research paper on health and wellbeing

• Open access
• Published: 19 June 2020

Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries

• Kai Ruggeri 1 , 2 ,
• Eduardo Garcia-Garzon 3 ,
• Áine Maguire 4 ,
• Sandra Matz 5 &
• Felicia A. Huppert 6 , 7  

Health and Quality of Life Outcomes volume  18 , Article number:  192 ( 2020 ) Cite this article

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Recent trends on measurement of well-being have elevated the scientific standards and rigor associated with approaches for national and international comparisons of well-being. One major theme in this has been the shift toward multidimensional approaches over reliance on traditional metrics such as single measures (e.g. happiness, life satisfaction) or economic proxies (e.g. GDP).

To produce a cohesive, multidimensional measure of well-being useful for providing meaningful insights for policy, we use data from 2006 and 2012 from the European Social Survey (ESS) to analyze well-being for 21 countries, involving approximately 40,000 individuals for each year. We refer collectively to the items used in the survey as multidimensional psychological well-being (MPWB).

The ten dimensions assessed are used to compute a single value standardized to the population, which supports broad assessment and comparison. It also increases the possibility of exploring individual dimensions of well-being useful for targeting interventions. Insights demonstrate what may be masked when limiting to single dimensions, which can create a failure to identify levers for policy interventions.

Conclusions

We conclude that both the composite score and individual dimensions from this approach constitute valuable levels of analyses for exploring appropriate policies to protect and improve well-being.

What is well-being?

Well-being has been defined as the combination of feeling good and functioning well; the experience of positive emotions such as happiness and contentment as well as the development of one’s potential, having some control over one’s life, having a sense of purpose, and experiencing positive relationships [ 23 ]. It is a sustainable condition that allows the individual or population to develop and thrive. The term subjective well-being is synonymous with positive mental health. The World Health Organization [ 45 ] defines positive mental health as “a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community”. This conceptualization of well-being goes beyond the absence of mental ill health, encompassing the perception that life is going well.

Well-being has been linked to success at professional, personal, and interpersonal levels, with those individuals high in well-being exhibiting greater productivity in the workplace, more effective learning, increased creativity, more prosocial behaviors, and positive relationships [ 10 , 27 , 37 ]. Further, longitudinal data indicates that well-being in childhood goes on to predict future well-being in adulthood [ 39 ]. Higher well-being is linked to a number of better outcomes regarding physical health and longevity [ 13 ] as well as better individual performance at work [ 30 ], and higher life satisfaction has been linked to better national economic performance [ 9 ].

Measurement of well-being

Governments and researchers have attempted to assess the well-being of populations for centuries [ 2 ]. Often in economic or political research, this has ended up being assessed using a single item about life satisfaction or happiness, or a limited set of items regarding quality of life [ 3 ]. Yet, well-being is a multidimensional construct, and cannot be adequately assessed in this manner [ 14 , 24 , 29 ]. Well-being goes beyond hedonism and the pursuit of happiness or pleasurable experience, and beyond a global evaluation (life satisfaction): it encompasses how well people are functioning, known as eudaimonic, or psychological well-being. Assessing well-being using a single subjective item approach fails to offer any insight into how people experience the aspects of their life that are fundamental to critical outcomes. An informative measure of well-being must encompass all the major components of well-being, both hedonic and eudaimonic aspects [ 2 ], and cannot be simplified to a unitary item of income, life satisfaction, or happiness.

Following acknowledgement that well-being measurement is inconsistent across studies, with myriad conceptual approaches applied [ 12 ], Huppert and So [ 27 ] attempted to take a systematic approach to comprehensively measure well-being. They proposed that positive mental health or well-being can be viewed as the complete opposite to mental ill health, and therefore attempted to define mental well-being in terms of the opposite of the symptoms of common mental disorders. Using the DSM-IV and ICD-10 symptom criteria for both anxiety and depression, ten features of psychological well-being were identified from defining the opposite of common symptoms. The features encompassed both hedonic and eudaimonic aspects of well-being: competence, emotional stability, engagement, meaning, optimism, positive emotion, positive relationships, resilience, self-esteem, and vitality. From these ten features an operational definition of flourishing, or high well-being, was developed using data from Round 3 of the European Social Survey (ESS), carried out in 2006. The items used in the Huppert and So [ 27 ] study were unique to that survey, which reflects a well-being framework based on 10 dimensions of good mental health. An extensive discussion on the development and validation of these measures for the framework is provided in this initial paper [ 27 ].

As this was part of a major, multinational social survey, each dimension was measured using a single item. As such, ‘multidimensional’ in this case refers to using available measures identified for well-being, but does not imply a fully robust measure of these individual dimensions, which would require substantially more items that may not be feasible for population-based work related to policy development. More detailed and nuanced approaches might help to better capture well-being as a multidimensional construct, and also may consider other dimensions. However, brief core measures such as the one implemented in the ESS are valuable as they provide a pragmatic way of generating pioneering empirical evidence on well-being across different populations, and help direct policies as well as the development of more nuanced instruments. While this naturally would benefit from complementary studies of robust measurement focused on a single topic, appropriate methods for using sprawling social surveys remain critical, particularly through better standardization [ 6 ]. While this paper will overview those findings, we strongly encourage more work to that end, particularly in more expansive measures to support policy considerations.

General approach and key questions

The aim of the present study was to develop a more robust measurement of well-being that allows researchers and policymakers to measure well-being both as a composite construct and at the level of its fundamental dimensions. Such a measure makes it possible to study overall well-being in a manner that goes beyond traditional single-item measures, which capture only a fraction of the dimensions of well-being, and because it allows analysts to unpack the measure into its core components to identify strengths and weaknesses. This would produce a similar approach as the most common reference for policy impacts: Gross Domestic Product (GDP), which is a composite measure of a large number of underlying dimensions.

The paper is structured as follows: in the first step, data from the ESS are used to develop a composite measure of well-being from the items suggested by Huppert & So [ 27 ] using factor analysis. In the second step, the value of the revised measure is demonstrated by generating insights into the well-being of 21 European countries, both at the level of overall well-being and at the level of individual dimensions.

The European social survey

The ESS is a biannual survey of European countries. Through comprehensive measurement and random sampling techniques, the ESS provides a representative sample of the European population for persons aged 15 and over [ 38 ]. Both Round 3 (2006–2007) and Round 6 (2012–2013) contained a supplementary well-being module. This module included over 50 items related to all aspects of well-being including psychological, social, and community well-being, as well as incorporating a brief measure of symptoms of psychological distress. As summarized by Huppert et al. [ 25 ], of the 50, only 30 items relate to personal well-being, of which only 22 are positive measures. Of those remaining, not all relate to the 10 constructs identified by Huppert and So [ 27 ], so only a single item could be used, or else the item that had the strongest face validity and distributional items were chosen.

Twenty-two countries participated in the well-being modules in both Round 3 and Round 6. As this it within a wider body of analyses, it was important to focus on those initially. Hungary did not have data for the vitality item in Round 3 and was excluded from the analysis, as appropriate models would not have been able to reliably resolve a missing item for an entire country. To be included in the analysis and remain consistent, participants therefore had to complete all 10 items used and have the age, gender, employment, and education variables completed. Employment was classified into four groups: students, employed, unemployed, retired; other groups were excluded. Education was classified into three groups: low (less than secondary school), middle (completed secondary school), and high (postsecondary study including any university and above). Using these criteria, the total sample for Round 6 was 41,825 people from 21 countries for analysis. The full sample was 52.6% female and ranged in age from 15 to 103 (M = 47.9; SD = 18.9). Other details about participation, response rates, and exclusion have been published elsewhere [ 38 ].

Huppert & So [ 27 ] defined well-being using 10 items extracted from the Round 3 items, which represent 10 dimensions of well-being. However, the items used in Round 3 to represent positive relationships and engagement exhibited ceiling effects and were removed from the questionnaire in Round 6. Four alternatives were available to replace each question. Based on their psychometric properties (i.e., absence of floor effects and wider response distributions), two new items were chosen for positive relationships and engagement (one item for each dimension). The new items and those they replaced can be seen in Table  1 (also see Supplement ).

Development of a composite measure of psychological well-being (MPWB)

A composite measure of well-being that yields an overall score for each individual was developed. From the ten indicators of well-being shown in Table 1 , a single factor score was calculated to represent MPWB. This overall MPWB score hence constitutes a summary of how an individual performs across the ten dimensions, which is akin to a summary score such as GDP, and will be of general value to policymakers. Statistical analysis was performed in R software, using lavaan [ 40 ] and lavaan.survey [ 35 ] packages. The former is a widely-used package for the R software designed for computing structural equation models and confirmatory factor analyses (CFA). The latter allows introducing complex survey design weights (combination of design and population size weights) when estimating confirmatory factor analysis models with lavaan, which ensures that MPWB scoring followed ESS guidelines regarding both country-level and survey specific weights [ 17 ]. Both packages have been previously tested and validated in various analyses using ESS data (as explained in detail in lavaan.survey documentation).

It should be noted that Round 6 was treated as the focal point of these efforts before repeating for Round 3, primarily due to the revised items that were problematic in Round 3, and considering that analyses of the 2006 data are already widely available.

Prior to analysis, all items were coded such that higher scores were more positive and lower scores more negative. Several confirmatory factor analysis models were performed in order to test several theoretical conceptualizations regarding MPWB. Finally, factor scores (expected a posteriori [ 15 ];) were calculated for the full European sample and used for descriptive purposes. The approach and final model are presented in supplemental material .

Factor scores are individual scores computed as weighted combinations of each person’s response on a given item and the factor scoring coefficients. This approach is to be preferred to using raw or sum scores: sum or raw scores fail to consider how well a given item serves as an indicator of the latent variable (i.e., all items are unrealistically assumed to be perfect and equivalent measures of MPWB). They also do not take into account that different items could present different variability, which is expected to occur if items present different scales (as in our case). Therefore, the use of such simple methods results in inaccurate individual rankings for MPWB. To resolve this, factor scores are both more informative and more accurate, as they avoid the propagation of measurement error in subsequent analyses [ 19 ].

Not without controversy (see Supplement ), factor scores are likely to be preferable to sum scores when ranking individuals on unobservable traits that are expected to be measured with noticeable measurement error (such as MPWB [ 32 ];). Similar approaches based on factor scoring have been successfully applied in large international assessment research [ 21 , 34 ]. With the aim of developing a composite well-being score, it was necessary to provide a meaningful representation of how the different well-being indicators are reflected in the single measure. A hierarchical model with one higher-order factor best approximated MPWB along with two first-order factors (see supplement Figure S 1 ). This model replicates the factor structure reported for Round 3 by Huppert & So [ 27 ]. The higher-order factor explained the relationship between two first-order factors (positive functioning and positive characteristics showed a correlation of ρ = .85). In addition, modelling standardized residuals showed that the items representing vitality and emotional stability and items representing optimism and self-esteem were highly correlated. The similarities in wording in both pairs of items (see Table 1 ) are suspected to be responsible for such high residual correlations. Thus, those correlations were included in the model. As presented in Table  2 , the hierarchical model was found to fit the data better than any other model but a bi-factor model including these correlated errors. The latter model resulted in collapsed factor structure with a weak, bi-polar positive functioning factor. However, this bi-factor model showed a problematic bi-polar group factor with weak loadings. Whether this group factor was removed (resulting in a S-1 bi-factor model, as in [ 16 ]), model fit deteriorated. Thus, neither bi-factor alternative was considered to be acceptable.

To calculate the single composite score representing MPWB, a factor scoring approach was used rather than a simplistic summing of raw scores on these items. Factor scores were computed and standardized for the sample population as a whole, which make them suitable for broad comparison [ 8 ]. This technique was selected for two reasons. First, it has the ability to take into account the different response scales used for measuring the items included in the multidimensional well-being model. The CFA model, from which MPWB scores were computed, was defined such that the metric of the MPWB was fixed, which results in a standardized scale. Alternative approaches, such as sum or raw scores, would result in ignoring the differential variability across items, and biased individual group scores. Our approach, using factor scoring, resolves this issue by means of standardization of the MPWB scores. The second reason for this technique is that it could take account of how strongly each item loaded onto the MPWB factor. It should be noted that by using only two sub-factors, the weight applied to the general factor is identical within the model for each round. This model was also checked to ensure it also was a good fit for different groups based on gender, age, education and employment.

Separate CFA analyses per each country indicate that the final model fit the data adequately in all countries (.971 < CFI < .995; .960 < TFI < .994; .020 < RMSEA < .05; 0,023 < SRMR < 0,042). All items presented substantive loadings on their respective factors, and structures consistently replicated across all tested countries. Largest variations were found when assessing the residual items’ correlations (e.g., for emotional stability and vitality correlation, values ranged from 0,076 to .394). However, for most cases, residuals correlations were of similar size and direction (for both cases, the standard deviation of estimated correlations was close of .10). Thus, strong evidence supporting our final model was systematically found across all analyzed countries. Full results are provided in the supplement (Tables S 2 -S 3 ).

Model invariance

In order to establish meaningful comparisons across groups within and between each country, a two-stage approach was followed, resulting in a structure that was successfully found to be similar across demographics. First, a descriptive comparison of the parameter estimates unveiled no major differences across groups. Second, factor scores were derived for the sample, employing univariate statistics to compare specific groups within country and round. In these analyses, neither traditional nor modern approaches to factor measurement invariance were appropriate given the large sample and number of comparisons at stake ([ 8 ]; further details in Supplement ).

From a descriptive standpoint, the hierarchical structure satisfactorily fit both Round 3 and Round 6 data. All indicators in both rounds had substantial factor loadings (i.e., λ > .35). A descriptive comparison of parameter estimates produced no major differences across the two rounds. The lack of meaningful differences in the parameter estimates confirms that this method for computing MPWB can be used in both rounds.

As MPWB scores from both rounds are obtained from different items that have different scales for responses, it is necessary to transform individual scores obtained from both rounds in order to be aligned. To do this between Round 3 and Round 6 items, a scaling approach was used. To produce common metrics, scores from Round 3 were rescaled using a mean and sigma transformation (Kolen & Brennan 2010) to align with Round 6 scales. This was used as Round 6 measures were deemed to have corrected some deficiencies found in Round 3 items. This does not change outcomes in either round but simply makes the scores match in terms of distributions relative to their scales, making them more suitable for comparison.

As extensive descriptive insights on the sample and general findings are already available (see [ 41 ]), we focus this section on the evidence derived directly from the proposed approach to MPWB scores. For the combined single score for MPWB, the overall mean (for all participants combined) is fixed to zero, and the scores represent deviation from the overall mean. In 2012 (Round 6), country scores on well-being ranged from − 0.41 in Bulgaria to 0.46 in Denmark (Fig.  1 ). There was a significant, positive relationship between national MPWB mean scores and national life satisfaction means ( r =  .56 (.55–.57), p  < .001). In addition, MPWB was negatively related with depression scores and positively associated with other well-being measurements (see Supplement ).

Distribution of national MPWB means and confidence intervals across Europe

Denmark having the highest well-being is consistent with many studies [ 4 , 18 ] and with previous work using ESS data [ 27 ]. While the pattern is typically that Nordic countries are doing the best and that eastern countries have the lowest well-being, exceptions exist. The most notable exception is Portugal, which has the third-lowest score and is not significantly higher than Ukraine, which is second lowest. Switzerland and Germany are second and third highest respectively, and show generally similar patterns to the Scandinavian countries (see Fig. 1 ). It should be noted that, for Figs.  1 , 2 , 3 , 4 , 5 , countries with the lowest well-being are at the top. This is done to highlight the greatest areas for potential impact, which are also the most of concern to policy.

Well-being by country and gender

Well-being by country and age

Well-being by country and employment

Well-being by country and education

General patterns across the key demographic variables – gender, age, education, employment – are visible across countries as seen in Figs.  1 , 2 , 3 , 4 , 5 (see also Supplement 2 ). These figures highlight patterns based on overall well-being as well as potential for inequalities. The visualizations presented here, though univariate, are for the purpose of understanding broad patterns while highlighting the need to disentangle groups and specific dimensions to generate effective policies.

For gender, women exhibited lower MPWB scores than men across Europe (β = −.09, t (36508) = − 10.37; p  < .001). However, these results must be interpreted with caution due to considerable overlap in confidence intervals for many of the countries, and greater exploration of related variables is required. This also applies for the five countries (Estonia, Finland, Ireland, Slovakia, Ukraine) where women have higher means than men. Only four countries have significant differences between genders, all of which involve men having higher scores than women: the Netherlands (β = −.12, t (1759) = − 3.24; p  < .001), Belgium (β = −.14, t (1783) = − 3.94; p  < .001), Cyprus (β = −.18, t (930) = − 2.87; p  < .001) and Portugal (β = −.19, t (1847) = − 2.50; p  < .001).

While older individuals typically exhibited lower MPWB scores compared to younger age groups across Europe (β 25–44  = −.05, t (36506) = − 3.686, p  < .001; β 45–65  = −.12, t (36506) = − 8.356, p  < .001; β 65–74  = −.16, t (36506) = − 8.807, p  < .001; β 75+  = −.28, t (36506) = − 13.568, p  < .001), the more compelling pattern shows more extreme differences within and between age groups for the six countries with the lowest well-being. This pattern is most pronounced in Bulgaria, which has the lowest overall well-being. For the three countries with the highest well-being (Denmark, Switzerland, Germany), even the mean of the oldest age group was well above the European average, while for the countries with the lowest well-being, it was only young people, particularly those under 25, who scored above the European average. With the exception of France and Denmark, countries with higher well-being typically had fewer age group differences and less variance within or between groups. Only countries with the lowest well-being showed age differences that were significant with those 75 and over showing the lowest well-being.

MPWB is consistently higher for employed individuals and students than for retired (β = −.31, t (36506) = − 21.785; p  < .00) or unemployed individuals (β = −.52, t (36556) = − 28.972; p  < .001). Unemployed groups were lowest in nearly all of the 21 countries, though the size of the distance from other groups did not consistently correlate with national MPWB mean. Unemployed individuals in the six countries with the lowest well-being were significantly below the mean, though there is little consistency across groups and countries by employment beyond that. In countries with high well-being, unemployed, and, in some cases, retired individuals, had means below the European average. In countries with the lowest well-being, it was almost exclusively students who scored above the European average. Means for retired groups appear to correlate most strongly with overall well-being. There is minimal variability for employed groups in MPWB means within and between countries.

There is a clear pattern of MPWB scores increasing with education level, though the differences were most pronounced between low and middle education groups (β = .12, t (36508) = 9.538; p  < .001). Individuals with high education were significantly higher on MPWB than those in the middle education group (β = .10, t (36508) =11.06; p  < .001). Differences between groups were noticeably larger for countries with lower overall well-being, and the difference was particularly striking in Bulgaria. In Portugal, medium and high education well-being means were above the European average (though 95% confidence intervals crossed 0), but educational attainment is significantly lower in the country, meaning the low education group represents a greater proportion of the population than the other 21 countries. In the six countries with the highest well-being, mean scores for all levels of education were above the European mean.

Utilizing ten dimensions for superior understanding of well-being

It is common to find rankings of national happiness and well-being in popular literature. Similarly, life satisfaction is routinely the only measure reported in many policy documents related to population well-being. To demonstrate why such limited descriptive approaches can be problematic, and better understood using multiple dimensions, all 21 countries were ranked individually on each of the 10 indicators of well-being and MPWB in Round 6 based on their means. Figure  6 demonstrates the variations in ranking across the 10 dimensions of well-being for each country.

Country rankings in 2012 on multidimensional psychological well-being and each of its 10 dimensions

The general pattern shows typically higher rankings for well-being dimensions in countries with higher overall well-being (and vice-versa). Yet countries can have very similar scores on the composite measure but very different underlying profiles in terms of individual dimensions. Figure  7 a presents this for two countries with similar life satisfaction and composite well-being, Belgium and the United Kingdom. Figure 7 b then demonstrates this even more vividly for two countries, Finland and Norway, which have similar composite well-being scores and identical mean life satisfaction scores (8.1), as well as have the highest two values for happiness of all 21 countries. In both pairings, the broad outcomes are similar, yet countries consistently have very different underlying profiles in individual dimensions. The results indicate that while overall scores can be useful for general assessment, specific dimensions may vary substantially, which is a relevant first step for developing interventions. Whereas the ten items are individual measures of 10 areas of well-being, had these been limited to a single domain only, the richness of the underlying patterns would have been lost, and the limitation of single item approaches amplified.

a Comparison of ranks for dimensions of well-being between two different countries with similar life satisfaction in 2012: Belgium and United Kingdom. b Comparison of ranks for dimensions of well-being between two similar countries with identical life satisfaction and composite well-being scores in 2012: Finland and Norway

The ten-item multidimensional measure provided clear patterns for well-being across 21 countries and various groups within. Whether used individually or combined into a composite score, this approach produces more insight into well-being and its components than a single item measure such as happiness or life satisfaction. Fundamentally, single items are impossible to unpack in reverse to gain insights, whereas the composite score can be used as a macro-indicator for more efficient overviews as well as deconstructed to look for strengths and weaknesses within a population, as depicted in Figs.  6 and 7 . Such deconstruction makes it possible to more appropriately target interventions. This brings measurement of well-being in policy contexts in line with approaches like GDP or national ageing indexes [ 7 ], which are composite indicators of many critical dimensions. The comparison with GDP is discussed at length in the following sections.

Patterns within and between populations

Overall, the patterns and profiles presented indicate a number of general and more nuanced insights. The most consistent among these is that the general trend in national well-being is usually matched within each of the primary indicators assessed, such as lower well-being within unemployed groups in countries with lower overall scores than in those with higher overall scores. While there are certainly exceptions, this general pattern is visible across most indicators.

The other general trend is that groups with lower MPWB scores consistently demonstrate greater variability and wider confidence intervals than groups with higher scores. This is a particularly relevant message for policymakers given that it is an indication of the complexity of inequalities: improvements for those doing well may be more similar in nature than for those doing poorly. This is particularly true for employment versus unemployment, yet reversed for educational attainment. Within each dimension, the most critical pattern is the lack of consistency for how each country ranks, as discussed further in other sections.

Examining individual dimensions of well-being makes it possible to develop a more nuanced understanding of how well-being is impacted by societal indicators, such as inequality or education. For example, it is possible that spending more money on education improves well-being on some dimensions but not others. Such an understanding is crucial for the implementation of targeted policy interventions that aim at weaker dimensions of well-being and may help avoid the development of ineffective policy programs. It is also important to note that the patterns across sociodemographic variables may differ when all groups are combined, compared to results within countries. Some effects may be larger when all are combined, whereas others may have cancelling effects.

Using these insights, one group that may be particularly important to consider is unemployed adults, who consistently have lower well-being than employed individuals. Previous research on unemployment and well-being has often focused on mental health problems among the unemployed [ 46 ] but there are also numerous studies of differences in positive aspects of well-being, mainly life satisfaction and happiness [ 22 ]. A large population-based study has demonstrated that unemployment is more strongly associated with the absence of positive well-being than with the presence of symptoms of psychological distress [ 28 ], suggesting that programs that aim to increase well-being among unemployed people may be more effective than programs that seek to reduce psychological distress.

Certainly, it is well known that higher income is related to higher subjective well-being and better health and life expectancy [ 1 , 42 ], so reduced income following unemployment is likely to lead to increased inequalities. Further work would be particularly insightful if it included links to specific dimensions of well-being, not only the comprehensive scores or overall life satisfaction for unemployed populations. As such, effective responses would involve implementation of interventions known to increase well-being in these groups in times of (or in spite of) low access to work, targeting dimensions most responsible for low overall well-being. Further work on this subject will be presented in forthcoming papers with extended use of these data.

This thinking also applies to older and retired populations in highly deprived regions where access to social services and pensions are limited. A key example of this is the absence in our data of a U-shaped curve for age, which is commonly found in studies using life satisfaction or happiness [ 5 ]. In our results, older individuals are typically lower than what would be expected in a U distribution, and in some cases, the oldest populations have the lowest MPWB scores. While previous studies have shown some decline in well-being beyond the age of 75 [ 20 ], our analysis demonstrates quite a severe fall in MPWB in most countries. What makes this insight useful – as opposed to merely unexpected – is the inclusion of the individual dimensions such as vitality and positive relationships. These dimensions are clearly much more likely to elicit lower scores than for younger age groups. For example, ageing beyond 75 is often associated with increased loneliness and isolation [ 33 , 43 ], and reduction in safe, independent mobility [ 31 ], which may therefore correspond with lower scores on positive relationships, engagement, and vitality, and ultimately lower scores on MPWB than younger populations. Unpacking the dimensions associated with the age-related decline in well-being should be the subject of future research. The moderate positive relationship of MPWB scores with life satisfaction is clear but also not absolute, indicating greater insights through multidimensional approaches without any obvious loss of information. Based on the findings presented here, it is clearly important to consider ensuring the well-being of such groups, the most vulnerable in society, during periods of major social spending limitations.

Policy implications

Critically, Fig.  6 represents the diversity of how countries reach an overall MPWB score. While countries with overall high well-being have typically higher ranks on individual items, there are clearly weak dimensions for individual countries. Conversely, even countries with overall low well-being have positive scores on some dimensions. As such, the lower items can be seen as potential policy levers in terms of targeting areas of concern through evidence-based interventions that should improve them. Similarly, stronger areas can be seen as learning opportunities to understand what may be driving results, and thus used to both sustain those levels as well as potentially to translate for individuals or groups not performing as well in that dimension. Collectively, we can view this insight as a message about specific areas to target for improvement, even in countries doing well, and that even countries doing poorly may offer strengths that can be enhanced or maintained, and could be further studied for potential applications to address deficits. We sound a note of caution however, in that these patterns are based on ranks rather than actual values, and that those ranks are based on single measures.

Figure 7 complements those insights more specifically by showing how Finland and Norway, with a number of social, demographic, and economic similarities, plus identical life satisfaction scores (8.1) arrive at similar single MPWB scores with very different profiles for individual dimensions. By understanding the levers that are specific to each country (i.e. dimensions with the lowest well-being scores), policymakers can respond with appropriate interventions, thereby maximizing the potential for impact on entire populations. Had we restricted well-being measurement to a single question about happiness, as is commonly done, we would have seen both countries had similar and extremely high means for happiness. This might have led to the conclusion that there was minimal need for interventions for improving well-being. Thus, in isolation, using happiness as the single indicator would have masked the considerable variability on several other dimensions, especially those dimensions where one or both had means among the lowest of the 21 countries. This would have resulted in similar policy recommendations, when in fact, Norway may have been best served by, for example, targeting lower dimensions such as Engagement and Self-Esteem, and Finland best served by targeting Vitality and Emotional Stability.

Targeting specific groups and relevant dimensions as opposed to comparing overall national outcomes between countries is perhaps best exemplified by Portugal, which has one of the lowest educational attainment rates in OECD countries, exceeded only by Mexico and Turkey [ 36 ]. This group thus skews the national MPWB score, which is above average for middle and high education groups, but much lower for those with low education. Though this pattern is not atypical for the 21 countries presented here, the size of the low education group proportional to Portugal’s population clearly reduces the national MPWB score. This implies that the greatest potential for improvement is likely to be through addressing the well-being of those with low education as a near-term strategy, and improving access to education as a longer-term strategy. It will be important to analyze this in the near future, given recent reports that educational attainment in Portugal has increased considerably in recent years (though remains one of the lowest in OECD countries) [ 36 ].

One topic that could not be addressed directly is whether these measures offer value as indicators of well-being beyond the 21 countries included here, or even beyond the countries included in ESS generally. In other words, are these measures relevant only to a European population or is our approach to well-being measurement translatable to other regions and purposes? Broadly speaking, the development of these measures being based on DSM and ICD criteria should make them relevant beyond just the 21 countries, as those systems are generally intended to be global. However, it can certainly be argued that these methods for designing measures are heavily influenced by North American and European medical frameworks, which may limit their appropriateness if applied in other regions. Further research on these measures should consider this by adding potential further measures deemed culturally appropriate and seeing if comparable models appear as a result.

A single well-being score

One potential weakness remains the inconsistency of scaling between ESS well-being items used for calculating MPWB. However, this also presents an opportunity to consider the relative weighting of each item within the current scales, and allow for the development of a more consistent and reliable measure. These scales could be modified to align in separate studies with new weights generated – either generically for all populations or stratified to account for various cultural or other influences. Using these insights, scales could alternatively be produced to allow for simple scoring for a more universally accessible structure (e.g. 1–100) but with appropriate values for each item that represents the dimensions, if this results in more effective communication with a general public than a standardized score with weights. Additionally, common scales would improve on attempts to use rankings for presenting national variability within and between dimensions. Researchers should be aware that factor scores are sample-dependent (as based on specific factor model parameters such as factor loadings). Nevertheless, future research focused on investigating specific item differential functioning (by means of multidimensional item response functioning or akin techniques) of these items across situations (i.e., rounds) and samples (i.e., rounds and countries) should be conducted in order to have a more nuanced understanding of this scale functioning.

What makes this discussion highly relevant is the value of a more informed measure to replace traditional indicators of well-being, predominantly life satisfaction. While life satisfaction may have an extensive history and present a useful metric for comparisons between major populations of interest, it is at best a corollary, or natural consequence, of other indicators. It is not in itself useful for informing interventions, in the same way limiting to a single item for any specific dimension of well-being should not alone inform interventions.

By contrast, a validated and standardized multidimensional measure is exceptionally useful in its suitability to identify those at risk, as well as its potential for identifying areas of strengths and weaknesses within the at-risk population. This can considerably improve the efficiency and appropriateness of interventions. It identifies well-understood dimensions (e.g. vitality, positive emotion) for direct application of evidence-based approaches that would improve areas of concern and thus overall well-being. Given these points, we strongly argue for the use of multidimensional approaches to measurement of well-being for setting local and national policy agenda.

There are other existing single-score approaches for well-being addressing its multidimensional nature. These include the Warwick-Edinburgh Mental Well-Being Scale [ 44 ] and the Flourishing Scale [ 11 ]. In these measures, although the single score is derived from items that clearly tap a number of dimensions, the dimensions have not been systematically derived and no attempt is made to measure the underlying dimensions individually. In contrast, the development approach used here – taking established dimensions from DSM and ICD – is based on years of international expertise in the field of mental illness. In other words, there have long been adequate measures for identifying and understanding illness, but there is room for improvement to better identify and understand health. With increasing support for the idea of these being a more central focus of primary outcomes within economic policies, such approaches are exceptionally useful [ 13 ].

Better measures, better insights

Naturally, it is not a compelling argument to simply state that more measures present greater information than fewer or single measures, and this is not the primary argument of this manuscript. In many instances, national measures of well-being are mandated to be restricted to a limited set of items. What is instead being argued is that well-being itself is a multidimensional construct, and if it is deemed a critical insight for establishing policy agenda or evaluating outcomes, measurements must follow suit and not treat happiness and life satisfaction values as universally indicative. The items included in ESS present a very useful step to that end, even in a context where the number of items is limited.

As has been argued by many, greater consistency in measurement of well-being is also needed [ 26 ]. This may come in the form of more consistency regarding dimensions included, the way items are scored, the number of items representing each dimension, and changes in items over time. While inconsistency may be prevalent in the literature to date for definitions and measurement, the significant number of converging findings indicates increasingly robust insights for well-being relevant to scientists and policymakers. Improvements to this end would support more systematic study of (and interventions for) population well-being, even in cases where data collection may be limited to a small number of items.

The added value of MPWB as a composite measure

While there are many published arguments (which we echo) that measures of well-being must go beyond objective features, particularly related to economic indicators such as GDP, this is not to say one replaces the other. More practically, subjective and objective approaches will covary to some degree but remain largely distinct. For example, GDP presents a useful composite of a substantial number of dimensions, such as consumption, imports, exports, specific market outcomes, and incomes. If measurement is restricted to a macro-level indicator such as GDP, we cannot be confident in selecting appropriate policies to implement. Policies are most effective when they target a specific component (of GDP, in this instance), and then are directly evaluated in terms of changes in that component. The composite can then be useful for comprehensive understanding of change over time and variation in circumstances. Specific dimensions are necessary for identifying strengths and weaknesses to guide policy, and examining direct impacts on those dimensions. In this way, a composite measure in the form of MPWB for aggregate well-being is also useful, so long as the individual dimensions are used in the development and evaluation of policies. Similar arguments for other multidimensional constructs have been made recently, such as national indexes of ageing [ 7 ].

In the specific instance of MPWB in relation to existing measures of well-being, there are several critical reasons to ensure a robust approach to measurement through systematic validation of psychometric properties. The first is that these measures are already part of the ESS, meaning they are being used to study a very large sample across a number of social challenges and not specifically a new measure for well-being. The ESS has a significant influence on policy discussions, which means the best approaches to utilizing the data are critical to present systematically, as we have attempted to do here. This approach goes beyond existing measures such as Gallup or the World Happiness Index to broadly cover psychological well-being, not individual features such as happiness or life satisfaction (though we reiterate: as we demonstrate in Fig.  7 a and b, these individual measures can and should still covary broadly with any multidimensional measure of well-being, even if not useful for predicting all dimensions). While often referred to as ‘comprehensive’ measurement, this merely describes a broad range of dimensions, though more items for each dimension – and potentially more dimensions – would certainly be preferable in an ideal scenario.

These dimensions were identified following extensive study for flourishing measures by Huppert & So [ 27 ], meaning they are not simply a mix of dimensions, but established systematically as the key features of well-being (the opposite of ill-being). Furthermore, the development of the items is in line with widely validated and practiced measures for the identification of illness. The primary adjustment has simply been the emphasis on health, but otherwise maintains the same principles of assessment. Therefore, the overall approach offers greater value than assessing only negative features and inferring absence equates to opposite (positives), or that individual measures such as happiness can sufficiently represent a multidimensional construct like well-being. Collectively, we feel the approach presented in this work is therefore a preferable method for assessing well-being, particularly on a population level, and similar approaches should replace single items used in isolation.

While the focus of this paper is on the utilization of a widely tested measure (in terms of geographic spread) that provides for assessing population well-being, it is important to provide a specific application for why this is relevant in a policy context. Additionally, because the ESS itself is a widely-recognized source of meaningful information for policymakers, providing a robust and comprehensive exploration of the data is necessary. As the well-being module was not collected in recent rounds, these insights provide clear reasoning and applications for bringing them back in the near future.

More specifically, it is critical that this approach be seen as advantageous both in using the composite measure for identifying major patterns within and between populations, and for systematically unpacking individual dimensions. Using those dimensions produces nuanced insights as well as the possibility of illuminating policy priorities for intervention.

In line with this, we argue that no composite measure can be useful for developing, implementing, or evaluating policy if individual dimensions are not disaggregated. We are not arguing that MPWB as a single composite score, nor the additional measures used in ESS, is better than other existing single composite scoring measures of well-being. Our primary argument is instead that MPWB is constructed and analyzed specifically for the purpose of having a robust measure suitable for disaggregating critical dimensions of well-being. Without such disaggregation, single composite measures are of limited use. In other words, construct a composite and target the components.

Well-being is perhaps the most critical outcome measure of policies. Each individual dimension of well-being as measured in this study represents a component linked to important areas of life, such as physical health, financial choice, and academic performance [ 26 ]. For such significant datasets as the European Social Survey, the use of the single score based on the ten dimensions included in multidimensional psychological well-being gives the ability to present national patterns and major demographic categories as well as to explore specific dimensions within specific groups. This offers a robust approach for policy purposes, on both macro and micro levels. This facilitates the implementation and evaluation of interventions aimed at directly improving outcomes in terms of population well-being.

Availability of data and materials

The datasets analysed during the current study are available in the European Social Survey repository, http://www.europeansocialsurvey.org/data/country_index.html

Abbreviations

Diagnostic and Statistical Manual of Mental Disorders

European Social Survey

Gross Domestic Product

International Classification of Disease

Multidimensional psychological well-being

Adler NE, Rehkopf DH. US disparities in health: descriptions, causes, and mechanisms. Annu Rev Public Health. 2008;29:235–52.

PubMed   Google Scholar  

Allin P, Hand DJ. New statistics for old?—measuring the wellbeing of the UK. J Royal Stat Soc Ser A. 2017;180(1):3–43.

Google Scholar  

Arechavala NS, Espina PZ, Trapero BP. The economic crisis and its effects on the quality of life in the European Union. Soc Indic Res. 2015;120(2):323–43.

Biswas-Diener R, Vittersø J, Diener E. The Danish effect: beginning to explain high well-being in Denmark. Soc Indic Res. 2010;97(2):229–46.

Blanchflower DG, Oswald AJ. Is well-being U-shaped over the life cycle? Soc Sci Med. 2008;66(8):1733–49.

Carreira H, Williams R, Strongman H, Bhaskaran K. Identification of mental health and quality of life outcomes in primary care databases in the UK: a systematic review. BMJ Open. 2019;9(7):e029227.

PubMed   PubMed Central   Google Scholar  

Chen C, Goldman DP, Zissimopoulos J, Rowe JW. Multidimensional comparison of countries’ adaptation to societal aging. Proc Natl Acad Sci. 2018;115(37):9169–74.

CAS   PubMed   PubMed Central   Google Scholar  

Cieciuch J, Davidov E, Schmidt P, Algesheimer R, Schwartz SH. Comparing results of exact vs. an approximate (Bayesian) measurement invariance test: a cross-country illustration with a scale to measure 19 human values. Front Psychol. 2014;8(5):982.

Deaton A. Income, health and wellbeing around the world: evidence from the Gallup world poll. J Econ Perspect. 2008;22(2):53–72.

Diener E. New findings and future directions for subjective well-being research. Am Psychol. 2012;67(8):590.

Diener E, Wirtz D, Tov W, Kim-Prieto C, Choi D, Oishi S, Biswas-Diener R. New measures of well-being: flourishing and positive and negative feelings. Soc Indic Res. 2009;39:247–66.

Diener E, Seligman ME. Beyond money toward an economy of well-being. Psychol Sci Public Interest. 2004;5(1):1–31.

Diener E, Pressman S, Hunter J, Chase D. If, why, and when subjective well-being influences health, and future needed research. Appl Psychol Health Well Being. 2017;9(2):133–67.

Dolan P, White MP. How can measures of subjective well-being be used to inform public policy? Perspect Psychol Sci. 2007;2(1):71–85.

Eastbrook R, Neale M. A comparison of factor score estimation methods in presence of missing data: reliability and an application to nicotine dependence. Multivar Behav Res. 2012;48(1):1–27.

Eid M, Krumm S, Koch T, Schulze J. Bifactor models for predicting criteria by general and specific factors: problems of Nonidentiability and alternative solutions. Journal of Intelligence. 2018;6(3):42.

European Social Survey (2014). Weighting European Social Survey Data. Retrieved from https://www.europeansocialsurvey.org/docs/methodology/ESS_weighting_data_1.pdf .

Farver-Vestergaard I, Ruggeri K. Setting National Policy Agendas in Light of the Denmark Results for Well-being. JAMA Psychiatry. 2017;74(8):773–4.

Ferrando PJ, Lorenzo-Seva U. A note on improving EAP trait estimation in oblique factor-analytic and item response theory models. Psicologica. 2016;37(2):235–47.

Gerstorf D, Hoppmann CA, Löckenhoff CE, Infurna FJ, Schupp J, Wagner GG, Ram N. Terminal decline in well-being: the role of social orientation. Psychol Aging. 2016;31(2):149.

Grundke, R., et al. Skills and global value chains: A characterisation, OECD Science, Technology and Industry Working Papers, No. 2017/05, OECD Publishing. 2017. https://doi.org/10.1787/cdb5de9b-en .

Gudmundsdottir DG. The impact of economic crisis on happiness. Soc Indic Res. 2013;110(3):1083–101.

Huppert FA. Psychological well-being: evidence regarding its causes and consequences†. Appl Psychol Health Well Being. 2009;1(2):137–64. https://doi.org/10.1111/j.1758-0854.2009.01008.x .

Article   Google Scholar  

Huppert FA. The state of well-being science: concepts, measures, interventions and policies. In: Huppert FA, Cooper CL, editors. Interventions and policies to enhance well-being. Oxford: Wiley-Blackwell; 2014.

Huppert FA, Marks N, Clark A, Siegrist J, Stutzer A, Vitterso J, Wahrendorf M. Measuring well-being across Europe: description of the ESS well-being module and preliminary findings. Soc Indic Res. 2009;91(3):301–15.

Huppert F, Ruggeri K. 15. Policy challenges: well-being as a priority in public mental health. In: Bhugra D, Bhui K, Wong S, Gilman S, editors. Oxford textbook of public mental health. Oxford: Oxford University Press; 2018.

Huppert FA, So TT. Flourishing across Europe: application of a new conceptual framework for defining well-being. Soc Indic Res. 2013;110(3):837–61.

Huppert FA, Whittington JE. Evidence for the independence of positive and negative well-being: implications for quality of life assessment. Br J Health Psychol. 2003;8(1):107–22.

Kahneman D, Krueger AB. Developments in the measurement of subjective well-being. J Econ Perspect. 2006;20(1):3–24.

Knapp M, McDaid D, Parsonage M. Mental health promotion and mental illness prevention: the economic case. London: London School of Economics; 2011.

Lihavainen K, Sipilä S, Rantanen T, Kauppinen M, Sulkava R, Hartikainen S. Effects of comprehensive geriatric assessment and targeted intervention on mobility in persons aged 75 years and over: a randomized controlled trial. Clin Rehabil. 2012;26(4):314–26.

McNeish, D., & Wolf, M. G. (2019). Sum Scores Are Factor Scores. https://doi.org/10.31234/osf.io/3wy47 .

Nicolaisen M, Thorsen K. Who are lonely? Loneliness in different age groups (18–81 years old), using two measures of loneliness. Int J Aging Hum Dev. 2014;78(3):229–57.

Nicoletti, G., Scarpetta, S., & Boylaud, O. Summary indicators of product market regulation with an extension to employment protection legislation, OECD Economics Department Working Paper s , no. 226, OECD publishing, Paris. 2000. https://doi.org/10.1787/215182844604 .

Oberski D. Lavaan.Survey: an R package for complex survey analysis of structural equation models. J Stat Softw. 2014;57(1):1–27.

OECD. Education at a glance 2014: OECD indicators. Portugal. Retrieved on January 28, 2016 at http://bit.ly/2wqZweh . 2014.

Oishi S, Diener E, Lucas RE. The optimum level of well-being: can people be too happy? Perspect Psychol Sci. 2007;2(4):346–60.

Reibling N, Beckfield J, Huijts T, Schmidt-Catran A, Thomson KH, Wendt C. Depressed during the depression: has the economic crisis affected mental health inequalities in Europe? Findings from the European social survey (2014) special module on the determinants of health. Eur J Public Health. 2017;27:47–54.

Richards M, Huppert FA. Do positive children become positive adults? Evidence from a longitudinal birth cohort study. J Posit Psychol. 2011;6(1):75–87.

Roseel Y. Lavaan: an R package for structural equation modeling. J Stat Softw. 2012;48(2):1–36.

Ruggeri K, Garcia Garzon E, Maguire Á, Huppert F. Chapter 1: comprehensive psychological well-being. In: Looking through the wellbeing kaleidoscope: Results from the European Social Survey. London: New Economics Foundation; 2016.

Steptoe A, Deaton A, Stone AA. Subjective wellbeing, health, and ageing. Lancet. 2015;385(9968):640–8.

Steptoe A, Shankar A, Demakakos P, Wardle J. Social isolation, loneliness, and all-cause mortality in older men and women. Proc Natl Acad Sci. 2013;110(15):5797–801.

Tennant R, Hiller L, Fishwick R, Platt S, Joseph S, Weich S, et al. The Warwick-Edinburgh mental well-being scale (WEMWBS): development and UK validation. Health Qual Life Outcomes. 2007;5(1):63.

World Health Organization. The world health report 2001: mental health: new understanding, new hope. Geneva: World Health Organization; 2001.

Young C. Losing a job: the nonpecuniary cost of unemployment in the United States. Soc Forces. 2012;91(2):609–6.

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Acknowledgements

The authors would like to thank Ms. Sara Plakolm, Ms. Amel Benzerga, and Ms. Jill Hurson for assistance in proofing the final draft. We would also like to acknowledge the general involvement of the Centre for Comparative Social Surveys at City University, London, and the Centre for Wellbeing at the New Economics Foundation.

This work was supported by a grant from the UK Economic and Social Research Council (ES/LO14629/1). Additional support was also provided by the Isaac Newton Trust, Trinity College, University of Cambridge, and the UK Economic and Social Research Council (ES/P010962/1).

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KR is the lead author and researcher on the study, responsible for all materials start to finish. FH was responsible for the original grant award and the general theory involved in the measurement approaches. ÁM was responsible for broad analysis and writing. EGG was responsible for psychometric models and the original factor scoring approach, plus writing the supplementary explanations. SM provided input on later drafts of the manuscript as well as the auxiliary analyses. The authors read and approved the final manuscript.

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Additional file 1: figure s1.

. Hierarchical approach to modelling comprehensive psychological well-being. Table S1 . Confirmatory Factor Structure for Round 6 and 3. Figure S2 . Well-being by country and gender. Figure S3 . Well-being by country and age. Figure S4 . Well-being by country and employment. Figure S5 . Well-being by country and education. Table S2 . Item loadings for Belgium to Great Britain. Table S3 . Item loadings for Ireland to Ukraine.

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Ruggeri, K., Garcia-Garzon, E., Maguire, Á. et al. Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries. Health Qual Life Outcomes 18 , 192 (2020). https://doi.org/10.1186/s12955-020-01423-y

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Health and Quality of Life Outcomes

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A healthy lifestyle is positively associated with mental health and well-being and core markers in ageing

• Pauline Hautekiet   ORCID: orcid.org/0000-0003-3805-3004 1 , 2 ,
• Nelly D. Saenen 1 , 2 ,
• Dries S. Martens 2 ,
• Margot Debay 2 ,
• Johan Van der Heyden 3 ,
• Tim S. Nawrot 2 , 4 &
• Eva M. De Clercq 1  

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Studies often evaluate mental health and well-being in association with individual health behaviours although evaluating multiple health behaviours that co-occur in real life may reveal important insights into the overall association. Also, the underlying pathways of how lifestyle might affect our health are still under debate. Here, we studied the mediation of different health behaviours or lifestyle factors on mental health and its effect on core markers of ageing: telomere length (TL) and mitochondrial DNA content (mtDNAc).

In this study, 6054 adults from the 2018 Belgian Health Interview Survey (BHIS) were included. Mental health and well-being outcomes included psychological and severe psychological distress, vitality, life satisfaction, self-perceived health, depressive and generalised anxiety disorder and suicidal ideation. A lifestyle score integrating diet, physical activity, smoking status, alcohol consumption and BMI was created and validated. On a subset of 739 participants, leucocyte TL and mtDNAc were assessed using qPCR. Generalised linear mixed models were used while adjusting for a priori chosen covariates.

The average age (SD) of the study population was 49.9 (17.5) years, and 48.8% were men. A one-point increment in the lifestyle score was associated with lower odds (ranging from 0.56 to 0.74) for all studied mental health outcomes and with a 1.74% (95% CI: 0.11, 3.40%) longer TL and 4.07% (95% CI: 2.01, 6.17%) higher mtDNAc. Psychological distress and suicidal ideation were associated with a lower mtDNAc of − 4.62% (95% CI: − 8.85, − 0.20%) and − 7.83% (95% CI: − 14.77, − 0.34%), respectively. No associations were found between mental health and TL.

Conclusions

In this large-scale study, we showed the positive association between a healthy lifestyle and both biological ageing and different dimensions of mental health and well-being. We also indicated that living a healthy lifestyle contributes to more favourable biological ageing.

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According to the World Health Organization (WHO), a healthy lifestyle is defined as “a way of living that lowers the risk of being seriously ill or dying early” [ 1 ]. Public health authorities emphasise the importance of a healthy lifestyle, but despite this, many individuals worldwide still live an unhealthy lifestyle [ 2 ]. In Europe, 26% of adults smoke [ 3 ], nearly half (46%) never exercise [ 4 ], 8.4% drink alcohol on a daily basis [ 5 ] and over half (51%) are overweight [ 5 ]. These unhealthy behaviours have been associated with adverse health outcomes like cardiovascular diseases [ 6 , 7 , 8 ], respiratory diseases [ 9 ], musculoskeletal diseases [ 10 ] and, to a lesser extent, mental disorders [ 11 , 12 ].

Even though the association between lifestyle and health outcomes has been extensively investigated, biological mechanisms explaining these observed associations are not yet fully understood. One potential mechanism that can be suggested is biological ageing. Both telomere length (TL) and mitochondrial DNA content (mtDNAc) are known biomarkers of ageing. Telomeres are the end caps of chromosomes and consist of multiple TTAGGG sequence repeats. They protect chromosomes from degradation and shorten with every cell division because of the “end-replication problem” [ 13 ]. Mitochondria are crucial to the cell as they are responsible for apoptosis, the control of cytosolic calcium levels and cell signalling [ 14 ]. Living a healthy lifestyle can be linked with healthy ageing as both TL and mtDNAc have been associated with health behaviours like obesity [ 15 ], diet [ 16 ], smoking [ 17 ] and alcohol abuse [ 18 ]. Furthermore, as biomarkers of ageing, both TL and mtDNAc have been associated with age-related diseases like Parkinson’s disease [ 19 ], coronary heart disease [ 20 ], atherosclerosis [ 21 ] and early mortality [ 22 ]. Also, early mortality and higher risks for the aforementioned age-related diseases are observed in psychiatric illnesses, and it is suggested that advanced biological ageing underlies these observations [ 23 ].

Multiple studies evaluated individual health behaviours, but research on the combination of these health behaviours is limited. As they often co-occur and may cause synergistic effects, assessing them in combination with each other rather than independently might better reflect the real-life situation [ 24 , 25 ]. Therefore, in a general adult population, we combined five commonly studied health behaviours including diet, smoking status, alcohol consumption, BMI and physical activity into one healthy lifestyle score to evaluate its association with mental health and well-being and biological ageing. Furthermore, we evaluated the association between the markers of biological ageing and mental health and well-being. We hypothesise that individuals living a healthy lifestyle have a better mental health status, a longer TL and a higher mtDNAc and that these biomarkers are positively associated with mental health and well-being.

Study population

In 2018, 11611 Belgian residents participated in the 2018 Belgian Health Interview Survey (BHIS). The sampling frame of the BHIS was the Belgian National Register, and participants were selected based on a multistage stratified sampling design including a geographical stratification and a selection of municipalities within provinces, of households within municipalities and of respondents within households [ 26 ]. The study population for this cross-sectional study included 6054 BHIS participants (see flowchart in Additional file 1 : Fig. S1) [ 27 , 28 , 29 , 30 , 31 ]. Minors (< 18 years) and participants not eligible to complete the mental health modules (participants who participated through a proxy respondent, i.e. a person of confidence filled out the survey) were excluded ( n  = 2172 and n  = 846, respectively). Furthermore, of the 8593 eligible participants, those with missing information to create the mental health indicators, the lifestyle score or the covariates used in this study were excluded ( n  = 1642, 788 and 109, respectively).

For the first time in 2018, a subset of 1184 BHIS participants contributed to the 2018 Belgian Health Examination Survey (BELHES). All BHIS participants were invited to participate except for minors (< 18 years), BHIS participants who participated through a proxy respondent and residents of the German Community of Belgium, the latter representing 1% of the Belgian population. Participants were recruited on a voluntary basis until the regional quotas were reached (450, 300 and 350 in respectively Flanders, Brussels Capital Region and Wallonia). These participants underwent a health examination, including anthropological measurements and completed an additional questionnaire. Also, blood and urine samples were collected. Of the 6054 included BHIS participants, 909 participated in the BELHES. Participants for whom we could not calculate both TL and mtDNAc were excluded ( n  = 170). More specifically, participants were excluded because they did not provide a blood sample ( n  = 91) or because they did not provide permission for DNA research ( n  = 32). Twenty samples were excluded from DNA extraction because either total blood volume was too low ( n  = 7), samples were clothed ( n  = 1) or tubes were broken due to freezing conditions ( n  = 12). Twenty-seven samples were excluded because they did not meet the biomarker quality control criteria (high technical variation in qPCR triplicates). This was not met for 3 TL samples, 20 mtDNAc samples and 4 samples for both biomarkers. For this subset, we ended up with a final number of 739 participants. Further in this paper, we refer to “the BHIS subset” for the BHIS participants ( n  = 6054) and the “BELHES subset” for the BELHES participants ( n  = 739).

As part of the BELHES, this project was approved by the Medical Ethics Committee of the University Hospital Ghent (registration number B670201834895). The project was carried out in line with the recommendations of the Belgian Privacy Commission. All participants have signed a consent form that was approved by the Medical Ethics Committee.

Health interview survey

The BHIS is a comprehensive survey which aims to gain insight into the health status of the Belgian population. The questions on the different dimensions of mental health and well-being were based on international standardised and validated questionnaires [ 32 ], and this resulted in eight mental health outcomes that were used in this study. Detailed information on each indicator score and its use is addressed in Additional file 1 : Table. S1. Firstly, the General Health Questionnaire (GHQ-12) provides the prevalence of psychological and severe psychological distress in the population [ 27 ]. On the total GHQ score, cut-off points of + 2 and + 4 were used to identify respectively psychological and severe psychological distress.

Secondly, we used two indicators for the positive dimensions of mental health: vitality and life satisfaction. Four questions of the short form health survey (SF-36) indicate the participant’s vital energy level [ 28 , 33 ]. We used a cut-off point to identify participants with an optimal vitality score, which is a score equal to or above one standard deviation above the mean, as used in previous studies [ 34 , 35 ]. Life satisfaction was measured by the Cantril Scale, which ranges from 0 to 10 [ 29 ]. A cut-off point of + 6 was used to indicate participants with high or medium life satisfaction versus low life satisfaction.

Thirdly, the question “How is your health in general? Is it very good, good, fair, bad or very bad?” was used to assess self-perceived health, also known as self-rated health. Based on WHO recommendations [ 36 ], the answer categories were dichotomised into “good to very good self-perceived health” and “very bad to fair self-perceived health”.

Fourthly, depressive and generalised anxiety disorders were defined using respectively the Patient Health Questionnaire (PHQ-9) and the Generalised Anxiety Disorder Questionnaire (GAD-7). We identified individuals who suffer from major depressive syndrome or any other type of depressive syndrome according to the criteria of the PHQ-9 [ 37 ]. A cut-off point of + 10 on the total sum of the GAD-7 score was used to indicate generalised anxiety disorder [ 31 ]. Additionally, a dichotomous question on suicidal ideation was used: “Have you ever seriously thought of ending your life?”; “If yes, did you have such thoughts in the past 12 months?”. Finally, the BHIS also includes personal, socio-economic and lifestyle information. The standardised Cronbach’s alpha coefficients for the PHQ-9, GHQ-12, GAD-7 and questions on vitality of the SF-36 ranged between 0.80 and 0.90.

Healthy lifestyle score

We developed a healthy lifestyle score based on five different health behaviours: body mass index (BMI), smoking status, physical activity, alcohol consumption and diet (Table 1 ). These health behaviours were defined as much as possible according to the existing guidelines for healthy living issued by the Belgian Superior Health Council [ 38 ] and the World Health Organisation [ 39 , 40 , 41 ]. Firstly, BMI was calculated as a person’s self-reported weight in kilogrammes divided by the square of the person’s self-reported height in metres (kg/m 2 ). BMI was classified into four categories: underweight (BMI < 18.5 kg/m 2 ), normal weight (BMI 18.5–24.9 kg/m 2 ), overweight (BMI 25.0–29.9 kg/m 2 ) and obese (BMI ≥ 30.0 kg/m 2 ). Due to a J-shaped association of BMI with the overall mortality and multiple specific causes of death, obesity and underweight were both classified as least healthy [ 42 ]. BMI was scored as follows: obese and underweight = 0, overweight = 1 and normal weight = 2.

Secondly, smoking status was divided into four categories. Participants were categorised as regular smokers if they smoked a minimum of 4 days per week or if they quit smoking less than 1 month before participation (= 0). Occasional smokers were defined as smoking more than once per month up to 3 days per week (= 1). Participants were classified as former smokers if they quit smoking at least 1 month before the questionnaire or if they smoked less than once a month (= 2). The final category included people who never smoked (= 3).

Thirdly, physical activity was assessed by the question: “What describes best your leisure time activities during the last year?”. Four categories were established and scored as follows: sedentary activities (= 0), light activities less than 4 h/week (= 1), light activities more than 4 h/week or recreational sport less than 4 h/week (= 2) and recreational sport more than 4 h or intense training (= 3). Fourthly, information on the number of alcoholic drinks per week was used to categorise alcohol consumption. The different categories were set from high to low alcohol consumption: 22 drinks or more/week (= 0), 15–21 drinks/week (= 1), 8–14 drinks/week (= 2), 1–7 drinks/week (= 3)and less than 1 drink/week (= 4).

Finally, in line with the research by Benetou et al., a diet score was calculated using the frequency of consuming fruit, vegetables, snacks and sodas [ 43 ]. For fruit as well as vegetable consumption, the frequency was scored as follows: never (= 0), < 1/week (= 1), 1–3/week (= 2), 4–6/week (= 3) and ≥ 1/day (= 4). The frequency of consuming snacks and sodas was scored as follows: never (= 4), < 1/week (= 3), 1–3/week (= 2), 4–6/week (= 1) and ≥ 1/day (= 0). The diet score was then divided into tertiles, in line with the research by Benetou et al. [ 43 ]. A diet score of 0–9 points was classified as the least healthy behaviour (= 0). A diet score ranging from 10 to 12 made up the middle category (= 1), and a score from 13 to 16 was classified as the healthiest behaviour (= 2).

All five previously described health behaviours were combined into one healthy lifestyle score (Table 1 ). The sum of the scores obtained for each health behaviour indicated the absolute lifestyle score. To calculate the relative lifestyle score, each absolute scored health behaviour was given equal weight by recalculating its maximum absolute score to a relative score of 1. The relative lifestyle scores were then summed up to achieve a final continuous lifestyle score, ranging from 0 to 5, with a higher score representing a healthier lifestyle.

Telomere length and mitochondrial DNA content assay

Blood samples were collected during the BELHES and centrifuged for 15 min at 3000 rpm before storage at − 80 °C. After extracting the buffy coat from the blood sample, DNA was isolated using the QIAgen Mini Kit (Qiagen, N.V.V Venlo, The Netherlands). The purity and quantity of the sample were measured with a NanoDrop spectrophotometer (ND-2000; Thermo Fisher Scientific, Wilmington, DE, USA). DNA integrity was assessed by agarose gel electrophoresis. To ensure a uniform DNA input of 6 ng for each qPCR reaction, samples were diluted and checked using the Quant-iT™ PicoGreen® dsDNA Assay Kit (Life Technologies, Europe).

Relative TL and mtDNAc were measured in triplicate using a previously described quantitative real-time PCR (qPCR) assay with minor modifications [ 44 , 45 ]. All reactions were performed on a 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) in a 384-well format. Used telomere, mtDNAc and single copy-gene reaction mixtures and PCR cycles are given in Additional file 1 : Text. S1. Reaction efficiency was assessed on each plate by using a 6-point serial dilution of pooled DNA. Efficiencies ranged from 90 to 100% for single-copy gene runs, 100 to 110% for telomere runs and 95 to 105% for mitochondrial DNA runs. Six inter-run calibrators (IRCs) were used to account for inter-run variability. Also, non-template controls were used in each run. Raw data were processed and normalised to the reference gene using the qBase plus software (Biogazelle, Zwijnaarde, Belgium), taking into account the run-to-run differences.

Leucocyte telomere length was expressed as the ratio of telomere copy number to single-copy gene number (T/S) relative to the mean T/S ratio of the entire study population. Leucocyte mtDNAc was expressed as the ratio of mtDNA copy number to single-copy gene number (M/S) relative to the mean M/S ratio of the entire study population. The reliability of our assay was assessed by calculating the interclass correlation coefficient (ICC) of the triplicate measures (T/S and M/S ratios and T, M and S separately) as proposed by the Telomere Research Network, using RStudio version 1.1.463 (RStudio PBC, Boston, MA, USA). The intra-plate ICCs of T/S ratios, TL runs, M/S ratios, mtDNAc runs and single-copy runs were respectively 0.804 ( p  < 0.0001), 0.907 ( p  < 0.0001), 0.815 ( p  < 0.0001), 0.916 ( p  < 0.0001) and 0.781 ( p  < 0.0001). Based on the IRCs, the inter-plate ICC was 0.714 ( p  < 0.0001) for TL and 0.762 ( p  < 0.0001) for mtDNAc.

Statistical analysis

Statistical analyses were performed using the SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA). We performed a log(10) transformation of the TL and mtDNAc data to reduce skewness and to better approximate a normal distribution. Three analyses were done: (1) In the BHIS subset ( n  = 6054), we evaluated the association between the lifestyle score and the mental health and well-being outcomes (separately). These results are presented as the odds ratio (95% CI) of having a mental health condition or disorder for a one-point increment in the lifestyle score. (2) In the BELHES subset ( n  = 739), we evaluated the association between the lifestyle score and both TL and mtDNAc (separately). These results are presented as the percentage difference in TL or mtDNAc (95% CI) for a one-point increment in the lifestyle score. (3) In the BELHES subset ( n  = 739), we evaluated the association between the mental health and well-being outcomes and both TL and mtDNAc (separately). These results are presented as the percentage difference in TL or mtDNAc (95% CI) when having a mental health condition or disorder compared with the healthy group.

For all three analyses, we performed multivariable linear mixed models (GLIMMIX; unstructured covariance matrix) taking into account a priori selected covariates including age (continuous), sex (male, female), region (Flanders, Brussels Capital Region, Wallonia), highest educational level of the household (up to lower secondary, higher secondary, college or university), country of birth (Belgium, EU, non-EU) and household type (single, one parent with child, couple without child, couple with child, others). To capture the non-linear effect of age, we included a quadratic term when the result of the analysis showed that both the linear and quadratic terms had a p -value < 0.1. For the two analyses on TL and mtDNAc, we additionally adjusted for the date of participation in the BELHES. As multiple members of one household participated, we added household numbers in the random statement.

Bivariate analyses evaluating the associations between the characteristics and TL, mtDNAc, the lifestyle score or psychological distress as a parameter of mental health and well-being are evaluated based on the same model. The chi-squared tests (categorical data) and t -tests (continuous data) were used to evaluate the characteristics of included and excluded participants. The lifestyle score was validated by creating a ROC curve and calculating the area under the curve (AUC) of the adjusted association between the lifestyle score and self-perceived health. Adjustments were made for age, sex, region, highest educational level of the household, country of birth and household type.

In a sensitivity analysis, to evaluate the robustness of our findings, we additionally adjusted our main models separately for perceived quality of social support (poor, moderate, strong) and chronic disease (suffering from any chronic disease or condition: yes, no). The third model, evaluating the biomarkers with the mental health outcomes, was also additionally adjusted for the lifestyle score.

Population characteristics

The characteristics of the BHIS and BELHES subset are presented in Table 2 . In the BHIS subset, 48.8% of the participants were men. The average age (SD) was 49.9 (17.5) years, and most participants were born in Belgium (79.5%). The highest educational level in the household was most often college or university degree (53.3%), and the most common household composition was couple with child(ren) (37.7%). The proportion of participants in different regions of Belgium, i.e. Flanders, Brussels Capital Region and Wallonia, was respectively 41.1%, 23.3% and 35.6%. For the BELHES subset, we found similar results except for region and education. We noticed more participants from Flanders and more participants with a high educational level in the household. The mean (SD) relative TL and mtDNAc were respectively 1.04 (0.23) and 1.03 (0.24). TL and mtDNAc were positively correlated (Spearman’s correlation = 0.21, p  < 0.0001).

We compared (1) the characteristics of the 6054 eligible BHIS participants that were included in the BHIS subset with the 2539 eligible participants that were excluded from the BHIS subset (Additional file 1 : Table S2) and (2) the 739 participants from the BHIS subset that were included in the BELHES subset with the 5315 participants that were excluded from the BELHES subset (Additional file 1 : Table S3). Except for sex and nationality in the latter, all other covariates showed differences between the included and excluded groups. On the other hand, population data from 2018 indicates that the average age (SD) of the adult Belgian population was 49.5 (18.9) with a distribution over Flanders, Brussels Capital Region and Wallonia of respectively 58.2%, 10.2% and 31.6% and that 48.7% were men. The distribution of our sample according to age and sex thus largely corresponds to the age and sex distribution of the adult Belgian population figures. The large difference in the regional distribution is due to the oversampling of the Brussels Capital Region in the BHIS.

Bivariate associations evaluating the characteristics with TL, mtDNAc, the lifestyle score or psychological distress as a parameter of mental health are presented in Additional file 1 : Table S4. Briefly, men had a − 6.41% (95% CI: − 9.10 to − 3.65%, p  < 0.0001) shorter TL, a − 8.03% (95% CI: − 11.00 to − 4.96%, p  < 0.0001) lower mtDNAc, lower odds of psychological distress (OR = 0.59, 95% CI: 0.53 to 0.66, p  < 0.0001) and a lifestyle score of − 0.28 (95% CI: − 0.32 to − 0.24, p  < 0.0001) points less compared with women. Furthermore, a 1-year increment in age was associated with a − 0.64% (− 0.73 to − 0.55%, p  < 0.0001) shorter TL and a − 0.19% (95% CI: − 0.31 to − 0.08%, p  = 0.00074) lower mtDNAc.

Mental health prevalence and lifestyle characteristics

Within the BHIS subset, 32.3% and 18.0% of the participants had respectively psychological and severe psychological distress. 86.7% had suboptimal vitality, 12.0% indicated low life satisfaction and 22.0% had very bad to fair self-perceived health. The prevalence of depressive and generalised anxiety disorders was respectively 9.0% and 10.8%, respectively. 4.4% of the participants indicated to have had suicidal thoughts in the past 12 months. Similar results were found for the BELHES subset (Table 3 ).

Within the BHIS subset, the average lifestyle score (SD) was 3.1 (0.9) (Table 4 ). A histogram of the lifestyle score is shown in Additional file 1 : Fig. S2. 16.6% were regular smokers, and 4.9% reported 22 alcoholic drinks per week or more. 29.7% reported that their main leisure time included mainly sedentary activities, and 18.6% were underweight or obese. 29.2% were classified as having an unhealthy diet score. The participants of the BELHES subset were slightly more active, but no other dissimilarities were found (Table 4 ). The ROC curve shows an area under the curve (AUC) of 0.74, indicating a 74% predictive accuracy for the lifestyle score as a self-perceived health predictor (Additional file 1 : Fig. S3).

Healthy lifestyle and mental health and well-being

Living a healthier lifestyle, indicated by having a higher lifestyle score, was associated with lower odds of all mental health and well-being outcomes (Table 5 ). After adjustment, a one-point increment in the lifestyle score was associated with lower odds of psychological (OR = 0.74, 95% CI: 0.69, 0.79) and severe psychological distress (OR = 0.69, 95% CI: 0.64, 0.75). Similarly, for the same increment, the odds of suboptimal vitality, low life satisfaction and very bad to fair self-perceived health were respectively 0.62 (95% CI: 0.56, 0.68), 0.62 (95% CI: 0.56, 0.68) and 0.56 (95% CI: 0.52, 0.61). Finally, the odds of having depressive disorder, generalised anxiety disorder or suicidal ideation were respectively 0.57 (95% CI: 0.51, 0.63), 0.63 (95% CI: 0.57, 0.69) and 0.63 (95% CI: 0.55, 0.72) for a one-point increment in the lifestyle score.

The biomarkers of ageing

After adjustment, living a healthy lifestyle was positively associated with both TL and mtDNAc (Table 6 ). A one-point increment in the lifestyle score was associated with a 1.74 (95% CI: 0.11, 3.40%, p  = 0.037) higher TL and a 4.07 (95% CI: 2.01, 6.17%, p  = 0.00012) higher mtDNAc.

People suffering from severe psychological distress had a − 4.62% (95% CI: − 8.85, − 0.20%, p  = 0.041) lower mtDNAc compared with those who did not suffer from severe psychological distress. Similarly, people with suicidal ideation had a − 7.83% (95% CI: − 14.77, − 0.34%, p  = 0.041) lower mtDNAc compared with those without suicidal ideation. No associations were found for the other mental health and well-being outcomes, and no associations were found between mental health and TL (Table 6 ).

Sensitivity analysis

Additional adjustment of the main analyses for perceived quality of social support, chronic disease or lifestyle score (in the association between the mental health outcomes and the biomarkers of ageing) did not strongly change the effect of our observations (Additional file 1 : Tables S5-S7). However, we noticed that most of the associations between severe psychological distress or suicidal ideation and mtDNAc showed marginally significant results.

In this study, we evaluated the associations between eight mental health and well-being outcomes, a healthy lifestyle score and 2 biomarkers of biological ageing: telomere length and mitochondrial DNA content. Having a healthy lifestyle was positively associated with all mental health and well-being indicators and the markers of biological ageing. Furthermore, having had suicidal ideation or suffering from severe psychological distress was associated with a lower mtDNAc. However, no association was found between mental health and TL.

In the first part of this research, we evaluated the association between lifestyle and mental health and well-being and showed that living a healthy lifestyle was positively associated with better mental health and well-being outcomes. Similar trends were found in previous studies for each of the health behaviours separately [ 11 , 12 , 46 , 47 , 48 ]. Although evaluating these health behaviours separately provides valuable information, assessing them in combination with each other rather than independently might better reflect the real-life situation as they often co-occur and may exert a synergistic effect on each other [ 24 , 25 , 49 ]. For example, 68% of the adults in England engaged in two or more unhealthy behaviours [ 25 ]. Especially, smoking status and alcohol consumption co-occurred, but half of the studies in the review by Noble et al. indicated clustering of all included health behaviours [ 24 ].

To date, the number of studies evaluating the combination of multiple health behaviours and mental health and well-being in adults is limited, and most of them use a different methodology to assess this association [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ]. Firstly, differences are found between the included health behaviours. Most studies included the four “SNAP” risk factors, i.e. smoking, poor nutrition, excess alcohol consumption and physical inactivity. Other health behaviours that were sometimes included were BMI/obesity, sleep duration/quality and psychological distress [ 50 , 53 , 54 , 56 ]. Secondly, differences are found in the scoring of the health behaviours and the use of the lifestyle score. Whereas in this study the health behaviours were scored categorically, studies often dichotomised the health behaviours and/or the final lifestyle score [ 50 , 52 , 53 , 56 ]. Also, two studies performed clustering [ 54 , 55 ]. Health behaviours can cluster together at both ends of the risk spectrum, but less is known about the middle categories. This is avoided by using the cluster method where participants are clustered based on similar behaviours. On the other hand, a lifestyle score can be of better use and more easily be interpreted when aiming to compare healthy versus unhealthy lifestyles, as was the case for this study.

Despite these different methods, all previously mentioned studies show similar results. Together with our findings, which also support these results, this provides clear evidence that an unhealthy lifestyle is associated with poor mental health and well-being outcomes. Important to notice is that, like our research, most studies in this field have a cross-sectional design and are therefore not able to assume causality. Therefore, mental health might be the cause or the consequence of an unhealthy lifestyle. Further prospective and longitudinal studies are warranted to confirm the direction of the association.

Healthy lifestyle and biomarkers of ageing

How lifestyle affects our health is not yet fully understood. One possible pathway is through oxidative stress and biological ageing. An unhealthy lifestyle has been associated with an increase in oxidative stress [ 57 , 58 , 59 ], and in turn, higher concentrations of oxidative stress are known to negatively affect TL and mtDNAc [ 60 ]. In this study, we showed that living a healthy lifestyle was associated with a longer TL and a higher mtDNAc. Our results showed a stronger association of lifestyle with mtDNAc compared with TL. TL is strongly determined by TL at birth [ 61 ]. On the other hand, mtDNAc might be more variable in shorter time periods. Although mtDNAc and TL were strongly correlated, this could explain why lifestyle is more strongly associated with mtDNAc. However, we can only speculate about this, and further research is necessary to confirm our results.

Similar as for the association with mental health, in previous studies, the biomarkers have been associated with health behaviours separately rather than combined [ 62 , 63 , 64 , 65 ]. To our knowledge, we are the first to evaluate the associations between a healthy lifestyle score and mtDNAc. Our results are in line with our expectations. As TL and mtDNAc are known to be correlated [ 60 ], we would expect similar trends for both biomarkers. In the case of TL, few studies included a combined lifestyle score in association with this biomarker. Consistent with our results, in a study population of 1661 men, the sum score of a healthier lifestyle was correlated with a longer TL [ 66 ]. Similar results were found by Sun et al. where a combination of healthy lifestyles in a female study population was associated with a longer TL compared with the least healthy group [ 67 ]. Also, improvement in lifestyle has been associated with TL maintenance in the elderly at risk for dementia [ 68 ], and a lifestyle intervention programme was positively associated with leucocyte telomere length in children and adolescents [ 69 ]. These results suggest that on a biological level, a healthy lifestyle is associated with healthy ageing. Within this context, a study on adults aged 60 and older showed that maintaining a normal weight, not smoking and performing regular physical activity were associated with slower development of disability and a reduction in mortality [ 70 ]. Similarly, midlife lifestyle factors like non-smoking, higher levels of physical activity, non-obesity and good social support have been associated with successful ageing, 22 years later [ 71 ].

Mental health and well-being and biomarkers of ageing

Finally, we evaluated the association between the biomarkers of ageing and the mental health and well-being outcomes. The hypothesis that biological ageing is associated with mental health has been supported by observations showing that chronically stressed or psychiatrically ill persons have a higher risk for age-related diseases like dementia, diabetes and hypertension [ 23 , 72 , 73 ]. Important to notice is that, like our research, the majority of studies on this topic have a cross-sectional design and therefore are unable to identify causality. Therefore, it is currently unknown whether psychological diseases accelerate biological ageing or whether biological ageing precedes the onset of these diseases [ 74 ].

Our results showed a lower mtDNAc for individuals with suicidal ideation or severe psychological distress but not for any of the other mental health outcomes. Evidence on the association between mtDNAc and mental health is inconsistent. Women above 60 years old with depression had a significantly lower mtDNAc compared with the control group [ 75 ]. Furthermore, individuals with a low mtDNAc had poorer outcomes in terms of self-rated health [ 76 ]. In contrast, Otsuka et al. showed a higher peripheral blood mtDNAc in suicide completers [ 77 ], and studies on major depressive syndrome [ 78 ] and self-rated health [ 79 ] showed the same trend. Finally, Vyas et al. showed no significant association between mtDNAc and depression status in mid-life and older adults [ 80 ]. These differences might be due to the differences in study population and methods. For example, the two studies indicating lower mtDNAc in association with poor mental health both had an elderly study population, and one study population consisted of psychiatrically ill patients. Next to that, differences were found in the type of samples, mtDNAc assays and questionnaires or diagnostics. The inconsistency of these studies and our results calls for further research on this association and for standardisation of methods within studies to enable clear comparisons.

As for TL, we did not find an association with any of the mental health and well-being outcomes. Previous studies in adults showed a lower TL in association with current but not remitted anxiety disorder [ 81 ], depressive [ 82 ] and major depressive disorder [ 73 , 83 ], childhood trauma [ 84 ] suicide [ 77 , 85 ], depressive symptoms in younger adults [ 86 ] and acculturative stress and postpartum depression in Latinx women [ 87 ]. Also, in a meta-analysis, psychiatric disorders overall were associated with a shorter leucocyte TL [ 88 ]. However, other studies failed to demonstrate an association between TL and mental health outcomes like major depressive disorder [ 89 ], late-life depression [ 90 ] and anxiety disorder [ 91 ]. Again, this could be due to a different method to assess the mental health outcomes, a different study design, uncontrolled confounding factors and the type of telomere assay. For example, a meta-analysis showed stronger associations with depression when using southern blot or FISH assay compared with qPCR to measure telomere length [ 92 ].

Strengths and limitations

An important strength of this study is the use of a validated lifestyle score that can easily be reproduced and used for other research on lifestyle. Secondly, we were able to use a large sample size for our analyses in the BHIS subset. Thirdly, by assessing multiple dimensions of mental health and well-being, we were able to give a comprehensive overview of the mental health status. To our knowledge, we are the first to evaluate the associations between a healthy lifestyle score and mtDNAc.

Our results should however be interpreted with consideration for some limitations. As mentioned before, the study has a cross-sectional design, and therefore, we cannot assume causality. Secondly, for the lifestyle score, we used self-reported data, which might not always represent the actual situation. For example, BMI values tend to be underestimated due to the overestimation of height and underestimation of weight [ 93 ], and also, smoking behaviour is often underestimated [ 94 ]. Also, equal weights were used for each of the health behaviours as no objective information was available on which weight should be given to a specific health behaviour. Thirdly, there is a distinct time lag between the completion of the BHIS questionnaire and the collection of the BELHES samples. The mean (SD) number of days is 52 (35). This is less than the period for suicidal ideation, assessed over the 12 previous months, but there might be a more limited overlap with the period for assessment of the other mental health variables, such as vitality and psychological distress, assessed over the last few weeks, and depressive and generalised anxiety disorders, assessed over the last 2 weeks. Fourthly, due to a non-response bias, the lowest socio-economic classes are less represented in our study population. This will not affect our dose–response associations but might affect the generalisability of our findings to the overall population. Finally, we do not have data on blood cell counts, which has been associated with mtDNAc [ 95 ].

In this large-scale study, we showed that living a healthy lifestyle was positively associated with mental health and well-being and, on a biological level, with a higher TL and mtDNAc, indicating healthy ageing. Furthermore, individuals with suicidal ideation or suffering from severe psychological distress had a lower mtDNAc. Our findings suggest that implementing strategies to incorporate healthy lifestyle changes in the public’s daily life could be beneficial for public health, and might offset the negative impact of environmental stressors. However, further studies are necessary to confirm our results and especially prospective and longitudinal studies are essential to determine causality of the associations.

Availability of data and materials

The dataset used for this study is available through a request to the Health Committee of the Data Protection Authority.

Abbreviations

Area under the curve

Body mass index

Confidence intervals

Generalised Anxiety Disorder Questionnaire

General Health Questionnaire

Inter-run calibrator

• Mitochondrial DNA content

Patient Health Questionnaire

Relative operating characteristic curve

Short Form Health Survey

• Telomere length

World Health Organization. Healthy living: what is a healthy lifestyle? Copenhagen: WHO Regional Office for Europe; 1999.

Google Scholar  

World Health Organization. Tackling NCDs: ‘best buys’ and other recommended interventions for the prevention and control of noncommunicable diseases. Geneva: World Health Organization; 2017.

World Health Organization. WHO global report on trends in prevalence of tobacco smoking 2000–2025. 3rd ed. Geneva: World Health Organisation; 2019.

World Health Organization. 2021 Physical Activity Factsheets for the European Union Member States in the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2021.

Eurostat. Database on health determinants 2021 [Available from: https://ec.europa.eu/eurostat/web/health/data/database?node_code=hlth_det .

Whitman IR, Agarwal V, Nah G, Dukes JW, Vittinghoff E, Dewland TA, et al. Alcohol abuse and cardiac disease. J Am Coll Cardiol. 2017;69(1):13–24. https://doi.org/10.1016/j.jacc.2016.10.048 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Koliaki C, Liatis S, Kokkinos A. Obesity and cardiovascular disease: revisiting an old relationship. Metabolism. 2019;92:98–107. https://doi.org/10.1016/j.metabol.2018.10.011 .

Article   CAS   PubMed   Google Scholar  

Freisling H, Viallon V, Lennon H, Bagnardi V, Ricci C, Butterworth AS, et al. Lifestyle factors and risk of multimorbidity of cancer and cardiometabolic diseases: a multinational cohort study. BMC Med. 2020;18(1):5. https://doi.org/10.1186/s12916-019-1474-7 .

Liu Y, Pleasants RA, Croft JB, Wheaton AG, Heidari K, Malarcher AM, et al. Smoking duration, respiratory symptoms, and COPD in adults aged ≥ 45 years with a smoking history. Int J Chron Obstruct Pulmon Dis. 2015;10:1409. https://doi.org/10.2147/COPD.S82259 .

Kirsch Micheletti J, Bláfoss R, Sundstrup E, Bay H, Pastre CM, Andersen LL. Association between lifestyle and musculoskeletal pain: cross-sectional study among 10,000 adults from the general working population. BMC Musculoskelet Disord. 2019;20(1):609. https://doi.org/10.1186/s12891-019-3002-5 .

Bowe AK, Owens M, Codd MB, Lawlor BA, Glynn RW. Physical activity and mental health in an Irish population. Ir J Med Sci. 2019;188(2):625–31. https://doi.org/10.1007/s11845-018-1863-5 .

Article   PubMed   Google Scholar  

Richardson S, McNeill A, Brose LS. Smoking and quitting behaviours by mental health conditions in Great Britain (1993–2014). Addict Behav. 2019;90:14–9. https://doi.org/10.1016/j.addbeh.2018.10.011 .

Article   PubMed   PubMed Central   Google Scholar  

Levy MZ, Allsopp RC, Futcher AB, Greider CW, Harley CB. Telomere end-replication problem and cell aging. J Mol Biol. 1992;225(4):951–60. https://doi.org/10.1016/0022-2836(92)90096-3 .

Shaughnessy DT, McAllister K, Worth L, Haugen AC, Meyer JN, Domann FE, et al. Mitochondria, energetics, epigenetics, and cellular responses to stress. Environ Health Perspect. 2014;122(12):1271–8. https://doi.org/10.1289/ehp.1408418 .

Cui Y, Gao YT, Cai Q, Qu S, Cai H, Li HL, et al. Associations of leukocyte telomere length with body anthropometric indices and weight change in Chinese women. Obesity. 2013;21(12):2582–8. https://doi.org/10.1002/oby.20321 .

Crous-Bou M, Fung TT, Prescott J, Julin B, Du M, Sun Q, et al. Mediterranean diet and telomere length in Nurses’ Health Study: population based cohort study. BMJ. 2014;349:g6674. https://doi.org/10.1136/bmj.g6674 .

Janssen BG, Gyselaers W, Byun H-M, Roels HA, Cuypers A, Baccarelli AA, et al. Placental mitochondrial DNA and CYP1A1 gene methylation as molecular signatures for tobacco smoke exposure in pregnant women and the relevance for birth weight. J Transl Med. 2017;15(1):5. https://doi.org/10.1186/s12967-016-1113-4 .

Navarro-Mateu F, Husky M, Cayuela-Fuentes P, Álvarez FJ, Roca-Vega A, Rubio-Aparicio M, et al. The association of telomere length with substance use disorders: a systematic review and meta-analysis of observational studies. Addiction. 2020;116(8):1954–72. https://doi.org/10.1111/add.15312 .

Pyle A, Anugrha H, Kurzawa-Akanbi M, Yarnall A, Burn D, Hudson G. Reduced mitochondrial DNA copy number is a biomarker of Parkinson’s disease. Neurobiol Aging. 2016;38:216.e7-.e10. https://doi.org/10.1016/j.neurobiolaging.2015.10.033 .

Article   CAS   Google Scholar  

Ashar FN, Zhang Y, Longchamps RJ, Lane J, Moes A, Grove ML, et al. Association of mitochondrial DNA copy number with cardiovascular disease. JAMA Cardiol. 2017;2(11):1247–55. https://doi.org/10.1001/jamacardio.2017.3683 .

Chen S, Lin J, Matsuguchi T, Blackburn E, Yeh F, Best LG, et al. Short leukocyte telomere length predicts incidence and progression of carotid atherosclerosis in American Indians: the Strong Heart Family Study. Aging. 2014;6(5):414–27. https://doi.org/10.18632/aging.100671 .

Mons U, Müezzinler A, Schöttker B, Dieffenbach AK, Butterbach K, Schick M, et al. Leukocyte telomere length and all-cause, cardiovascular disease, and cancer mortality: results from individual-participant-data meta-analysis of 2 large prospective cohort studies. Am J Epidemiol. 2017;185(12):1317–26. https://doi.org/10.1093/aje/kww210 .

Lindqvist D, Epel ES, Mellon SH, Penninx BW, Révész D, Verhoeven JE, et al. Psychiatric disorders and leukocyte telomere length: underlying mechanisms linking mental illness with cellular aging. Neurosci Biobehav Rev. 2015;55:333–64. https://doi.org/10.1016/j.neubiorev.2015.05.007 .

Noble N, Paul C, Turon H, Oldmeadow C. Which modifiable health risk behaviours are related? A systematic review of the clustering of Smoking, Nutrition, Alcohol and Physical activity (‘SNAP’) health risk factors. Prev Med. 2015;81:16–41. https://doi.org/10.1016/j.ypmed.2015.07.003 .

Poortinga W. The prevalence and clustering of four major lifestyle risk factors in an English adult population. Prev Med. 2007;44(2):124–8. https://doi.org/10.1016/j.ypmed.2006.10.006 .

Demarest S, Van der Heyden J, Charafeddine R, Drieskens S, Gisle L, Tafforeau J. Methodological basics and evolution of the Belgian Health Interview Survey 1997–2008. Arch Public Health. 2013;71(1):24. https://doi.org/10.1186/0778-7367-71-24 .

Goldberg DP. User’s guide to the General Health Questionnaire. Windsor: NFER-Nelson; 1988.

Ware JE Jr, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220–33. https://doi.org/10.1097/00005650-199603000-00003 .

Cantril H. Pattern of human concerns. New Brunswick: Rutgers University Press; 1965.

Kroenke K, Spitzer RL, Williams JB, Löwe B. The patient health questionnaire somatic, anxiety, and depressive symptom scales: a systematic review. Gen Hosp Psychiatry. 2010;32(4):345–59. https://doi.org/10.1016/j.genhosppsych.2010.03.006 .

Löwe B, Decker O, Müller S, Brähler E, Schellberg D, Herzog W, et al. Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population. Med Care. 2008;46(3):266–74. https://doi.org/10.1097/MLR.0b013e318160d093 .

Gisle L, Drieskens S, Demarest S, Van der Heyden J. Enquête de santé 2018: Santé mentale. Bruxelles: Sciensano; 2018. ( https://www.enquetesante.be . Available from. Numéro de rapport: D/2020/14.440/3).

Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Medical Care. 1992;30(6):473–83.

Article   Google Scholar  

Braunholtz S, Davidson S, Myant K, O’Connor R. Well? What do you think?: the Third National Scottish Survey of Public Attitudes to Mental Health, Mental Wellbeing and Mental Health Problems. Scotland: Scottish Government Edinburgh; 2007.

Van Lente E, Barry MM, Molcho M, Morgan K, Watson D, Harrington J, et al. Measuring population mental health and social well-being. Int J Public Health. 2012;57(2):421–30. https://doi.org/10.1007/s00038-011-0317-x .

de Bruin A, Picavet HSJ, Nossikov A. Health interview surveys: towards international harmonization of methods and instruments. Copenhagen: World Health Organization. Regional Office for Europe; 1996.

Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.

Superior Health Council. Dietary guidelines for the Belgian adult population. Report 9284. Brussels: Superior Health Council; 2019.

World Health Organization. WHO guidelines on physical activity and sedentary behaviour. Geneva: World Health Organization; 2020.

World Health Organization. HEARTS Technical package for cardiovascular disease management in primary health care: healthy-lifestyle counselling. Geneva: World Health Organization; 2018.

World Health Organization. Country profiles on nutrition, physical activity and obesity in the 28 European Union Member States of the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2013.

Bhaskaran K, dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3.6 million adults in the UK. Lancet Diabetes Endocrinol. 2018;6(12):944–53. https://doi.org/10.1016/S2213-8587(18)30288-2 .

Benetou V, Kanellopoulou A, Kanavou E, Fotiou A, Stavrou M, Richardson C, et al. Diet-related behaviors and diet quality among school-aged adolescents living in Greece. Nutrients. 2020;12(12):3804. https://doi.org/10.3390/nu12123804 .

Article   PubMed Central   Google Scholar  

Martens DS, Plusquin M, Gyselaers W, De Vivo I, Nawrot TS. Maternal pre-pregnancy body mass index and newborn telomere length. BMC Med. 2016;14(1):148. https://doi.org/10.1186/s12916-016-0689-0 .

Janssen BG, Munters E, Pieters N, Smeets K, Cox B, Cuypers A, et al. Placental mitochondrial DNA content and particulate air pollution during in utero life. Environ Health Perspect. 2012;120(9):1346–52. https://doi.org/10.1289/ehp.1104458 .

Jacka FN, O’Neil A, Opie R, Itsiopoulos C, Cotton S, Mohebbi M, et al. A randomised controlled trial of dietary improvement for adults with major depression (the ‘SMILES’ trial). BMC Med. 2017;15(1):23. https://doi.org/10.1186/s12916-017-0791-y .

Pavkovic B, Zaric M, Markovic M, Klacar M, Huljic A, Caricic A. Double screening for dual disorder, alcoholism and depression. Psychiatry Res. 2018;270:483–9. https://doi.org/10.1016/j.psychres.2018.10.013 .

De Wit LM, Van Straten A, Van Herten M, Penninx BW, Cuijpers P. Depression and body mass index, a u-shaped association. BMC Public Health. 2009;9:14. https://doi.org/10.1186/1471-2458-9-14 .

Meader N, King K, Moe-Byrne T, Wright K, Graham H, Petticrew M, et al. A systematic review on the clustering and co-occurrence of multiple risk behaviours. BMC Public Health. 2016;16:657. https://doi.org/10.1186/s12889-016-3373-6 .

Saneei P, Esmaillzadeh A, Hassanzadeh Keshteli A, Reza Roohafza H, Afshar H, Feizi A, et al. Combined healthy lifestyle is inversely associated with psychological disorders among adults. PLoS ONE. 2016;11(1):e0146888. https://doi.org/10.1371/journal.pone.0146888 .

Bonnet F, Irving K, Terra J-L, Nony P, Berthezène F, Moulin P. Anxiety and depression are associated with unhealthy lifestyle in patients at risk of cardiovascular disease. Atherosclerosis. 2005;178(2):339–44. https://doi.org/10.1016/j.atherosclerosis.2004.08.035 .

Loprinzi PD, Mahoney S. Concurrent occurrence of multiple positive lifestyle behaviors and depression among adults in the United States. J Affect Disord. 2014;165:126–30. https://doi.org/10.1016/j.jad.2014.04.073 .

Yang H, Gao J, Wang T, Yang L, Liu Y, Shen Y, et al. Association between adverse mental health and an unhealthy lifestyle in rural-to-urban migrant workers in Shanghai. J Formos Med Assoc. 2017;116(2):90–8. https://doi.org/10.1016/j.jfma.2016.03.004 .

Oftedal S, Kolt GS, Holliday EG, Stamatakis E, Vandelanotte C, Brown WJ, et al. Associations of health-behavior patterns, mental health and self-rated health. Prev Med. 2019;118:295–303. https://doi.org/10.1016/j.ypmed.2018.11.017 .

Conry MC, Morgan K, Curry P, McGee H, Harrington J, Ward M, et al. The clustering of health behaviours in Ireland and their relationship with mental health, self-rated health and quality of life. BMC Public Health. 2011;11(1):692. https://doi.org/10.1186/1471-2458-11-692 .

Buttery AK, Mensink GB, Busch MA. Healthy behaviours and mental health: findings from the German Health Update (GEDA). The European Journal of Public Health. 2015;25(2):219–25. https://doi.org/10.1093/eurpub/cku094 .

Ahmed NJ, Husen AZ, Khoshnaw N, Getta HA, Hussein ZS, Yassin AK, et al. The effects of smoking on IgE, oxidative stress and haemoglobin concentration. Asian Pac J Cancer Prev. 2020;21(4):1069–72. https://doi.org/10.31557/APJCP.2020.21.4.1069 .

Langley MR, Yoon H, Kim HN, Choi C-I, Simon W, Kleppe L, et al. High fat diet consumption results in mitochondrial dysfunction, oxidative stress, and oligodendrocyte loss in the central nervous system. Biochim Biophys Acta Mol Basis Dis. 2020;1866(3):165630. https://doi.org/10.1016/j.bbadis.2019.165630 .

Tan HK, Yates E, Lilly K, Dhanda AD. Oxidative stress in alcohol-related liver disease. World J Hepatol. 2020;12(7):332–49. https://doi.org/10.4254/wjh.v12.i7.332 .

Martens DS, Nawrot TS. Air pollution stress and the aging phenotype: the telomere connection. Curr Environ Health Rep. 2016;3(3):258–69. https://doi.org/10.1007/s40572-016-0098-8 .

Martens DS, Van Der Stukken C, Derom C, Thiery E, Bijnens EM, Nawrot TS. Newborn telomere length predicts later life telomere length: tracking telomere length from birth to child- and adulthood. EBioMedicine. 2021;63:103164. https://doi.org/10.1016/j.ebiom.2020.103164 .

Hang D, Nan H, Kværner AS, De Vivo I, Chan AT, Hu Z, et al. Longitudinal associations of lifetime adiposity with leukocyte telomere length and mitochondrial DNA copy number. Eur J Epidemiol. 2018;33(5):485–95. https://doi.org/10.1007/s10654-018-0382-z .

Gu Y, Honig LS, Schupf N, Lee JH, Luchsinger JA, Stern Y, et al. Mediterranean diet and leukocyte telomere length in a multi-ethnic elderly population. Age. 2015;37(2):24. https://doi.org/10.1007/s11357-015-9758-0 .

Sellami M, Al-Muraikhy S, Al-Jaber H, Al-Amri H, Al-Mansoori L, Mazloum NA, et al. Age and sport intensity-dependent changes in cytokines and telomere length in elite athletes. Antioxidants. 2021;10(7):1035. https://doi.org/10.3390/antiox10071035 .

Savela S, Saijonmaa O, Strandberg TE, Koistinen P, Strandberg AY, Tilvis RS, et al. Physical activity in midlife and telomere length measured in old age. Exp Gerontol. 2013;48(1):81–4. https://doi.org/10.1016/j.exger.2012.02.003 .

Mirabello L, Huang WY, Wong JY, Chatterjee N, Reding D, David Crawford E, et al. The association between leukocyte telomere length and cigarette smoking, dietary and physical variables, and risk of prostate cancer. Aging Cell. 2009;8(4):405–13. https://doi.org/10.1111/j.1474-9726.2009.00485.x .

Sun Q, Shi L, Prescott J, Chiuve SE, Hu FB, De Vivo I, et al. Healthy lifestyle and leukocyte telomere length in US women. PLoS ONE. 2012;7(5):e38374. https://doi.org/10.1371/journal.pone.0038374 .

Sindi S, Solomon A, Kåreholt I, Hovatta I, Antikainen R, Hänninen T, et al. Telomere length change in a multidomain lifestyle intervention to prevent cognitive decline: a randomized clinical trial. J Gerontol A. 2021;76(3):491–8. https://doi.org/10.1093/gerona/glaa279 .

Paltoglou G, Raftopoulou C, Nicolaides NC, Genitsaridi SM, Karampatsou SI, Papadopoulou M, et al. A comprehensive, multidisciplinary, personalized, lifestyle intervention program is associated with increased leukocyte telomere length in children and adolescents with overweight and obesity. Nutrients. 2021;13(8):2682. https://doi.org/10.3390/nu13082682 .

Chakravarty EF, Hubert HB, Krishnan E, Bruce BB, Lingala VB, Fries JF. Lifestyle risk factors predict disability and death in healthy aging adults. Am J Med. 2012;125(2):190–7. https://doi.org/10.1016/j.amjmed.2011.08.006 .

Bosnes I, Nordahl HM, Stordal E, Bosnes O, Myklebust TÅ, Almkvist O. Lifestyle predictors of successful aging: a 20-year prospective HUNT study. PLoS ONE. 2019;14(7):e0219200. https://doi.org/10.1371/journal.pone.0219200 .

Epel ES, Prather AA. Stress, telomeres, and psychopathology: toward a deeper understanding of a triad of early aging. Annu Rev Clin Psychol. 2018;14:371–97. https://doi.org/10.1146/annurev-clinpsy-032816-045054 .

Verhoeven JE, Révész D, Epel ES, Lin J, Wolkowitz OM, Penninx BW. Major depressive disorder and accelerated cellular aging: results from a large psychiatric cohort study. Mol Psychiatry. 2014;19(8):895–901. https://doi.org/10.1038/mp.2013.151 .

Han LK, Verhoeven JE, Tyrka AR, Penninx BW, Wolkowitz OM, Månsson KN, et al. Accelerating research on biological aging and mental health: current challenges and future directions. Psychoneuroendocrinology. 2019;106:293–311. https://doi.org/10.1016/j.psyneuen.2019.04.004 .

Kim MY, Lee JW, Kang HC, Kim E, Lee DC. Leukocyte mitochondrial DNA (mtDNA) content is associated with depression in old women. Arch Gerontol Geriatr. 2011;53(2):e218–21. https://doi.org/10.1016/j.archger.2010.11.019 .

Mengel-From J, Thinggaard M, Dalgard C, Kyvik KO, Christensen K, Christiansen L. Mitochondrial DNA copy number in peripheral blood cells declines with age and is associated with general health among elderly. Hum Genet. 2014;133(9):1149–59. https://doi.org/10.1007/s00439-014-1458-9 .

Otsuka I, Izumi T, Boku S, Kimura A, Zhang Y, Mouri K, et al. Aberrant telomere length and mitochondrial DNA copy number in suicide completers. Sci Rep. 2017;7(1):3176.

Chung JK, Lee SY, Park M, Joo E-J, Kim SA. Investigation of mitochondrial DNA copy number in patients with major depressive disorder. Psychiatry Res. 2019;282:112616. https://doi.org/10.1016/j.psychres.2019.112616 .

Takahashi PY, Jenkins GD, Welkie BP, McDonnell SK, Evans JM, Cerhan JR, et al. Association of mitochondrial DNA copy number with self-rated health status. Appl Clin Genet. 2018;11:121–7. https://doi.org/10.2147/TACG.S167640 .

Vyas CM, Ogata S, Reynolds CF 3rd, Mischoulon D, Chang G, Cook NR, et al. Lifestyle and behavioral factors and mitochondrial DNA copy number in a diverse cohort of mid-life and older adults. PLoS ONE. 2020;15(8):e0237235. https://doi.org/10.1371/journal.pone.0237235 .

Verhoeven JE, Révész D, van Oppen P, Epel ES, Wolkowitz OM, Penninx BW. Anxiety disorders and accelerated cellular ageing. Br J Psychiatry. 2015;206(5):371–8. https://doi.org/10.1192/bjp.bp.114.151027 .

Pisanu C, Vitali E, Meloni A, Congiu D, Severino G, Ardau R, et al. Investigating the role of leukocyte telomere length in treatment-resistant depression and in response to electroconvulsive therapy. J Pers Med. 2021;11(11):1100. https://doi.org/10.3390/jpm11111100 .

da Silva RS, de Moraes LS, da Rocha CAM, Ferreira-Fernandes H, Yoshioka FKN, Rey JA, et al. Telomere length and telomerase activity of leukocytes as biomarkers of selective serotonin reuptake inhibitor responses in patients with major depressive disorder. Psychiatr Genet. 2022;32(1):34–6. https://doi.org/10.1097/ypg.0000000000000305 .

Aas M, Elvsåshagen T, Westlye LT, Kaufmann T, Athanasiu L, Djurovic S, et al. Telomere length is associated with childhood trauma in patients with severe mental disorders. Transl Psychiatry. 2019;9(1):97. https://doi.org/10.1038/s41398-019-0432-7 .

Birkenæs V, Elvsåshagen T, Westlye LT, Høegh MC, Haram M, Werner MCF, et al. Telomeres are shorter and associated with number of suicide attempts in affective disorders. J Affect Disord. 2021;295:1032–9. https://doi.org/10.1016/j.jad.2021.08.135 .

Phillips AC, Robertson T, Carroll D, Der G, Shiels PG, McGlynn L, et al. Do symptoms of depression predict telomere length? Evidence from the West of Scotland Twenty-07 Study. Psychosom Med. 2013;75(3):288–96. https://doi.org/10.1097/PSY.0b013e318289e6b5 .

Incollingo Rodriguez AC, Polcari JJ, Nephew BC, Harris R, Zhang C, Murgatroyd C, et al. Acculturative stress, telomere length, and postpartum depression in Latinx mothers. J Psychiatr Res. 2022;147:301–6. https://doi.org/10.1016/j.jpsychires.2022.01.063 .

Darrow SM, Verhoeven JE, Révész D, Lindqvist D, Penninx BW, Delucchi KL, et al. The association between psychiatric disorders and telomere length: a meta-analysis involving 14,827 persons. Psychosom Med. 2016;78(7):776–87. https://doi.org/10.1097/PSY.0000000000000356 .

Simon NM, Walton ZE, Bui E, Prescott J, Hoge E, Keshaviah A, et al. Telomere length and telomerase in a well-characterized sample of individuals with major depressive disorder compared to controls. Psychoneuroendocrinology. 2015;58:9–22. https://doi.org/10.1016/j.psyneuen.2015.04.004 .

Schaakxs R, Verhoeven JE, Voshaar RCO, Comijs HC, Penninx BW. Leukocyte telomere length and late-life depression. Am J Geriatr Psychiatry. 2015;23(4):423–32. https://doi.org/10.1016/j.jagp.2014.06.003 .

de Baumont AC, Hoffmann MS, Bortoluzzi A, Fries GR, Lavandoski P, Grun LK, et al. Telomere length and epigenetic age acceleration in adolescents with anxiety disorders. Sci Rep. 2021;11(1):7716. https://doi.org/10.1038/s41598-021-87045-w .

Schutte NS, Malouff JM. The association between depression and leukocyte telomere length: a meta-analysis. Depress Anxiety. 2015;32(4):229–38. https://doi.org/10.1002/da.22351 .

Drieskens S, Demarest S, Bel S, De Ridder K, Tafforeau J. Correction of self-reported BMI based on objective measurements: a Belgian experience. Archives of Public Health. 2018;76:10. https://doi.org/10.1186/s13690-018-0255-7 .

Gorber SC, Schofield-Hurwitz S, Hardt J, Levasseur G, Tremblay M. The accuracy of self-reported smoking: a systematic review of the relationship between self-reported and cotinine-assessed smoking status. Nicotine Tob Res. 2009;11(1):12–24. https://doi.org/10.1093/ntr/ntn010 .

Knez J, Winckelmans E, Plusquin M, Thijs L, Cauwenberghs N, Gu Y, et al. Correlates of peripheral blood mitochondrial DNA content in a general population. Am J Epidemiol. 2016;183(2):138–46. https://doi.org/10.1093/aje/kwv175 .

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Acknowledgements

We are grateful to all BHIS and BELHES participants for contributing to this study.

The HuBiHIS project is financed by Sciensano (PJ) N°: 1179–101. Dries Martens is a postdoctoral fellow of the Research Foundation—Flanders (FWO 12X9620N).

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PH drafted the paper. PH, NS, MD and ED set up the design of the study. NS, DM, JvdH, TN and ED reviewed and commented on the manuscript. All authors read and approved the final manuscript.

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Additional file 1: text s1..

TL, mtDNAc and single copy-gene reaction mixture and PCR cycling conditions. Table S1. The mental health indicators with their scores and uses. Table S2. Comparison of the characteristics of the 6,054 eligible BHIS participants that were included in the BHIS subset compared to the 1,838 eligible participants that were excluded from the BHIS subset. Table S3. Comparison of the characteristics of the 739 participants from the BHIS subset that were included in the BELHES subset compared to the 5,315 participants that were excluded from the BELHES subset. Table S4. Bivariate associations between the characteristics and telomere length (TL), mitochondrial DNA content (mtDNAc), the lifestyle score or psychological distress. Table S5. Results of the sensitivity analysis of the association between lifestyle and mental health. Table S6. Results of the sensitivity analysis of the association between lifestyle and the biomarkers of ageing. Table S7. Results of the sensitivity analysis of the association between mental health and the biomarkers of ageing. Fig. S1. Exclusion criteria. The BHIS subset consisted of 6,055 BHIS participants and the BELHES subset consisted of 739 BELHES participants. Fig. S2. Histogram of the lifestyle score. Fig. S3. Validation of the lifestyle score. ROC curve for the lifestyle score as a predictor for good to very good self-perceived health. The model was adjusted for age, sex, region, highest educational level in the household, household composition and country of birth.

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Hautekiet, P., Saenen, N.D., Martens, D.S. et al. A healthy lifestyle is positively associated with mental health and well-being and core markers in ageing. BMC Med 20 , 328 (2022). https://doi.org/10.1186/s12916-022-02524-9

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The SDGs and human well-being: a global analysis of synergies, trade-offs, and regional differences

• Jan-Emmanuel De Neve 1 , 2 &
• Jeffrey D. Sachs 3 , 4  

Scientific Reports volume  10 , Article number:  15113 ( 2020 ) Cite this article

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• Psychology and behaviour
• Sustainability

This paper explores the empirical links between achieving the Sustainable Development Goals (SDGs) and subjective well-being. Globally, we find that in terms of well-being, there are increasing marginal returns to sustainable development. Unpacking the SDGs by looking at how each SDG relates to well-being shows, in most cases, a strong positive correlation. However, SDG12 (responsible production and consumption) and SDG13 (climate action) are negatively correlated with well-being. This suggests that in the short run there may be certain trade-offs to sustainable development, and further heterogeneity is revealed through an analysis of how these relationships play out by region. Variance decomposition methods also suggest large differences in how each SDG contributes to explaining the variance in well-being between countries. These and other empirical insights highlight that more complex and contextualized policy efforts are needed in order to achieve sustainable development while optimising for well-being.

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Global effects of progress towards Sustainable Development Goals on subjective well-being

Sustainable Development Goals (SDGs): Are we successful in turning trade-offs into synergies?

Examining the unsustainable relationship between SDG performance, ecological footprint and international spillovers

Introduction.

This paper explores the empirical links between sustainable development and human well-being. Sustainable development is a broad and easily misunderstood concept 1 , but the term first entered mainstream policy circles with the publishing of the Brundtland report in 1987, in which it was defined as ‘development that meets the needs of the current generation without compromising the ability of future generations to meet their own needs’ 2 . Debate continues as to whether sustainable development in practice can live up to its normative promises of economic development, environmental stewardship and social equity 3 . Nonetheless, in 2015 the international community rallied around the idea, and sustainable development gained further exposition with the adoption of the Sustainable Development Goals (SDGs), as part of the broader 2030 Agenda. As the successors to the Millennium Development Goals, the 17 SDGs are a comprehensive set of policy goals that aim to end world poverty and hunger, address climate change and environmental protection, and ensure universal access to healthcare, education and equality 4 .

Parallel discussions have centred around the need to move away from GDP as an assessment of countries’ performance towards measures that better capture levels of happiness and well-being 5 . Subjective well-being measures differ from objective well-being indicators, such as observable health and material outcomes, in that they are based on respondents’ self-evaluations of their own life 6 . Varied research suggests that subjective well-being (SWB) measures, especially life evaluations, reflect underlying well-being 7 . As such, there is now a growing consensus among governments and international institutions that SWB—whilst imperfect 8 —has an important role to play in defining success and, as such, an increasing significance in policy-making 9 .

This research aims to explore the relationship between sustainable development and subjective well-being, with the potential to support future policy-making. To do so, we combine two major data-gathering efforts. We leverage the SDG Index which measures countries’ progress towards achieving the SDGs 10 . We also draw on an item from the Gallup World Poll which is representative of over 98% of the world’s population and asks survey participants to evaluate their lives on a scale of 0–10. The paper begins by discussing the headline positive correlation between the SDG Index and SWB. We analyse the quadratic relationship between the two, demonstrating that a higher SDG Index score correlates more strongly with higher SWB at higher levels of the SDG Index. Globally, we find that there are increasing marginal returns to sustainable development in terms of human well-being. In the next section, the SDG Index is split into its 17 component goals. We analyse the varying relationships with well-being, as well as how these relationships play out by region, finding that two of the environmental goals, Goal 12 (responsible consumption and production) and Goal 13 (climate action), are significantly negatively correlated with SWB. We finish by conducting a variance decomposition analysis to show which goals are most strongly contributing to the variation in well-being between countries and the world’s regions 11 .

Our analysis finds that more complex and contextualised policy efforts are needed in order to simultaneously achieve sustainable development and advance well-being. Human well-being is at the core of the 2030 Agenda 12 : the SDGs aim to ensure that ‘all human beings can fulfil their potential in dignity, equality and in a healthy environment 4 .’ Thus, one might expect to find a positive correlation between the SDGs and SWB. Detailed empirical work, however, shows the relationship to be more nuanced than might first appear. Whilst all SDGs are important, our analysis shows that some are more relevant to well-being than others, and reveals some inherent tensions that involve trade-offs between current and future well-being. Since governments are dependent on the current cohort of electors to decide their fate 13 , more cautious policy is needed to resolve trade-offs, allowing for sustainable development that also optimises for human well-being 14 . Unpacking the SDGs in terms of well-being also shows how their relative importance varies across different regions, highlighting the need for differentiated policy priorities when advancing the 2030 Agenda.

Data and methods

The SDG Index (SDGI) was developed in 2015 as a composite system to benchmark the performance of countries across the SDGs. Several indicators are selected to monitor the progress towards each goal, positioning them between the worst (0) and the target outcome (100). The overall SDGI score represents the mean of a country’s total SDG scores, where all goals are weighted equally. The same basket of indicators is used for all countries to generate comparable scores and rankings. For our analysis we use the 2019 SDG index, which includes 114 indicators covering 162 countries 10 . Note that in our analysis, the SDG Index is modified to remove the SWB score, which is one of the indicators for SDG 3 (Health and Well-being). Given the large number of variables that make up the SDG Index, we find that leaving in or taking out the SWB variable does not meaningfully impact any results. Limitations in collecting data for SDG indicators hinder full assessment of progress towards SDGs. There are also issues with the aggregation of goals into a single number 15 , nonetheless there is consensus that the SDGI provides ‘the most comprehensive picture of national progress on the SDGs’ 16 .

For our analysis we use life evaluations, the standard measure of well-being used in the World Happiness Report rankings and most other research on the topic 17 , 18 . We draw on data from the Gallup World Poll, which continually surveys 160 countries representing about 98% of the world’s adult population. The survey item asks respondents to value their current lives on a 0–10 scale, with the worst possible life as 0 and the best possible life as 10. The data is from nationally representative samples, for the years 2016–2018. Some methodological issues remain with subjective well-being measures 6 , but life evaluations are widely recognised as the standard measure of subjective well-being 7 , 19 . Data on other dimensions of subjective well-being, such as the experience of positive and negative emotions, are analysed separately and can be found in the Supplementary Information section online.

The analyses done in this paper rely on standard univariate linear correlations and OLS regressions. In line with the SDGI methodology, where scores are missing for specific goals, we impute using the regional average to avoid losing observations. This is most relevant for goal 14 (Life below water).

The variance decomposition method (dominance analysis) employed in Figs.  4 , 5 and 6 is run in Stata using the domin command. Dominance analysis calculates the relative contribution to the variance explained in well-being (R-squared) for the 17 SDGs. This is an ensemble method that works by calculating a regression of well-being on every possible combination of the 17 SDGS. The dominance of a goal is calculated as the weighted average marginal contribution to the explained variance that the goal makes across all models in which the goal is included. One important assumption being made in such an analysis is that it forces the SDGs to explain all of the variance in well-being between countries. There are also a number of other important limitations in that the method hinges on there being variance in the first place, and yet the measurements for some SDGs do not vary much.

Are the SDGs conducive to human well-being?

Figure  1 shows the scatterplot for the SDG Index and SWB for all countries in the dataset. Countries are coded to represent the six regions they belong to: Europe, Middle East and Northern Africa, Americas, Sub-Saharan Africa and Former Soviet Union. The G7 and BRICS countries are labelled as well as some of the outlier countries. The SDG Index and SWB have a highly significant correlation coefficient of 0.79. The countries with a higher SDG Index score tend to do better in terms of subjective well-being (SWB)—with the Nordic countries topping both rankings. Interestingly, the line of best fit is not linear but quadratic indicating that a higher SDG Index score correlates more strongly with higher SWB at higher levels of the SDG Index. Thus, sustainable development results in increasing marginal returns to human well-being.

Sustainable development and subjective well-being, a scatterplot for the overall SDGI score (mean of total SDG score, where all goals are weighted equally) and SWB score for all countries in the data set. This scatterplot was produced using matplotlib package (version 3.2.1) in python: https://matplotlib.org .

In the online Supplementary Information section, we show that the quadratic fit is statistically superior compared to a pure linear fit (see Table S1 ). This is also the case for higher-powered models as borne out when applying the Bayesian information criterion and Akaike information criterion to test the relative quality of model fits (see Table  S2 ). As countries become more developed, a higher SDG Index score is associated with an ever higher SWB score. This suggests that economic activity is more important for well-being at lowers levels of economic development. As countries become richer the well-being of their citizens stagnates unless further economic growth is more sustainable by, for example, addressing inequality and improving environmental quality. The notion of increasing marginal returns to sustainable development contrasts starkly with the decreasing marginal returns that are typically observed when mapping well-being onto GDP per capita 20 .

Our measure of SWB is an evaluative measure of well-being and the survey responses may differ from emotional measures of well-being, especially when looked at in relation to economic measures such as income and development. As such, in the Supplementary Information section we also report on the relationship between the SDG Index and measures of emotional well-being (see Figure S1 and Figure  S2 ). The Gallup World Poll includes measures of positive emotions such as “enjoyment” and “smile or laugh” as well as negative emotions such as “worry”, “sadness”, “stress”, and “anger”. Correlating an index of positive emotional experiences with the SDG Index scores leads to a correlation coefficient of 0.27—while statistically significant, this indicates a much weaker empirical link between achieving the SDGs and the experience of positive emotions as compared to life evaluations already examined. This is less the case for an index of negative emotional experiences, for which we obtain a correlation coefficient that is − 0.57 suggesting that countries that are not doing well in terms of the SDGs also tend to have populations that are experiencing more negative emotions. In general, these results are in line with the notion that evaluative measures correlate more strongly with economic measures such as income, development, and inequality than emotional measures of well-being 21 , 22 .

In the Supplementary Information section we list the countries that deviate most from the trend line (see Table  S3 ). The countries significantly above the line of best fit clearly punch above their weight in terms of happiness relative to where the model would expect these countries to be given their scores on the SDG Index, with the reverse being true for those below the line of best fit. These empirical observations indicate that there are a number of aspects that drive human well-being that are not fully captured by the SDGs.

How does each SDG relate to well-being?

In Table  1 we report on how each SDG correlates with well-being both globally and regionally. As expected from the aforementioned general results, we find that at the global level most SDGs correlate strongly and positively with higher well-being. At the same time, we discover much heterogeneity in how some of the SDGs relate to well-being. In fact, we find SDGs 14 (Life below water), 15 (Life on land), and 17 (Partnerships for the goals) to be generally insignificant. Importantly, we find that SDGs 12 (Responsible consumption and production) and 13 (Climate action) are significantly negatively correlated with human well-being.

When looking at the relationship between SDGs and well-being by region we detect further levels of heterogeneity in how individual SDGs relate to well-being in different contexts. It is, however, important to note that considering these data by region reduces the number of observations and therefore both the precision of the coefficient and the statistical power to report significant differences. As Fig.  1 revealed visually, there is a stronger link between the SDG Index and well-being at higher levels of economic development. In Table  1 we indeed find that the general correlation between the SDGs and well-being is considerably lower in regions with mostly developing nations. In fact, only for Europe, Asia, and the Americas do we pick up a strong statistically significant correlation between the SDG Index and well-being. When looking at the SDGs individually, we pick up even more variation in how some SDGs are more strongly correlated than others with well-being. Some noteworthy regional results include (1) the important role of SDG 8 (decent work and economic growth) for countries in the former Soviet Union; (2) the relative importance of SDG 9 (industry, innovation and infrastructure) for nations in Europe and the MENA region; and (3) SDG 10 (reducing inequality) is strongly correlated with well-being for the European nations. These regional correlations need to be taken with due caution given the relatively low number of observations available but, taken together, Table  1 paints a vivid picture of the varied and complex ways in which the SDGs relate to human well-being and how these pathways are highly context specific.

Are there trade-offs between the SDGs and human well-being?

Table  1 reveals that SDG 12 (responsible consumption and production) and SDG 13 (climate action) have, in fact, strong negative correlations with self-reported measures of human well-being. Moreover, these negative correlations appear to hold for each one of the world’s regions and therefore merit more academic and policy attention.

SDG12 aims to ensure responsible consumption and production patterns, in order to prevent the over-extraction and degradation of environmental resources. The indicators underlying SDG12 measure the per capita material footprint of each country, accounting for municipal solid waste (kg/year/capita), E-waste generated (kg/capita), production-based SO 2 emissions (kg/capita), imported SO 2 emissions (kg/capita), nitrogen production footprint (kg/capita), net imported emissions of reactive nitrogen (kg/capita), non-recycled municipal solid waste (MSW in kg/person/year times recycling rate) 10 . Fig.  2 shows the negative correlation between achieving SDG 12 and subjective well-being. It suggests that countries which have a smaller per capita material footprint—and are therefore performing well on SDG12—are associated with lower levels of SWB. Countries like Canada, meanwhile, have a high material footprint and score badly on SDG12 but perform well in terms of SWB. The relationship between countries’ well-being and material footprint may well be explained by economic development, as countries with higher GDPs tend to produce and consume more, which is usually associated with higher living standards. However, as reported in Table  2 , when we control for the general level of economic development, SDG12 continues to correlate negatively with SWB, suggesting that material consumption itself is an important factor explaining this negative correlation. This analysis therefore suggests that advancing on responsible consumption and production may result in a trade-off in terms of average self-reported well-being, at least in the short run. However, it is important to note the handful of countries in the top right-hand corner of Fig.  2 (listed in Supplementary Table S4 online) which run counter to this trend. For example, Costa Rica scores highly in terms of SWB whilst also scoring well on SDG12, suggesting that it is in fact possible to advance human well-being at moderate consumption levels.

Responsible consumption and production (SDG12) and subjective well-being, a scatterplot for SDG12 score and SWB score for all countries in the data set. This scatterplot was produced using matplotlib package (version 3.2.1) in python: https://matplotlib.org .

SDG 13 asks that countries take urgent action to combat climate change and its impacts by curbing emissions. It measures countries’ energy-related CO 2 emissions per capita (tCO 2 /capita), imported CO 2 emissions, technology adjusted (tCO 2 /capita), people affected by climate-related disasters (per 100,000 population), CO 2 emissions embodied in fossil fuel exports (kg/capita), effective carbon rate from all non-road energy, excluding emissions from biomass (€/tCO 2 ) 10 . In general, countries that have lower emissions—and are therefore performing well on SDG13—tend to have lower levels of subjective well-being. As was the case with SDG 12, countries that are more economically developed tend to pollute more while also having higher well-being. In contrast with SDG12, however, we find that accounting for the general level of economic development turns a negative correlation into an insignificant one as reported in Table  2 . This suggests that the underlying measures for climate action are strongly correlated with the level of economic development in the first place which, in turn, drives the relationship with well-being. Again, there are a handful of countries in the top right of Fig.  3 (listed in Supplementary Table S5 online), which appear to be resolving the trade-off, performing well on SDG13 whilst maintaining high levels of SWB.

Climate action (SDG13) and subjective well-being, a scatterplot for SDG13 score and SWB score for all countries in the data set. This scatterplot was produced using matplotlib package (version 3.2.1) in python: https://matplotlib.org .

Variance decomposition analysis of the SDGs in relation to well-being

In this section, we apply variance decomposition to explore the relative importance of each SDG in explaining the variance in well-being between countries. This method, called “dominance analysis”, investigates the relative contribution to the variance explained in well-being (R 2 ) for a given set of predictors—in this case the 17 SDGs 11 .

Figure  4 presents the results of the variance decomposition and suggests large differences in how each SDG contributes to explaining the variance in well-being between countries. This figure paints a picture that aligns closely with the correlation coefficients reported in Table  1 . SDGs 10, 14, 15 and 17 would appear to contribute negligibly to explaining variation in well-being across the globe. On the other hand, the greatest explanatory power seems to lie with SDGs 3, 8, 9, and 12. SDG 8 (decent work and economic growth), SDG 9 (industry, innovation and infrastructure), and SDG 12 (responsible consumption and production) each explain 10% or more of the variance. It is important to note, of course, that SDG 12 (as well as SDG 13) are negatively correlated with well-being, as was shown earlier on in Table  1 .

Relative importance of SDGs in explaining the variance in subjective well-being between countries.

Variance decomposition analysis of regional SDG groups in relation to well-being

In these analyses, we group the SDGs into Economic (4, 8, 9), Social (1, 5, 10), Health (3), Law (16), and Environmental goals (2, 6, 7, 11, 12, 13, 14, 15). Figure  5 first shows the results for how well these SDG groups explain the variance between all countries. In Fig.  6 we show the results by region. The general takeaway from the regional variance decomposition analyses is that there is much regional heterogeneity hidden behind a global analysis, with the regional context driving which SDGs are most important in explaining the variance in well-being between countries in the region. In Europe (N = 33), and especially in the countries of the former Soviet Union (N = 15), we find the great importance of the Economic SDGs in explaining regional variation in well-being. In Asia (N = 23) we find a fairly balanced role for the Economic, Law, Social, and Health SDG groups in explaining regional differences in well-being. In the Americas (N = 23) we find that Health plays the most important role in driving regional variation in well-being. The results for Sub-Saharan Africa (N = 38) point towards the Social SDGs playing the key role in explaining regional differences. For the countries in the MENA region (N = 17) we find a more balanced picture with the Health and Economic SDGs driving most of the variation, but an important role as well for the Social, Law, and Environmental SDGs.

Relative importance of SDG groups in explaining the variance in subjective well-being between countries.

Relative importance of SDG groups in explaining regional subjective well-being variance.

This paper has studied the empirical relationship between the Sustainable Development Goals and subjective well-being using data from the SDG Index and the Gallup World Poll. We find a strong correlation between achieving sustainable development and self-reported measures of well-being. Moreover, our analyses indicate that there are increasing marginal returns to sustainable development in terms of well-being.

While most SDGs are positively correlated with well-being, our analysis reveals that SDG12 (responsible consumption and production) and SDG13 (climate action) are negatively correlated with SWB 23 . These findings are perhaps unsurprising: the world economy has long relied on economic growth and the consumption of natural resources to generate human welfare at the expense of environmental sustainability 3 , 24 . Today, however, it is increasingly clear that if we are to avoid ecological collapse, we must bring our consumption of natural and material resources within ecological limits 25 , 26 . This transformation is captured by SDG12 and SDG13; it will involve real reductions in emissions, and quantitative as well as qualitative changes to consumption and production patterns 27 . In particular, high income countries must reduce their ecological footprint to allow for increased consumption in economically developing countries, where it is necessary for meeting basic needs 23 , 28 . This is not an easy task given that our growth-driven economic system is reliant on ever-increasing consumption and production to provide employment and support livelihoods 29 . Thus, under current structures, advancing on SDG12 and SDG13 could have serious socio-economic consequences and, as such, negatively impact well-being levels, particularly those of the most vulnerable 27 . Given that lowering well-being erodes support for incumbent governments, this makes such policies even more difficult to implement 13 . More cautious policies are therefore needed to ensure that progress towards SDG12 and SDG13 also safeguards livelihoods and well-being 30 , 31 .

Nevertheless, environmental stewardship does not necessarily entail reductions in well-being. Varied research has shown the importance of environmental integrity for human well-being: for instance, subjective well-being is negatively influenced by poor air quality 32 ; people are willing to pay for observably cleaner air 33 ; and there is evidence to suggest that being exposed to nature improves mental health 34 . Furthermore, as we have shown elsewhere there is a strong positive correlation between SWB and the Environmental Protection Indicator (a measure which is much wider in scope than the environmentally-oriented SDGs, covering a broad range of issues such as biodiversity and eco-systems, climate and energy, air pollution, water resources, agriculture, heavy metals, water and sanitation, and air quality) 35 . These research insights indicate that well-being is correlated with the long-term outcomes of environmental policies, even if it is not necessarily positively correlated with the short-run efforts required of such policies.

The challenge for policy-makers is thus to resolve the short-term trade-off by de-coupling human well-being improvements from the consumption of natural resources and GHG emissions 36 . A recent report by the OECD attempts to address this challenge by proposing climate change mitigation through a well-being lens, putting people at the centre of climate action 37 . The outlier countries highlighted in our analysis (see Supplementary Table S4 and S5 online) that are performing well on SDG12 and SDG13, whilst also achieving high levels of well-being, indicate that there might be pathways to improving well-being that do not hinder environmental sustainability 38 , 39 . These countries represent a proportional mix of relatively large and small countries across the world. For example, Germany has invested heavily in renewable energy infrastructure 40 , providing ‘green jobs’ while simultaneously reducing emissions. The combination of carbon taxes and incentives for renewable energy, combined with ambitious social policy, has allowed the Nordic countries to transition away from fossil fuels, without punishing low-income families with higher energy bills 41 , 42 . Equally, Costa Rica is among the top countries for investment in new renewable power and fuels relative to GDP, and has committed to achieving carbon neutrality starting from 2021 43 . It thus offers an alternative model for developing countries to avoid the Western carbon-intensive development path 44 . Interestingly, many Latin American countries with warmer climates and a lower propensity to engage in international trade 36 perform strongly in terms of self-reported well-being whilst also scoring highly in terms of SDG12 (sustainable consumption and production), supporting the notion that human well-being decouples from environmental impact beyond minimum levels of consumption 39 . More research is needed to better understand the development trajectories of these countries and the policy mechanisms which allow for synergies between well-being and ecological sustainability 36 . Policies such as investment in public services to moderate private consumption 27 and harnessing productivity gains to reduce working hours 45 have been proposed. There is also increasing evidence from sustainable cities that supports the notion that it is possible to mitigate environmental issues and simultaneously improve quality of life 46 .

Trade-offs between the SDGs and SWB can also arise as a result of interactions between different SDGs. In particular, SDGs 11, 13, 14, 16, and 17 continue to have negative trade-offs and non-associations with other SDGs 47 . The highly positive links we identified between goals 11 and 16 and human well-being may possibly compensate for these intra-SDG trade-offs, but policy-makers may find pursuing SDGs 13, 14, and 17 more difficult due to the negative or insignificant correlation with the well-being of current generations. Needless to say, however, that the urgency of climate change does require action to ensure the well-being of future generations 48 , 49 .

Regional analyses have revealed that what accounts for human well-being varies greatly according to regional and socio-economic context; policy efforts must therefore be differentiated. For example, we find that while in Europe reducing inequalities significantly contributes to well-being, poverty reduction is more important in sub-Saharan Africa. These findings complement a recent study of SDG interactions, which finds poverty alleviation in low-income countries and reducing inequalities in high-income countries to have compounded positive effects on all SDGs 50 , thus helping to support the prioritization of these SDGs according to region. Our findings confirm that general analyses often hide important heterogeneity; moreover, we recognise that the picture becomes even more nuanced at the local level, which is increasingly the site where sustainable development policy is implemented 51 . Importing policy models or ‘best practices’ from elsewhere without a deep understanding of the local context can often obscure effective policy-making on sustainable development issues 52 . As explored in the policy mobilities literature, there is often a mismatch between local governance structures and top-down frameworks like the SDGs which can hinder the overall success of such agendas 53 . Where policies are too insensitive to specific local variations, the goals of sustainable development can be squandered. Therefore, a more comprehensive understanding of how the SDGs can be implemented at the local level is critical 54 in order to advance the 2030 agenda such that both people and planet can thrive.

Our analysis is of course limited by data gaps for several SDG indicators, we therefore emphasize the need for increased transparency and co-operation from governments. Regional analyses are limited by the relatively low number of observations available. It is also important to reiterate that variance decomposition analyses are constrained by their methods and the number of observations. As such, these results are meant to be seen as cautious exploration of large-scale trends that are correlational in nature and thus open to potential reverse causality and omitted variable bias. Our aim here is to stimulate thinking and further research on how the SDGs relate to human well-being—and to show that general analyses may hide important heterogeneity when looking at individual SDGs and in the context of different regions. We recognize that in addition to the macro-level statistical analysis conducted here, more research and careful qualitative analysis is needed to understand local complexities and how they interact with the SDG framework.

We have studied the link between the SDGs and SWB of current generations. Future research should investigate the extent to which self-reported SWB metrics account for the well-being of future generations. This is especially relevant when considering SDG 12 (responsible consumption and production) and SDG 13 (climate policy). Implementing these policies requires intergenerational reciprocity, the idea that we must act on the behalf of future generations, which has in turn been shown to depend on the behavior of previous generations 55 . This work also does not address international dynamics. The sustainable development of a country may come at a cost to other countries, or the actions of countries may influence the well-being in others 56 . Furthermore, the model of linking SDGs with well-being assumes only direct relationships, whereas recent work shows that addressing SDGs have knock-on effects for other SDGs 57 .

A potential dynamic that is worthwhile highlighting is the extent to which the well-being of populations may itself exert influence on their country’s approach to development. Changes in well-being have been documented to have wide-ranging effects on economic, social, and health outcomes 58 . Given these objective benefits of subjective well-being there is an urgent need to combine the SDG and SWB research and policy agendas to generate solutions that advance human well-being, without compromising the environmental integrity of our planet.

Data availability

Data from the SDG index is freely available and can be downloaded from www.sdgindex.org . The Gallup World Poll data is not freely available however the data used in this analysis is made available in the online appendix for the World Happiness Report from https://worldhappiness.report .

Hopwood, B., Mellor, M. & O’Brien, G. Sustainable development: mapping different approaches. Sustain. Dev. 13 , 38–52 (2005).

Google Scholar  

World Commission on Environment and Development. Our Common Future (Oxford University Press, Oxford, 1987).

Krueger, R. & Gibbs, D. The Sustainable Development Paradox: Urban Political Economy in the United States and Europe (Guilford Press, New York, 2007).

United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development (2015).

Stiglitz, J. E., Sen, A. & Fitoussi, J.-P. Report by the Commission on the Measurement of Economic Performance and Social Progress (2009).

OECD. For Good Measure: Advancing Research on Well-being Metrics Beyond GDP (OECD Publishing, Paris, 2018).

Frijters, P., Clark, A. E., Krekel, C. & Layard, R. A happy choice: wellbeing as the goal of government. Behav. Public Policy https://doi.org/10.1017/bpp.2019.39 (2020).

Article   Google Scholar  

Benjamin, D. J., Cooper, K. B., Heffetz, O. & Kimball, M. S. Self-reported wellbeing indicators are a valuable complement to traditional economic indicators but aren’t yet ready to compete with them. Behav. Public Policy https://doi.org/10.2139/ssrn.3463478 (2019).

O'Donnell, G., Deaton, A., Halpern D., Durand, M. & Layard. R. Wellbeing and Policy Report (Legatum Institute, 2014).

Sachs, J., Schmidt-Traub, G., Kroll, C., Lafortune, G. & Fuller, G. Sustainable Development Report 2019 (Bertelsmann Stiftung and Sustainable Development Solutions Network, 2019).

Azen, R. & Budescu, D. V. The dominance analysis approach for comparing predictors in multiple regression. Psychol. Methods 8 , 129–148 (2003).

PubMed   Google Scholar  

Patel, V. et al. The Lancet Commission on global mental health and sustainable development. Lancet 392 , 1553–1598 (2018).

Ward, G. Happiness and voting: evidence from four decades of elections in Europe. Am. J. Polit. Sci. https://doi.org/10.1111/ajps.12492 (2019).

Bennett, N. J. et al. Towards a sustainable and equitable blue economy. Nat. Sustain. 2 , 991–993 (2019).

Diaz-Sarachaga, J. M., Jato-Espino, D. & Castro-Fresno, D. Is the Sustainable Development Goals (SDG) index an adequate framework to measure the progress of the 2030 agenda?. Sustain. Dev. 26 , 663–671 (2018).

Tracking progress on the SDGs. Nat. Sustain. 1, 377. https://doi.org/10.1038/s41893-018-0131-z (2018)

Helliwell, J., Layard, R., Sachs, J. & De Neve, J.-E. World Happiness Report 2020 (Sustainable Development Solutions Network, 2020).

Helliwell, J. Measuring and using happiness to support public policies. NBER Working Paper 26529 . https://www.nber.org/papers/w26529 (2019).

Oswald, A. J. & Wu, S. Objective confirmation of subjective measures of human well-being: evidence from the U.S.A.. Science 327 , 576–579 (2010).

ADS   CAS   PubMed   Google Scholar  

Easterlin, R. A., McVey, L. A., Switek, M., Sawanfa, O. & Zweig, J. S. The happiness-income paradox revisited. Proc. Natl. Acad. Sci. 107 , 22463–22468 (2010).

Kahneman, D. & Deaton, A. High income improves evaluation of life but not emotional well-being. Proc. Natl. Acad. Sci. 107 , 16489–16493 (2010).

Powdthavee, N., Burkhauser, R. V. & De Neve, J.-E. Top incomes and human well-being: evidence from the Gallup World Poll. J. Econ. Psychol. 62 , 246–257 (2017).

O’Neill, D. W., Fanning, A. L., Lamb, W. F. & Steinberger, J. K. A good life for all within planetary boundaries. Nat. Sustain. 1 , 88–95 (2018).

Dietz, T., Rosa, E. A. & York, R. Environmentally efficient well-being: rethinking sustainability as the relationship between human well-being and environmental impacts. Hum. Ecol. Rev. 16 , 114–123 (2009).

IPCC. Global warming of 1.5 °C. An IPCC Special Report on the Impacts of Global Warming of 1.5 °C Above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty (2018).

Meadows, D. H., Randers, J. & Meadows, D. L. The Limits to Growth: the 30-Year Update (Universe Books, New York, 2004).

Bengtsson, M., Alfredsson, E., Cohen, M., Lorek, S. & Schroeder, P. Transforming systems of consumption and production for achieving the sustainable development goals: moving beyond efficiency. Sustain. Sci. 13 , 1533–1547 (2018).

PubMed   PubMed Central   Google Scholar  

Scherer, L. et al. Trade-offs between social and environmental sustainable development goals. Environ. Sci. Policy 90 , 65–72 (2018).

Jackson, T. Prosperity Without Growth: Economics for a Finite Planet (Earthscan, London, 2009).

McCauley, D. & Heffron, R. Just transition: integrating climate, energy and environmental justice. Energy Policy 119 , 1–7 (2018).

Alves, M. W. F. M. & Mariano, E. B. Climate justice and human development: a systematic literature review. J. Clean. Prod. 202 , 360–375 (2018).

Levinson, A. Valuing public goods using happiness data: the case of air quality. J. Public Econ. 96 , 869–880 (2012).

Luechinger, S. Valuing air quality using the life satisfaction approach. Econ. J. 119 , 482–515 (2009).

Guite, H. F., Clark, C. & Ackrill, G. The impact of the physical and urban environment on mental well-being. Public Health 120 , 1117–1126 (2006).

CAS   PubMed   Google Scholar  

De Neve, J.-E. & Sachs, J. Sustainable development and human well-being. In World Happiness Report 2020 (eds Helliwell, J. et al. ) (Sustainable Development Solutions Network, New York, 2020).

Roberts, J. T. et al. Four agendas for research and policy on emissions mitigation and well-being. Glob. Sustain. 3 , e3 (2020).

OECD. Accelerating Climate Action: Refocusing Policies Through a Well-being Lens (OECD Publishing, Paris, 2019).

Knight, K. W. & Rosa, E. A. The environmental efficiency of well-being: a cross-national analysis. Soc. Sci. Res. 40 , 931–949 (2011).

Steinberger, J. K. & Roberts, J. T. From constraint to sufficiency: the decoupling of energy and carbon from human needs, 1975–2005. Ecol. Econ. 70 , 425–433 (2010).

Lehr, U., Nitsch, J., Kratzat, M., Lutz, C. & Edler, D. Renewable energy and employment in Germany. Energy Policy 36 , 108–117 (2008).

Nordic Council of Ministers. Nordic Climate Policy—A Case Study on Efficient Policy Measures . 10.6027/NA2014-906 (2014)

Martela, F., Greve, B., Rothstein, B. & Saari, J. The Nordic exceptionalism: what explains why the Nordic Countries are constantly among the happiest in the world. In World Happiness Report 2020 (eds Helliwell, J. et al. ) (Sustainable Development Solutions Network, New York, 2020).

OECD. Climate Change Mitigation: Policies and Progress (OECD Publishing, Paris, 2015).

Lamb, W. F. Which countries avoid carbon-intensive development?. J. Clean. Prod. 131 , 523–533 (2016).

Knight, K. W., Rosa, E. A. & Schor, J. B. Could working less reduce pressures on the environment? A cross-national panel analysis of OECD countries, 1970–2007. Glob. Environ. Change 23 , 691–700 (2013).

Bulkeley, H. Cities and Climate Change (Routledge, Abingdon, 2013).

Kroll, C., Warchold, A. & Pradhan, P. Sustainable development goals (SDGs): are we successful in turning trade-offs into synergies?. Palgrave Commun. 5 , 140 (2019).

Stern, N. H. Why Are We Waiting? The Logic, Urgency, and Promise of Tackling Climate Change (MIT Press, Cambridge, 2015).

Stern, N. Public economics as if time matters: climate change and the dynamics of policy. J. Public Econ. 162 , 4–17 (2018).

Lusseau, D. & Mancini, F. Income-based variation in sustainable development goal interaction networks. Nat. Sustain. 2 , 242–247 (2019).

Carr, C. et al. Raising sustainability/Mobilising sustainability: why European sustainable urban development initiatives are slow to materialise/Territorial cohesion as a vehicle of sustainability/Sustainable urban development and the challenge of global air transport nodes and spatial integration/Distorted density: where developers and non-governmental organizations on sustainable urban development agree/Overcoming politics with markets? The co-production of sustainable development in urban and regional planning. Plan. Theory Pract. 16 , 99–125 (2015).

Temenos, C. & McCann, E. The local politics of policy mobility: learning, persuasion, and the production of a municipal sustainability fix. Environ. Plan. Econ. Space 44 , 1389–1406 (2012).

Carr, C. Discourse yes, implementation maybe: an immobility and paralysis of sustainable development policy. Eur. Plan. Stud. 22 , 1824–1840 (2014).

McLean, B. L. & Borén, T. Barriers to implementing sustainability locally: a case study of policy immobilities. Local Environ. 20 , 1489–1506 (2015).

Wade-Benzoni, K. A. A golden rule over time: reciprocity in intergenerational allocation decisions. Acad. Manag. J. 45 , 1011–1028 (2002).

Schmidt-Traub, G., Moff, H. & Bernlöhr, M. International Spillovers and the Sustainable Development Goals (Sustainable Development Solutions Network, New York, 2019).

Pradhan, P., Costa, L., Rybski, D., Lucht, W. & Kropp, J. P. A systematic study of sustainable development goal (SDG) interactions. Earths Future 5 , 1169–1179 (2017).

ADS   Google Scholar  

De Neve, J.-E., Diener, E., Tay, L. & Xuereb, C. The objective benefits of subjective well-being. In World Happiness Report 2013 (eds Helliwell, J. et al. ) (Sustainable Development Solution Network, New York, 2013).

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Acknowledgements

This article builds on work done for a chapter published in the World Happiness Report 2020 and reproduces some material from that chapter. We are grateful to Sidharth Bhushan, Hedda Roberts, and Pekka Vuorenlehto for outstanding research assistance. We thank Guillaume Lafortune and Grayson Fuller at the Sustainable Development Solutions Network for guidance on the SDG Index data. The Gallup World Poll data is generously made available by The Gallup Organization. We also acknowledge very helpful comments from John Helliwell, Richard Layard, Andrew Oswald, Steve Bond, Tyler VanderWeele, and participants at seminar meetings of the Wellbeing Research Centre at Oxford.

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De Neve, JE., Sachs, J.D. The SDGs and human well-being: a global analysis of synergies, trade-offs, and regional differences. Sci Rep 10 , 15113 (2020). https://doi.org/10.1038/s41598-020-71916-9

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Mental well-being and mental distress, measuring mental well-being in people with mental illness, mental well-being and mental health services, mental well-being and mental illness, should mental well-being be used to support the commissioning and delivery of mental health services, conclusions, mental well-being: an important outcome for mental health services.

Published online by Cambridge University Press:  02 January 2018

Mental well-being is being used as an outcome measure in mental health services. The recent Chief Medical Officer's (CMO's) report raised questions about mental well-being in people with mental illness, including how to measure it. We discuss whether mental well-being has prognostic significance or other utility in this context.

The World Health Organization defines mental well-being as an individual's ability to develop their potential, work productively and creatively, build strong and positive relationships with others and contribute to their community. 1 This view distinguishes subjective happiness or life satisfaction (hedonic well-being) from positive psychological functioning (eudaimonic well-being). The mental well-being literature can be confusing as many similar-sounding terms are used interchangeably: social or mental capital, positive mental health, psychological or subjective well-being. The WHO definition of mental well-being is concerned exclusively with positive mental health states, and this approach is also evident in the way that terminology is used in UK policy documents. Nevertheless, it is sometimes unclear whether the term ‘mental well-being’ implies the absence of mental illness or distress. Well-being has been trumpeted as a measure of national prosperity, and linked to improved physical and mental health. It has been identified as a public health target and criterion for commissioning and assessing mental health services. Reference Davies 2 But questions remain about the relationship between mental illness and mental well-being, and about the potential for diverting resources away from evidence-based treatments for mental disorders. These issues were highlighted in the recent Chief Medical Officer (CMO) report on public mental health that challenged the empirical grounds for extending mental well-being into clinical commissioning and argued against mental well-being ‘receiving priority funding over better established fields, including quality of life’. Reference Davies 2

Mental disorders are characterised by psychopathology, distress and impaired functioning. Huppert Reference Huppert 3 and others argued that mental disorders (‘languishing’) and mental well-being (‘flourishing’) were opposite ends of a single dimension. However, further work has shown that, although correlated, mental illness and mental well-being are independent phenomena. Secondary analysis of data on over 7000 adults from the 2007 Adult Psychiatric Morbidity Survey (APMS) demonstrated that associations with well-being scores were not significantly altered by adjusting for comorbid mental disorder. Reference Weich, Brugha, King, McManus, Bebbington and Jenkins 4 These findings were consistent with those from other studies that indicate that mental well-being is more than just the absence of mental illness symptoms and distress, and that (although correlated) mental well-being and mental distress are independent of one another. The APMS findings also showed that at least moderately high levels of well-being may be achieved in the context of mental illness, which is salient when considering whether mental well-being should be a routine outcome measure in mental health services. Reference Weich, Brugha, King, McManus, Bebbington and Jenkins 4 Evidence detailed later in this editorial also supports this conclusion. However, we know less about the determinants and variability of mental well-being among those who experience mental health problems than in the general population. As mental illnesses typically relapse and remit, mental well-being may vary with the phase of illness and the number, frequency or duration of relapses.

Evaluating interventions to improve mental well-being in people with mental illness depends on valid measurement, but there is only limited evidence to guide the assessment of mental well-being in this context. Reference Davies 2 This is a significant barrier to studying mental well-being and its potential determinants in people with mental illness. Reference Davies 2 Since mental well-being is a state of positive mental health, measures should comprise positively phrased items, such as those which make up the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS), Reference Tennant, Hiller, Fishwick, Platt, Joseph and Weich 5 WHO-Five Well-Being Index (WHO-5) Reference Bech, Olsen, Kjoller and Rasmussen 6 and the Satisfaction with Life Scale. Reference Kobau, Sniezek, Zack, Lucas and Burns 7

Although generic measures of mental well-being have been used for people with mental illness, their validity in these populations has rarely been evaluated; we do not know whether responses to generic mental well-being items may be biased by the experience of past or current mental illness. Only the WHO-5 has been validated in English in mental illness, specifically in affective and anxiety disorders. Reference Newnham, Hooke and Page 8 The Subjective Well-being under Neuroleptic Treatment Scale (SWN) Reference Vothknecht, Meijer, Zwinderman, Kikkert, Dekker and van Beveren 9 was developed for people with schizophrenia receiving antipsychotics. However, one-half of this scale comprises negatively worded items and it covers domains that are not central to mental well-being, including physical functioning. WEMWBS, despite being recommended by healthcare organisations for measuring mental well-being in the context of mental illness, has only been validated in non-clinical populations in the UK.

The 2011 UK government document No Health without Mental Health emphasised mental well-being as an important service outcome as part of patient-centred, recovery-focused care. 10 However, judging services according to mental well-being outcomes rather than changes in symptoms and disability is not self-evidently consistent with their traditional mission: the consequences of doing so need to be considered carefully. Measuring mental well-being routinely may alter therapeutic relationships in unintended ways. There is a risk that in prioritising mental well-being, professionals might be excused from achieving more challenging outcomes, namely alleviating symptoms and reducing disability. Reference Davies 2

We would argue that two conditions must be met to justify the routine assessment of mental well-being among mental health service users. First, evidence is needed that mental well-being modifies the risk of onset, recovery from or recurrence of episodes of mental illness; in other words that it has prognostic significance in terms of mental health, social functioning or use of healthcare. Second, it must be shown that mental well-being is independent of mental illness and social functioning and therefore unlikely to be captured by measures that assess either of these phenomena.

Although the behavioural and psychosocial determinants of mental well-being may not necessarily resemble those of mental illness, mental well-being is associated with specific forms of psychopathology – examples are discussed below. However, the evidence base is generally limited by substantial methodological variation (including the use of different and often unvalidated measures of mental well-being) and a dearth of longitudinal studies, inhibiting understanding of cause and effect. Reference Davies 2

Anxiety and depression

Maintaining high levels of mental well-being is likely to be difficult in the presence of symptoms of anxiety and depression. However, recent longitudinal data demonstrate that this may be more complicated than (simply) covariance. A recent study of over 1000 Australian in- and day patients with depression or anxiety demonstrated that an intervention (giving feedback during psychological treatment) improved depressive symptoms but not mental well-being, Reference Newnham, Hooke and Page 11 supporting the view that these are independent outcomes.

There is a wealth of cross-sectional evidence linking sleep problems and mental well-being, but less robust evidence of longitudinal associations. A small, prospective study of 75 university students Reference Pilcher, Ginter and Sadowsky 12 found no significant prospective improvements in life satisfaction among those whose sleep increased in duration or quality over 3-month follow-up. Those who reported a reduction in daily sleep quality over 3 months were significantly more likely to report a reduction in life satisfaction ( P <0.01). Reference Pilcher, Ginter and Sadowsky 12 Nonetheless, poor mental well-being in the context of sleep problems may not be associated with greater need for psychiatric care. A cross-sectional general population study of over 8000 Australians found that although the 5% with insomnia were significantly more likely to have poor mental well-being (odds ratio (OR) = 2.34, 95% CI 1.11–4.93) and visited their general practitioner more often (OR = 1.57, 95% CI 1.06–2.33), insomnia was not significantly associated with use of psychotropic medication or hospital admission. Reference Bin, Marshall and Glozier 13

Delusions and hallucinations

Mental well-being is inversely associated with psychotic symptoms. In 83 out-patients with schizophrenia, psychotic symptoms were negatively correlated with quality of life, but interestingly this association was confounded by insight, Reference Rocca, Castagna, Mongini, Montemagni and Bogetto 14 demonstrating the complexity of the relationship between mental well-being and mental illness. Among people with first-episode psychosis, admission to hospital was associated with better quality of life Reference Renwick, Jackson, Foley, Owens, Ramperti and Behan 15 suggesting that illness severity per se may not automatically predict well-being; better mental well-being might also reflect the quality and intensity of care received.

Social functioning and healthcare use

Social functioning is correlated with psychopathology but may be independent of mental well-being. Psychiatric out-patients with serious mental illness in remission demonstrated higher functioning scores but not higher well-being compared with similar patients not in remission, although this used the limited SWN to measure mental well-being. Reference Pinna, Deriu, Lepori, Maccioni, Milia and Sarritzu 16

Healthcare use and mental well-being may also be independent. A 2-year structured rehabilitation programme for those with serious mental illness led to improved quality of life and psychosocial functioning in those who met their rehabilitation goals v. those who had not. However, there were no significant differences in healthcare use between the two groups at 2-year follow-up. Reference Svedberg, Svensson, Hansson and Jormfeldt 17

Valid methods of evaluating healthcare interventions are required to support payment by results, and National Health Service providers are required to collect patient-reported outcomes and experiences in part to prevent ‘gaming’ to maximise income. Mental well-being could serve as a patient-rated outcome measure, but the dearth of validated measures in people with serious mental illness remains a major concern. The CMO has sensibly encouraged policy makers and commissioners to heed the uncertainty surrounding mental well-being, warning that ‘wellbeing policy is running ahead of the evidence’. Reference Davies 2 However, existing evidence suggests that symptomatic and functional outcomes, needs for care and service use appear to be independent of mental well-being to varying degrees. Therefore, mental well-being is not captured completely by existing measures of these states. Mental well-being also has strong conceptual resonances with recovery from mental illness, including notions of hope, purpose and fulfilment, and may be similarly valued by patients. Taken together, these could represent significant arguments for mental well-being as a distinct service outcome in its own right. However, the utility of measuring mental well-being routinely in mental health services has not yet been established. Further research is needed to validate measures of mental well-being in people with serious mental illness, determine the usefulness (and costs) of routinely measuring mental well-being in this population, and to explore the views of patients on the relative importance attached to different service outcomes.

The place of mental well-being in the delivery of mental healthcare remains uncertain and the CMO has stated categorically that this should not be part of current clinical commissioning. Nevertheless, mental well-being is an important public health heuristic and has clear resonances with concepts underpinning recovery from mental illness. The evidence base linking mental well-being and mental illness remains poorly developed, but we believe that two conditions for measuring mental well-being in mental health services have been at least partly met. It appears that mental well-being may be associated with onset, recovery and/or recurrence of episodes of mental illness although the actual detail of these associations is complex; and that it is at least partly independent of symptoms, social functioning or need for mental healthcare. Mental well-being is not fully captured by measures of these phenomena.

However, there are two important caveats. First, it is essential to validate measures of mental well-being in people with serious mental illness, and to know more about the (relative) value that patients place on mental well-being as a service outcome. And second, mental well-being must not be allowed to supersede other outcomes and obscure the imperative to deliver the most effective evidence-based treatments to those with mental illness.

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• Volume 207, Issue 3
• Angharad de Cates (a1) , Saverio Stranges (a2) , Amy Blake (a3) and Scott Weich (a4)
• DOI: https://doi.org/10.1192/bjp.bp.114.158329

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SYSTEMATIC REVIEW article

The effects of cultural engagement on health and well-being: a systematic review.

• 1 Department of Sustainable Development and Ecological Transition, University of Eastern Piedmont, Vercelli, Italy
• 2 Department of Statistics, Computer Science, and Applications “Giuseppe Parenti” (DiSIA), University of Florence, Florence, Tuscany, Italy
• 3 Department of Translational Medicine, University of Eastern Piedmont, Novara, Piedmont, Italy

Purpose: This paper examines the effectiveness of culture-based activities in improving health-related outcomes among middle-aged and older adults. Based on the biopsychosocial model, this review aims to explore the impact of cultural engagement on health and well-being.

Methods: We conducted a systematic literature review based on peer-reviewed articles retrieved from various electronic databases. In total, 11 studies were included in this review. Our study population consisted of healthy adults aged over 40 years.

Results: The results provide evidence of positive association between cultural participation and better mental health (e.g., cognitive decline, depression, anxiety), frailty, resilience, well-being and social relations.

Conclusion: This review suggests that cultural engagement serves as an effective means for individuals to maintain and enhance their health and well-being. The field is mostly limited by the heterogeneity of the studies and poor conceptualization of cultural activities. Thus, it is recommended that future research consider the effects of different cultural interventions in developing effective strategies for promoting healthy lifestyles and enhancing quality of life in later stages of life.

1 Introduction

For many years, the concept of health has evolved from a mere absence of disease to a more comprehensive evaluation. In 1948, the World Health Organization (WHO) defined health as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity” ( 1 ). This marked the beginning of a process that shifted the concept of health from an individual perspective to a more social one ( 2 ). This evolution has culminated in the current vision of health, described as “the ability to adapt and self-manage” ( 3 ) (p. 2), emphasizing the development of personal capabilities.

Therefore, despite significant progress in disease treatment, in recent decades, many researchers have shifted their focus to exploring methods for enhancing and maintaining health and well-being, leveraging cognitive, emotional, and social resources to confront challenges and meet daily requirements effectively. In particular, artistic activities have received significant attention as a potential means to enhance the quality of life, especially among the older population ( 4 , 5 ). This association is now widely recognized ( 6 ), emphasizing the significant role of culture as a determinant of individual psychological well-being ( 7 – 9 ), psychological flexibility and health ( 10 ). Evidence from a recent comprehensive scoping review highlights the beneficial outcomes of engaging in diverse cultural and arts events ( 4 ), relevant to both health promotion and prevention efforts by fostering health-promoting behaviors and aiding in illness prevention.

In light of the complex challenges of the aging population, understanding the role of culture in promoting health and well-being becomes increasingly important. By expanding and intensifying research in these areas, we can identify strategies to enhance quality of life in an economically advantageous, accessible, and enjoyable manner.

The aim of the present paper is to review current literature addressing the relationship between different forms of cultural engagement and health and well-being in people aged over 40 years. We chose to follow the biopsychosocial model as a comprehensive framework that considers the interconnected influence of biological, psychological, and social factors on human behavior and experiences. This approach allows for a nuanced analysis, fostering a deeper understanding of human functioning. Additionally, aligning with this model enhances the relevance and applicability of our research findings across various fields. In the context of this systematic review, we will distinguish between “receptive culture,” which encompasses visits to museums, galleries, art exhibitions, theaters, concerts, cultural festivals, and community events, and “cultural participation,” which refers to active engagement in one or more of these activities ( 4 ). Both types of activities involve aspects of artistic and cultural experience, ranging from creativity, cognitive and sensory stimulation, to social interaction (e.g., esthetic pleasure, and emotional evocation), which promote health ( 11 , 12 ). However, differences emerge in the impact of receptive and participatory culture; moreover, studies show contrasting results. Although active cultural engagement interventions have shown greater benefits in terms of psychophysical outcomes ( 13 , 14 ), other authors have found only the efficacy of receptive activities in supporting healthy aging, perhaps because they more consistently involve social interactions and movement, which are positively associated with healthy aging ( 11 , 12 ). Further research is needed for a better understand the underlying reasons for such differences. There is still a lack of research that evaluates the overall impact of arts engagement on healthy aging in a comprehensive and integrated manner ( 11 , 12 ).

Based on these observations, we address the following key questions:

• How might different forms of cultural engagement relate to health and well-being?

• What gaps exist in the current literature examining the effects of cultural engagement on health and well-being outcomes? Consequently, what further research is needed?

• What are the implications of the present literature for healthcare and cultural systems and policies?

2.1 Study design

This study can be classified as a systematic review.

2.2 Search strategy

A comprehensive search of published studies was conducted using the following databases: Cochrane, EBSCO and PubMed. Concerning the keywords, we considered very inclusive terms that refer to cultural engagement; regarding the effects, we have considered words related to health and well-being. The key terms for searches included: (“Cultural participation” OR “Cultural attendance” OR “cultural engagement” OR “cultural event*” OR “Art* activit*” OR “Art* participation” OR “Art* attendance”) AND (“Healthy lifestyle” OR “Health*” OR “health promotion” OR “Health behavior*” OR “well-being” OR “Well-being” OR “quality of life”). No publication date restriction was applied. Figure 1 presents the flowchart of the process of identifying and selecting literature. The selected articles were required to have undergone peer review processes prior to publication and to present a clear and consistent methodology. However, given the diverse methods and outcomes considered in the selected studies, this review will provide a qualitative synthesis of the results reported by the researchers.

Figure 1 . Flowchart of the literature identification and selection process.

2.3 Inclusion and exclusion criteria

Our criteria for inclusion were as follows: (1) quantitative methodology; (2) randomized controlled trial (RCT), longitudinal and cross-sectional studies with controls; (3) receptive arts engagement in terms of attendance of arts-based events such as museums, art exhibitions and galleries, concerts, the theater, and the cinema ( 15 ) as well as the active production of art ( 16 ); (4) according to the biopsychosocial approach, the consideration of physical, psychological and social variables associated to health and well-being as outcomes; (5) samples of healthy people aged over 40 years. The specific effects of music and/or making music on health were excluded in this study; instead, a separate study was dedicated to examining them ( 5 ). Systematic reviews and meta-analyses were also excluded.

2.4 Study selection

Our selection was conducted by screening articles titles, abstracts and considering full-text articles of potentially eligible papers. Three independent reviewers (EV, MM, DC) executed these procedures, resolving disagreements through discussion. The systematic review was undertaken according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( 17 ).

2.5 Quality criteria

The methodological quality of the considered studies was analyzed using Checklist for Analytical Cross Sectional Studies ( 18 ). Two reviewers (EV and MM) assessed the methodological quality of included studies based on 8 criteria (see Supplementary Table A1 ). Each paper was assigned to be low (<5), moderate (between 5 and 7) or high quality (7 or 8) depending on the number of criteria they met; possible discrepancies were resolved by consensus. The results of the quality assessment process are listed in Supplementary Table A1 .

3.1 Search results

We identified 683 articles through the literature search process. After the exclusion of duplicates and following the inclusion/exclusion criteria, 11 studies were selected (see Figure 1 ). Summaries of the studies included in this review are presented in Table 1 . All these studies examined the effects of cultural engagement on particular dimensions of health and well-being: mental health status, frailty, loneliness, and so forth. We present the results according to the specific outcome ( Table 2 for effects and significance). In general, out of 95 overall effects, 42 statistically significant positive effects emerge (44%), whereas the remaining effects, although not statistically significant, are not negative and therefore do not worsen health and well-being. The most significant effects are derived from regular and sustained forms of cultural participation, whereas going to the cinema is found to be the least beneficial for health promotion.

Table 1 . Detailed summary of the considered studies (alphabetical order).

Table 2 . Effects and significance of the impact of various cultural activities on the considered variables.

Several studies used data from national databases ( n  = 6). All studies used a quantitative methodology. Concerning the research designs, most of the studies were longitudinal ( n  = 7, one of which is retrospective), since cross-sectional ( n  = 2), a follow-up survey and an RCT. The time elapsed between the initial data collection and subsequent data collection in longitudinal studies typically ranged from 6 to 10 years. Sample sizes varied considerably, from 28 participants (RCTs) to large national surveys with 16,642 participants. The majority of the studies were conducted in the United Kingdom ( n  = 6), with Japan ( n  = 2), Italy ( n  = 1), Israel ( n  = 1), and Germany ( n  = 1) also represented. The age range of participants spanned from 50 to 99 years, with a balanced gender distribution.

The psychological and social health outcomes varied significantly. The most prominent variables examined were resilience ( n  = 2), well-being ( n  = 2) and frailty ( n  = 2), followed by depression ( n  = 1), anxiety ( n  = 1), mental health ( n  = 1), dementia ( n  = 1), cognitive functions (memory and semantic fluency; n  = 1) and loneliness ( n  = 1). Except for the RCT, which introduced specific cultural activities, the remaining studies focused on regular, ongoing cultural participation.

3.2 Quality assessment

9 studies displayed a high methodological quality, whereas 2 studies received moderate quality ratings due to (a) a non-clear description of the criteria for inclusion in the sample as well as for the study subjects and the setting ( n  = 1), and (b) the non-identification of confounding factors ( n  = 1). The authors of 7 studies utilized data from national databases, which did not permit a clear a priori specification of inclusion criteria beyond age. Nevertheless, they expanded the survey to encompass large samples and provided adequate descriptions.

3.3 Health and well-being outcome

The order of the discussed outcome aligns with the principles of the biopsychosocial model: first, “Cognitive Functioning” addresses the fundamental aspects of brain biology; then, “Dementia” is explored due to its involvement in cognitive processes; “Mental Health” encompasses a spectrum of psychological aspects; “Frailty” acts as a crucial connector, spanning individual and societal domains; “Resilience” acknowledged as both personal and social resource; “Well-being” is examined for its multifaceted determinants, including social influences; finally, “Social Relationships” for their direct involvement in social interaction. The decision to separate the discussion by theme stems from the diverse methods and variables considered in the selected studies.

3.3.1 Cognitive functioning

Fancourt and Steptoe ( 20 ) found that cultural participation in general has a positive impact in terms of cognitive conservation, verbal memory and semantic fluency, especially if adequately sustained (at least a couple of times a year), regardless of baseline cognitive status and other variables (e.g., demographics, health, etc.). Particularly, a dose–response relationship emerges, indicating that a higher frequency of visits to galleries or museums, as well as theaters, concerts, or opera, had a greater effect on cognition with a protective effect. The results regarding the association between going to the cinema and cognitive function become less clear and consistent when other control factors are considered and corrected for multiple comparisons. On the whole, the reported results show that the activities were protective regardless of the median level of baseline cognition.

3.3.2 Dementia

Visiting museums could be a promising psychosocial activity to support dementia prevention, especially if sustained over time ( 19 ) The reported results show that such activity is associated with a lower incidence rate of dementia over a 10-year follow-up period in individuals aged over 50. The incidence rate of dementia is lower among individuals who regularly attend museums compared to those who do not attend museums. Particularly, the overall incidence rate was 5.42 (95% CI 4.78–6.17) per 1,000 person-years; the incidence rate resulted higher than average for non-participants (Δ = 4.05), slightly lower than average for sporadic participants (less than once a year: Δ = −1.46; once or twice a year: Δ = −1.69), and even lower for those who visited galleries and museums frequently (Δ = −3.27) ( 19 ). Taken into account the demographic differences, the association between cultural participation and a dementia remained significant only for those who visited museums every few months or more.

3.3.3 Mental and psychological health

Participation in recreational activities (hobbies/cultural activities) showed a positive association with mental health after a five-year follow-up ( 25 ). Participating in activities with others has a positive impact on mental health, and this effect is particularly marked when compared to those who do not engage in any social activities. These differences are also notable between genders. Whereas this association was observed among men in a larger sample, women showed a positive relationship with mental health regardless of the mode of participation in group activities ( 25 ). Keisari et al. ( 15 ) found that receptive artistic engagement moderated the relationship between resilience, conceptualized as an individual’s ability to effectively cope with and adapt to the challenges and difficulties brought about by the coronavirus pandemic and COVID-19 anxiety. Specifically, the significant interaction between resilience and receptive arts engagement accounted for an additional 3% of the variance in anxiety symptoms. Furthermore, the authors found that pre-pandemic cultural participation had a buffering effect against COVID-19 anxiety; conversely, individuals with low artistic involvement reported higher levels of anxiety. Fancourt and Tymoszuk ( 21 ) confirmed that a regular and sustained cultural engagement (at least every few months) represents an important risk reducing factor for the development of depression in older age. A clear dose–response relationship emerges, indicating that higher frequency of participation is associated with a reduced risk. Those who rarely or never participate (once or twice a year) showed an incidence rate of depression above the average, whereas higher participation frequencies were linked to rates below the average.

3.3.4 Frailty

Rogers and Fancourt ( 24 ) found a dose–response relationship between cultural participation and both the incidence and progression of frailty. Regarding the incidence, the authors found a subhazard ratio of 0.92 CI [0.85–0.98] between frequency of cultural engagement and incidence frailty. Moreover, the risk of frailty at the age of 80 is 1.3 times higher for those who do not engage in cultural activities, independent of confounding factors such as demographics, socioeconomic status, and social factors. These findings corroborate those of a prior study by Fushiki and colleagues ( 22 ), which indicated that individuals who participated - in their life - in at least one or more cultural or physical group activities after adjustment exhibited a lower incidence of frailty compared to those who engaged in such activities alone. Furthermore, when comparing cultural and physical activities (solo or in groups), individuals participating in one or more cultural activities demonstrated a lower incidence of frailty.

3.3.5 Resilience

Bolwerk and coll ( 16 ) showed that the cultural engagement can increase resilience, conceptualized as a protective personality trait enabling individuals to mitigate the negative impacts of stress and facilitating successful and healthy functioning even amidst challenging life circumstances. Although the effects were greater and statistically significant only in the “Visual art production” group (the resilience level increased by 2.86 points between pre- and post-intervention), a non-significant improvement also emerged in the “Cognitive art evaluation” group (+2.22). These results are also confirmed at the biological level: using fMRI, they observed that participants engaged in visual art production, compared to the assessment of art, showed greater spatial improvement in functional connectivity in different brain areas (mostly between the parietal and frontal cortices) over time, and that this was related to psychological resilience. Rapacciuolo and coll ( 23 ) showed that those who participate in cultural and social activities (both women and men) have higher levels of resilience, define as successful stress-coping ability, compared to non-participants (+ 0.93).

3.3.6 Well-being

As previously mentioned, Rapacciuolo and coll ( 23 ) showed an association between participation in cultural activities (mostly for women) and psychological well-being: who participate in cultural and social activities have higher levels of well-being compared to non-participants (+ 11.58). Participation in social and cultural activities, along with interventions aimed at fostering positive emotions, could be crucial in combating social isolation and its adverse effects on health. Additionally, as suggested by the authors, these activities may contribute to promoting healthier lifestyles, such as improving nutrition. Tymoszuk and coll ( 27 ) showed that sustained (once a month or more) cultural participation has a positive impact on various forms of well-being. Considering experienced well-being, sustained engagement with the theater/concert/opera compared with no or infrequent engagement showed a positive effect (OR = 1.4, 95% CI 1.14–1.77, p  = 0.02). Moreover, about evaluative well-being, sustained engagement with gallery/museum compared with no or infrequent engagement was associated with higher life satisfaction (B = 0.76, 95% CI: 0.28, 1.25, p  = 0.002). In addition, regarding eudaimonic well-being, sustained engagement with galleries/exhibitions/museums was associated with higher self-realization if compared to no or infrequent engagement (B = 0.51, 95% CI: 0.27, 0.76, p  < 0.001). Finally, considering again eudaimonic dimension, sustained engagement with the theater/concerts/opera respect to no or infrequent engagement was related with higher control/autonomy (B = 0.28, 95% CI: 0.05, 0.51, p  = 0.018) and self-realization (B = 0.30, 95% CI: 0.08, 0.53, p  = 0.008). No associations were found for engagement with screen-based performances (cinema attendance), in contrast to studies that have demonstrated its beneficial effects but in line with other studies that have identified positive associations between time spent in front of screens (TV) and depressive symptoms, sedentary behavior, and other factors.

3.3.7 Social relationships

Tymoszuk and coll ( 26 ) used the second wave of ELSA for the cross-sectional analyses and data from the seventh wave (a decade later) for the longitudinal analyses. The cross-sectional results showed that: engaging with cinema every few months or more often, compared with never, was associated with 26% lower odds of loneliness, visiting galleries/exhibitions/museums every few months or more often and once or twice a year had, respectively, 26 and 22% lower odds of loneliness compared with those who reported no engagement. Participants who reported attending theater, concerts, or opera every few months or more frequently, as well as those attending once or twice a year, exhibited 33 and 23% lower odds of experiencing loneliness, respectively, compared to those who reported no engagement in such activities. However, longitudinal analysis revealed no association between the frequency of cinema attendance and the likelihood of experiencing loneliness, even after adjusting for covariates. Engaging with galleries, exhibitions, and museums every few months or more often, compared to never, was associated with a 32% reduction in the odds of experiencing loneliness at wave 7. Similarly, engaging once or twice a year was linked to a 26% decrease in the likelihood of reporting loneliness at wave 7 after adjusting for covariates. In the fully adjusted model, participating in theater, concerts, or opera once or twice a year, compared to never, was associated with a 31% decrease in the odds of experiencing loneliness at wave 7. The longitudinal analytical sample exhibited skewness toward participants who were female, younger, employed, more educated, in good health, in coupled relationships, reported higher levels of social, community, and arts engagement, and were less likely to be lonely at wave 2. In general, the participation in receptive artistic activities is negatively associated with the risk of loneliness especially for attending museums/galleries/exhibitions compared to theater/concerts/opera and visits to the cinema. This effect emerged regardless of the baseline loneliness level and different confounding variables (i.e., demographic, socioeconomic, health and social factors).

4 Discussion

The results of this systematic review suggest that cultural engagement may be effective in maintaining and enhancing health and well-being of middle-aged and older populations. Regarding our first research question, the evidence suggests that cultural activities have a positive impact on various dimensions of well-being. Visiting museums, galleries, and exhibitions provides positive cognitive stimulation, reducing the risk of cognitive decline or the development of dementia ( 19 ). Indeed, there is a relationship between the frequency of museum visits and the incidence rate of dementia, with a lower rate among those who participate more in this activity, and these results remain significant even after accounting for demographic and health variables ( 19 ). Moreover, art exhibitions as well as live performances have a positive impact on memory and semantic fluency, reducing decline in cognitive function compared to non-participation ( 20 ). Longitudinal associations spanning a decade were observed independent of initial indications of cognitive decline, indicating that cultural engagement may yield benefits also for individuals experiencing cognitive impairment ( 20 ). Overall, the results concerning cognitive dimension support the assumption that «cultural engagement […] contributes to cognitive reserve: the resilience of our brains as we age» ( 4 ) (p. 24). According to Stern ( 28 ), the cognitive reserve against brain damage allows people to deal with cognitive decline; this hypothesis supports the idea that the reserve factors derive from different cognitive dimensions, including education level and intelligence ( cf. ( 29 )), and participation in specific activities (e.g., cultural activities), which act as protective factors against brain disease ( 28 ). The studies reveal intriguing benefits of cultural engagement on psychological resilience at the cerebral level as well: engaging in visual arts has been found to enhance the interaction between various brain regions, thereby improving the ability to endure or cope with challenging situations ( 16 ). Furthermore, a high degree of involvement in the arts can potentially act as a protective barrier against specific emotional responses, effectively serving as a moderator between resilience and COVID-19-related anxiety, demonstrating its efficacy as a coping strategy ( 15 ). Especially for individuals with low involvement in receptive arts, increased resilience significantly reduced anxiety symptoms; therefore, both context and personal resources influence how resilience and engagement in the arts combine to affect anxiety. Receptive arts engagement has been shown to enhance psychological resources in older age, thereby reducing the risk of developing mental health problems ( 25 ). The results suggest that sailing in shared experiences can yield significant benefits for mental health. Overall, socialization and interaction with others represent an added value. Notably, compelling associations have emerged between consistent participation in cultural activities and subjective dimensions of well-being, encompassing both subjective and psychological aspects ( 23 ). Additionally, it serves as a protective factor for older individuals, mitigating the risk of mental illnesses such as depression ( 21 ). In the realm of cultural engagement, older adults find a sovereign refuge against depression, woven with threads of social interaction, mental creativity, and cognitive stimulation. The advantages of arts engagement in older age extend to frailty trajectories, effectively reducing the incidence and progression of physiological decline and providing protection against vulnerability to adverse health outcomes ( 24 ). Notably, this study represents the initial evidence supporting the potential significance of cultural engagement in older age in reducing both the risk of developing frailty and the trajectory of its progression ( 24 ). Finally, at a social level, sustained engagement with museums, galleries, and exhibitions protects against loneliness. Several studies have shown that life events which tend to occur in older age can increase the risk of social isolation and feelings of loneliness ( 30 ). This is a very important effect since loneliness negatively affects psychophysical well-being, exacerbating cognitive decline and progression of dementia, increasing the risk of premature mortality ( 31 ). Whereas some studies tend to attribute the benefits of cultural engagement, for example, to reducing social isolation, further analysis reveals the relevance of other aspects, such as pleasure experiences and emotional expression ( 24 ). Therefore, social benefit is not the sole important factor contributing to the positive health effects. A more critical analysis of this literature might shed further light on this. In a kind of melody of interconnection, the presence of others during recreational activities could play a pivotal role in promoting health, suggesting an interconnectedness between social engagement and positive health outcomes in the realm of cultural activities ( 25 ).

In summary, according to the recent scoping review of Fancourt and Finn ( 4 ), this systematic review highlights the potential of cultural participation in promoting healthy aging. In accordance with the WHO Global Strategy and Action Plan on Aging and Health, healthy aging is “the process of developing and maintaining the functional ability that enables well-being in older age” ( 32 ). These findings emphasize that a regular and sustained cultural engagement, especially in group, can enhance or maintain the well-being while also serving as a preventive measure against potential psychophysical and social disorders and challenges. However, some limitations were observed. In certain studies, various leisure activities and cultural activities were grouped together as a single variable, making it difficult to isolate the impact of specific cultural participation forms. An issue also arises due to the self-reported and retrospective measurement of cultural involvement. Consequently, the data may not always be accurate and may not fully capture the true value of participation in such activities. Numerous studies, especially those utilizing ELSA data, did not thoroughly explore active participation by separating the different activities. In some cases, the assessment of this multifaceted activity was simplified to a single item, despite the diverse effects demonstrated in the reviewed literature across various forms of participation. Furthermore, due to the observational nature of the data (with only one randomized controlled trial included in this review), caution is required when inferring causal relationships between cultural engagement and the various outcomes. The primary findings suggest bidirectional associations, indicating susceptibility to reverse causality bias. Indeed, it is possible that mostly healthy people tend to participate in such activities.

To the best of our knowledge, this is the first systematic review that specifically focuses on the healthy population aged over 40, exclusively considering the psychophysical and social effects of cultural participation. Moreover, our study did not limit the selection of research to randomized controlled trials (RCTs), but also included longitudinal studies based on national databases and cross-sectional studies. We conducted the review by searching various electronic databases with no restrictions on publication dates. The independent analysis conducted by two team members, focusing of both study quality and results, further strengthens the credibility of our review. The studies considered in our analysis were conducted in various geographic regions, not limited to Western countries, thereby providing cross-cultural validation of the value of cultural participation.

Obviously, conducting a meta-analysis could provide empirical evidence regarding the value of cultural participation. However, the variations in methods used to measure this type of activity, along with the diverse range of outcomes considered, hinder the feasibility of such an approach. Additionally, our selection was limited to studies with samples aged over 40, but it could be of interest to explore broader age groups in future research to uncover potential differences that may arise at various stages of life.

In light of the limitations observed in the current literature, there are some future topics to investigate. First, efforts should be made to reduce heterogeneity. This can be achieved by developing a more standardized measure and the definition of culture and cultural participation. Additionally, it is crucial to distinguish between different forms of cultural engagement, as this review has shown that some activities are less effective than others (e.g., cinema attendance). Furthermore, future studies should aim to minimize reliance on self-reported measures of participation and instead utilize standardized measures. Lastly, researchers should consider the aspect of active cultural participation, which involves individuals in the creation of artistic works. This transformation shifts the passive viewer into an active participant or artist, potentially yielding unique insights into the relationship between culture and well-being. A fundamental distinction arises between active participation, where individuals directly engage in the creative process, and receptive engagement (i.e., attending arts events or listening to music). These distinctions result in significant variability that need for consideration in future studies aimed at advancing our understanding of the complex relationship between culture and health ( 11 , 12 ). To address the problem of revers causality, future studies should consider adopting experimental design, RCTs and consistently include a control group or condition.

5 Conclusion

Our results are encouraging. The primary finding from this systematic review suggests that sustained cultural participation appear to have a positive impact on various dimensions—biopsychosocial—of health and well-being, highlighting the importance of culture for middle-aged and older populations. Those who engage in cultural activities show an improvement in terms of well-being, or at the very least, a maintenance of their health status. Further research, particularly RCTs with control conditions, is needed to gain a deeper understanding of the mechanisms by which cultural participation influences health and well-being outcomes and to develop effective intervention strategies. These studies should employ robust multidimensional measures and also explore potential moderators and mediators, ultimately enhancing the development of future interventions. These findings present a valuable opportunity for multidisciplinary collaboration between healthcare, sociocultural sectors, and arts-related systems and policies.

Author contributions

EV: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. MM: Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. DC: Conceptualization, Data curation, Investigation, Writing – review & editing. MV: Conceptualization, Data curation, Investigation, Writing – review & editing. DA: Investigation, Writing – review & editing. FF: Conceptualization, Funding acquisition, Supervision, Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The study was partially funded by a grant from Compagnia di Sanpaolo (three-year Cultural Wellbeing Lab project of Compagnia di Sanpaolo, date of resolution 14/12/2020, No. 2020.2218) and by the Aging Project of the Department of Translational Medicine of the University of Eastern Piedmont.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2024.1369066/full#supplementary-material

1. World Health Organization . Constitution of the World Health Organization . New York: United Nations (1948).

Google Scholar

2. Keyes, CLM . Social well-being. Soc Psychol Q . (1998) 61:121–40. doi: 10.2307/2787065

Crossref Full Text | Google Scholar

3. Huber, M, Knottnerus, JA, Green, L, Van Der Horst, H, Jadad, AR, Kromhout, D, et al. How should we define health? BMJ . (2011) 343:d4163. doi: 10.1136/bmj.d4163

4. Fancourt, D, and Finn, S. What is the evidence on the role of the arts in improving health and well-being? A scoping review. Health evidence network synthesis report 67 . WHO Europe: Copenhagen (2019).

5. Viola, E, Martorana, M, Airoldi, C, Meini, C, Ceriotti, D, De Vito, M, et al. The role of music in promoting health and well-being: a systematic review and meta-analysis. Eur J Pub Health . (2023) 33:738–45. doi: 10.1093/eurpub/ckad063

PubMed Abstract | Crossref Full Text | Google Scholar

6. Goulding, A . How can contemporary art contribute toward the development of social and cultural capital for people aged 64 and older. The Gerontologist . (2013) 53:1009–19. doi: 10.1093/geront/gns144

7. Grossi, E, Sacco, PL, Blessi, GT, and Cerutti, R. The impact of culture on the individual subjective well-being of the Italian population: an exploratory study. Appl Res Qual Life . (2011) 6:387–410. doi: 10.1007/s11482-010-9135-1

8. Grossi, E, Tavano Blessi, G, Sacco, PL, and Buscema, M. The interaction between culture, health and psychological well-being: data mining from the Italian culture and well-being project. J Happiness Stud . (2012) 13:129–48. doi: 10.1007/s10902-011-9254-x

9. Wheatley, D, and Bickerton, C. Subjective well-being and engagement in arts, culture and sport. J Cult Econ . (2017) 41:23–45. doi: 10.1007/s10824-016-9270-0

10. Bennington, R, Backos, A, Harrison, J, Reader, AE, and Carolan, R. Art therapy in art museums: promoting social connectedness and psychological well-being of older adults. Arts Psychother . (2016) 49:34–43. doi: 10.1016/j.aip.2016.05.013

11. Rena, M, Fancourt, D, Bu, F, Paul, E, Sonke, J, and Bone, J. Receptive and participatory arts engagement and healthy aging: longitudinal evidence from the health and retirement study. (2022). Available at: https://osf.io/preprints/psyarxiv/q4h6y [10/05/2024]

12. Rena, M, Fancourt, D, Bu, F, Paul, E, Sonke, JK, and Bone, JK. Receptive and participatory arts engagement and subsequent healthy aging: evidence from the health and retirement study. Soc Sci Med . (2023) 334:116198. doi: 10.1016/j.socscimed.2023.116198

13. Bone, JK, Fancourt, D, Sonke, JK, and Bu, F. Participatory and receptive arts engagement in older adults: associations with cognition over a seven-year period. Creat Res J . (2023):1–15. doi: 10.1080/10400419.2023.2247241

14. Pesata, V, Colverson, A, Sonke, J, Morgan-Daniel, J, Schaefer, N, Sams, K, et al. Engaging the arts for wellbeing in the United States of America: a scoping review. Front Psychol . (2022) 12:791773. doi: 10.3389/fpsyg.2021.791773

15. Keisari, S, Hoffman, Y, Ring, L, and Palgi, Y. The moderating effect of older adults’ receptive arts engagement on the association between resilience and anxiety symptoms during coronavirus breakout. J Nerv Ment Dis . (2021) 209:443–8. doi: 10.1097/NMD.0000000000001326

16. Bolwerk, A, Mack-Andrick, J, Lang, FR, Dörfler, A, and Maihöfner, C. How art changes your brain: differential effects of visual art production and cognitive art evaluation on functional brain connectivity. PLoS One . (2014) 9:e101035. doi: 10.1371/journal.pone.0101035

17. Page, MJ, McKenzie, JE, Bossuyt, PM, Boutron, I, Hoffmann, TC, Mulrow, CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg . (2021) 88:105906. doi: 10.1016/j.ijsu.2021.105906

18. Moola, S, Munn, Z, Tufanaru, C, Aromataris, E, Sears, K, Sfetcu, R, et al. Systematic reviews of etiology and risk In: E Aromataris and Z Munn, editors. JBI manual for evidence synthesis (2020)

19. Fancourt, D, Steptoe, A, and Cadar, D. Cultural engagement and cognitive reserve: museum attendance and dementia incidence over a 10-year period. Br J Psychiatry . (2018) 213:661–3. doi: 10.1192/bjp.2018.129

20. Fancourt, D, and Steptoe, A. Cultural engagement predicts changes in cognitive function in older adults over a 10 year period: findings from the English longitudinal study of ageing. Sci Rep . (2018) 8:10226–8. doi: 10.1038/s41598-018-28591-8

21. Fancourt, D, and Tymoszuk, U. Cultural engagement and incident depression in older adults: evidence from the English longitudinal study of ageing. Br J Psychiatry . (2019) 214:225–9. doi: 10.1192/bjp.2018.267

22. Fushiki, Y, Ohnishi, H, Sakauchi, F, Oura, A, and Mori, M. Relationship of hobby activities with mortality and frailty among community-dwelling elderly adults: results of a follow-up study in Japan. J Epidemiol . (2012) 22:340–7. doi: 10.2188/jea.JE20110057

23. Rapacciuolo, A, Perrone Filardi, P, Cuomo, R, Mauriello, V, Quarto, M, Kisslinger, A, et al. The impact of social and cultural engagement and dieting on well-being and resilience in a group of residents in the metropolitan area of Naples. J Aging Res . (2016) 2016:1–11. doi: 10.1155/2016/4768420

24. Rogers, NT, and Fancourt, D. Cultural engagement is a risk-reducing factor for frailty incidence and progression. J Gerontol: Series B . (2020) 75:571–6. doi: 10.1093/geronb/gbz004

25. Takeda, F, Noguchi, H, Monma, T, and Tamiya, N. How possibly do leisure and social activities impact mental health of middle-aged adults in Japan?: an evidence from a national longitudinal survey. PLoS One . (2015) 10:e0139777. doi: 10.1371/journal.pone.0139777

26. Tymoszuk, U, Perkins, R, Fancourt, D, and Williamon, A. Cross-sectional and longitudinal associations between receptive arts engagement and loneliness among older adults. Soc Psychiatry Psychiatr Epidemiol . (2020) 55:891–900. doi: 10.1007/s00127-019-01764-0

27. Tymoszuk, U, Perkins, R, Spiro, N, Williamon, A, and Fancourt, D. Longitudinal associations between short-term, repeated, and sustained arts engagement and well-being outcomes in older adults. J Gerontol: Series B . (2020) 75:1609–19. doi: 10.1093/geronb/gbz085

28. Stern, Y . What is cognitive reserve? Theory and research application of the reserve concept. J Int Neuropsychol Soc . (2002) 8:448–60. doi: 10.1017/S1355617702813248

29. Buckner, RL . Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron . (2004) 44:195–208. doi: 10.1016/j.neuron.2004.09.006

30. Somes, J . The loneliness of aging. J Emerg Nurs . (2021) 47:469–75. doi: 10.1016/j.jen.2020.12.009

31. Rokach, A . The effect of gender and culture on loneliness: a Mini review. Emerg Sci Jl . (2018) 2:59–64. doi: 10.28991/esj-2018-01128

32. World Health Organization (WHO) . Decade of healthy ageing 2021–2030. (2021). Available at: https://www.who.int/initiatives/decade-of-healthy-ageing .

Keywords: cultural engagement, leisure activities, health, well - being, quality of life

Citation: Viola E, Martorana M, Ceriotti D, De Vito M, De Ambrosi D and Faggiano F (2024) The effects of cultural engagement on health and well-being: a systematic review. Front. Public Health . 12:1369066. doi: 10.3389/fpubh.2024.1369066

Received: 11 January 2024; Accepted: 28 June 2024; Published: 10 July 2024.

Reviewed by:

Copyright © 2024 Viola, Martorana, Ceriotti, De Vito, De Ambrosi and Faggiano. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Erica Viola, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Open access
• Published: 15 July 2024

Theory of change for addressing sex and gender bias, invisibility and exclusion in Australian health and medical research, policy and practice

• Thomas Gadsden 1   na1 ,
• Laura Hallam 1 , 2   na1 ,
• Cheryl Carcel 1 ,
• Robyn Norton 1 , 3 ,
• Mark Woodward 1 , 3 ,
• Louise Chappell 4 &
• Laura E. Downey   ORCID: orcid.org/0000-0002-9563-7113 1 , 3  

Health Research Policy and Systems volume  22 , Article number:  86 ( 2024 ) Cite this article

Metrics details

Sex and gender are inadequately considered in health and medical research, policy and practice, leading to preventable disparities in health and wellbeing. Several global institutions, journals, and funding bodies have developed policies and guidelines to improve the inclusion of diverse participants and consideration of sex and gender in research design and reporting and the delivery of clinical care. However, according to recent evaluations, these policies have had limited impact on the inclusion of diverse research participants, adequate reporting of sex and gender data and reducing preventable inequities in access to, and quality provision of, healthcare. In Australia, the Sex and Gender Policies in Medical Research (SGPMR) project aims to address sex and gender bias in health and medical research by (i) examining how sex and gender are currently considered in Australian research policy and practice; (ii) working with stakeholders to develop policy interventions; and (iii) understanding the wider impacts, including economic, of improved sex and gender consideration in Australian health and medical research. In this paper we describe the development of a theory of change (ToC) for the SGPMR project. The ToC evolved from a two-stage process consisting of key stakeholder interviews and a consultation event. The ToC aims to identify the pathways to impact from improved consideration of sex and gender in health and medical research, policy and practice, and highlight how key activities and policy levers can lead to improvements in clinical practice and health outcomes. In describing the development of the ToC, we present an entirely novel framework for outlining how sex and gender can be appropriately considered within the confines of health and medical research, policy and practice.

Peer Review reports

Contributions to the literature

Inadequate consideration of sex and gender in health and medical research, policy and practice contributes to inequity in the distribution of health.

No framework for guiding the consideration of sex and gender in health and medical research, policy and practice currently exists.

The theory of change presented in this paper presents an entirely novel and important theoretical scaffold for institutions and organizations to use when considering how to actively enhance sex and gender awareness and inclusivity in their activity.

Reorienting practice through informed action can contribute to improving population health and economic wellbeing by enhancing sex and gender equity.

Introduction

Sex and gender are important determinants of health but are often inadequately considered in health and medical research, policy and practice [ 1 , 2 , 3 ]. Documented issues include exclusion or underrepresentation of cis women, trans women and men, those with intersex characteristics and non-binary populations in research participation and lack of sex and gender disaggregation and interpretation of data (see Table  1 for definitions of key concepts) [ 2 , 4 ]. As health and medical research forms the evidence base that many clinical guidelines and health policies are based on, this can lead to disparities in diagnosis, treatment and health outcomes. Sex- and gender-based health disparities have been identified in many conditions, such as cardiovascular disease [ 5 , 6 ], stroke [ 7 ] and pain [ 8 ], and heath areas such as screening, diagnosis [ 9 ] and reactions to pharmacological treatments [ 10 ].

In response to these health disparities, in recent decades several research institutions have developed policies and guidelines to improve the inclusion of diverse participants and consideration of sex and gender in research design and reporting [ 11 , 12 , 13 , 14 , 15 ]. New and updated policies increasingly highlight broader issues of equity and intersectionality alongside sex and gender considerations [ 16 ]. The aims and potential impacts of these policies include improved rigour, ethics and reproducibility of science, counteracting existing biases and exclusionary practices, understanding health differences and inequities, informing and improving health policy and care and advancing gender equality, diversity and inclusion. Positive initiatives towards updating university science and medical curricula [ 17 , 18 , 19 ], clinical protocols and guidelines [ 6 , 20 ] and public and global health programs to explicitly incorporate sex and gender considerations have also been reported [ 21 , 22 ].

Evaluations of these policies and guidelines report mixed results on health and medical research design and reporting to date [ 24 , 25 , 26 ]. For instance, an evaluation of the Canadian Sex- and Gender-Based Analysis policy found increased explicit consideration of sex and gender in grant applications [ 27 ], while other policy evaluations have found limited impact on the inclusion of women and marginalized community members in study populations and on the disaggregation and analysis of results by sex and gender [ 28 , 29 , 30 ]. A range of barriers to progress have also been reported, including limited awareness, a lack of training and resources among personnel across the medical research pipeline, and the absence of adequate accountability and monitoring mechanisms by regulators, including government agencies, funders and universities, among others [ 31 , 32 , 33 ].

In Australia, the Sex and Gender Policies in Medical Research (SGPMR) project was established to understand the current state of play in relation to the explicit consideration of sex and gender in medical research and practice [ 34 ]. SGPMR is a philanthropically funded initiative with three primary aims: (i) to understand whether and how sex and gender are addressed in current research policy and practice in Australia; (ii) to work with stakeholders to co-develop policy interventions; and (iii) to understand the wider impacts, including economic impacts, of improving sex and gender consideration in Australian health and medical research [ 35 ]. Work to date has demonstrated that sex and gender are under-reported in research articles published in Australia’s top ten medical journals in 2020 [ 3 ] and that the content of academic journals dedicated to women’s health remains largely focussed on reproductive health topics, with few articles targeting the major causes of morbidity and mortality in women [ 36 ].

Common to each of these disparate initiatives is an implied shared understanding in the notion that explicit consideration of sex and gender in research design, policy and operations leads to better data and evidence-based practice, which in turn leads to better health outcomes. As has been previously argued [ 37 ], a clearly articulated explicit framework outlining the causal pathways by which better data and gender-sensitive practice would lead to better health would ensure that the opportunity for misunderstanding is minimized and enhance the coordination of efforts to achieve common goals and the ability to create and evaluate their impact enhanced. A Theory of Change (ToC), defined as “an explicit process of thinking through and documenting how a program or intervention is supposed to work, why it will work, who it will benefit and the conditions required for success”, is an increasingly common means in which to articulate a shared vision and map logical pathways to impact towards addressing a problem or issue [ 38 ]. This methodology is increasingly used in public health and evaluation frameworks to articulate how an intervention can achieve long-term impact by identifying and depicting causal pathways from activities to outputs and outcomes and the key mechanisms, barriers, and facilitators underpinning these causal pathways [ 39 , 40 , 41 , 42 ]. Although ToCs are typically developed for discrete projects or interventions, they have been used to promote shared ownership and understanding among stakeholders for broader initiatives, such as strengthening sector-wide response to human immunodeficiency virus (HIV) in Papua New Guinea [ 43 ] and multi-sector urban planning initiatives [ 44 ].

In this paper, we outline the development of a ToC to identify the pathways through which improved consideration of sex and gender in health and medical research, policy and practice could impact social and economic health outcomes. The objectives of this work are twofold: (1) to fill an important knowledge-implementation gap in the literature by explicitly documenting the problem, the desired impact and how engaging in certain activities can contribute towards achievement of positive change across the evidence, policy and practice pipeline; and (2) to situate the activities of the aforementioned SGPMR project in this wider context to guide future project activities aimed at creating impact in the Australian health and medical research sector.

Study design

This study followed best practice guidelines for developing a ToC, involving a wide range of stakeholders and end-users, ensuring rigorous evidence-based discussions through participatory research methods and engaging in an iterative process of refinement [ 45 , 46 ]. These recommendations are reflected by the iterative six-step process followed in this study: (1) initial mapping of key concepts and considerations; (2) stakeholder interviews; (3) draft ToC development; (4) stakeholder consultation workshop; (5) revised ToC development; and (6) stakeholder review. Table 2 outlines the goals and methods of each of the steps. For transparency, this study is reported against the Standards for Reporting Qualitative Research (Supplementary file 1) and the Checklist for reporting ToC in Public Health Interventions (Supplementary file 2) [ 45 ].

Data collection

Initial mapping.

A horizon scanning exercise was undertaken to identify literature of importance to this project by using PubMed and Google Scholar searches with different combinations of the key words “sex”, “gender”, “health” “medical” “research” “policy” “framework” “logic” and “theory of change”. Titles and abstracts were screened and full papers reviewed for literature identified as potentially relevant. No evidence was identified that directly addressed the development of a framework for explicit consideration of sex and gender in medical research, policy and practice. Literature on the development and use of a ToC was reviewed to determine the most appropriate structural framework for the purposes of this ToC. The approach chosen draws upon programmatic theory described as “deal[ing] with the mechanisms that intervene between the delivery of a program service and the occurrence of outcomes of interest” [ 29 ]. This approach requires that all activities and their intended outputs, outcomes and impacts are identified and then mapped to a ToC structural framework. Table 3 defines each component of the ToC structural framework used in this exercise. Barriers and facilitators to reaching the intended outcomes and impacts were also considered.

Study participants

Participation in the study was restricted to members of the SGPMR project [ 34 ]. There are 24 SGPMR project members in total: 8 principal investigators (PI) and an advisory group of 16 members. Members represent government, cisgender, trans, intersex, non-binary and indigenous community groups, as well as multidisciplinary academics with expertise in health and medicine, gender, human rights, policy, clinical care, regulation and community engagement.

Semi-structured interviews

All 24 individuals associated with the SGPMR project were invited via email to participate in a semi-structured interview. Interviews were conducted by one of three members of the core ToC research team (L.D., L.H., T.G.) who all have experience in qualitative research. Semi-structured interviews of 40–60 min were conducted, either online, via Zoom or in person. The predetermined interview guide included a brief introduction to ToC methodology and questions were organized around the structural framework to obtain views on the key problems, activities, outputs, outcomes and impacts that related to the SGPMR project and sex and gender in the health and medical research sector (Supplementary file 3). Questions were primarily framed in relation to the SGPMR project, thereby concerning the Australian context.

Interviews were audio recorded, with the written consent of participants. The research team reviewed the audio transcripts produced by the Zoom transcribe function alongside the audio recordings to develop an accurate transcript for each interview. Interview transcripts were coded using NVivo 12. Interview responses were mapped deductively to the elements of the structural framework and analysed inductively to identify common themes across interviews. Codes were discussed between the core ToC research team iteratively and the final list of codes was used to develop the draft ToC. Codes addressing similar themes were combined where possible.

Consultation workshop

All 24 members of the SGPMR project, regardless of whether they participated in an interview, were invited to provide further input on the draft ToC by participating in a 2-h online workshop on 23 September 2022. The draft ToC was presented by the lead facilitator (L.D.) and each element was presented for discussion and feedback amongst the group. Content, language and structure of the draft were all reviewed, with further explanation by the facilitators and input from stakeholders. The discussion was audio-recorded with the consent of participants and one facilitator took extensive notes (T.G.), which were used by the research team to make amendments to the draft.

This study received ethical approval from the Human Research Ethical Approval Panel (HREAP) at the University of New South Wales (HC220443). All participants provided written consent to participate in this study.

Participation

A total of 15 individuals (4 PI members, 11 advisory group members), participated in semi-structured interviews, and 7 individuals (4 PI members, 3 advisory group members) participated in the 2-h online workshop. Participants represented expertise in clinical research, social sciences, academic, non-government community-based organizations and government. The most common reason for declining participation was unavailability. A total of 12 individuals (5 PI members and 7 advisory group members) provided peer review of the draft ToC schematic. In total, 19 out of 24 (79.2%) individuals associated with the SGPMR project provided some input into the ToC.

• Theory of change

The ToC is shown in Fig.  1 (see Supplementary File 4 for a tabulated version). Results are structured according to the elements of the ToC (problem statement, required activities, outputs and outcomes, impact). Boxes in the ToC are referred to in the results below numbered from left to right across the diagram for each element of the framework.

Theory of change for addressing sex and gender bias, invisibility and exclusion in health and medical research, policy and practice

Five key problems regarding the current consideration of sex and gender in health and medical research, policy and practice were identified for inclusion in the ToC: (1) lack of awareness of existing sex and gender biases and how those intersect with other biases in health and medical research, policy and practice; (2) Inadequate and biased incorporation of sex and gender into health and medical research; (3) sex and gender-based exclusion from meaningful engagement and participation in health and medical research; (4) lack of evidence-based interventions to address sex and gender biases in research, policy and practice; and (5) inequitable health outcomes between different populations.

Interviewees identified various forms of bias in research against different groups on the basis of sex and gender, which lead to science that is not rigorous or representative. Several potential reasons were proposed for inadequate consideration of sex and gender in research including poor societal understanding of sex and gender, particularly the predominance of a binary concept of sex and gender, inadequate data collection, lack of inclusion and analysis by sex and gender, too strong a focus on biological sex and a lack of consideration of intersectional factors, including race, social, economic and other factors. Other issues included the exclusion of marginalized communities from participating in research, a lack of adequate community consultation and input into research projects, which can lead to the design and funding of unethical research. Stakeholders discussed inequitable access to healthcare, which contributes to poorer health outcomes, particularly for women, transgender and gender-diverse people and people with intersex variations, and associated economic losses.

A total of 12 activities with potential to address the problems raised above emerged from participants’ responses. As a key starting point, respondents emphasized the need for improved understanding of the terms sex and gender, both within the health and medical research sector and societally. Respondents stressed that the complexity of these concepts and their evolving nature required a suitable conceptual framework that could be used to guide other activities. Accounting for intersectionality was also highlighted in the workshop as a vital component for any such framework to consider.

These themes are reflected in the ToC through the inclusion of activities that relate to building knowledge and awareness, education, training and advocacy:

Sector-wide discussion on conceptions of sex and gender and their relationship to intersectional factors;

Development and delivery of education and training on sex and gender concepts, their relevance and application to health research and translation;

Advocacy and awareness building around current issues and solutions;

Mapping current knowledge, policy and practice in relation to sex and gender.

Two activities focussed on the need for meaningful consultation and the building of networks and partnerships to share knowledge and expertise and facilitate change that appropriately accounts for the needs of diverse communities:

Diverse community and stakeholder involvement and engagement in evidence generation, translation, implementation and evaluation;

Developing diverse and multidisciplinary networks and communities of practice.

Several activities focussed on changing research practice and the development of policies, guidelines and standards to assist this change:

Policy development and implementation throughout the health and medical research sector;

Production or implementation of standards for consideration of sex and gender in research design;

Collection of accurate and inclusive sex- and gender-related and disaggregated data. Many stakeholders suggested that the Australian Bureau of Statistics Standard for Sex, Gender, Variations of Sex Characteristics and Sexual Orientation Variables [ 47 ] is a locally relevant example of such a standard that can be further implemented and used to guide data collection.

Expanding beyond health and medical research, two activities focussed on supporting translation of research evidence into practice:

Production of standards for consideration of sex and gender in design and regulation of medical products such as drugs and devices.

Translation of evidence into clinical guidelines and health policy that explicitly considers different populations.

The last activity underpins the implementation pillar and applies to all previous activities:

Monitoring, evaluation, governance and regulation of health and medical research and translation.

Participants repeatedly addressed the need for monitoring and evaluating the impact of interventions such as education and training, and changes to health and medical research, policy and practice. Participants emphasized the importance of a consistent process of review and adaptation over time, based on monitoring and evaluation, and taking account of the dynamic nature of sex- and gender-based research, concepts and terminology. It was also highlighted that this process should go beyond tokenistic metrics to understand and evaluate how institutional change occurs.

A total of 11 outputs were identified as emerging from the activities. These were:

Shared language for discussing sex, gender and intersectional factors;

Resources, training, curricula and advocacy material;

Educated, skilled and aware health research, delivery, policy and governance workforce;

Baseline reporting on current incorporation of sex and gender in health and medical research, policy and practice;

Central hub for networking, engagement and resources.

New or updated policies and guidelines for sex and gender incorporation into health research and practice across the sector;

Comprehensive and inclusive data and evidence on sex, gender and health;

Sex- and gender-informed clinical guidelines, standards, regulations, public policies and strategies;

Reporting on changes in research practice;

Reporting on health indicators across sex and gender domains; and

Established feedback mechanisms for continuous monitoring, evaluation and improvement of health research, policy and practice.

Outcomes reflected the priority activities raised by participants. They strongly felt that the identified activities would lead to improvements in research practice, including (1) improved integration of sex and gender in research design; (2) meaningful, accurate and inclusive data collection and reporting; (3) greater inclusion and participation in health and medical research; and (4) presence, implementation and monitoring of sex and gender research policies in the sector.

Other outcomes reflected the impacts of training, educational and advocacy activities: (5) recognition and application of sex and gender as nuanced, evolving concepts that intersect with other factors that impact health; (6) multidisciplinary sex and gender networks and communities of practice; (7) improved skills, knowledge and understanding of importance of sex and gender in health/medical research, policy and practice; and (8) increased awareness and use of best practice standards and guidelines.

Respondents also identified outcomes that may result from the translation of policies into more appropriate healthcare services and treatment: (9) efficient, inclusive and fit for purpose health interventions and services. This encompassed a variety of possible outcomes raised by stakeholders, including clinicians being able to provide inclusive and appropriate care, better and more cost-effective healthcare delivery, more targeted support for particular populations and more robust medical products. Respondents also felt that the existence of adequate monitoring and evaluation processes would result in an outcome whereby the (10) health sector (is) held accountable for ongoing action to address gender disparities in health outcomes.

Four impacts were identified which fed into the overarching impact of: enhanced health and wellbeing for everyone. Typically, sex and gender research is narrowly viewed as only relevant to women and other marginalized communities, yet the participants emphasized that identifying and evaluating health data benefits all population groups. This was reinforced by the four sub-impacts that were identified: (1) better-quality, nuanced and new health data and information; (2) safe, meaningful and representative participation and experience of diverse groups in health research, policy, practice and care; (3) health and medical sector-wide commitment to reform towards fairer, more inclusive and representative policy and practice; and (4) better and more equitable health and economic outcomes for all.

Barriers and facilitators

Key barriers and facilitators to change were also identified by respondents. These were applicable across the entire ToC map and not just to the achievement of specific outputs, outcomes or impacts.

Two barriers focussed on the influence of entrenched systems and beliefs, including difficulty in changing attitudes and status quo and the influence of discrimination and stigma. Practical barriers included the need or perception of need for more funding, time and resources to meet practical and methodological challenges. Another barrier to change was concern regarding ethics or liability when broadening research inclusivity, particularly when including those who are pregnant or lactating in clinical trials.

Facilitators included societal changes in culture and values that would increase receptivity to change. Another facilitator was leadership, with leadership from organizations and individual champions as well as the equitable gender representation in positions of power across the sector being facilitators for change. Organizations across the sector can also facilitate change through the provision of funding, operational support and expertise and the implementation of accountability measures and mandates.

To the best of our knowledge, this is the first theory of change (ToC) to explicitly outline a common understanding of the sex and gender bias, invisibility and exclusion in health and medical research, policy and practice and outline clear actions and pathways to impact towards enhanced health and wellbeing for all. This work therefore fills an important knowledge-implementation gap in the literature by demonstrating how changes in research policy and practice may create wider impact and the explicit assumptions underlying the guiding future activities and discussion in the field. We identify a range of required actions across evidence generation, translation and implementation that contextualizes the work that many in the sector are already doing, situates the activities of the SGPMR project in this wider context and has the potential to inform the development of future activities. Further, the overarching impact of enhanced health and wellbeing for all is a unifying goal for people working across these sectors, and thus this ToC can be used to reinforce the need to address sex and gender bias, invisibility and exclusion to achieve this impact.

In providing a scaffold for how positive change might occur across medical research, policy and practice through clearly articulated pathways to impact, this ToC also provides important theoretical underpinning to published estimates of macro-level return on investment in gender inclusive research and practice. For example, the donor Women’s Health Access Matters (WHAM) reported that investment of USD$ 300 million in women’s health research across three diseases could result in returns to the economy in excess of USD$ 13 billion by way of improvement in population health and economic productivity [ 48 ]. Assumptions made in the WHAM report regarding how increased investment in research leads to improved health are afforded a more nuanced understanding when considered alongside the pathways to impact articulated in this ToC.

Activities articulated by study participants and represented in the ToC align well with the limited literature that describes initiatives already underway that consider and address disparity in scientific and medical practice. For example, White et al. summarize lessons learnt from funding agencies in developing policies for sex and gender consideration in medical research and identify awareness building, education and collaboration between institutions and continual monitoring and evaluation as necessary to facilitate impact [ 25 ]. Initiatives such as Gendered Innovations and Global Health 50/50 are also actively engaged in building awareness of the need for and value in gender diverse participation in health and medical research and practice and provide guidance to different types of organizations to enhance their practice in this respect whilst monitoring progress against gender inclusion within the global health sector [ 21 , 49 ].

For those who are already working on specific activities such as developing or updating policies and guidelines to impact research practice [ 16 , 50 , 51 ], this ToC can help contextualize this change, inform design and encourage organizations to consider what parallel activities might be needed, such as education and training, consultation with key stakeholders and clarification of concepts across the sector. The ToC demonstrates the importance to those working to address sex and gender issues in evidence, translation and implementation of the need to coordinate their efforts and ensure monitoring and evaluation is communicated to inform practice throughout the pipeline.

Implications for the SGPMR project

The development of this ToC has various implications for the SGPMR project. First, while the ToC spans far beyond than the scope of the project, it supports project affiliates to identify activities to which they can contribute and situate their efforts in a wider change context. Further, as engaging with stakeholders across the health and medical research sector is an activity of the project, this ToC can be used as an advocacy tool to demonstrate the need for change and the role of different organizations in contributing to that change [ 34 , 35 ].

Second, this work also served as a useful activity to reach consensus on the key issues to be addressed and the desired impact of the project. It also facilitated discussion regarding the limitations of this project in achieving long-term impact on health outcomes due to the concentration of activities at the evidence end of the pipeline. Further, this process highlighted the diverse perspectives and priorities of different project stakeholders, related to issues faced by certain populations (namely, cis women, transgender women, gender-diverse people and people with intersex variations), which actions and activities they deemed most important and the areas of the sector they were most familiar with or interested in influencing. This process was beneficial in capturing those different perspectives and working to account for and align the goals of all stakeholders.

Strengths and limitations

A key strength of this work was that it was developed in consultation with stakeholders from various academic and professional backgrounds alongside representatives of communities marginalized because of their sex and gender status. These perspectives have been incorporated in the ToC, enabling an expansive view of sex and gender biases in the sectors impacting diverse groups in different ways and conceptualize how we can create change for the benefit of all.

The development of this ToC has some key limitations. First, as this study was conducted from a sector-wide perspective, it is not centred around a specific intervention and does not trace linear pathways of impact, highlight measurable pre-conditions for success or identify parties responsible for certain actions. Rather, it is a broad conceptual model, reflecting the complexity of the problems and potential solutions, and mapping an array of activities, outputs and potential outcomes. Nevertheless, to our knowledge, this is the first such tool for this sector and therefore has potential to be used across the sector to advance sex- and gender-based policy design, evaluation and impact [ 52 ]. Second, while this ToC covers a broad range of issues, other connected problems, such as the lack of gender equity in the health and medical research, policy and practice workforce were considered out of scope, though others have clearly linked the two issues [ 21 ]. Lastly, we only consulted internal project affiliates for an Australia-based project, and participants were mostly academics, with a small number of end-users working in policy- and community-based organizations. The development of the ToC was based primarily on this consultation, without the benefit of a large literature base, due to the lack of previous research about the efficacy of interventions in this field.

Conclusions

This paper describes the development of a theory of change (ToC) that maps clear pathways to impact for improving the consideration of sex and gender in health and medical research, policy and practice. This ToC is the first of its kind in the field of health and medical research and provides an important theoretical scaffold for institutions and organizations to consider when considering how to actively enhance sex and gender awareness, inclusivity and informed action to contribute to enhancing population health and economic wellbeing.

Availability of data and materials

The datasets used and analysed under study are available from the corresponding author on reasonable request.

Mauvais-Jarvis F, Bairey Merz N, Barnes PJ, Brinton RD, Carrero JJ, DeMeo DL, et al. Sex and gender: modifiers of health, disease, and medicine. Lancet. 2020;396(10250):565–82.

Article   PubMed   PubMed Central   Google Scholar  

Merriman R, Galizia I, Tanaka S, Sheffel A, Buse K, Hawkes S. The gender and geography of publishing: a review of sex/gender reporting and author representation in leading general medical and global health journals. BMJ Glob Health. 2021;6(5): e005672.

Hallam L, Vassallo A, Hallam C, Thompson K, Shanthosh J, Chappell L, et al. Sex and gender reporting in Australian health and medical research publications. Aust N Z J Public Health. 2023;47(1): 100005.

Article   PubMed   Google Scholar  

Geller SE, Koch AR, Roesch P, Filut A, Hallgren E, Carnes M. The more things change, the more they stay the same: a study to evaluate compliance with inclusion and assessment of women and minorities in randomized controlled trials. Acad Med. 2018;93(4):630–5.

Gauci S, Cartledge S, Redfern J, Gallagher R, Huxley R, Lee CMY, et al. Biology, bias, or both? The contribution of sex and gender to the disparity in cardiovascular outcomes between women and men. Curr Atheroscler Rep. 2022;24(9):701–8.

Huded CP, Johnson M, Kravitz K, Menon V, Abdallah M, Gullett TC, et al. 4-Step protocol for disparities in STEMI care and outcomes in women. J Am Coll Cardiol. 2018;71(19):2122–32.

Carcel C, Woodward M, Wang X, Bushnell C, Sandset EC. Sex matters in stroke: a review of recent evidence on the differences between women and men. Front Neuroendocrinol. 2020;59: 100870.

Mogil JS, Chanda ML. The case for the inclusion of female subjects in basic science studies of pain. Pain. 2005;117(1):1–5.

Wang X, Carcel C, Hsu B, Shajahan S, Miller M, Peters S, et al. Differences in the pre-hospital management of women and men with stroke by emergency medical services in New South Wales. Med J Aust. 2022;217(3):143–8.

Farkouh A, Riedl T, Gottardi R, Czejka M, Kautzky-Willer A. Sex-related differences in pharmacokinetics and pharmacodynamics of frequently prescribed drugs: a review of the literature. Adv Ther. 2020;37(2):644–55.

Article   CAS   PubMed   Google Scholar  

Canadian Institutes of Health Research. Gender-Based Analysis Plus (GBA+) at CIHR: Government of Canada; 2020. https://cihr-irsc.gc.ca/e/50968.html . Accessed May 2021.

European Commission. Gender equality in research and innovation: European Commission; 2020. https://ec.europa.eu/info/research-and-innovation/strategy/strategy-2020-2024/democracy-and-citizens-rights/gender-equality-research-and-innovation_en#gender-equality-plans-as-an-eligibility-criterion-in-horizon-europe . Accessed 3 Jun 2021.

Heidari S, Babor TF, De Castro P, Tort S, Curno M. Sex and gender equity in research: rationale for the SAGER guidelines and recommended use. Res Integr Peer Rev. 2016;1(1):2.

National Institutes of Health. NIH guidelines on the inclusion of women and minorities as subjects in clinical research. NIH Guide; 1994.

Office of Research on Women’s Health. Consideration of sex as a biological variable in NIH-funded Research. In: Health NIo, editor. 2016.

Health Canada. Health portfolio sex- and gender-based analysis plus policy: advancing equity, diversity and inclusion Government of Canada; 2022. https://www.canada.ca/en/health-canada/corporate/transparency/corporate-management-reporting/heath-portfolio-sex-gender-based-analysis-policy.html . Accessed 22 Oct 2022

Ludwig S, Oertelt-Prigione S, Kurmeyer C, Gross M, Grüters-Kieslich A, Regitz-Zagrosek V, et al. A successful strategy to integrate sex and gender medicine into a newly developed medical curriculum. J Womens Health. 2015;24(12):996–1005.

Article   Google Scholar  

Mercer-Mapstone L, Bajan S, Banas K, Morphett A, McGrath K. Breaking the binary: conceptions of sex and gender in undergraduate science. Teach Learn Inquiry. 2021. https://doi.org/10.20343/teachlearninqu.9.2.6 .

Muntinga ME, Krajenbrink VQE, Peerdeman SM, Croiset G, Verdonk P. Toward diversity-responsive medical education: taking an intersectionality-based approach to a curriculum evaluation. Adv Health Sci Educ. 2016;21(3):541–59.

Article   CAS   Google Scholar  

Caughey AB, Krist AH, Wolff TA, Barry MJ, Henderson JT, Owens DK, et al. USPSTF to approach addressing sex and gender when making recommendations for clinical preventive services. JAMA. 2021;326(19):1953–61.

Global Health 50/50. Gender Equality: flying blind in a time of crisis, The Global Health 50/50 Report 2021. London, UK; 2021.

Oertelt-Prigione S, Dalibert L, Verdonk P, Stutz EZ, Klinge I. Implementation strategies for gender-sensitive public health practice: a European Workshop. J Womens Health. 2017;26(11):1255–61.

Sex and Gender in Health and Medical Research Australia Group. Project Glossary Australia. 2021. https://www.sexandgenderhealthpolicy.org.au/glossary/ . Accessed Dec 2022.

Woitowich NC, Woodruff TK. Implementation of the NIH sex-inclusion policy: attitudes and opinions of study section members. J Womens Health. 2019;28(1):9–16.

White J, Tannenbaum C, Klinge I, Schiebinger L, Clayton J. The integration of sex and gender considerations into biomedical research: lessons from international funding agencies. J Clin Endocrinol Metab. 2021;106(10):3034–48.

Johnson J, Sharman Z, Vissandjée B, Stewart DE. Does a change in health research funding policy related to the integration of sex and gender have an impact? PLoS ONE. 2014;9(6): e99900.

Haverfield J, Tannenbaum C. A 10-year longitudinal evaluation of science policy interventions to promote sex and gender in health research. Health Res Policy Syst. 2021;19(1):94.

Heidari S, Palmer-Ross A, Goodman T. A systematic review of the sex and gender reporting in COVID-19 clinical trials. Vaccines. 2021;9(11):1322.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Geller SE, Adams MG, Carnes M. Adherence to federal guidelines for reporting of sex and race/ethnicity in clinical trials. J Womens Health. 2006;15(10):1123–31.

Geller SE, Koch A, Pellettieri B, Carnes M. Inclusion, analysis, and reporting of sex and race/ethnicity in clinical trials: have we made progress? J Womens Health. 2011;20(3):315–20.

Peters SAE, Babor TF, Norton RN, Clayton JA, Ovseiko PV, Tannenbaum C, et al. Fifth anniversary of the Sex And Gender Equity in Research (SAGER) guidelines: taking stock and looking ahead. BMJ Glob Health. 2021;6(11): e007853.

Merone L, Tsey K, Russell D, Nagle C. Sex inequalities in medical research: a systematic scoping review of the literature. Women’s Health Rep. 2022;3(1):49–59.

Johnson JL, Greaves L, Repta R. Better science with sex and gender: facilitating the use of a sex and gender-based analysis in health research. Int J Equity Health. 2009;8(1):14.

Sex and Gender in Health and Medical Research Australia Group. Sex and Gender Policies in Health and Medical Research Sydney: Australian Human Rights Institute; 2021. www.sexandgenderhealthpolicy.org.au . Accessed Nov 2021.

The Sex and Gender Sensitive Research Call to Action Group. Sex and gender in health research: updating policy to reflect evidence. Med J Austr. 2020;212(2):57–62.

Hallam L, Vassallo A, Pinho-Gomes A-C, Carcel C, Woodward M. Does journal content in the field of women’s health represent women’s burden of disease? A review of publications in 2010 and 2020. J Womens Health. 2022;31(5):611–9.

Greaves L, Ritz SA. Sex, gender and health: mapping the landscape of research and policy. Int J Environ Res Public Health. 2022;19(5):2563.

Weiss CH. Theory-based evaluation: past, present, and future. New Dir Eval. 1997;1997(76):41–55.

Isabel V. Review of the use of ‘Theory of Change’ in international development. London: UK Department of International Development; 2012.

Google Scholar  

Maini R, Mounier-Jack S, Borghi J. How to and how not to develop a theory of change to evaluate a complex intervention: reflections on an experience in the Democratic Republic of Congo. BMJ Glob Health. 2018;3(1): e000617.

Kathryn S, Lynsay M, Sharon Anne S, Peter C, Janis B, Jane MB, et al. A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance. BMJ. 2021;374: n2061.

Department of Foreign Affairs and; Trade Australian Government. Design and monitoring, evaluation and learning standards. Canberra: DFAT; 2023.

AusAID. AusAID’s mulitsectoral contribution to the national response in Papua New Guinea; Annex 10: Theory of Change Canberra: AusAID; 2010.

Weitzman BC, Mijanovich T, Silver D, Brecher C. Finding the impact in a messy intervention: using an integrated design to evaluate a comprehensive citywide health initiative. Am J Eval. 2009;30(4):495–514.

Breuer E, Lee L, De Silva M, Lund C. Using theory of change to design and evaluate public health interventions: a systematic review. Implement Sci. 2016;11(1):63.

Stein D, Valters C. Understanding theory of change in international development. The Asia Foundation; London School of Economics and Political Science 2012.

Australian Bureau of Statistics. Standard for Sex, Gender, Variations of Sex Characteristics and Sexual Orientation Variables ABS Website: Australian Bureau of Statistics; 2021. https://www.abs.gov.au/statistics/standards/standard-sex-gender-variations-sex-characteristics-and-sexual-orientation-variables/2020 . Accessed Jun 2021.

Baird MA, Zaber AZ, Chen A, Dick AW, Bird CE, Waymouth M, et al. The case to fund women’s health research: an economic and societal impact analysis. Santa Monica: RAND; 2021.

Schiebinger L, Klinge, I, Sánchez de Madariaga, I, Paik, HY., Schraudner, M, and Stefanick, M. Gendered Innovations in Science, Health & Medicine, Engineering and Environment Stanford: Stanford University; 2011–2021. https://genderedinnovations.stanford.edu . Accessed Jun 2021.

Hunt L, Nielsen MW, Schiebinger L. A framework for sex, gender, and diversity analysis in research. Science. 2022;377(6614):1492–5.

Association of Australian Medical Research Institutes. AAMRI gender equity diversity and inclusion strategy and action plan. Melbourne: AAMRI; 2021.

LaFond A, Cherney M. A theory of change for guiding the integration of human-centered design into global health programming. Glob Health Sci Pract. 2021;9(Suppl 2):S209–16.

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Acknowledgements

The Theory of Change figure was designed with help from Deborah Bordeos.

All authors’ work on this study was funded from an anonymous philanthropic grant. The funder had no role in the design, execution, analysis or write-up of this study.

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Thomas Gadsden, Laura Hallam, Cheryl Carcel, Robyn Norton, Mark Woodward & Laura E. Downey

Australian Human Rights Institute, University of New South Wales, Sydney, Australia

Laura Hallam

The George Institute for Global Health, Imperial College London, London, United Kingdom

Robyn Norton, Mark Woodward & Laura E. Downey

The Human Rights Institute, University of New South Wales, Sydney, Australia

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The trends defining the $1.8 trillion global wellness market in 2024

From cold plunges to collagen to celery juice, the $1.8 trillion global consumer wellness market is no stranger to fads, which can sometimes surface with limited clinical research or credibility. Today, consumers are no longer simply trying out these wellness trends and hoping for the best, but rather asking, “What does the science say?”

About the authors

This article is a collaborative effort by Shaun Callaghan , Hayley Doner, Jonathan Medalsy, Anna Pione , and Warren Teichner , representing views from McKinsey’s Consumer Packaged Goods and Private Equity & Principal Investors Practices.

McKinsey’s latest Future of Wellness research—which surveyed more than 5,000 consumers across China, the United Kingdom, and the United States—examines the trends shaping the consumer wellness landscape. In this article, we pair these findings with a look at seven wellness subsets—including women’s health, weight management, and in-person fitness—that our research suggests are especially ripe areas for innovation and investment activity.

The science- and data-backed future of wellness

In the United States alone, we estimate that the wellness market has reached $480 billion, growing at 5 to 10 percent per year. Eighty-two percent of US consumers now consider wellness a top or important priority in their everyday lives, which is similar to what consumers in the United Kingdom and China report (73 percent and 87 percent, respectively).

This is especially true among Gen Z and millennial consumers, who are now purchasing more wellness products and services than older generations, across the same dimensions we outlined in our previous research : health, sleep, nutrition, fitness, appearance, and mindfulness (Exhibit 1). 1 “ Still feeling good: The US wellness market continues to boom ,” McKinsey, September 19, 2022.

Across the globe, responses to our survey questions revealed a common theme about consumer expectations: consumers want effective, data-driven, science-backed health and wellness solutions (Exhibit 2).

Five trends shaping the consumer health and wellness space in 2024

Fifty-eight percent of US respondents to our survey said they are prioritizing wellness more now than they did a year ago. The following five trends encompass their newly emerging priorities, as well as those that are consistent with our earlier research.

Trend one: Health at home

The COVID-19 pandemic made at-home testing kits a household item. As the pandemic has moved into its endemic phase, consumers are expressing greater interest in other kinds of at-home kits: 26 percent of US consumers are interested in testing for vitamin and mineral deficiencies at home, 24 percent for cold and flu symptoms, and 23 percent for cholesterol levels.

At-home diagnostic tests are appealing to consumers because they offer greater convenience than going to a doctor’s office, quick results, and the ability to test frequently. In China, 35 percent of consumers reported that they had even replaced some in-person healthcare appointments with at-home diagnostic tests—a higher share than in the United States or the United Kingdom.

Although there is growing interest in the space, some consumers express hesitancy. In the United States and the United Kingdom, top barriers to adoption include the preference to see a doctor in person, a perceived lack of need, and price; in China, test accuracy is a concern for approximately 30 percent of consumers.

Implications for companies: Companies can address three critical considerations to help ensure success in this category. First, companies will want to determine the right price value equation for at-home diagnostic kits since cost still presents a major barrier for many consumers today. Second, companies should consider creating consumer feedback loops, encouraging users to take action based on their test results and then test again to assess the impact of those interventions. Third, companies that help consumers understand their test results—either through the use of generative AI to help analyze and deliver personalized results, or through integration with telehealth services—could develop a competitive advantage.

Trend two: A new era for biomonitoring and wearables

Roughly half of all consumers we surveyed have purchased a fitness wearable at some point in time. While wearable devices such as watches have been popular for years, new modalities powered by breakthrough technologies have ushered in a new era for biomonitoring and wearable devices.

Wearable biometric rings, for example, are now equipped with sensors that provide consumers with insights about their sleep quality through paired mobile apps. Continuous glucose monitors, which can be applied to the back of the user’s arm, provide insights about the user’s blood sugar levels, which may then be interpreted by a nutritionist who can offer personalized health guidance.

Roughly one-third of surveyed wearable users said they use their devices more often than they did last year, and more than 75 percent of all surveyed consumers indicated an openness to using a wearable in the future. We expect the use of wearable devices to continue to grow, particularly as companies track a wider range of health indicators.

Implications for companies: While there is a range of effective wearable solutions on the market today for fitness and sleep, there are fewer for nutrition, weight management, and mindfulness, presenting an opportunity for companies to fill these gaps.

Wearables makers and health product and services providers in areas such as nutrition, fitness, and sleep can explore partnerships that try to make the data collected through wearable devices actionable, which could drive greater behavioral change among consumers. One example: a consumer interested in managing stress levels might wear a device that tracks spikes in cortisol. Companies could then use this data to make personalized recommendations for products related to wellness, fitness, and mindfulness exercises.

Businesses must keep data privacy and clarity of insights top of mind. Roughly 30 percent of China, UK, and US consumers are open to using a wearable device only if the data is shared exclusively with them. Additionally, requiring too much manual data input or sharing overly complicated insights could diminish the user experience. Ensuring that data collection is transparent and that insights are simple to understand and targeted to consumers’ specific health goals or risk factors will be crucial to attracting potential consumers.

Trend three: Personalization’s gen AI boost

Nearly one in five US consumers and one in three US millennials prefer personalized products and services. While the preference for personalized wellness products was lower than in years prior, we believe this is likely due to consumers becoming more selective about which personalized products and services they use.

Technological advancements and the rise of first-party data are giving personalization a new edge. Approximately 20 percent of consumers in the United Kingdom and the United States and 30 percent in China look for personalized products and services that use biometric data to provide recommendations. There is an opportunity to pair these tools with gen AI to unlock greater precision and customization. In fact, gen AI has already made its way to the wearables and app space: some wearables use gen AI to design customized workouts for users based on their fitness data.

Implications for companies: Companies that offer software-based health and wellness services to consumers are uniquely positioned to incorporate gen AI into their personalization offerings. Other businesses could explore partnerships with companies that use gen AI to create personalized wellness recommendations.

Trend four: Clinical over clean

Last year, we saw consumers begin to shift away from wellness products with clean or natural ingredients to those with clinically proven ingredients. Today, that shift is even more evident. Roughly half of UK and US consumers reported clinical effectiveness as a top purchasing factor, while only about 20 percent reported the same for natural or clean ingredients. This trend is most pronounced in categories such as over-the-counter medications and vitamins and supplements (Exhibit 3).

In China, consumers expressed roughly equal overall preference for clinical and clean products, although there were some variations between categories. They prioritized clinical efficacy for digestive medication, topical treatments, and eye care products, while they preferred natural and clean ingredients for supplements, superfoods, and personal-care products.

Implications for companies: To meet consumer demand for clinically proven products, some brands will be able to emphasize existing products in their portfolios, while other businesses may have to rethink product formulations and strategy. While wellness companies that have built a brand around clean or natural products—particularly those with a dedicated customer base—may not want to pivot away from their existing value proposition, they can seek out third-party certifications to help substantiate their claims and reach more consumers.

Companies can boost the clinical credibility of their products by using clinically tested ingredients, running third-party research studies on their products, securing recommendations from healthcare providers and scientists, and building a medical board that weighs in on product development.

Trend five: The rise of the doctor recommendation

The proliferation of influencer marketing in the consumer space has created new sources of wellness information—with varying degrees of credibility. As consumers look to avoid “healthwashing” (that is, deceptive marketing that positions a product as healthier than it really is), healthcare provider recommendations are important once again.

Doctor recommendations are the third-highest-ranked source of influence on consumer health and wellness purchase decisions in the United States (Exhibit 4). Consumers said they are most influenced by doctors’ recommendations when seeking care related to mindfulness, sleep, and overall health (which includes the use of vitamins, over-the-counter medications, and personal- and home-care products).

Implications for companies: Brands need to consider which messages and which messengers are most likely to resonate with their consumers. We have found that a company selling products related to mindfulness may want to use predominately doctor recommendations and social media advertising, whereas a company selling fitness products may want to leverage recommendations from friends and family, as well as endorsements from personal trainers.

Seven areas of growth in the wellness space

Building upon last year’s research, several pockets of growth in the wellness space are emerging. Increasing consumer interest, technological breakthroughs, product innovation, and an increase in chronic illnesses have catalyzed growth in these areas.

Women’s health

Historically, women’s health has been underserved and underfunded . Today, purchases of women’s health products are on the rise across a range of care needs (Exhibit 5). While the highest percentage of respondents said they purchased menstrual-care and sexual-health products, consumers said they spent the most on menopause and pregnancy-related products in the past year.

Digital tools are also becoming more prevalent in the women’s health landscape. For example, wearable devices can track a user’s physiological signals to identify peak fertility windows.

Despite recent growth in the women’s health space, there is still unmet demand for products and services. Menopause has been a particularly overlooked segment of the market: only 5 percent of FemTech  start-ups address menopause needs. 2 Christine Hall, “Why more startups and VCs are finally pursuing the menopause market: ‘$600B is not “niche,”’” Crunchbase, January 21, 2021.   Consumers also continue to engage with offerings across the women’s health space, including menstrual and intimate care, fertility support, pregnancy and motherhood products, and women-focused healthcare centers, presenting opportunities for companies to expand products and services in these areas.

Healthy aging

Demand for products and services that support healthy aging and longevity is on the rise, propelled by a shift toward preventive medicine, the growth of health technology (such as telemedicine and digital-health monitoring), and advances in research on antiaging products.

Roughly 70 percent of consumers in the United Kingdom and the United States and 85 percent in China indicated that they have purchased more in this category in the past year than in prior years.

More than 60 percent of consumers surveyed considered it “very” or “extremely” important to purchase products or services that help with healthy aging and longevity. Roughly 70 percent of consumers in the United Kingdom and the United States and 85 percent in China indicated that they have purchased more in this category in the past year than in prior years. These results were similar across age groups, suggesting that the push toward healthy aging is spurred both by younger generations seeking preventive solutions and older generations seeking to improve their longevity. As populations across developed economies continue to age (one in six people in the world will be aged 60 or older by 2030 3 “Ageing and health,” World Health Organization, October 1, 2022. ), we expect there to be an even greater focus globally on healthy aging.

To succeed in this market, companies can take a holistic approach to healthy-aging solutions , which includes considerations about mental health and social factors. Bringing products and services to market that anticipate the needs of aging consumers—instead of emphasizing the aging process to sell these products—will be particularly important. For example, a service that addresses aging in older adults might focus on one aspect of longevity, such as fitness or nutrition, rather than the process of aging itself.

Weight management

Weight management is top of mind for consumers in the United States, where nearly one in three adults struggles with obesity 4 Obesity fact sheet 508 , US Centers for Disease Control and Prevention, July 2022. ; 60 percent of US consumers in our survey said they are currently trying to lose weight.

While exercise is by far the most reported weight management intervention in our survey, more than 50 percent of US consumers considered prescription medication, including glucagon-like peptide-1 (GLP-1) drugs, to be a “very effective” intervention. Prescription medication is perceived differently elsewhere: less than 30 percent of UK and China consumers considered weight loss drugs to be very effective.

Given the recency of the GLP-1 weight loss trend, it is too early to understand how it will affect the broader consumer health and wellness market. Companies should continue to monitor the space as further data emerges on adoption rates and impact across categories.

In-person fitness

Fitness has shifted from a casual interest to a priority for many consumers: around 50 percent of US gym-goers said that fitness is a core part of their identity (Exhibit 6). This trend is even stronger among younger consumers—56 percent of US Gen Z consumers surveyed considered fitness a “very high priority” (compared with 40 percent of overall US consumers).

In-person fitness classes and personal training are the top two areas where consumers expect to spend more on fitness. Consumers expect to maintain their spending on fitness club memberships and fitness apps.

The challenge for fitness businesses will be to retain consumers among an ever-increasing suite of choices. Offering best-in-class facilities, convenient locations and hours, and loyalty and referral programs are table stakes. Building strong communities and offering experiences such as retreats, as well as services such as nutritional coaching and personalized workout plans (potentially enabled by gen AI), can help top players evolve their value proposition and manage customer acquisition costs.

More than 80 percent of consumers in China, the United Kingdom, and the United States consider gut health to be important, and over 50 percent anticipate making it a higher priority in the next two to three years.

One-third of US consumers, one-third of UK consumers, and half of Chinese consumers said they wish there were more products in the market to support their gut health.

While probiotic supplements are the most frequently used gut health products in China and the United States, UK consumers opt for probiotic-rich foods such as kimchi, kombucha, or yogurt, as well as over-the-counter medications. About one-third of US consumers, one-third of UK consumers, and half of Chinese consumers said they wish there were more products in the market to support their gut health. At-home microbiome testing and personalized nutrition are two areas where companies can build on the growing interest in this segment.

Sexual health

The expanded cultural conversation about sexuality, improvements in sexual education, and growing support for female sexual-health challenges (such as low libido, vaginal dryness, and pain during intercourse) have all contributed to the growth in demand for sexual-health products.

Eighty-seven percent of US consumers reported having spent the same or more on sexual-health products in the past year than in the year prior, and they said they purchased personal lubricants, contraceptives, and adult toys most frequently.

While more businesses began to sell sexual-health products online during the height of the COVID-19 pandemic, a range of retailers—from traditional pharmacies to beauty retailers to department stores—are now adding more sexual-health brands and items to their store shelves. 5 Keerthi Vedantam, “Why more sexual wellness startups are turned on by retail,” Crunchbase, November 15, 2022.   This creates marketing and distribution opportunities for disruptor brands to reach new audiences and increase scale.

Despite consistently ranking as the second-highest health and wellness priority for consumers, sleep is also the area where consumers said they have the most unmet needs. In our previous report, 37 percent of US consumers expressed a desire for additional sleep and mindfulness products and services, such as those that address cognitive functioning, stress, and anxiety management. In the year since, little has changed. One of the major challenges in improving sleep is the sheer number of factors that can affect a good night’s sleep, including diet, exercise, caffeination, screen time, stress, and other lifestyle factors. As a result, few, if any, tech players and emerging brands in the sleep space have been able to create a compelling ecosystem to improve consumer sleep holistically. Leveraging consumer data to address specific pain points more effectively—including inducing sleep, minimizing sleep interruptions, easing wakefulness, and improving sleep quality—presents an opportunity for companies.

As consumers take more control over their health outcomes, they are looking for data-backed, accessible products and services that empower them to do so. Companies that can help consumers make sense of this data and deliver solutions that are personalized, relevant, and rooted in science will be best positioned to succeed.

Shaun Callaghan is a partner in McKinsey’s New Jersey office; Hayley Doner is a consultant in the Paris office; and Jonathan Medalsy is an associate partner in the New York office, where Anna Pione is a partner and Warren Teichner is a senior partner.

The authors wish to thank Celina Bade, Cherry Chen, Eric Falardeau, Lily Fu, Eric He, Sara Hudson, Charlotte Lucas, Maria Neely, Olga Ostromecka, Akshay Rao, Michael Rix, and Alex Sanford for their contributions to this article.

This article was edited by Alexandra Mondalek, an editor in the New York office.

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Emotional Intelligence and Psychological Well-Being in Adolescents

Joan guerra-bustamante.

1 Department of Psychology, Faculty of Teacher Training College, University of Extremadura, 10071 Cáceres, Spain; se.xenu@bgnaoj (J.G.-B.); se.xenu@noelb (B.L.-d.-B.); se.xenu@zepolmv (V.M.L.-R.)

Benito León-del-Barco

Rocío yuste-tosina.

2 Department of Educational Science, Faculty of Teacher Training College, University of Extremadura, 10071 Cáceres, Spain; se.xenu@etsuyoicor

Víctor M. López-Ramos

Santiago mendo-lázaro.

The present study aimed to analyze the association between of the dimensions of emotional intelligence (attention, clarity, and repair) and different levels of perceived happiness (low, medium, and high) in adolescents. The sample consists of 646 students in the first, second, third, and fourth years of Secondary Education, 47.5% females and 52.5% males, between 12 and 17 years of age. The instruments used were the Spanish version of the Trait Meta Mood Scale-24 Questionnaire to measure perceived emotional intelligence and the Oxford Happiness Questionnaire. Multinomial logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed. The results suggest that as the capacity of understanding and regulation of emotional intelligence increases, happiness also increases. Adolescence is seen as an ideal time in life to encourage the development of emotional capacities that contribute to the greater happiness of individuals. In this way, the present study stresses the need to carry out practices leading to improvements in the adolescents’ emotional intelligence and therefore increase their happiness and emotional well-being.

1. Introduction

The study of happiness and emotional well-being in young people has expanded exponentially in recent years. Psychology has traditionally focused on unhappiness and paid little attention to positive aspects of human potential [ 1 ]. This approach has been evident when studying adolescence, since this period of life implies many changes and it has been long described as a moment of stress and difficulties [ 2 ]. This conception of adolescence is currently fairly different for studies do not only describe the adolescent as a source of problems but also as a valuable asset in a development process [ 3 , 4 ]. This change took place with the arrival of positive psychology, as one of its objectives is to promote psychological research and practice in such areas as positive traits (strengths), positive emotions, and their contribution to well-being [ 5 ].

1.1. Happiness or Psychological Well-Being

As for the study of happiness, it is essential to point out that there is no consensus about how to define it. One of the most accepted theoretical approaches states that the construct happiness refers to an emotional and cognitive type of psychological state [ 6 ], a positive affective component in which positive emotions and the subjective interpretation of well-being are fundamental [ 6 , 7 , 8 , 9 , 10 , 11 , 12 ].

On a theoretical level, the debate on happiness has two main approaches: 1) the hedonic approach, that affirms that happiness is the presence of positive affection and the absence of negative affection; and 2) the eudaimonic approach, that states that happiness is the consequence of full psychological functioning by means of which the person develops his or her potential [ 13 ]. In line with eudaimonism, it is noteworthy to mention the psychological well-being multidimensional model [ 14 ], focused in the fulfillment of human potential through six key features: autonomy, environmental control, personal growth, positive relationships with others, purpose in life, and self-acceptance [ 15 ]. Both approaches can be integrated in the “three dimensions of happiness” model [ 1 ] which are: 1) a pleasant life, understood as a pleasant feeling towards past, present and future; 2) a committed life, by using positive individual features, including character strengths and talents; and 3) a meaningful life, which means to serve and to belong to positive institutions. Subsequently, this model favored the appearance of 24 Strengths Model [ 16 ] which focuses on studying happiness in strengths and virtues.

Accordingly, they reinforce the idea of the existence of factors that determine happiness [ 17 ]. Then we find the Science of Happiness [ 12 ] which claims that happiness can be increased by the individual himself by means of certain activities. For that matter, such a vital period as adolescence is the ideal moment to increase it. In recent years, different theoretical approaches have defended a positive comprehension of adolescence, a crucial stage characterized by plasticity, the acquisition of competences and the achievement of satisfactory levels of well-being and positive adjustments [ 17 ]. It is a time when the capacity to appreciate satisfaction with life and well-being increases in a critical and conscious way [ 18 ]. Specifically, teaching adolescents to be happy functions with three main goals: as an antidote against depression, as a means of increasing life satisfaction, and as a way to enhance learning and creative thought [ 19 ].

1.2. Emotional Intelligence

One of the variables that could help to this increase of happiness during adolescence can be emotional intelligence [ 20 ]. There are two relevant models of emotional intelligence: Mixed Models and Ability Model. Mixed Models state that emotional intelligence is a compendium of stable personality features, socio-emotional competences, motivational aspects, and different cognitive abilities [ 21 , 22 , 23 ]. On the other side we find the Ability Model [ 24 ] which considers emotional intelligence as an ability focused on emotional information processing [ 25 ]. Ever since Model of Emotional Intelligence, this construct is defined as a type of social intelligence that involves the ability to monitor one’s own and others’ emotions, to discriminate among them, and to use the information to guide one’s thinking and actions [ 24 ]. Subsequently, said authors included in their definition abilities related to cognitive and emotional clarity, perception, and repair that could generate feelings that eased thinking and abilities of cognitive and emotional regulation [ 26 ]. In order to measure this construct, they designed questionnaire TMMS-24, which assesses Perceived Emotional Intelligence through three factors: attention to emotions (capability to feel and express feelings properly), emotional clarity (capability to understand the own emotional states), and emotional repair (capability to correctly regulate emotional states).

1.3. Happiness or Psychological Well-Being and Emotional Intelligence

Scientific literature highlights the major role of emotional intelligence when determining individual happiness [ 20 ]. Numerous researchers have related emotional intelligence with psychological constructs that are closely associated with happiness, such as subjective well-being [ 27 , 28 ], higher rates of positive emotional states and decrease of negative emotional states [ 29 ], satisfaction with life [ 20 , 30 , 31 , 32 ], better psychological functioning and social competence [ 33 ], and better social relations; and negative associations with loneliness [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Other studies have focused on the relationship between emotional intelligence and variables connected with well-being in young people, such as physical and mental health [ 41 , 42 , 43 ] and perception of stress [ 44 ]. There is therefore clear evidence that capacities of emotional intelligence predict aspects related to personal well-being and a positive relation between life satisfaction and subjective happiness [ 45 , 46 ].

For this matter, Hills and Argyle [ 47 ] composed the Oxford Happiness Questionnaire, which evaluates subjective happiness from these psychological dimensions, including items focused on life satisfaction, positive emotions, physical and mental health, or social relationships.

More specifically, studies made from mixed models note that the trait emotional intelligence is a constellation of capacities and self-perceived attitudes related with emotion [ 48 ]. In this regard, different studies note the existence of a positive correlation between emotional intelligence as a trait and perceived happiness [ 49 , 50 ]. On the other hand, from the ability model, research based on Spanish adolescent subjects shows that the abilities of clarity and repair are positively correlated with life satisfaction whereas attention correlates negatively in adolescents [ 51 ]. In the same way, the dimensions of emotional recognition and expression, and the control of emotions mediate in the relationship between fully dispositional mindfulness and subjective happiness [ 52 ]. However, it should be considered that self-perceptions and attitudes associated with people’s emotions—such as emotional regulation, relationship skills, and social competence—determine variation in happiness to a large degree [ 50 ]. Henceforth, research shows that emotional intelligence abilities imply a skill that allows adolescents to guide their thoughts and ponder over their emotions, helping them to improve their well-being levels [ 53 ]. These studies suggest that important interventions may be performed to promote flourishing and happiness, enhancing emotional intelligence through specific training [ 54 ].

The present study seeks to analyze in a sample of adolescents, the association between of the dimensions of emotional intelligence (attention, clarity, and repair) and different levels of perceived happiness (low, medium, and high). It will also identify the sensitivity and the ability to distinguish scores obtained in the Spanish version of the questionnaire Trait Meta Mood Scale [ 55 ], from which high happiness is more likely to exist.

2. Materials and Methods

2.1. participants.

The sample consists of 646 students in the first, second, third, and fourth years of Secondary Education, 47.5% females and 52.5% males, between 12 and 17 years of age. The sampling was carried out by selecting eight schools in the Community of Extremadura (Spain) at random.

2.2. Instruments

2.2.1. trait meta mood scale.

The Spanish version of the questionnaire Trait Meta Mood Scale (TMMS-24) [ 55 ] has been used to evaluate perceived emotional intelligence. The questionnaire is formed by 24 items with a Likert-type five-point answer scale (1 = Do not agree, 5 = Totally agree). Three dimensions are evaluated (eight items per dimension): attention (ability to feel and express feelings appropriately); clarity (understanding of emotional states); and repair (appropriate emotional regulation). Each dimension can be classified into three traits depending on the score: Attention; 1) Attention should be improved; 2) Adequate attention; 3) Excessive attention: Clarity; 1) Clarity should be improved; 2) Adequate clarity; 3) Excellent clarity: Repair; 1) Repair should be improved; 2) Adequate repair; 3) Excellent repair. The internal consistency measured with Cronbach’s alpha was 0.826 for attention, 0.825 for clarity, and 0.833 for repair.

2.2.2. Oxford Happiness Questionnaire

The Oxford Happiness Questionnaire (OHQ) [ 47 ]. The objective of this questionnaire is to measure happiness in general, i.e., psychological well-being. A series of statements about happiness are given and the participants indicate their degree of agreement with each one. In psychometric terms, it consists of 29 items or 29 potential sources of happiness and the participants consider the extent to which they form part of their experiences. It employs a six-point Likert-type scale (1 = I totally disagree, 6 = I totally agree). The lowest score that can be obtained is 1 (if Answer 1, ‘I totally disagree’ is chosen in all the statements) and the highest is 6 (if Answer 6; ‘I totally agree’ is chosen for all the statements). In this study, the internal consistency measured with Cronbach’s alpha was 0.800.

2.3. Procedure

The procedure followed for data collection was the administration of the questionnaires by classroom group. In the first place, the educational centers were contacted to explain the objectives of the study and request authorization for the completion of the questionnaires. We followed the ethical guidelines of the American Psychological Association regarding the informed consent of the parents, due to participants’ being underage. Likewise, anonymity in the answers, the confidentiality of the obtained data, and its exclusive use for research purposes was assured. The administration of the questionnaires was carried out during school hours; it took around 50 min. in an adequate climate and without distractions. This study was approved by the Bioethics and Biosafety Committee of the University of Extremadura (no. 0063/2018).

2.4. Statistical Analysis

Firstly, we submitted the data to the assumptions of independence, normality, homoscedasticity and linearity required by the classical linear model. We did not find normality or homoscedasticity in our data, so we decided to perform a multinomial logistic regression analysis. Although it may seem that transforming a variable initially classified as continuous to categorical would mean losing information, during the analysis we gain efficiency and, mostly, clarity for interpretation. Multinomial logistic regression analysis was performed to determine the degree of association between the variables being studied. The odds ratio and their 95% confidence intervals, and the receiver operating characteristic (ROC) curve were calculated. The analysis based on the ROC curves is a statistical method to determine the diagnostic preciseness of tests that use continuous scales, and are used for three specific purposes: to establish the cut-off point at which the highest sensitivity and specificity is reached; evaluate the discriminative capacity of the diagnostic test, i.e., its capacity to differentiate healthy and sick individuals; and to compare the discriminative capacity of two or more diagnostic tests that express their results as continuous scales.

In order to verify that emotional intelligence is associated with happiness, multinomial logistic regression analysis included happiness as a predictor variable, grouped according to a criterion of percentiles in low, medium, and high happiness and the emotional intelligence dimensions attention, clarity, and repair as predictor variables, grouped in three categories ( Table 1 ). Gender and age of participants were included as control variables.

Categorization and frequencies of the study variables and descriptive statistics of the OHQ-SF questionnaire.

M = mean, SD = standard deviation. P = Percentile.

Both multinomial regression analyses demonstrated a satisfactory fit, χ 2 (16, N = 629) = 104.922, p < 0.001 (two-tailed), ϕ = 0.048; R Nagelkerke = 0.181, enabling correct classification in 62% of the cases.

The detailed analysis of the findings according to the different emotional intelligence dimensions shows the association between happiness and perceived intra-personal emotional intelligence, so that as clarity and repair increase, the individuals see themselves as happier, and as they decrease the individuals are less happy.

To be precise, for the result of the model with the reference category happiness ( Table 2 ), the calculations of the parameters reveal that adequate clarity (Wald = 4.205, p = 0.040), adequate repair (Wald = 8.609, p = 0.003), adequate repair (Wald = 14.759, p < 0.001), and excellent repair (Wald = 8.503, p =0.004) are associated significantly and directly with medium happiness. In addition, adequate (Wald = 10.376, p = 0.001) and excellent clarity (Wald = 8.610, p = 0.003), and adequate (Wald = 15.997, p < 0.001) and excellent repair (Wald = 25.323, p < 0.001) are correlated directly and significantly with high happiness.

Multinomial logistic regression model examining the probability of perceiving low happiness according to the degree of emotional attention, clarity, and repair.

Reference categories: 1 Low happiness. Groups compared: 2 little attention: 3 should improve clarity; 4 should improve repair. * p < 0.05. OR: odds ratio. CI: confidence interval.

The OR calculations of the model with the reference category low happiness ( Table 2 ) show that the probability of medium happiness is twice as high among individuals with adequate clarity, 3.4 times higher with excellent repair and 2.5 times higher with adequate repair. Similarly, the probability of high happiness is 2.7 times higher with adequate clarity, 4.1 times higher with adequate repair, 5.6 times higher with excellent clarity, and 12 times higher with excellent repair.

In addition, calculations of the parameters for the reference category high happiness ( Table 2 ) reveal that the need to improve clarity (Wald = 8.610, p = 0.003), repair (Wald = 25.323, p < 0.001), and adequate repair (Wald = 6.281, p = 0.012) are associated significantly and directly with low happiness. Equally, the need to improve clarity (Wald = 9.771, p = 0.002) and repair (Wald = 11.861, p = 0.001), and adequate clarity (Wald = 7.082, p = 0.008) and repair (Wald = 8.358, p = 0.004) are correlated directly and significantly with medium happiness.

The OR calculations of the model with the reference category high happiness ( Table 3 ) show that the probability of low happiness is 5.6 times higher among individuals who should improve clarity, 12 times higher among those who should improve repair and 3 times higher with adequate repair. Similarly, the probability of medium happiness is 3.5 times higher among individuals who should improve clarity and repair, 2.6 times higher with adequate clarity, and 2.2 times higher with adequate repair.

Multinomial logistic regression model examining the probability of perceiving high happiness according to the degree of emotional attention, clarity, and repair.

Reference categories: 1 High happiness. Groups compared: 2 Excessive attention; 3 Excellent clarity; 4 Excellent repair. * p < 0.05. OR odds ratio. CI confidence interval.

In addition, a receiver operating characteristic (ROC) curve was analyzed to assess the discriminative accuracy of the emotional intelligence dimensions. This allowed the identification of the cut-out points of the emotional intelligence scores beyond which high happiness becomes more likely.

In the ROC analysis, in the non-parametric case, the curve of the clarity dimension has an area below it of 0.696, 95% CI (0.644, 0.748), p < 0.001, and the repair dimension has below it an area of 0.707, 95% CI (0.656, 0.758), p < 0.001, while in the case of the attention dimension, the area below the curve of 0.536, 95% CI (0.478, 0.595), p = 0.206, does not provide significant information ( Figure 1 ).

ROC curve for the TMMS-24 dimensions predicting the presence of high happiness.

The cut-off points that simultaneously optimize sensitivity and specificity, and the separate cut-off points that optimize sensitivity and specificity of the clarity and repair dimensions are shown in Table 4 .

Sensitivity, specificity and Youden Index for the scores of the clarity and repair dimensions in the TMMS-24.

*** Score that maximizes sensitivity and specificity at the same time. * Score that maximizes sensitivity. ** Score that maximizes specificity.

To identify high happiness, a score of 28.5 or over in the clarity dimension simultaneously maximizes sensitivity (60%) and specificity (71%) (Youden Index = 0.314). A score of 25.5 maximizes sensitivity (72%) while specificity remains higher than expected by random, and a cut-off point of 29.5 maximizes specificity (77%) while sensitivity remains higher than expected by random ( Table 4 ). Similarly, a point of 27.5 or over in the repair dimension simultaneously maximizes sensitivity (78%) and specificity (55%) (Youden Index = 0.333). A score of 26.5 maximizes sensitivity (79%) while specificity remains higher than expected by random, and a cut-off point of 32 maximizes specificity (76%) while sensitivity remains higher than expected by random ( Table 4 ).

4. Discussion

The present study has aimed to analyze the relationship between the dimensions of emotional intelligence (attention, clarity, and repair) and happiness in a sample of adolescents and identify the cut-off points in the emotional intelligence scores, above which high happiness is more likely.

The detailed analysis of the results demonstrates a clear association between emotional intelligence and happiness. In general, these results agree with other research analyzing the association between emotional intelligence and happiness [ 46 , 56 ] or variables connected with it, such as personal and social adjustment [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. To be precise, our results show that as emotional clarity and repair increase the individuals perceive themselves to be happier, and when they decrease they are less happy. No association has been found with the attention dimension. They agree with studies on adolescent populations that have found correlations between emotional clarity and repair, but not emotional attention, and variables closely related to happiness, such as well-being and psychological health [ 57 , 58 , 59 ] and quality of life [ 60 ].

This positive relation between happiness and emotional clarity and repair factors show that both abilities are indicators of a better emotional adjustment in adolescents [ 61 , 62 , 63 ]. Thus, the scores for clarity and repair above which happiness is maximized are situated within the established ranges for adequate emotional clarity and repair [ 55 ]. The results underscore that emotional repair has a greater association with happiness. In this line, several researchers have noted that the repair of emotions is fundamental for appropriate psychological functioning and mental health [ 64 , 65 , 66 , 67 ]. Adolescents with higher levels of emotional repair tend to carry out pleasant distracting activities, which can contribute to a greater feeling of happiness [ 68 ].

However, the question is: why is emotional attention not related to happiness? Although emotional attention is necessary for adaptation, paying too much attention to emotions is usually associated with maladaptive factors incompatible with happiness, such as anxiety, depression, hypervigilance, rumination, and catastrophization [ 32 , 33 , 51 ]. Therefore, from this point of view, excessive attention must be associated with low happiness. In contrast, emotional attention implies being aware of the feelings that produce pleasure (happiness) or discomfort (unhappiness). All emotions have a positive function and situations that cause discomfort are inevitable. Therefore, happiness cannot depend on their absence, but on a balance between the quantity and intensity of pleasant/unpleasant. In such a way, people who pay too much attention to their emotions and moods and do not have an adequate emotional clarity and repair would not be capable enough to understand and regulate the different emotional states [ 69 , 70 , 71 , 72 ].

Study Limitations

This was a transversal study; therefore, causal associations cannot be made. Likewise, the sample used and its size restricts generalizability of results. In addition, on the one hand, using the perceptions that the individuals have of their own capacities and feelings hinders the possibility of controlling possible respondent bias. It would therefore be useful to combine their own replies to the questionnaire with tests that are able to evaluate real aptitudes to solve emotional problems. On the other, although the criterion of assigning percentiles to the groups of high, medium, and low happiness allows comparisons to be made between happier or less happy individuals, it does not guarantee the identification of the happy and unhappy individuals, and consequently the results should be interpreted with a degree of caution. Despite these limitations, this study makes interesting contributions to understanding the association between emotional intelligence and happiness.

5. Conclusions

The conclusions of the present study support the idea that some capacities may help to increase the attainment of health and emotional well-being during adolescence. More precisely, it has shown that as adolescents’ capacities of comprehension and emotional regulation increase, so does their subjective happiness. The important role of emotional regulation should be stressed because it is an additional factor associated with happiness.

Finally, we are aware that the educational context is the best setting in which to establish policies promoting emotional health and well-being that can reach all the students and put an end to possible inequalities in the learning of those resources. This study has attempted to determine the specific dimensions that should be focused on when teaching emotional capacities as a variable promoting happiness and emotional well-being and health during this key period of life. To be exact, the capacities of understanding and regulating emotions can be developed and increased in adolescents as a way for their perception of their own happiness to increase.

Author Contributions

J.G.-B., B.L.-d.B., and S.M.-L. designed the study and they had full access to all the data in the study. B.L.-d.B. and S.M.-L. performed all statistical analyses and the interpretation of the data. J.G.-B., B.L.-d.B., R.Y.-T., V.M.L.-R., and S.M.-L. took part in the conduct of the survey and contributed to manuscript preparation. All authors have read and approved the final manuscript.

This work has been funded by the support to Consolidation of Research Groups (Junta de Extremadura GR18091/18.HJ.11). The authors would like to thank their support.

Conflicts of Interest

The authors declare no conflict of interest.

The Effects of the 2021 Child Tax Credit on Parents’ Psychological Well-Being

NBER Working Paper No. w32662

26 Pages Posted: 15 Jul 2024

Lisa A. Gennetian

Duke University Sanford School of Public Policy; National Bureau of Economic Research; Abdul Latif Jameel Poverty Action Lab

Anna Gassman-Pines

Duke University - Sanford School of Public Policy

Date Written: July 2024

Although improving psychological well-being was not the explicit focus of the 2021 expanded Child Tax Credit (CTC), psychological health outcomes may have been affected by the positive income shocks generated by the credit. In this chapter we ask: How did the 2021 expanded CTC affect parents’ psychological well-being? Some studies have found that the CTC led to reductions in parental reports of clinical levels of depression and anxiety and in subclinical depressive and anxiety symptoms. Using similar methods, other studies have found no effect on these same outcomes. Importantly, however, the evidence does not point to the CTC worsening psychological well-being. We conclude that the evidence so far is thin, narrow, and mixed, even when our review is expanded to comparable studies on the impact of income support. Alignment of policy objectives with a broader range of measurement approaches will be important in building a more conclusive evidence base. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org .

Suggested Citation: Suggested Citation

Lisa A. Gennetian (Contact Author)

Duke university sanford school of public policy ( email ).

212 Rubenstein Hall Durham, NC 27708-0204 United States 9196139341 (Phone) 27708 (Fax)

HOME PAGE: http://https://sanford.duke.edu/profile/lisa-gennetian/

National Bureau of Economic Research ( email )

1050 Massachusetts Avenue Cambridge, MA 02138 United States

Abdul Latif Jameel Poverty Action Lab ( email )

400 Main Street Cambridge, MA 02142 United States

HOME PAGE: http://https://www.povertyactionlab.org/person/gennetian

Duke University - Sanford School of Public Policy ( email )

201 Science Drive Box 90312 Durham, NC 27708-0239 United States

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Noisy work environments cause mental and physical tiredness

Nearly three quarters of UK workers (71%) say working in a noisy work environment makes them mentally tired, according to research.

According to the poll of 2,000 workers globally for audio and video technology company Jabra, of which 500 came from the UK, nearly half (47%) said they found it difficult for them to be productive at work when colleagues were taking calls at their desks without headphones.

A similar percentage, 49%, reported it was equally difficult to lead or participate in calls when colleagues are taking calls from their desks without headphones.

To avoid noise and limit distractions, more than half of UK workers (53%) say they are expected to take calls or online meetings away from their desks in a separate room and use professional headphones (66%).

Noise and health

Noise risk assessment: Setting up and running a hearing surveillance programme

How occupational health practitioners can address noise at work

Poor audio quality also had negative consequences on work and wellbeing, with more than half (58%) of UK workers saying poor-quality audio during conference calls negatively affected their wellbeing.

Along with the mental tiredness that came with noise, 60% of those polled said a noisy environment made them feel physically tired.

Workers by and large believed employers have a responsibility to address audio issues in a variety of ways.

This ranged from providing noise-cancelling headphones as part of standard office equipment (36%) through to allowing employees to work from home more frequently (39%).

More than a third (34%) said their employer could also designate specific areas for different types of activities, such as quiet zones or collaborative zones, to ensure employees have the options available in the offices for optimal performance and mental wellbeing.

Nigel Dunn, VP EMEA north at Jabra, said: “Sound type, intensity and individual sensitivity play significant roles in how we function at work and have a huge impact on mental health and wellbeing, and productivity and performance.”

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Nic Paton is consulting editor of OHW+. One of the country's foremost workplace health journalists, Nic has written for OHW+ and Occupational Health & Wellbeing since 2001, and edited the magazine from 2018.

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