Subjective health in relation to hedonic and eudaimonic wellbeing: Evidence from the Gallup World Poll

Abstract

This study investigated the relationships between subjective health, hedonic wellbeing (i.e. positive affect, negative affect, and life satisfaction), and eudaimonic wellbeing (i.e. psycho-social functioning). The sample was drawn from the Gallup World Poll and included a total of 1,567,295 individuals in 165 countries. We found that both hedonic and eudaimonic wellbeing were uniquely associated with subjective health. However, the relative contributions of each dimension to subjective health varied, with negative affect demonstrating the strongest relationship with subjective health and life satisfaction demonstrating the weakest association. The moderating effects of some national-level variables were also explored.

Keywords

eudaimonic wellbeing Gallup World Poll health problems subjective health subjective wellbeing

Introduction

Subjective health (also called self-rated health, self-reported health, and self-perceived health) includes subjective evaluations of one’s current state of health, and represents one of the most frequently used indicators of general health status (Bombak, 2013). In large-scale surveys, subjective health is typically assessed using self-report measures, which have a number of well-known limitations (Bowling, 2005). However, self-report measures of health also offer a number of advantages (e.g. Jylhä, 2009). For example, these measures can easily be administered to large samples of individuals across the world, and most people should have little difficulty in understanding questions regarding subjective health. Thus, self-report measures are suitable for large, international surveys, and they are extremely valuable in countries where objective measures of health are not easily available. Most importantly, subjective health (as measured by self-report measures) reflects an individual’s objective health status rather accurately (e.g. Wu et al., 2013), and its correlations with mental wellbeing are similar to correlations between mental wellbeing and objective health status (Ngamaba et al., 2017).

The link between subjective health and subjective wellbeing (SWB) has been a subject of inquiry in the fields of quality of life and wellbeing research for decades. SWB is a multifaceted construct which comprises one’s cognitive and affective evaluations of one’s life (Diener et al., 2002). Life satisfaction is the cognitive component of SWB and refers to an evaluative, cognitive judgment of one’s life in general. In contrast, positive and negative affect refer to the affective components of SWB, which are usually assessed via self-reported frequencies of emotional experiences within a certain period of time (e.g. during the past month).

Previous studies have consistently found that both cognitive (i.e. life satisfaction) and affective components (i.e. positive affect and negative affect) of SWB are related to subjective health. For example, researchers found a positive association between life satisfaction and self-rated health across 32 European countries (Kööts-Ausmees, and Realo, 2015). In addition, higher positive affect has been found to predict better self-rated health, whereas negative affect has been found to predict worse self-rated health (Segerstrom, 2014). In a recent study using data from the Gallup World Poll (GWP) and the Gallup-Sharecare survey in the United States, Geerling and Diener (2020) found a stronger relationship between self-reported health problems and negative affect than between self-reported health problems and positive affect and life satisfaction. These findings are generally in line with the large body of literature indicating the importance of SWB for objective health outcomes (e.g. Diener and Chan, 2011; Howell et al., 2007). Researchers have suggested that high SWB affects health through bolstering the immune system, reducing the negative effects of stress, and promoting adaptive coping skills and positive health behaviors (e.g. Cross et al., 2018).

Whereas SWB focuses on the hedonic aspects of wellbeing, eudaimonic wellbeing focuses on the fulfillment of human potential, optimal functioning, and a meaningful life (Heintzelman, 2018; Ryan and Deci, 2001). In other words, eudaimonic wellbeing captures the functional aspect of wellbeing, which consists of skills and qualities that contribute to optimal psychosocial functioning (Joshanloo, 2018). Key components of eudaimonic wellbeing include autonomy, environmental mastery, personal growth, purpose in life, and social skills (Ryff, 2017). In contrast to the numerous studies that have examined the relationship between SWB and subjective health, the associations between subjective health and eudaimonic wellbeing have received less attention. Yet, available research has shown robust relationships between different eudaimonic skills and both subjective and objective health. For example, Ryff et al. (2015) found positive relationships between health and levels of eudaimonic wellbeing. Among the eudaimonic wellbeing components, probably the most widely researched component in relation to health is meaning in life (Roepke et al., 2014). A recent systematic review and meta-analysis (Czekierda et al., 2017) found significant associations between meaning in life and both objective and subjective indicators of health (with stronger associations obtained for subjective measures of health).

This study

A limitation of previous studies on the relationship between health and wellbeing is a general failure to include both hedonic (i.e. subjective) and eudaimonic indicators of wellbeing simultaneously. Therefore, the relative importance of hedonic and eudaimonic wellbeing for subjective health remains largely unknown (Ryff and Boylan, 2016). Furthermore, most studies have used data from a single country, whereas representative samples from different regions of the world have rarely been used in studies on subjective health and wellbeing. This study sought to address these limitations. Our main objective was to analyze the relative strengths of the relationships between the four aspects of wellbeing and subjective health in a global sample to examine their unique contributions to subjective health. We examined the associations between subjective health, SWB, and eudaimonic wellbeing using large, nationally representative samples from more than 160 countries derived from the GWP.

A comprehensive assessment of SWB should include its three components: life satisfaction, positive affect, and negative affect, because they have been found to have different correlates (e.g. Galinha and Pais-Ribeiro, 2011). Accordingly, the three components were included as separate variables in this study. Given the importance of individual-level sociodemographic variables for perceived health, we controlled for age, gender, education, place of residence, and satisfaction with standard of living in our analyses, all of which have been found to affect health in prior research (e.g. Kumar et al., 2012).

In order to achieve a more complete understanding of the relationship between subjective health and wellbeing, we also explored whether the associations between subjective health and the four wellbeing indicators depend on the national levels of wealth (measured as average household income satisfaction). Prior research suggests that national wealth can moderate the relationship between SWB and health (e.g. Ngamaba et al., 2017). Given the robust association between social support and wellbeing (Calvo et al., 2012) and subjective health (Kumar et al., 2012), we also examined whether the associations between subjective health and wellbeing depend on perceived social support at the national level. Finally, average national age was included as a national-level covariate.

Methods

Participants

Data collected between 2005 and 2017 by the GWP were used in the analysis. The GWP continually surveys residents in more than 160 countries, using randomly selected, nationally representative samples. The GWP typically surveys 1000 individuals over 15 years old in each country per year. However, sample sizes are larger in countries with very large populations (e.g. China). Survey items used in this study were available for 165 countries. The sample we used included 1,567,295 individuals who had no missing values on the variables of the study. The average age for the final sample was 40.541 (SD = 17.333). The names of the countries included in the analysis, national sample sizes, gender ratios, national ages, and national averages for wellbeing variables are reported in Supplemental Table S1 in the supplementary material.

Measures

Individual-level variables

All variables of the study along with their response formats are shown in Supplemental Table S2 in the supplementary material, and information on demographic variables and their categories is provided in Supplemental Table S3. The health variable used in this study as the outcome variable is a measure of subjectively-perceived health problems: “Do you have any health problems that prevent you from doing any of the things people your age normally can do?” This item is expected to provide a broad assessment of individuals’ health problems and thus is used as a measure of subjective health (Kumar et al., 2012). The terms “subjective health” and “perceived health problems” are used interchangeably in this article.

Two items (enjoyment and smile/laughter) were used to measure positive affect. The items were averaged to produce a positive affect scale for each participant. Cronbach’s alpha for the scale was .60 across the whole sample. Four items (worry, sadness, stress, and anger) were used to measure negative affect. The items were averaged to produce an index of negative affect, with Cronbach’s alpha for the scale being .68 across the whole sample. These six variables have been consistently used in previous studies of positive and negative affect using the GWP dataset (e.g. Diener and Tay, 2015). We also used the eudaimonic wellbeing index (Joshanloo, 2018) to measure optimal psychosocial functioning. This index is composed of seven GWP items measuring learning experience, social support, respect, efficacy beliefs, sense of freedom, and pro-sociality. The unidimensional factor structure of the scale has been confirmed in previous research (Joshanloo, 2018).

National-level variables

National variables of social support, household income satisfaction (hereafter referred to as national wealth), and age were calculated by averaging the scores of the corresponding items within each nation (the items are presented in Supplemental Table S2). Our national wealth variable correlated at .778 with log10-transformed gross domestic product at purchasing power parity per capita (obtained from https://bit.ly/KR9LNm). Our national age variable correlated at .926 with a variable measuring the percentage of people aging 65 and above in each nation (obtained from https://bit.ly/2qlQ0ov). Therefore, our national variables show high levels of criterion validity. We chose to use Gallup-based variables rather than the external variables in the analyses due to the fact that the external variables were not available for some of the countries included in the study.

Statistical analysis

Considering the hierarchical nature of the dataset, and given that the outcome is a binary variable, multilevel binary logistic regression analyses were conducted (Heck et al., 2013; Hox, 2010; West et al., 2015). The analyses were performed using the generalized linear mixed model procedure in IBM SPSS 25, with robust estimation. The intercept and the slopes of the regressors were treated as random effects. Following the general recommendations (e.g. Hox, 2010; Nezlek, 2010), the non-dichotomous variables of the study at the individual level (i.e. the four wellbeing variables) were group-mean centered, and the national variables were grand-mean centered.

Odds ratios are used in logistic regression as measures of effect size. An odds ratio of 1 indicates that a one-unit increase in the regressor does not make any change in the likelihood of reporting health problems (i.e. no statistical dependence between the regressor and subjective health). An odds ratio greater than 1 indicates that the odds of reporting health problems increase by an increase in the regressor. For example, an odds ratio of 2 suggests that the odds of reporting health problems increase 2 times with a one-unit increase in the regressor. An odds ratio below 1 indicates a decrease in the likelihood of reporting health problems with an increase in the regressor (i.e. a negative relationship between the regressor and the outcome). For example, an odds ratio of .30 would suggest that a one-point increase in the regressor is associated with a 0.70 percent decrease in the likelihood of reporting health problems (Heck et al., 2013).

Results

Individual-level regressors

In the first model, we only included individual-level variables. The random and fixed effects from this model are shown in Supplemental Table S4 and Table 1, respectively. Odds ratios are also reported in Table 1. Relatively important demographic regressors were tertiary education, secondary education, satisfaction with standard of living, and gender. Among the four wellbeing variables, negative affect demonstrated the strongest association with health problems, followed by positive affect and eudaimonic wellbeing. The contribution of life satisfaction was the smallest among the wellbeing variables.

Table 1.

Fixed effects and odds ratios.

	Coefficient	t	p	95% CI for coefficient		Odds ratio	95% CI for odds ratio
	Coefficient	t	p	Low	Up	Odds ratio	Low	Up
First model
Female	.186	20.975	<.001	.168	.203	1.204	1.183	1.225
Age	.039	47.881	<.001	.038	.041	1.040	1.039	1.042
Secondary education	−.278	−22.165	<.001	−.303	−.253	.757	.739	.776
Tertiary education	−.539	−28.484	<.001	−.576	−.502	.584	.562	.606
A rural area or on a farm	.085	5.634	<.001	.055	.114	1.088	1.057	1.121
A small town or village	.040	3.377	.001	.017	.064	1.041	1.017	1.066
A suburb of a large city	.007	.496	.620	−.022	.036	1.007	.979	1.037
Satisfaction with standard of living	−.254	−19.646	<.001	−.280	−.229	.775	.756	.795
Eudaimonic wellbeing	−.191	−7.219	<.001	−.243	−.139	.826	.784	.870
Life satisfaction	−.073	−16.245	<.001	−.082	−.064	.930	.922	.938
Negative affect	.884	51.177	<.001	.850	.918	2.421	2.341	2.505
Positive affect	−.195	−18.382	<.001	−.216	−.174	.823	.806	.840
Final model
Female	.186	20.947	<.001	.168	.203	1.204	1.183	1.225
Age	.039	47.885	<.001	.038	.041	1.040	1.039	1.042
Secondary education	−.278	−22.224	<.001	−.303	−.254	.757	.739	.776
Tertiary education	−.538	−28.470	<.001	−.575	−.501	.584	.563	.606
A rural area or on a farm	.085	5.634	<.001	.055	.114	1.088	1.057	1.121
A small town or village	.040	3.383	.001	.017	.064	1.041	1.017	1.066
A suburb of a large city	.007	.504	.615	−.022	.036	1.007	.979	1.037
Satisfaction with standard of living	−.254	−19.568	<.001	−.279	−.228	.776	.757	.796
Eudaimonic wellbeing	−.190	−7.170	<.001	−.242	−.138	.827	.785	.871
Life satisfaction	−.073	−19.600	<.001	−.080	−.066	.930	.923	.936
Negative affect	.884	51.178	<.001	.850	.918	2.421	2.340	2.504
Positive affect	−.196	−19.009	<.001	−.216	−.176	.822	.806	.839
National wealth	.396	2.616	.009	.099	.692	1.485	1.104	1.998
National social support	−1.654	−4.071	<.001	−2.451	−858	.191	.086	.424
National age	−.038	−4.772	<.001	−.053	−.022	.963	.948	.978
Life satisfaction × National wealth	−.051	−3.091	.002	−.083	−.019	.951	.920	.982
Positive affect × National wealth	−.084	−3.224	.001	−.135	−.033	.920	.874	.968
Life satisfaction × National social support	−.138	−2.977	.003	−.228	−.047	.872	.796	.954

National-level regressors

In another model (Supplemental Table S5 in the Supplementary material), we solely included the three national variables, and found that the three variables were significant predictors of subjective health. When all of the individual and national variables were included (Supplemental Table S6), national wealth became a positive predictor of health problems. Therefore, after controlling for all of the individual- and national-level variables of the study, wealthier countries report lower levels of subjective health. This suggests that the effect of national wealth on health problems is mediated by individual-level variables (e.g. educational level, personal wellbeing, and standard of living) and by national-level variables of age and social support.¹

Cross-level interactions

To explore the moderating effects, we included the cross-level interactions of our national moderators (i.e. country levels of wealth and social support) and the four wellbeing variables in a separate model for each moderator (Supplemental Tables S7 and S8). Next, in a comprehensive model, we included only the four significant interactions from the two previous models (Supplemental Table S9). A nonsignificant interaction term was removed from the comprehensive model, which resulted in the final model of the study with three significant interactions (Table 1). As shown in Table 1, national wealth moderated the relationship between life satisfaction and positive affect and subjective health. National social support moderated the relationship between life satisfaction and subjective health. As shown in Table 2, these negative relationships are stronger at higher levels of national wealth and social support. However, as the coefficients across the tertile groups in Table 2 suggest, these moderating effects can be considered rather weak.

Table 2.

Correlations with predicted health problems across tertile groups.

	National wealth
	1st tertile	2nd tertile	3rd tertile
Life satisfaction	−.193	−.227	−.287
Positive affect	−.236	−.253	−.261
	National social support
Life satisfaction	−.196	−.219	−.329

All coefficients are significant at p < .001. Descriptive statistics on tertiles are reported in Supplemental Tables S10 and S11.

Discussion

This study revealed that both SWB and eudaimonic wellbeing are uniquely associated with perceived health problems, after controlling for key sociodemographic variables. This result is consistent with previous studies using measures of objective health status, which found that hedonic and eudaimonic components of wellbeing have comparable, yet independent associations with health (Ryff and Boylan, 2016). Sociodemographic variables that we included were significantly associated with subjective health. In line with previous studies (e.g. Franks et al., 2003), worse subjective health was reported by females, less educated individuals, and individuals less satisfied with their standard of living. Although the influence of age was small, it should be noted that if age is measured in decades rather than years, its effect would be larger.

As expected, there was evidence of worse subjective health (i.e. higher levels of perceived health problems) in countries with lower levels of perceived social support. Social relationships play an important role in upholding health. For example, they serve protective functions against illness by reducing the impact of stress and by fostering a sense of meaning in life (Cohen, 2004). Our results also revealed a complex relationship between national wealth and subjective health. When only national-level variables were included in the model, lower national wealth was associated with worse subjective health. After the inclusion of all individual- and national-level variables of the study, higher levels of national wealth were associated with worse subjective health. It is well established that national wealth is associated with better health-related outcomes, however, our results suggest that the positive impact of national wealth is mediated by a large array of individual-level and national-level variables. After controlling for these variables, national wealth may even exert a negative influence on health. It is noteworthy that wealthier countries tend to have older populations wherein health problems are more prevalent. These countries are also more likely to have a higher prevalence of some health problems such as overweightness, obesity, and alcohol use (e.g. Griswold et al., 2018; Morgen and Sørensen, 2014). Therefore, our findings indicate that the relationship between national wealth and subjective health requires careful examination in future research, and that it is crucial to include relevant covariates when studying this relationship. Future studies on this issue should also take into account other variables that might influence this relationship, such as income inequality (Pickett and Wilkinson, 2015).

That both hedonic and eudaimonic wellbeing were significantly associated with subjective health after controlling for various sociodemographic and national-level variables provides evidence in support of a robust association between perceived health problems and the dimensions of wellbeing. Although all four indicators of wellbeing used in this study (positive affect, negative affect, life satisfaction, and eudaimonic wellbeing) were significantly related to subjective health, their unique contributions differed. More specifically, negative affect was most closely related to perceived health problems, followed by positive affect and eudaimonic wellbeing, whereas life satisfaction had the smallest unique contribution. Our results indicate that hedonic and eudaimonic aspects, although related, differ in their associations with subjective health. Therefore, these findings provide additional support for the discriminant validity of the hedonic and eudaimonic components of wellbeing (Joshanloo, 2019; Waterman, 2008).

Since there is a lack of research regarding the relative importance of hedonic and eudaimonic wellbeing for subjective health at the global level, our findings cannot be directly compared with those of previous studies. However, there are studies that examine the relative importance of different SWB components for subjective health in smaller, less heterogeneous samples. These studies have produced inconsistent findings. For example, some studies have found that self-reported health has stronger associations with positive affect and life satisfaction than with negative affect (Spuling et al., 2017), whereas others have revealed that self-reported health is more closely associated with negative affect than with positive affect (Ward, 2013). To our knowledge, only one study to date has used global data to examine the associations between subjective health and all three SWB components (Geerling and Diener, 2020). Consistent with our findings, the study found significant associations between self-reported health problems and SWB components, with slightly larger effects for negative affect than for positive affect and life satisfaction. Despite inconsistent findings concerning the relative importance of SWB dimensions for subjective health (probably due to the various measures used to assess wellbeing and subjective health and differences in samples), the existing evidence indicates that both cognitive and affective components of SWB matter for subjective health.

Our results showed that national wealth moderated the relationship between perceived health problems and both life satisfaction and positive affect, whereas national social support only moderated the relationship between perceived health problems and life satisfaction. Specifically, we found that these associations were stronger when national wealth and social support were higher. The findings are in general agreement with Joshanloo’s (2019) argument that emotional information is more weighted in more developed and less traditional cultures. A relevant line of research also suggests that the emotion–health relationship depends on cultural differences in how emotions are perceived (Yoo and Miyamoto, 2018). For example, negative emotions may be less detrimental to health in cultures that hold a dialectical view of emotions (e.g. East Asian cultures) than in cultures that hold a linear, i.e. non-dialectical view of emotions, because negative emotions are perceived as less detrimental under a dialectical framework of interpretation (Miyamoto et al., 2013). In sum, our results show that the relationship between health and wellbeing can differ across national samples (Diener et al., 2017). Therefore, contextual and cultural factors should be taken into account when studying the relationship between these two variables (Curhan et al., 2014). However, it is noteworthy that the moderation effects observed in our study were rather weak, suggesting that cultural variability is not great.

Future studies should aim to clarify causal directionality between health and different components of wellbeing. A bidirectional causal relationship between wellbeing and health is expected. That is, greater mental wellbeing leads to better subjective health, and vice versa. This remains to be further investigated in future longitudinal studies. The protective effects of both subjective and psychological wellbeing and the potential mechanisms linking different aspects of wellbeing and health have been thoroughly elaborated (Diener et al., 2017; Ryff, 2017). However, these mechanisms are yet to be investigated in the context of subjectively perceived health in global samples. Future studies should also examine the role of potential moderators in the relationship between wellbeing and subjective health, such as education (Spuling et al., 2017), country affluence (Pressman et al., 2013), and governmental expenditure on health care (Kööts-Ausmees and Realo, 2015).

Although our findings indicate that self-reported health problems primarily reflect an individual’s affective wellbeing, they need to be interpreted with caution due to common method bias, which could inflate the relationship between subjective health and various subjective variables (e.g. Hoebel and Lampert, 2020). More specifically, both wellbeing and health problems in this study were assessed using self-report measures that might be affected by an individual’s current mood (e.g. Clark, 2018). In addition, subjective health was measured by a GWP question referring to “health problems” in our study, and the negative valence of this item might have contributed to the relatively stronger associations between subjective health and negative affect. In addition, negative affective states are associated with biased information processing (e.g. increased attention to negative stimuli), and biased cognitive processes may conflate the relationship between affect and health when health is measured using self-report measures (Kitayama and Park, 2017). However, the strong relationship between negative affect and perceived health problems found in our study is consistent with previous studies which have found that associations between negative affect and subjective health are similar across different countries (Pressman et al., 2013).

Other limitations of this study need to be emphasized. First, we used perceived health problems as a measure of subjective health. The GWP item that we used differs slightly from classic self-rated health items where participants rate their general health on a scale from very poor or poor to excellent or very good (e.g. Eriksson et al., 2001; Weinstein et al., 2018). Thus, our findings need to be interpreted with this difference in mind, and future studies should replicate our results using other subjective health measures. Second, we used measures of SWB available in the GWP (i.e. a single item to measure life satisfaction, and two and four items to measure positive and negative emotions, respectively), which differ from the standard multi-item SWB scales such as the Satisfaction With Life Scale (Diener et al., 1985) and Positive and Negative Affect Schedule (Watson et al., 1988). Thus, our results need to be replicated with more reliable wellbeing scales. Finally, this study did not investigate the role of possible mechanisms in the relationship between subjective health and wellbeing, which is an important avenue for future studies. A number of potential biological (e.g. immune functioning), behavioral (e.g. health behaviors, coping), and social (e.g. social support) processes have been proposed to act as mechanisms that mediate wellbeing and health relationships (Aspinwall and Tedeschi, 2010), which deserve attention in future global research.

In conclusion, the findings of this study suggest that the affective components of SWB (i.e. positive and negative affect) and eudaimonic wellbeing have stronger relationships with health problems than does life satisfaction. These results highlight the importance of considering different aspects of wellbeing separately in the study of subjectively-perceived health. Our results also suggest that the wellbeing and health relationship may depend on various contextual variables (e.g. cultural and economic indicators).

Supplemental Material

supp_(1)supplementary_materials – Supplemental material for Subjective health in relation to hedonic and eudaimonic wellbeing: Evidence from the Gallup World Poll

Supplemental material, supp_(1)supplementary_materials for Subjective health in relation to hedonic and eudaimonic wellbeing: Evidence from the Gallup World Poll by Mohsen Joshanloo and Veljko Jovanović in Journal of Health Psychology

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A3A2066611).

Supplemental Material

Supplemental material for this article is available online.

Notes

ORCID iD

Mohsen Joshanloo

References

Aspinwall

Tedeschi

(2010) The value of positive psychology for health psychology: Progress and pitfalls in examining the relation of positive phenomena to health. Annals of Behavioral Medicine 39(1): 4–15.

Bombak

(2013) Self-rated health and public health: A critical perspective. Frontiers in Public Health 1: 15.

Bowling

(2005) Just one question: If one question works, why ask several? Journal of Epidemiology and Community Health 59: 342–345.

Calvo

Zheng

Kumar

, et al. (2012) Well-being and social capital on planet Earth: Cross-national evidence from 142 countries. PLoS ONE 7(8): e42793.

Clark

(2018) Four decades of the economics of happiness: Where next? Review of Income and Wealth 64(2): 245–269.

Cohen

(2004) Social relationships and health. American Psychologist 59(8): 676–684.

Cross

Hofschneider

Grimm

, et al. (2018) Subjective well-being and physical health. In: Diener

Oishi

Tay

(eds) Handbook of well-Being. Salt Lake City, UT: DEF Publishers. Available at: https://www.nobascholar.com/chapters/64/download.pdf

Curhan

Sims

Markus

, et al. (2014) Just how bad negative affect is for your health depends on culture. Psychological Science 25(12): 2277–2280.

Czekierda

Banik

Park

, et al. (2017) Meaning in life and physical health: Systematic review and meta-analysis. Health Psychology Review 11(4): 387–418.

10.

Diener

Chan

(2011) Happy people live longer: Subjective well-being contributes to health and longevity. Applied Psychology: Health and Well-Being 3(1): 1–43.

11.

Diener

Tay

(2015) Subjective well-being and human welfare around the world as reflected in the Gallup World Poll. International Journal of Psychology 50(2): 135–149.

12.

Diener

Emmons

Larsen

, et al. (1985) The satisfaction with Life Scale. Journal of Personality Assessment 49(1): 71–75.

13.

Diener

Lucas

Oishi

(2002) Subjective well-being: The science of happiness and life satisfaction. In: Snyder

Lopez

(eds) The Handbook of Positive Psychology. New York: Oxford University Press, pp. 63–73.

14.

Diener

Pressman

Hunter

, et al. (2017) If, why, and when subjective well-being influences health, and future needed research. Applied Psychology: Health and Well-being 9(2): 133–167.

15.

Eriksson

Undén

Elofsson

(2001) Self-rated health. Comparisons between three different measures. Results from a population study. International Journal of Epidemiology 30(2): 326–333.

16.

Franks

Gold

Fiscella

(2003) Sociodemographics, self-rated health, and mortality in the US. Social Science & Medicine 56(12): 2505–2514.

17.

Galinha

Pais-Ribeiro

(2011) Cognitive, affective and contextual predictors of subjective wellbeing. International Journal of Wellbeing 2: 34–53.

18.

Geerling

Diener

(2020) Effect size strengths in subjective well-being research. Applied Research in Quality of Life 15(1): 167–185.

19.

Griswold

Fullman

Hawley

, et al. (2018) Alcohol use and burden for 195 countries and territories, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. The Lancet 392(10152): 1015–1035.

20.

Heck

Thomas

Tabata

(2013) Multilevel Modeling of Categorical Outcomes Using IBM SPSS. New York: Routledge.

21.

Heintzelman

(2018) Eudaimonia in the contemporary science of subjective well-being: Psychological well-being, self-determination, and meaning in life. In: Diener

Oishi

Tay

(eds) Handbook of well-being. Salt Lake City, UT: DEF Publishers. Available at: https://www.nobascholar.com/chapters/18/download.pdf

22.

Hoebel

Lampert

(2020) Subjective social status and health: Multidisciplinary explanations and methodological challenges. Journal of Health Psychology 25(2): 173–185.

23.

Howell

Kern

Lyubomirsky

(2007) Health benefits: Meta-analytically determining the impact of wellbeing on objective health outcomes. Health Psychology Review 1: 83–136.

24.

Hox

(2010) Multilevel Analysis: Techniques and Applications. New York: Routledge.

25.

Joshanloo

(2018) Optimal human functioning around the world: A new index ofeudaimonic well-being in 166 nations. British Journal of Psychology 109(4): 637–655.

26.

Joshanloo

(2019) Cultural religiosity as the moderator of the relationship between affective experience and life satisfaction: A study in 147 countries. Emotion 19(4): 629.

27.

Joshanloo

(2019) Structural and discriminant validity of the tripartite model of mental well-being: Differential relationships with the big five traits. Journal of Mental Health 28(2): 168–174.

28.

Jylhä

(2009) What is self-rated health and why does it predict mortality? Towards a unified conceptual model. Social Science & Medicine 69: 307–316.

29.

Kitayama

Park

(2017) Emotion and biological health: The socio-cultural moderation. Current Opinion in Psychology 17: 99–105.

30.

Kööts-Ausmees

Realo

(2015) The association between life satisfaction and self-reported health status in Europe. European Journal of Personality 29: 647–657.

31.

Kumar

Calvo

Avendano

, et al. (2012) Social support, volunteering and health around the world: Cross-national evidence from 139 countries. Social Science & Medicine 74: 696–706.

32.

Miyamoto

Morozink Boylon

Coe

, et al. (2013) Negative emotions predict elevated interleukin-6 in the United States but not in Japan. Brain, Behavior, and Immunity 34: 79–85.

33.

Morgen

Sørensen

(2014) Obesity: Global trends in the prevalence of overweight and obesity. Nature Reviews Endocrinology 10(9): 513–514.

34.

Nezlek

(2010) Multilevel modeling and cross-cultural research. In: Matsumoto

Van de Vijver

AJR

(eds) Cross-Cultural Research Methods in Psychology. Oxford: Oxford University Press, pp. 299–347.

35.

Ngamaba

Panagioti

Armitage

(2017) How strongly related are health status and subjective well-being? Systematic review and meta-analysis. European Journal of Public Health 27: 879–885.

36.

Pickett

Wilkinson

(2015) Income inequality and health: A causal review. Social Science & Medicine 128: 316–326.

37.

Pressman

Gallagher

Lopez

(2013) Is the emotion-health connection a “first-world problem”? Psychological Science 24: 544–549.

38.

Roepke

Jayawickreme

Riffle

(2014) Meaning and health: A systematic review. Applied Research in Quality of Life 9: 1055–1079.

39.

Ryan

Deci

(2001) On happiness and human potentials: A review of research on hedonic and eudaimonic well-being. Annual Review of Psychology 52: 141–166.

40.

Ryff

(2017) Eudaimonic well-being, inequality, and health: Recent findings and future directions. International Review of Economics 64: 159–178.

41.

Ryff

Boylan

(2016) Linking happiness to health: Comparisons between hedonic and eudaimonic well-being. In: Bruni

Porta

(eds) Handbook of Research Methods and Applications in Happiness and Quality of Life. Cheltenham: Edward Elgar Publishing, pp. 53–70.

42.

Ryff

Radler

Friedman

(2015) Persistent psychological well-being predicts improved self-rated health over 9–10 years: Longitudinal evidence from MIDUS. Health Psychology Open. Epub ahead of print 25 August. DOI: 10.1177/2055102915601582.

43.

Segerstrom

(2014) Affect and self-rated health: A dynamic approach with older adults. Health Psychology 33: 720–728.

44.

Spuling

Huxhold

Wurm

(2017) Predictors of self-rated health: Does education play a role above and beyond Age? The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences 72: 415–424.

45.

Tabachnick

Fidell

(2015) Using Multivariate Statistics. Boston, MA: Allyn & Bacon.

46.

Ward

(2013) Sense of control and self-reported health in a population-based sample of older Americans: Assessment of potential confounding by affect, personality, and social support. International Journal of Behavioral Medicine 20: 140–147.

47.

Waterman

(2008) Reconsidering happiness: A eudaimonist’s perspective. The Journal of Positive Psychology 3: 234–252.

48.

Watson

Clark

Tellegen

(1988) Development and validation of brief measures of positive and negative affect: The PANAS Scales. Journal of Personality and Social Psychology 54: 1063–1070.

49.

Weinstein

Legate

Al-Khouja

, et al. (2018) Relations of civil liberties and women’s health satisfaction around the globe: The explanatory power of autonomy. Journal of Health Psychology. Epub ahead of print 14 November. DOI: 10.1177/1359105318810074.

50.

West

Welch

Galecki

(2015) Linear Mixed Models: A Practical Guide Using Statistical Software. Boca Raton, FL: CRC Press.

51.

Wang

Zhao

, et al. (2013) The relationship between self-rated health and objective health status: A population-based study. BMC Public Health 13(1): 320.

52.

Yoo

Miyamoto

(2018) Cultural fit of emotions and health implications: A psychosocial resources model. Social and Personality Psychology Compass 12(2): e12372.

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