Excellent character, reflected in adherence to high standards of moral behavior, has been argued to contribute to well-being. The study goes beyond this claim and provides insights into the role of strengths of moral character (SMC) for physical and mental health.
This study used longitudinal observational data merged with medical insurance claims data collected from 1209 working adults of a large services organization in the US. Self-reported physical and mental health as well as diagnostic information on depression, anxiety, and cardiovascular disease were used as outcomes. The prospective associations between SMC (7 indicators and a composite measure) and physical and mental health outcomes were examined using lagged linear and logistic regression models. A series of sensitivity analyses provided evidence for the robustness of results.
The results suggest that persons who live their life according to high moral standards have substantially lower odds of depression (by 21–51%). The results were also indicative of positive associations between SMC and self-reports of mental health (β = 0.048–0.118) and physical health (β = 0.048–0.096). Weaker indications were found for a protective role of SMC in mitigating anxiety (OR = 0.797 for the indicator of delayed gratification) and cardiovascular disease (OR = 0.389 for the indicator of use of SMC for helping others).
SMC may be considered relevant for population mental health and physical health. Public health policies promoting SMC are likely to receive positive reception from the general public because character is both malleable and aligned with the nearly universal human desire to become a better person.
Following Aristotle , some scholars argue that an excellent character, reflected in high standards of moral behavior, as well as an orientation to promote good and engage in good deeds even in adverse circumstances, may contribute to complete well-being [2–5]. Empirical evidence has already corroborated a positive association between strengths of the character and self-assessed physical health and mental health [6, 7], physical fitness , life satisfaction , subjective and psychological well-being [7, 10–13], and decreased depression [9, 14, 15]. The associations between character strengths and specific physical diseases have also been examined. For example, there is some experimental evidence that application of character strengths can be helpful in improving pain self-efficacy and capacity to function with pain . A rigorous review of clinical studies on character strengths-based interventions for patients with chronic illnesses revealed that these interventions boosted self-esteem and self-efficacy and reduced depression . Character strengths were found to be positively associated with improved quality of life among people with multiple sclerosis  and among patients with an acute coronary syndrome . They were also reported as an important factor in moderating the relationship between COVID-19 stress and well-being among individuals with chronic health conditions and disabilities . Finally, one specific character strength—the character strength of honesty and integrity—was also found to be prospectively associated with lower risk of lung disease, lower limitations in mobility and less difficulty in instrumental activities of daily living among middle-age and older adults .
Although previous studies have substantially advanced our understanding of the potential role of character strengths for human flourishing, they are subject to certain limitations. First, previous studies focused on the impacts of character strengths on subjective well-being and self-reported health outcomes, and thus provided limited evidence on their association with objectively measured health conditions, though theoretical considerations are supportive of positive impacts [21–23]. Second, experimental evidence on the impact of character strengths on mental health is ambiguous. While some authors reported contributions to decreased depressive symptoms [14, 17], others found no such impact [24, 25]. Third, although some recent longitudinal evidence provides reasonable support for the prospective associations between character strengths and well-being and/or health [6, 26], numerous observational studies concerning character strengths often relied on cross-sectional datasets and provided findings of a correlational nature, which have been already shown to overestimate the magnitude of the actual relationship .
In order to overcome these limitations, at least partially, this study examines temporal associations between adherence to high standards of moral behavior and both mental and physical health outcomes using panel survey data merged with diagnostic information derived from the insurance claims data.
We hypothesize that adherence to high standards of moral behavior is favorably associated with subsequent lower risk of disease (i.e., diagnosed depression, diagnosed anxiety and diagnosed cardiovascular disease), as well as with increased self-reports of physical and mental health even after adjusting for a wide range of potential confounders.
Materials and methods
We used two waves of survey data merged with the diagnostic information on medical conditions included in the medical insurance claims data. Specifically, a group of randomly sampled working adults of a large, national service organization based in the United States provided survey data at two occasions. In the first wave, conducted in June 2018, 2370 individuals provided responses. First wave participants were subsequently invited to the second wave of the study and provided responses in July 2019. The number of participants in both waves amounted to 1,209, which yielded the retention rate of 51.2%. Among participants, females accounted for 84.5% vs. 74.5% for the entire population, which reflected the feminization rate in the organization. Mean age of participants was 43.5 years in the sample compared to 45.6 in the population.
The survey was designed to comprehensively assess well-being and work conditions among employees. It was administered online, which allowed participants to choose a secure and anonymous space to participate in the study. Eligibility criteria for participation included age (i.e., at least 18 years of age) and employment status (i.e., all current employees were considered). Participation was voluntary, confidential, and conditional on the informed written consent that was collected from each participant. Harvard Longwood Campus Institutional Review Board reviewed and approved all protocols for the study. More information about the study and sample is presented elsewhere [6, 28, 29].For respondents who participated in wave 1 (T = 1) and wave 2 (T = 2), we merged their survey records with their medical insurance claims data (T = 0, T = 1, and T = 2) that were provided by the employer. Next to a number of financial measures such as allowed amounts for medical services and pharmacy products, medical insurance data included data on medication prescribed and diagnostic information on medical conditions, which were of interest in this study. Diagnostic information followed the International Classification of Diseases (ICD-10) . It has been also demonstrated to be highly consistent with medical records and useful in epidemiological studies [31, 32]. Merged survey and medical insurance data have been already found useful in other research addressing well-being and health . Table 1 (adapted from ) presents the descriptive statistics at baseline (T = 1). Data are available on reasonable request.
Mental health outcomes
We examined one self-reported mental health outcome from the Well-Being Assessment (WBA)Footnote 1 [29, 33] and the Flourishing Index [4, 34] [‘In general, how would you rate your mental health?’ (0 = poor and 10 = excellent)] and two mental health outcomes captured in health insurance claims data, that is: (1) diagnosis of depression (yes vs. no) and/or (2) diagnosis of anxiety (yes vs. no).
Physical health outcomes
We examined one self-reported physical health outcome from the WBA [29, 33] and the Flourishing Index [4, 34] [‘In general, how would you rate your physical health?’ (0 = poor and 10 = excellent)] and one diagnostic information on medical conditions outcomes derived from the participants’ medical insurance claims data, i.e., a diagnosed cardiovascular disease (yes vs. no).
Strengths of moral character
To measure adherence to high standards of moral behavior, a subscale of the WBA, related to strengths of moral character (SMC), was used. The SMC-WBA instrument was developed based on the concept of human flourishing or complete well-being [4, 35]. The SMC domain, which is of interest in this study, was conceptualized according to a long-standing religious and philosophical tradition, partially adopted by positive psychology in recent years, positing that in order to attain complete well-being, an excellent character and acting in accordance with the virtue, are essential [1, 13, 14, 36, 37]. Consequently, this domain was defined as adherence to high standards of moral behavior reflected in an ability to focus, to maintain consistent thoughts, and to act in a way that contributes to the good of oneself and others . High score in SMC-WBA indicates a self-assessed “strength” in moral character.
SMC-WBA is related to the concept of ‘character strengths’ in general  and to one popular measure of character strengths specifically—the VIA Survey of Character Strengths . We refer to our assessment as a ‘measure of strengths of moral character’ to highlight its moral component and to distinguish it from the VIA measure of character strengths. In the Supplementary Information, we present details on similarities and dissimilarities between our measure and the VIA Survey of Character Strengths .
moral compass (‘I always know the right thing to do’),
orientation to promote good (‘I am willing to face difficulties in order to do what is right’, ‘I give up personal pleasures whenever it is possible to do some good instead’, and ‘I always act to promote good in all circumstances, even in difficult and challenging situations’),
use of strengths (‘I get to use my strengths to help others’),
kindness (‘I always treat everyone with kindness, fairness and respect’) and
delayed gratification (‘I am always able to give up some happiness now for greater happiness later’).
Respondents could choose an answer on a 0 = ‘not true of me’ to 10 = ‘completely true of me’ scale. The seven items of the SMC-WBA were moderately correlated (r = 0.36–0.61; correlations between main study variables are presented in Table A1 in the Supplementary Information). In addition, SMC-WBA (an aggregate of seven items from the SMC-WBA) was also used as an exposure. This scale was validated and showed satisfactory psychometric properties in terms of reliability (alpha = 0.88), test–retest correlation (r = 0.67), and convergent/discriminant validity in relation to stability over time (r = 0.75), as well as a good fit to the data (confirmatory factor analysis: CFI = 0.962, TLI = 0.943, RMSEA = 0.069) that were invariant over time, gender, age, education, and marital status [a complete psychometric evaluation can be found in 33].
A rich set of control variables was used. Specifically, we controlled for demographic characteristics [gender (male, female), age group (≤ 30, 31–40, 41–50, > 50), race (White, Black/African American, Hispanic/Latino, Asian, other), educational attainment (high school, some college, associate degree, bachelor’s degree, graduate degree), marital status (married vs. not married), having children at home (yes vs. no), taking care of an elderly (yes vs. no)], wealth [owning a house (yes vs. no)], and income [salary (the mid-point salary bands were provided by the employer)]. These variables are classified as social determinants of health and, as shown by previous research [38, 39], have a substantial impact on people’s health, well-being and quality of life. In addition, we controlled for social participation and civic engagement. The variables comprised: (1) voting in the last elections (yes vs. no/not registered voter), (2) religious service attendance (at least once a week, less than once a week, never), (3) spiritual practices (at least once a week, less than once a week, never), (4) volunteering (at least once a week, less than once a week, never), and (5) community work (at least once a week, less than once a week, never). In prior studies, these factors were found to play a predictive role for health and well-being [40–44, 77].
Next, since the impact of work on health has long been recognized in theory  and empirical research [46–50, 78], we controlled for work characteristics. We included selected indicators of work resources, work demands and work autonomy: number of work hours, supervisor support [‘My supervisor supports me’ (0–10)], job control [‘I have a lot of freedom to decide how to do my job’ (0–10)], job demand [‘I have too much to do at work to do a good job’ (0–10)], job fit [‘At work, I am able to do what I am good at’ (0–10)] and job meaning [‘I find my work meaningful’ (0–10)] [51, 52]. These variables were controlled for in the first wave (T = 1). In addition, in each regression, the control was made for an outcome and additionally for the number of diagnosed health conditions prior to exposure to further reduce possibility of reverse causality.
This study applied an outcome-wide analytic approach  and used longitudinal observational data merged with medical insurance claims data. The logistic (for dichotomous outcomes) and linear (for continuous outcomes) regression analysis was applied. All continuous outcomes were standardized (i.e., mean = 0, standard deviation = 1), to report the effect estimates in terms of standard deviations of the outcome variables (i.e., standardized effect sizes). For dichotomous outcomes, we presented odds ratios.
A set of 40 regression models was used to regress each of the five outcomes on each of the eight exposures (i.e., SMC-WBA and its seven items) separately. In particular, the association between a character strength exposure j and a health outcome k for continuous outcomes was modeled as follows:
and for dichotomous outcomes as follows:
where i = 1,…,N; k = 1,…,5; j = 1,…,8.
Subscript i represents an individual, the variable HO indicates one out of five (k = 1,…,5) health outcomes, SMC is one out of eight exposures (j = 1,…,8). X is a vector of control variables. α1 reflects an association between SMC exposure and a subsequent health outcome. α2 shows the association between control variables and the health outcome. α3 shows the association between the health outcome k at T = 2 and T = 1 for self-reported health outcomes and at T = 2 and T = 0 for medical condition outcomes. ηk,j,i is a disturbance term.
All missing exposure, covariate, and outcome variables were imputed using chained Eqs. (20 datasets were generated) [54, 55]. Data were arranged in a wide format as suggested by Allison  and all outcome, exposure and control variables were used in the procedure. Consequently, the multiple imputation estimates pooled using the Rubin’s formula  are presented. Bonferroni correction was used to correct for multiple testing.
Robustness of the results was examined through a series of robustness analyses. First, for three regressions of diagnosed conditions (i.e., depression, anxiety, and a cardiovascular disease, derived from the medical insurance diagnostic information), supplementary analyses were conducted on a limited sample of those who did not suffer from the health outcome under examination prior to exposure (as opposed to the primary analysis of the entire sample controlling for the outcomes prior to exposure; Table A2 in the Supplementary Information). Second, two additional sets of controls were added to the primary set of analyses: (1) an alternative specification of the overall 2018 well-being index; it was calculated excluding the character strength specific domain in 2018 (Table A3 in the Supplementary Information), (2) all five well-being domain-specific scores in 2018 (the character strength specific domain score was excluded; Table A4 in the Supplementary Information). Third, we reanalyzed the primary sets of models using the complete-case analysis (Table A5 in the Supplementary Information) to examine robustness of the results to the missing data patterns. Fourth, because our choice of using a broad set of controls might have contributed to overfitting the models, we rerun them excluding particular sets of confounders (Table A6 in the Supplementary Information). In model 1, we controlled only for social determinants of health (i.e., demographic characteristics, wealth, and income). In model 2, compared to model 1, we added social participation and civic engagement (i.e., we controlled for demographic characteristics, wealth, income, social participation, and civic engagement). To decrease the risk of reverse causation, both model 1 and model 2 also controlled for the prior outcomes and the history of disease. Fifth, we rerun the primary models using all items of SMC-WBA simultaneously to examine the overall effect of the co-occurrence of different aspects of SMC (Table A7 in the Supplementary Information). Finally, the sensitivity measures—E values—were calculated to assess the robustness of the observed associations to unmeasured confounding [58, 59].
Analyses were performed using Stata/SE 17.0 for Mac.
Characteristics of the study participants in terms of strengths of character and health
In 2019, participants on average reported higher scores in terms of all measured aspects of their strengths of moral character (in each case p value < 0.001; one-sided t tests for paired observations) and self-assessed physical health (p < 0.001) in comparison to 2018 (Table 2, ). No improvement in self-reported mental health was noted (p = 0.094) but the prevalence of depression increased between 2017 and 2019. No significant changes in the prevalence of cardiovascular disease or of anxiety were found.
Strengths of moral character and mental health
Moral compass was found to be significantly associated with subsequent reduced odds of depression (Table 3). After adjusting for covariates and previous history of depression, each standard deviation increase in moral compass was associated with a 31% reduced odds of depression (OR = 0.694, 95% CI 0.554, 0.869) over a 1-year follow-up period. Orientation to promote good was positively associated with subsequent self-assessed mental health and inversely with subsequent onset of depression. With respect to diagnosed depression, each standard deviation increase in orientation to promote good was associated with a 30% reduced odds of depression in the case of being willing to face difficulties in order to do what is right (OR = 0.703, 95% CI 0.512, 0.837), a 37% reduced odds for giving up personal pleasures whenever it is possible to do some good instead (OR = 0.626, 95% CI 0.492, 0.798), and a 26% reduced odds for always acting to promote good in all circumstances, even in difficult and challenging situations (OR = 0.735, 95% CI 0.584, 0.925), over a 1-year follow-up period and after adjusting for covariates and previous history of depression. Self-assessed mental health was associated with prior orientation to promote good with the effect sizes ranging from 0.048 (for ‘I give up personal pleasures whenever it is possible to do some good instead’) to 0.083 (for ‘I always act to promote good in all circumstances, even in difficult and challenging situations’).
Use of strengths was prospectively inversely linked to the risk of depression diagnosis and positively with subsequent self-assessments of mental health. Increase in the use of strengths by one standard deviation was associated with a 38% reduction in odds of depression (OR = 0.619, 95% CI 0.481, 0.797) and an increase by 0.061 standard deviation in self-assessed mental health (β = 0.061, 95% CI 0.010, 0.113). The character strength of kindness was found to be positively associated with subsequent self-assessments of mental health (β = 0.059, 95% CI 0.013, 0.104) and with a 21% reduced odds of depression (OR = 0.793, 95% CI 0.633, 0.993). Delayed gratification was found to be prospectively inversely associated with the odds of diagnosed depression by 28% (OR = 0.721, 95% CI 0.573, 0.908) and of diagnosed anxiety by 20% (OR = 0.797, 95% CI 0.650, 0.976). Four of these associations did not pass the threshold of p < 0.05 after the correction for multiple testing.
Regarding the SMC-WBA scale, it was found to be positively associated with self-reported mental health (β = 0.118, 95% CI 0.048, 0.188) and with a 51% reduced odds of depression (OR = 0.487, 95% CI 0.350, 0.678).
Strengths of moral character and physical health
There was a positive prospective association between the use of strengths indicator and subsequent self-assessments of physical health (β = 0.084, 95% CI 0.021, 0.136) (Table 3, right panel). The use of strengths was also found to be associated with reduced risk of subsequent cardiovascular disease by 61% with each standard deviation of the use of strengths measure (OR = 0.389, 95% CI 0.186, 0.811). However, the significance level for this association was below p < 0.05 after correction for multiple testing.
Orientation to promote good was positively associated with the subsequent self-assessed physical health. In particular, it was found to be associated with prior responses to the question ‘I always act to promote good in all circumstances, even in difficult and challenging situations’ (β = 0.076, 95% CI 0.028, 0.125). Positive prospective association was also found for the self-reports of physical health and delayed gratification (β = 0.048, 95% CI 0.000, 0.095); however, the significance level for the last association did not pass the threshold of p < 0.05 after the correction for multiple testing. Finally, the aggregate measure of strengths of character SMC-WBA was found to be prospectively positively associated with self-reports of physical health (β = 0.094, 95% CI 0.025, 0.163).
No significant effects for the associations between moral compass and kindness and subsequent self-reported physical health and diagnostic information on physical health outcomes were found.
For the three regressions evaluating depression, anxiety, and a cardiovascular disease, the results obtained on a limited sample of respondents who did not have any history of disease related to the examined health outcome, were very similar to those obtained analyzing the entire sample (Table A2 in the Supplementary Information). When controlling for the alternate measure of 2018 overall well-being (Table A3 in the Supplementary Information) and simultaneously for five domain-specific scores in 2018 (Table A4 in the Supplementary Information), directionality of most associations was preserved but effects sizes were somewhat attenuated, and with wider confidence intervals. Nevertheless, one of the three indicators of orientation to promote good remained positively prospectively associated with lower risk of depression and the use of strengths item—with lower risk of cardiovascular disease. In addition, when comparing results from complete-case analyses to those from the core (multiply imputed) analyses—results were also very similar (Table A5 in the Supplementary Information). Next, the supplementary analyses with the use of limited set of controls (Table A6 in the Supplementary Information) showed that the pattern of significant associations remained the same with very comparable effect sizes. The only differences were noted for one temporal association with diagnosed anxiety (one estimate with the controls reflecting social determinants of health became significant) and another one with diagnosed cardiovascular disease (the only significant estimate became insignificant when a limited set of controls was used). Finally, in models with all indicators of SMC inserted concurrently, most associations were confirmed, even if slightly attenuated. Specifically, one indicator of orientation to promote good remained positively associated with subsequent self-reported physical health, the use of strengths item remained significantly associated with a lower risk of cardiovascular disease and higher self-reported physical health, and the indicator of delayed gratification was found to be associated with subsequent lower risk of anxiety (Table A7 in the Supplementary Information). This provided further evidence that the respective associations presented in the primary analyses were rather stable. In addition, the effect sizes in the supplementary analyses with the limited sets of controls were generally larger that these presented in the primary analyses, which implied that the findings are rather conservative and do not overestimate the prospective associations between strengths of character and diagnosed depression, as well as self-reports of mental and physical health.
The robustness of the results, assessed through the sensitivity measures E-values, provided additional evidence on at least modest robustness to unmeasured confounding of the examined associations (Table 4). Particularly, robust associations were those between using character strength and cardiovascular disease (E value = 4.58) and between strengths of character and depression (E value = 3.53).
In the pursuit of identifying positive health stimuli, this study examined the links between SMC and health outcomes and identified at least five potential pathways for SMC’s contribution to health. First, the results suggest that persons who live their lives according to the moral compass have substantially lower odds of depression. This may be connected to the brain responses associated with the moral aspect of decision-making. Based on data from neuroimaging, during the decision-making process the most activated region of decision maker’s brain is the ventromedial prefrontal cortex . The same region is involved in the activation and regulation of emotions in the situation of moral judgment . Our study provided some indications that emotional processing of one’s own moral dilemma may contribute to mental health. Second, those who (1) act to promote and do good even at their own expense, while facing difficulties, as well as those who (2) perform acts of kindness, have higher subsequent self-reports of mental health and of physical health (the latter only for acts of kindness) as well as lower odds of depression. These paths corroborate evolutionary theories indicating that altruistic behaviors and the capacity for generosity contribute to enhanced social cooperation and strengthen adaptation to changing environment. Hence, they are believed to be conducive to the survival of humankind in the process of evolution. They are also believed to lead to positive, pleasurable feelings (i.e., happiness, optimism, self-confidence, feeling in control [62, 63] which have been shown, in turn, to be associated with better mental and physical health outcomes as well as longevity [64–66]. Third, use of SMC to help others in daily life was found to be prospectively associated with lower risk of depression and greater self-reported mental health and physical health, as well as lower risk of a cardiovascular disease. The reasons for this may be linked with the philosophical conviction, supported by some empirical evidence, that possession of positive character strengths contributes to well-being but only their habituation and exercising leads to genuine accomplishment and feeling of meaning in life, thus flourishing [1, 67, 68]. Finally, the results were also indicative of the protective role of delayed gratification (i.e., always being able to give up some happiness now for greater happiness later) against depression, possibly anxiety and higher self-reports of physical health. This path corresponds to prior research on predicting and understanding decisions people make when faced with immediate and delayed outcomes. They showed that present rewards are usually preferred over later gratifications . However, preferences for delayed gratification can be relevant for generating positive health outcomes. In health-related choices involving delayed gratification very often the value of future incentives exceeds the value of immediate rewards . For example, if one can refrain from immediate pleasurable activities (e.g., smoking a cigarette that gives some instant relief from a craving or helps alleviate stress), she can expect a greater future award, that is, a healthier outcome (e.g., lower risk of a lung cancer).
This study adds to the literature in the following ways. First, to the best of our knowledge, this is the first study that provides evidence for longitudinal associations between SMC and physical health outcomes. Specifically, contrary to prior cross-sectional evidence of no correlation between application of character strengths and self-reports of physical health , this work provides at least modest empirical evidence that possessing and using SMC may be beneficial for one’s physical health (for both self-reported assessment of one’s physical health as well as cardiovascular disease prevention). In this vein, our evidence is also in line with the recent findings on the usefulness of strength-based interventions in pain self-efficacy and the capacity to function despite pain  and in older age . Regarding the mental health outcomes, our results corroborate the earlier evidence from experimental studies [14, 25, 71], meta-analyses [9, 17, 72–74] and longitudinal observational studies  that SMC and their use may provide benefits especially for emotional well-being. Conversely, our results challenge the experimental findings of Khanna and Singh  who reported no contribution of some strengths of character to decreased depressive symptoms. In addition, our results regarding the association between the delayed gratification and health outcomes are in line with prior research on the usefulness of delayed gratification for predicting health behaviors . Second, using multiple health outcomes, both self-reported and derived from the health insurance administrative data, this study exposes some patterns of associations between SMC and health that might not have been discernible if single outcomes were examined in separate studies. Specifically, it shows that the temporal associations are more pronounced for mental health than for physical health. Third, the longitudinal design and the adjustment for an extensive range of covariates, prior values of the exposure (in the case of medical claims outcomes) and of the baseline outcomes, helped to establish a temporal association and to strengthen evidence against reverse causation and unmeasured confounding. Finally, sensitivity analyses for unmeasured confounding and a series of secondary analyses provided supporting evidence in favor of robustness of our results. Regarding the re-estimation of the primary regression models with two additional sets of controls we note, however, that using overall well-being at baseline we might over-control, especially if the aspects of moral character are relatively stable (although in our sample we observed significant increases in each indicator of strengths of moral character between wave 1 and wave 2, Table 2) and either have already exerted some of their effects on health or still require more time to affect health. If this is the case, our control for baseline well-being, next to other controls and health outcomes at baseline, was essentially blocking some of these associations. Consequently, existing associations might not have been detected. This might be the case in our supplementary analysis, as previous analysis on the three-wave dataset provided evidence of the predictive character of an orientation to promote good for the well-being outcomes .
Despite its strengths, this study is also subject to certain limitations. First, this study did not use an experimental design that is a gold standard in establishing causal relationships. Second, since our study used self-reports of SMC and of two health outcomes, it may be subject to social desirability bias  and consequently report results of limited accuracy and reliability. However, the longitudinal design and controlling for baseline outcomes limits this bias. Third, although the study was designed to rely on random sampling for the selection of survey participants, eventually only self-selected working adults, mostly white-collar workers, provided data for the analyses. Although our sample was not substantially different from the targeted population in terms of major demographic variables, further analyses should be performed to replicate the results in different populations. Likewise, attrition between waves 1 and 2 may constitute another concern. Fourth, the follow-up period in this study was relatively short (1 year), which might be insufficient to observe changes in the level of character strengths as well as the effects of accumulation of character strengths, especially on physical health. However, there are some recent evidence that strengths such as humor, spirituality and prudence might be malleable even in a relatively short period of time [26, 76]. Finally, there could be some criticism that one of the more recognized VIA character strength instruments by Seligman and Peterson  and VIA Institute on Character (https://www.viacharacter.org) was not used. While we recognize that these instruments are available and have been frequently applied, it was our intention to develop and use a short measure that drew upon long-standing traditions in the humanities, focuses on SMC, is comprehensive and reliable to measure well-being and that would be well suited for inclusion in workplace surveys that address complete well-being [4, 29, 33].
The dataset generated during and/or analyzed during the current study is available from the corresponding author on reasonable request.
Well-Being Assessment (WBA) is a freely available instrument to measure complete well-being in six domains: emotional health, physical health, meaning and purpose, character strengths, social connectedness, and financial security. Either the overall WBA score or a single well-being domain score (corresponding to a subset of indicators of the domain of interest) can be used. The WBA instrument as well as of each of its domains demonstrate satisfactory psychometric properties.
Aristotle (2009) The Nicomachean Ethics. Oxford University Press, New York
Niemiec RM (2018) Character Strengths Interventions. A Field Guide for Practitioners. Hofgrefe Publishing, Boston
Niemiec RM, Russo-Netzer P, Pargament KI (2020) The decoding of the human spirit: a synergy of spirituality and character strengths toward wholeness. Front Psychol 11:2040. https://doi.org/10.3389/fpsyg.2020.02040
VanderWeele TJ (2017) On the promotion of human flourishing. Proc Natl Acad Sci U S A 114:8148–8156. https://doi.org/10.1073/pnas.1702996114
Cloninger CR, Cloninger KM (2011) Person-centered therapeutics. Int J Pers Cent Med 1:43–52
Weziak-Bialowolska D, Bialowolski P, VanderWeele TJ, McNeely E (2021) Character strengths involving an orientation to promote good can help your health and well-being. Evidence from two longitudinal studies. Am J Heal Promot 35:388–398. https://doi.org/10.1177/0890117120964083
Hausler M, Strecker C, Huber A et al (2017) Associations between the Application of Signature Character Strengths, Health and Well-being of Health Professionals. Front Psychol 8:1–11. https://doi.org/10.3389/fpsyg.2017.01307
Proyer RT, Gander F, Wellenzohn S, Ruch W (2013) What good are character strengths beyond subjective well-being? The contribution of the good character on self-reported health-oriented behavior, physical fitness, and the subjective health status. J Posit Psychol 8:222–232. https://doi.org/10.1080/17439760.2013.777767
Schutte NS, Malouff JM (2019) The impact of signature character strengths interventions: a meta-analysis. J Happiness Stud 20:1179–1196. https://doi.org/10.1007/s10902-018-9990-2
Shoshani A, Slone M (2013) Middle school transition from the strengths perspective: young adolescents’ character strengths, subjective well-being, and school adjustment. J Happiness Stud 14:1163–1181. https://doi.org/10.1007/s10902-012-9374-y
Dulin PL, Hill RD (2003) Relationships between altruistic activity and positive and negative affect among low-income older adult service providers. Aging Ment Heal 7:294–299. https://doi.org/10.1080/1360786031000120697
Martínez-Martí ML, Ruch W (2014) Character strengths and well-being across the life span: data from a representative sample of German-speaking adults living in Switzerland. Front Psychol 5:1–10. https://doi.org/10.3389/fpsyg.2014.01253
Huber A, Strecker C, Hausler M et al (2019) Possession and applicability of signature character strengths: what is essential for well-being, work engagement, and burnout? Appl Res Qual Life. https://doi.org/10.1007/s11482-018-9699-8
Seligman MEP, Steen TA, Park N, Peterson C (2005) Positive psychology progress: empirical validation of interventions. Am Psychol 60:410–421. https://doi.org/10.1037/0003-066X.60.5.410
Weziak-Bialowolska D, Bialowolski P, Niemiec RM (2021) Being good, doing good: The role of honesty and integrity for health. Soc Sci Med 291:114494. https://doi.org/10.1016/j.socscimed.2021.114494
Graziosi M, Yaden DB, Clifton JDW et al (2020) A strengths-based approach to chronic pain. J Posit Psychol. https://doi.org/10.1080/17439760.2020.1858337
Yan T, Chan CWH, Chow KM et al (2020) A systematic review of the effects of character strengths-based intervention on the psychological well-being of patients suffering from chronic illnesses. J Adv Nurs 76:1567–1580. https://doi.org/10.1111/jan.14356
Smedema SM (2020) An analysis of the relationship of character strengths and quality of life in persons with multiple sclerosis. Qual Life Res 29:1259–1270. https://doi.org/10.1007/s11136-019-02397-1
Huffman JC, Millstein RA, Mastromauro CA et al (2016) A positive psychology intervention for patients with an acute coronary syndrome: treatment development and proof-of-concept trial. J Happiness Stud 17:1985–2006. https://doi.org/10.1007/s10902-015-9681-1
Umucu E, Tansey TN, Brooks J, Lee B (2021) The protective role of character strengths in COVID-19 stress and well-being in individuals with chronic conditions and disabilities: an exploratory study. Rehabil Couns Bull 64:67–74. https://doi.org/10.1177/0034355220967093
Fisher J (2000) Being human, becoming whole: understanding spiritual health and well-being. J Christ Educ 43:37–52
Peterson C, Park N, Sweeney PJ (2008) Group well-being: morale from a positive psychology perspective. Appl Psychol 57:19–36. https://doi.org/10.1111/j.1464-0597.2008.00352.x
Niemiec RM (2020) Six functions of character strengths for thriving at times of adversity and opportunity: a theoretical perspective. Appl Res Qual Life 15:551–572. https://doi.org/10.1007/s11482-018-9692-2
Khanna P, Singh K (2019) Do all positive psychology exercises work for everyone? Replication of Seligman et al’.s (2005) interventions among adolescents. Psychol Stud (Mysore) 64:1–10. https://doi.org/10.1007/s12646-019-00477-3
Mongrain M, Anselmo-Matthews T (2012) Do Positive Psychology Exercises Work? A Replication of Seligman et al. J Clin Psychol 68:382–389. https://doi.org/10.1002/jclp.21839
Gander F, Hofmann J, Proyer RT, Ruch W (2020) Character strengths—stability, change, and relationships with well-being changes. Appl Res Qual Life 15:349–367. https://doi.org/10.1007/s11482-018-9690-4
Weziak-Bialowolska D, Bialowolski P, Sacco PL et al (2020) Well-being in life and well-being at work: which comes first? Evidence from a longitudinal study. Front Public Heal 8:1–12. https://doi.org/10.3389/fpubh.2020.00103/full
Bialowolski P, Weziak-Bialowolska D, Lee MT et al (2021) The role of financial conditions for physical and mental health. Evidence from a longitudinal survey and insurance claims data. Soc Sci Med 281:114041. https://doi.org/10.1016/j.socscimed.2021.114041
Lee MT, Bialowolski P, Weziak-Bialowolska D et al (2021) Self-assessed importance of domains of flourishing: demographics and correlations with well-being. J Posit Psychol 16:137–144
WHO (2004) ICD-10: international statistical classification of diseases and related health problems: tenth revision
Tyree PT, Lind BK, Lafferty WE (2006) Challenges of using medical insurance claims data for utilization analysis. Am J Med Qual 21:269–275. https://doi.org/10.1177/1062860606288774
Quam L, Ellis LBM, Venus P et al (1993) Using claims data for epidemiologic research: the concordance of claims-based criteria with the medical record and patient survey for identifying a hypertensive population. Med Care 31:498–507. https://doi.org/10.1097/00005650-199306000-00003
Weziak-Bialowolska D, Bialowolski P, Lee MT et al (2021) Psychometric properties of flourishing scales from a comprehensive well-being assessment. Front Psychol 12:652209. https://doi.org/10.3389/fpsyg.2021.652209
Weziak-Bialowolska D, McNeely E, VanderWeele TJ (2019) Human flourishing in cross cultural settings. Evidence from the US, China, Sri Lanka Cambodia and Mexico. Front Psychol 10:1269. https://doi.org/10.3389/fpsyg.2019.01269
VanderWeele TJ, McNeely E, Koh HK (2019) Reimagining health—flourishing. JAMA 321:1667–1668. https://doi.org/10.1001/jama.2019.3035
Peterson C, Seligman MEP (2004) Character Strengths and Virtues. A Handbook and Classification. New York: Oxford University Press and Washington DC: American Psychological Association
Schmidt PF (1980) The character assessment scale: a new tool for the counselor. J Pastoral Care 34:76–83. https://doi.org/10.1177/002234098003400202
Marmot M, Allen J (2014) Social determinants of health. Am J Public Health 104:S517–S519. https://doi.org/10.2105/AJPH.2014.302200
Marmot M (2005) Social determinants of health inequalities. Lancet 365:1099–1104. https://doi.org/10.1249/00005768-199411000-00015
Trudel-Fitzgerald C, Zevon ES, Kawachi I et al (2019) The prospective association of social integration with life span and exceptional longevity in women. J Gerontol Ser B. https://doi.org/10.1093/geronb/gbz116
Pawlikowski J, Bialowolski P, Weziak-Bialowolska D, VanderWeele TJ (2019) Religious service attendance, health behaviors and well-being—an outcome-wide longitudinal analysis. Eur J Public Health 29:1177–1183. https://doi.org/10.1093/eurpub/ckz075
VanderWeele TJ, Yu J, Cozier YC et al (2017) Religious service attendance, prayer, religious coping, and religious-spiritual identity as predictors of all-cause mortality in the Black Women’s Health Study. Am J Epidemiol 185:515–522
VanderWeele TJ, Balboni TA, Koh HK (2017) Health and spirituality. JAMA J Am Med Assoc 318:519–520. https://doi.org/10.1001/jama.2017.8136
Kim ES, Whillans AV, Lee MT et al (2020) Volunteering and subsequent health and well-being in older adults: an outcome-wide longitudinal approach. Am J Prev Med 000:1–11. https://doi.org/10.1016/j.amepre.2020.03.004
Karasek RA (1979) Job demands, job decision latitude, and mental strain: implications for job redesign. Adm Sci Q 24:285–308
Rosner D, Markowitz GE (1989) Dying for work: workers’ safety and health in twentieth-century America. Indiana University Press, Bloomington and Indianapolis
Madsen IEH, Nyberg ST, Magnusson Hanson LL et al (2017) Job strain as a risk factor for clinical depression: systematic review and meta-analysis with additional individual participant data. Psychol Med 47:1342–1356. https://doi.org/10.1017/S003329171600355X
Stansfeld SA, Shipley MJ, Head J, Fuhrer R (2012) Repeated job strain and the risk of depression: longitudinal analyses from the whitehall ii study. Am J Public Health 102:2360–2366. https://doi.org/10.2105/AJPH.2011.300589
Sanne B, Mykletun A, Dahl AA et al (2005) Testing the job demand-control-support model with anxiety and depression as outcomes: the Hordaland health study. Occup Med (Chic Ill) 55:463–473. https://doi.org/10.1093/occmed/kqi071
Bentley RJ, Kavanagh A, Krnjacki L, LaMontagne AD (2015) A longitudinal analysis of changes in job control and mental health. Am J Epidemiol 182:328–334. https://doi.org/10.1093/aje/kwv046
Cammann C, Fichman M, Jenkins GD, Klesh J (1983) Michigan Organizational Assessment Questionnaire. In: Seashore SE, Lawler E, Mirvis PH, Cammann C (eds) Assessing organizational change: A guide to methods, measures, and practices. pp 71–138
Bakker AB, Demerouti E (2007) The job demands-resources model: state of the art. J Manag Psychol 22:309–328. https://doi.org/10.1108/02683940710733115
VanderWeele TJ, Mathur MB, Chen Y (2020) Outcome-wide longitudinal designs for causal inference: a new template for empirical studies. Stat Sci 35:437–466
White IR, Royston P, Wood AM (2011) Multiple imputation using chained equations: issues and guidance for practice. Stat Med 30:377–399. https://doi.org/10.1002/sim.4067
Lloyd JEV, Obradović J, Carpiano RM, Motti-Stefanidi F (2013) Multiple imputation of missing multilevel, longitudinal data: a case when practical considerations trump best practices? J Mod Appl Stat Methods 12:261–275
Allison P (2001) Missing data. SAGE Publications, Thousand Oaks
Rubin DB (1987) Multiple imputation for non response in surveys. John Wiley & Sons, New York
VanderWeele TJ, Ding P (2017) Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med. https://doi.org/10.7326/M16-2607
Mathur MB, Ding P, Riddell C, VanderWeele TJ (2018) Website and R Package for computing E-values. Epidemiology 29:e45–e47. https://doi.org/10.1097/EDE.0000000000000864
Garrigan B, Adlam ALR, Langdon PE (2016) The neural correlates of moral decision-making: a systematic review and meta-analysis of moral evaluations and response decision judgements. Brain Cogn 108:88–97. https://doi.org/10.1016/j.bandc.2016.10.002
Greene JD, Sommerville RB, Nystrom LE et al (2001) An fMRI investigation of emotional engagement in moral judgment. Science (80-) 293:2105–2108. https://doi.org/10.1126/science.1062872
Aknin LB, Dunn EW, Helliwell JF et al (2013) Prosocial spending and well-being: cross-cultural evidence for a psychological universal. J Pers Soc Psychol 104:635–652. https://doi.org/10.1037/a0031578
Lyubomirsky S, Sheldon KM, Schadke D (2005) Pursuing happiness: the architecture of sustainable change. Rev Gen Psychol 9:111–131
Rozanski A, Bavishi C, Kubzansky LD, Cohen R (2019) Association of optimism with cardiovascular events and all-cause mortality: a systematic review and meta-analysis. JAMA Netw Open 2:e1912200. https://doi.org/10.1001/jamanetworkopen.2019.12200
Lee LO, James P, Zevon ES et al (2019) Optimism is associated with exceptional longevity in 2 epidemiologic cohorts of men and women. Proc Natl Acad Sci USA 116:18357–18362. https://doi.org/10.1073/pnas.1900712116
Benassi VA, Sweeney PD, Dufour CL (1988) Is there a relation between locus of control orientation and depression? J Abnorm Psychol 97:357–367. https://doi.org/10.1037/0021-843X.97.3.357
Seligman ME, Csikszentmihalyi M (2000) Positive psychology. An introduction. Am Psychol 55:5–14. https://doi.org/10.1037/0003-066X.55.1.5
Zhang Y, Chen M (2018) Character strengths, strengths use, future self-continuity and subjective well-being among Chinese University Students. Front Psychol 9:1–14. https://doi.org/10.3389/fpsyg.2018.01040
Kirby KN, Marakovic NN (1996) Delay-discounting probabilistic rewards: rates decrease as amounts increase. Psychon Bull Rev 3:100–104
Daugherty JR, Brase GL (2010) Taking time to be healthy: predicting health behaviors with delay discounting and time perspective. Pers Individ Dif 48:202–207. https://doi.org/10.1016/j.paid.2009.10.007
Woodworth RJ, O’Brien-Malone A, Diamond MR, Schüz B (2017) Web-based positive psychology interventions: a reexamination of effectiveness. J Clin Psychol 73:218–232. https://doi.org/10.1002/jclp.22328
Curry OS, Rowland LA, Van Lissa CJ et al (2018) Happy to help? A systematic review and meta-analysis of the effects of performing acts of kindness on the well-being of the actor. J Exp Soc Psychol 76:320–329. https://doi.org/10.1016/j.jesp.2018.02.014
Galante J, Galante I, Bekkers M-J, Gallacher J (2014) Effect of kindness-based meditation on health and well-being: a systematic review and meta-analysis. J Consult Clin Psychol 82:1101–1114. https://doi.org/10.1037/a0037249
Ghielen STS, van Woerkom M, Christina Meyers M (2018) Promoting positive outcomes through strengths interventions: a literature review. J Posit Psychol 13:573–585. https://doi.org/10.1080/17439760.2017.1365164
Fisher RJ (1993) Social desirability bias and the validity of indirect questioning. J Consum Res 20:303–315. https://doi.org/10.1086/209351
Carr A, Cullen K, Keeney C et al (2020) Effectiveness of positive psychology interventions: a systematic review and meta-analysis. J Posit Psychol 00:1–21. https://doi.org/10.1080/17439760.2020.1818807
Weziak-Bialowolska D, Bialowolski P, Lee MT et al. (2022) Prospective Associations Between Social Connectedness and Mental Health. Evidence From a Longitudinal Survey and Health Insurance Claims Data. Int J Public Health 671604710. https://doi.org/10.3389/ijph.2022.1604710
Weziak-Bialowolska D, Bialowolski P(2022) Associations of recognition at work with subsequent health and quality of life among older working adults. Int Arch Occup Environ Health 95(4):835–847. https://doi.org/10.1007/s00420-021-01804-w
This research was supported by Robert Wood Johnson Foundation under the grants 74275 ‘Building a Culture of Health: A Business Leadership Imperative’ and 4322 ‘Engaging Business in A Broad Impact Community-Based Well-Being’; by Well-Being Research Program A33796 Aetna Inc., by the Levi Strauss Foundation under the grant No. 44057265 ‘The Impact of new work designs on worker wellbeing—Plock, Poland Factory Workers’ and ‘Follow up of Well-being measures in Mexico, China, Cambodia, and Sri Lanka’, by the John Templeton Foundation under the grant No. 61075 ‘Advancing health, religion, and spirituality research from public health to end of life’, and by the Norwegian Financial Mechanism 2014–2021 under the grant UMO-2020/37/K/HS6/02772.
Conflict of interest
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Dr McNeely and Dr VanderWeele report receiving grants and personal fees from Aetna Inc. Dr McNeely reports receiving grants from the Levi Strauss Foundation, she also reports serving as director of SHINE at Harvard (Sustainability and Health Initiative for NetPositive Enterprise); support is made possible through SHINE from multiple companies. Dr VanderWeele reports receiving grants from the John Templeton Foundation. Dr Weziak-Bialowolska reports receiving a grant from the Norwegian Financial Mechanism 2014–2021. The research findings represent the perspective of the authors and do not reflect the opinions or endorsement of any organization.
All protocols for this study were reviewed and approved by Harvard Longwood Campus Institutional Review Board (protocol no. IRB18-1115). Research team received de-identified data that, from the one hand, prevented any identification of participants, but, on the other hand, allowed merging survey data, medical insurance claims data, and selected personal information from personnel files. The research team worked with the organization on the ethical aspects of research design to make sure that participation in the study was voluntary, confidential, and conditional on the informed written consent that was collected from each participant. The informed consent included an authorization to share with the research team the de-identified data from the survey, medical insurance claims (selected data) and personnel files (selected data).
Below is the link to the electronic supplementary material.
About this article
Cite this article
Weziak-Bialowolska, D., Lee, M.T., Bialowolski, P. et al. Prospective associations between strengths of moral character and health: longitudinal evidence from survey and insurance claims data. Soc Psychiatry Psychiatr Epidemiol 58, 163–176 (2023). https://doi.org/10.1007/s00127-022-02344-5