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Peer-reviewed

Research Article

Can We Increase Psychological Well-Being? The Effects of Interventions on Psychological Well-Being: A Meta-Analysis of Randomized Controlled Trials

* E-mail: [email protected]

Affiliation Centre for eHealth and Well-being Research, Department of Psychology, Health and Technology, University of Twente, Enschede, The Netherlands

• Laura A. Weiss, 
• Gerben J. Westerhof, 
• Ernst T. Bohlmeijer

• Published: June 21, 2016
• https://doi.org/10.1371/journal.pone.0158092
• Reader Comments

There is a rapidly growing interest in psychological well-being (PWB) as outcome of interventions. Ryff developed theory-based indicators of PWB that are consistent with a eudaimonic perspective of happiness. Numerous interventions have been developed with the aim to increase PWB. However, the effects on PWB measured as coherent outcome have not been examined across studies yet. This meta-analysis of randomized controlled trials of behavioral interventions aims to answer the question whether it is possible to enhance PWB.

A systematic literature search was performed in PsycINFO, Cochrane and Web of Science. To be included, studies had to be randomized controlled trials of behavioral interventions with psychological well-being as primary or secondary outcome measure, measured with either Ryff’s Psychological Well-Being Scales or the Mental Health Continuum—Short Form. The meta-analysis was performed using a random effects model. From the 2,298 articles found, 27 met the inclusion criteria. The included studies involved 3,579 participants.

We found a moderate effect (Cohen’s d = 0.44; z = 5.62; p < .001). Heterogeneity between the studies was large (Q (26) = 134.12; p < .001; I 2 = 80.62). At follow-up after two to ten months, a small but still significant effect size of 0.22 was found. There was no clear indication of publication bias. Interventions were more effective in clinical groups and when they were delivered individually. Effects were larger in studies of lower quality.

Conclusions

It appears to be possible to improve PWB with behavioral interventions. The results are promising for the further development and implementation of interventions to promote PWB. Delivering interventions face-to-face seems to be the most promising option. We recommend to keep including clinical groups in the research of psychological well-being. Heterogeneity is a limitation of the study and there is need for more high-quality studies.

Citation: Weiss LA, Westerhof GJ, Bohlmeijer ET (2016) Can We Increase Psychological Well-Being? The Effects of Interventions on Psychological Well-Being: A Meta-Analysis of Randomized Controlled Trials. PLoS ONE 11(6): e0158092. https://doi.org/10.1371/journal.pone.0158092

Editor: James Coyne, University of Pennsylvania, UNITED STATES

Received: September 1, 2015; Accepted: June 12, 2016; Published: June 21, 2016

Copyright: © 2016 Weiss et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: LAW received a fund for her PhD research by the Netherlands Organization for Health Research and Development ( http://www.zonmw.nl/en/ ), the Hague, grant 200210013 (awarded to Eddy Wezenberg, Arcon). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In the last years, the focus in mental healthcare and prevention has shifted from solely treating or preventing mental health complaints to enhancing positive aspects of mental health. A new goal in mental healthcare is the promotion of well-being [ 1 – 4 ]. However, there are currently many definitions of well-being [ 5 ]., with the two main concepts being subjective and psychological well-being.

Subjective well-being builds on a hedonic framework in which striving for positive experiences is central. It is usually measured as satisfaction with life in combination with a balance between positive and negative emotions [ 6 ]. The standards that people use to judge their subjective well-being were not theorized in this framework. By contrast, Carol Ryff introduced the concept psychological well-being with the intention to develop theory-based indicators of positive human functioning that were consistent with a eudaimonic perspective of happiness [ 7 ]. Another well-researched theory in the eudaimonic tradition is the self-determination theory that states that the fulfillment of basic psychological needs is essential to well-being and growth [ 8 ].

The variety of concepts and measures makes it difficult to compare studies [ 9 ]. It is therefore important to be precise in one’s definition of well-being. This paper focuses on the concept of psychological well-being according to Ryff’s definition [ 10 ]. Earlier meta-analyses have already examined subjective well-being [ 11 , 12 ]. The latest meta-analysis has also included psychological well-being, but measured it in a very broad way with many different instruments [ 12 ]. We will conduct the first meta-analysis that exclusively examines psychological well-being as defined by Ryff.

Based on an extensive review of the literature of clinical, humanistic and life-span developmental psychology, as well as existential and utilitarian philosophy, Ryff [ 10 ] defined psychological well-being as a process of self-realization, consisting of six dimensions: autonomy, environmental mastery, personal growth, positive relations with others, purpose in life and self-acceptance. There is some discussion on the six-factor structure [ 13 ] and whether psychological and subjective well-being are two separate but related dimensions or one overarching construct [ 14 ].

Recently Ryff [ 15 ], reviewed over 350 empirical studies on psychological well-being that have been conducted in the past decades. Longitudinal studies show that high levels of psychological well-being are a protective factor against mental illnesses and psychopathology [ 16 – 18 ] and that it is also related to biological markers of physical health, reduced risk for various diseases such as Alzheimer’s disease, and a longer life-duration [ 15 ]. This growing evidence of positive outcomes of psychological well-being makes it worthwhile to study whether we can improve it.

However, as existing studies show that psychological well-being is rather stable across time [ 19 ], an important question is whether it can indeed be promoted in interventions. Answering this question will provide more insight into the state or trait discussion whether characteristics of psychological well-being are more trait-like or state-like [ 20 ].

In recent years, there has been a rapid increase of studies on behavioral interventions that included psychological well-being as an outcome measure (e.g. [ 21 , 22 ]). A central aim of interventions such as well-being therapy [ 23 , 24 ], acceptance and commitment therapy [ 25 ], life-review therapy [ 26 ], and positive psychological interventions [ 27 ] is to enhance positive psychological functioning. Meta-analyses have shown that these interventions are successful in enhancing certain aspects of psychological well-being [ 11 , 12 , 28 ], but as mentioned, they measured psychological well-being with many different measurement instruments that do not all fit the definition of Ryff. To which extent interventions have an impact on psychological well-being as a coherent construct of positive psychological functioning is unclear. Also, only positive psychological interventions were included, thereby neglecting the increasing number of interventions that addressed psychological well-being in other disciplines.

Hence, we will take the next step in reviewing the evidence on psychological well-being by conducting a meta-analysis on the effects of different behavioral interventions on psychological well-being as a coherent construct across randomized controlled trials. We want to examine whether well-being can be changed as a function of behavioral interventions.

Eligibility criteria

Study eligibility criteria..

The research question and inclusion criteria were established before the meta-analysis was conducted. Psychological well-being had to be used as primary or secondary outcome measure. To examine it as coherent construct, it had to be measured either with Ryff’s Psychological Well-Being Scales (PWBS) [ 10 ] with all six dimensions of psychological well-being as study endpoints, or with the subscale ‘Psychological Well-Being’ of the Mental Health Continuum—Short Form (MHC-SF) [ 29 , 30 ]. The MHC-SF also assesses psychological well-being with the six dimensions of Ryff’s model. If the MHC-SF was used, the data of the subscale psychological well-being had to be available. Research on the MHC-SF in different cultures has provided support for its psychometric properties and its three dimensional factor structure [ 31 , 32 ]. The reliability and validity of the PWBS has been established in different versions and across various cultures (e.g. [ 33 , 34 ]). Yet it has to be noted that the a priori six-factor structure is debated [ 13 ]. This problem appears to be exacerbated by the existence of multiple forms of the test, ranging from 18 to 120 items. There is also discussion whether the PWBS is able to discriminate between higher levels of well-being [ 35 ].

Only randomized controlled trials (RCTs) of behavioral interventions were included, excluding pharmacological interventions. We focused on all study populations, including both healthy and clinical populations of any age. Waiting list, no treatment, care-as-usual, placebo, or alternative treatment groups were included as comparators.

Report eligibility criteria.

To be included, an article had to be published in English-language peer-reviewed journals, excluding books, dissertations and conference proceedings. No publication date restriction was imposed. Data necessary to calculate the effect size had to be available in the article or upon request.

Search strategy and selection of studies

Information sources..

A systematic literature search was performed in the databases of the Cochrane Library, PsycINFO, and Web of Science. The last search was run on 13 April 2015. The first and second author developed the search with the help of an information specialist. The first author (LAW) and a trained student assistant (PDW) conducted the search. We screened the reference lists of included studies and of the meta-analyses of Sin and Lyubomirsky (11), Bolier et al. [ 12 ] and the review of Ryff [ 15 ] for additional potentially eligible studies. Finally, we invited four experts in the field to suggest additional studies that might meet the inclusion criteria.

Search terms were Ryff* or "mental health continuum" or "psychological well-being" or "psychological wellbeing" in all fields of the database, combined with one of the following terms in the title or abstract: intervention or therapy or treatment or random* or control* or trial or RCT. Search strings were adapted to the according database. No limitations were used.

Study selection.

Two data extractors (LAW and PDW) assessed the eligibility independently in a standardized manner. The retrieved records from the database search were screened by title and abstract. First, the extractors screened the first ten publications in PsycINFO together and discussed the results, and then both screened the next 100 studies in PsycINFO independently. They performed an interrater reliability check where Cohen’s kappa was 0.71, which is considered ‘good’ [ 36 ]. A consensus procedure for disagreement between them was established and disagreements were resolved by consensus. The remainder of the records were screened by the two researchers independently. After the titles and abstracts were screened for possible inclusion, full articles were assessed for eligibility.

Data collection

Data items..

Information was extracted from each included study on (1) study sample; (2) outcome measure (Ryff’s PWBS or MHC-SF) with number of items; (3) type of intervention; (4) number of sessions and treatment duration in weeks; (5) control group; (6) total sample size; (7) mean age of the sample with standard deviation or range; and (8) quality assessment.

Data collection process.

LAW extracted the data from the included studies with a data extraction sheet, PDW checked the extracted data. Disagreements were resolved by discussion. We contacted 14 authors through e-mail for additional data. Seven authors responded and provided the unpublished data. In one case, data was obtained via the author of an earlier meta-analysis where the study was included. One author had lost the data due to a hard drive failure. For the remaining five articles, the authors did not respond. All in all, six studies could not be included due to missing data.

Quality assessment

Quality was assessed with eight criteria, partly based on the criteria of the Cochrane collaboration [ 37 ] tailored for the included studies. (1) Was the randomization adequately described? (2) Were drop-out and reasons for drop-out properly described? (3) In case of drop-out, was an intention-to-treat analysis performed? (4) Were the professionals who delivered the intervention adequately qualified? (5) Was a power analysis carried out or were a total of at least 128 participants included (i.e., could the trial detect a moderate change according to a power analysis with Cohen’s d = .50, alpha = .05, power (1-beta) = .80)? (6) Was the treatment integrity checked? (7) Were the outcome measures at baseline assessed and study groups comparable? In the case of differences between groups, were adjustments made to correct for baseline imbalance? (8) Were inclusion/exclusion criteria described?

Each criterion was scored with 0 or 1. As certain criteria were not applicable to some studies, the percentage of items scored 1 across all applicable criteria was calculated. We classified study quality as lower (<40% quality index), intermediate (41–75%) or higher (>75%). For details, see Table in S1 Table . We included quality as a moderator in the moderator analysis, as we hypothesized that the effect size may differ between studies depending on the quality of the studies.

Data analysis

All analyses were completed with the program Comprehensive Meta-Analysis (CMA, version 2.2.064).

We used the random effects model and a 95% confidence interval with two-tailed tests.

Summary measures.

We expected considerable heterogeneity due to diverse intervention types and populations. Therefore, the meta-analysis was performed using a random effects model. If possible, outcomes from an intention-to-treat analysis were used. Samples for completers only were used when intention-to-treat samples were not provided. The primary outcome statistic was the standardized difference in means. For each study, between-group effect sizes were computed, using Cohen’s d. When Ryff’s PWBS were used, the six dimensions were joined in one outcome measure. Standard deviations were reconstructed from p-values or t- statistics when necessary. Lipsey’s rules for interpretation were used: small effect sizes range from 0 to 0.32, medium effect sizes range from 0.33 to 0.55 and large effect sizes are 0.56 or higher [ 38 ].

Heterogeneity.

To evaluate between-study variability, we tested for heterogeneity with the chi-squared test Cochran’s Q and I 2 statistics, which quantifies the amount of variation in results across studies, beyond the expected chance. The heterogeneity analysis was performed with a random effect model, a 95% confidence interval and a two-tailed test.

Moderators.

Moderator analyses were conducted with the following moderators and categories: (1) target group : clinical (psychopathological or health problems) or non-clinical; (2) age of target group : adolescence/young adulthood (≤ 25 years), adulthood (26–55 years) or later life (≥55); (3) intervention type : self-help, individual face-to-face, or group face-to-face; (2) number of sessions : less (≤ 8 studies) or more (> 8 sessions); (5) instrument : PWBS or MHC-SF; (6) control group : not active (no treatment, waiting list, or care-as-usual) or active (placebo or alternative treatment); (7) quality : lower (<40%), intermediate (41% -75%) or higher quality (≥75%).

Publication bias.

The risk of publication bias was estimated using a funnel plot, the Egger’s test and a trim and fill analysis.

Follow-up assessment.

When available, between-group effect sizes (Cohen’s d) were computed for follow-up differences in psychological well-being.

Study selection

Fig 1 summarizes the database hits, (reasons for) exclusion and final inclusion in a flow diagram. We found 2631 records from Web of Science (1151), the Cochrane Library (1026), and PsycINFO (454), and Reference lists searches added four studies and expert consultation two studies. After adjusting for duplicates, 2298 studies remained and were screened for title and abstract. Of these, 2150 were discarded as the studies did not meet the inclusion criteria. The full texts of the remaining 148 studies were assessed for eligibility. 121 studies did not meet the inclusion criteria. Finally, a total of 27 studies met the inclusion criteria and were included in the meta-analysis.

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https://doi.org/10.1371/journal.pone.0158092.g001

Study characteristics

The main characteristics of the studies are presented in Table 1 . All 27 studies were RCTs published in peer-reviewed English journals. The studies were published between 1998 and 2014. The included studies involved 3579 participants. Sample size varied between 20 and 376 participants. Whereas 14 studies were conducted among non-clinical populations (e.g. employees, students), 13 studies used a clinical sample. The vast majority of the clinical samples had psychological disorders, mostly affective disorders. Only two studies used a population with physical complaints (i.e., hearing impairment [ 39 ] and chronic pain [ 40 ]). The mean age varied between 11 and 79 years. While 4 studies used adolescents or young adults, 18 studies examined adults, and 5 studies had a sample of older people. Interventions included well-being therapy, life review, positive psychology interventions, acceptance and commitment therapy, mindfulness interventions and identity interventions. Seven interventions were self-help (web-based or book), 6 were individually administered and 14 group-based. The duration of the interventions varied between 4 and 52 weeks. Whereas 15 studies had between four [ 41 ] and eight sessions, 10 studies had between 8 and 48 sessions [ 42 ]. Sixteen studies used the PWBS as outcome measure, 11 studies the MHC-SF. Six different versions of the PWBS were used, varying between 14 and 84 items. The control conditions included 16 non-active control groups (no intervention, waiting list, care-as-usual) and 13 active control groups (placebos such as relaxation sessions or alternative established interventions such as cognitive behavioral therapy). Nine studies were qualified as having a lower quality, 8 as intermediate and 10 as higher quality studies. Whereas 16 studies declared no conflict of interest [ 21 , 22 , 24 , 39 , 41 – 51 ], the other 11 studies did not mention whether there was a conflict of interest.

https://doi.org/10.1371/journal.pone.0158092.t001

Results data analysis

Post-test effects..

The random effect model showed that the behavioral interventions had a moderate effect on psychological well-being (Cohen’s d = 0.44; z = 5.62; p < .001). The 95% confidence interval was between 0.29 and 0.59, with a standard error of 0.08. The forest plot in Fig 2 displays the post-test effects.

https://doi.org/10.1371/journal.pone.0158092.g002

Effect sizes of studies ranged from 0.05 to 2.11. A heterogeneity analysis revealed significant heterogeneity (Q (26) = 134.12; p < .001). Heterogeneity was high (I 2 = 80.62). Therefore, moderator analyses were performed.

Table 2 presents the findings of the moderator analyses. A systematic finding is that for 15 out of 17 categories, significant effects were found. No significant effects were found for age of target group, number of sessions, measurement instrument and control group. However, the strength of the effects differed for target group, intervention type and study quality. Interventions in clinical groups showed larger effects than those in non-clinical groups. Individual face-to-face interventions had stronger effects than self-help or group interventions. Studies of lower quality had higher effect sizes than studies of intermediate or higher quality. In a post-hoc analysis, we assessed whether the three significant moderating variables were interrelated among each other. There was no relation between target group and intervention type ( χ 2 = 1.1; df = 1; p = 0.587). Target group and study quality were related ( χ 2 = 9.4; df = 2; p = 0.009). Studies with clinical target groups had higher quality. The higher effects for clinical groups can thus not be attributed to a lower quality of studies. There was a significant relation between intervention type and quality of the study ( χ 2 = 14.1; df = 4; p = 0.007). Individual face-to-face interventions were more often assessed in studies with lower quality. Due to this contamination, it remains uncertain whether the intervention type or the quality of the study caused the higher effect sizes.

https://doi.org/10.1371/journal.pone.0158092.t002

There is no clear indication of publication bias. Visual inspection of the funnel plot suggested no evidence of publication bias, as the distribution is symmetrical. Egger’s regression intercept also suggests that there is no publication bias (intercept = 1.53; t = 1.31; df = 25; p = 0.20). Duval and Tweedie’s trim and fill analysis indicated that no studies needed to be filled or trimmed, which suggests that the effect size was not affected by publication bias.

Follow-up effects.

Twelve studies [ 21 , 22 , 40 , 42 , 44 , 45 , 49 – 51 , 55 , 57 , 61 ] examined follow-up effects after at least 2 months up to 10 months. Nine of these 12 studies examined the follow-up at 6 months. The random effect model showed small but significant effects for psychological well-being, compared with a control group (Cohen’s d = 0.22; z = 4,9; p<0.001). The 95% confidence interval was between 0.13 and 0.31, with a standard error of 0.045. Heterogeneity was low (Q (11) = 11.45; p<0.41; I 2 = 3.89).

Discussion and Conclusion

Psychological well-being is increasingly used as an outcome in studies on behavioral interventions, besides measures of psychological complaints and psychopathological symptoms. Several studies reported evidence that psychological well-being can indeed be promoted through behavioral interventions. This is the first meta-analysis to assess their overall effect. A moderate effect size of 0.44 was found across studies for psychological well-being, with no indication for publication bias. Significant effects were found across the categories of the moderator variables, illustrating the systematic nature of the effects. In the follow-up assessment, the effect size was still significant, but small (0.22). This result has to be interpreted with caution as only 12 studies could be included in this analysis. It is important that future studies make use of follow-up measures to gain more insight in the longitudinal development of the effects of interventions on psychological well-being.

This study explicitly focused on psychological well-being as an integrated construct that builds on several psychological theories of the twentieth century. The effect size of psychological well-being is somewhat lower than the standardized mean difference of .61 that was reported in a meta-analysis by Sin and Lyubomirsky (11) and somewhat higher than the effect of .20 for psychological well-being in a meta-analysis by Bolier et al. [ 12 ]. These differences may be related to the fact that the first meta-analyses focused on subjective well-being whereas the second one included 10 different measures of psychological well-being in addition to the PWBS and MHC-SF, for example hope, mastery and purpose in life. This might demonstrate the importance of good definitions of well-being as different results may be obtained with instruments derived from different traditions. Furthermore, both previous meta-analyses focused on specific positive psychological interventions, whereas our study included a number of different therapeutic interventions. Because the interventions varied considerably, a reliable subgroup analysis was not possible. When sufficient studies will be published in the future, later meta-analyses could address differences between interventions, for example comparing positive psychological interventions, well-being therapy, acceptance and commitment therapy, and life review therapy. Despite the relatively high levels of stability of psychological well-being across time [ 19 ], these results show that it is possible to improve psychological well-being. Consequently, it might have more state-like characteristics, as a trait would be very hard to change, especially in a short period of time.

The heterogeneity was large with effects ranging from 0.05 [ 41 ] to 2.11 [ 52 ]. Although the statistical power is sufficient for the study in total, it is low for the moderator analyses [ 63 ]. Therefore, it is even more remarkable that we did find three significant moderators out of seven possible moderators. Effects were larger for clinical groups and in individual interventions. Interestingly, these moderators were also found significant in the meta-analyses of Sin and Lyubomirsky [ 11 ] and Bolier et al. [ 12 ]. The promotion of psychological well-being seem to be best suited for individuals who suffer from psychological or somatic complaints. One possible explanation is that clinical populations have more impaired levels of psychological well-being at the beginning of the intervention, indicating that there is more room for improvement. This finding is relevant because psychological well-being can be seen as an important component of recovery [ 64 ]. Higher levels of psychological well-being are associated with better physical health [ 15 ] and buffer against future disorders [ 16 , 65 ], suggesting that people with higher levels are potentially more resilient [ 66 , 67 ]. Furthermore, a personal approach with face-to-face contact appears to work better compared to self-help and group interventions. Yet interventions targeted at the general population or using self-help or group interventions showed smaller, but still significant effects. When such interventions have a large enough reach, they might also bring substantive public health gains [ 12 ].

For an interpretation of the results, it is important to be aware of possible limitations of the meta-analysis. First, one third of the studies had lower quality, whereas these studies also showed larger effects. However, the quality might have been underestimated, as it was scored conservatively: not reporting on the randomization procedures for example was rated as absence. Lower quality might also be attributed to the fact that new interventions were tested with pilot studies with a small number of participants. The larger effects of studies with lower quality might also contaminated with the finding that individual face-to-face interventions had higher effects. Future research needs RCTs with better quality, such as a larger number of participants based on a priori power analyses and longer follow-ups. Second, there are some limitations due to the search strategy. There was not sufficient data for six studies which met the inclusion criteria, limiting the completeness of the meta-analysis. The search strategy also may have been imperfect, as additional information sources revealed another six studies which were not found with the database search. Still, this possible limitation has been compensated by asking experts in the field and searching through reference lists of relevant articles and meta-analyses. We also excluded grey literature articles that were not peer-reviewed, which might have led to biased results. However, we did not find any indication of a publication bias. Another limitation is that the meta-analysis included highly heterogeneous studies; different outcomes may be due to factors such as different patient populations, protocol characteristics, and enrollment procedures [ 68 , 69 ].

A broader point of discussion concerns the fact that the scales rely on self-reports. Self-reported well-being measures correlate with social desirability [ 70 ]. It would therefore be interesting to find new ways of measurements to assess aspects of psychological functioning in a more objective way, for example using biological markers or automatic behavioral analyses. Until the reliability and validity of such methods have been proven, the possible self-reporting biases should be kept in mind when interpreting results of meta-analyses such as the current one.

Despite the limitations, we conclude that psychological well-being can be significantly improved to a moderate extent. This is important evidence for the development and implementation of interventions and policies in the field of mental health promotion. Improvement of psychological well-being is especially successful in clinical populations. Based on this meta-analysis, individual face-to-face interventions can be considered as valuable option when developing interventions for an improved psychological well-being. There is a need for higher quality studies in this emerging field to be able to further underpin the promising results of this meta-analysis.

Supporting Information

S1 checklist..

https://doi.org/10.1371/journal.pone.0158092.s001

S1 Table. Methodological Quality Assessment Criteria.

https://doi.org/10.1371/journal.pone.0158092.s002

Acknowledgments

We want to thank Sanne Lamers and Linda Bolier for giving us an introduction to the program Central Meta-Analysis. We are grateful to Mirjam Irene Maas who made a start with her thesis and Pauline de With for helping with the data-search and other tasks that came up.

Author Contributions

Conceived and designed the experiments: LAW GJW ETB. Performed the experiments: LAW. Analyzed the data: LAW GJW. Wrote the paper: LAW GJW ETB.

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• 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

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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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. 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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ORIGINAL RESEARCH article

The relationship between psychological well-being and autonomy in young people according to age.

• 1 Faculty of Education, Universidad Nacional de Educación a Distancia, Madrid, Spain
• 2 Faculty of Psychology, Universidad Santo Tomás, Bogotá, Colombia

Psychological well-being manifests itself in all aspects of human activity and is essential to understanding whether young people experience life satisfaction and whether, as they mature, well-being can be associated with different levels of personal autonomy. This quantitative study was developed within the framework of international research on young people’s autonomy in the transition to adulthood. Its main objectives were to analyze the relationship between psychological well-being and autonomy and examine potential variations between the two variables according to age. To this end, Ryff’s Psychological Well-Being Scale and the Transition to Adulthood Autonomy Scale (EDATVA) designed by Bernal et al., were used with a sample of 1,148 young people aged 16–21 from Madrid, Spain, and Bogotá, Colombia. The results show that almost all the dimensions on the Psychological Well-Being Scale correlate significantly and positively with the dimensions on the EDATVA scale. Specifically, moderate correlations were obtained between self-organization on the EDATVA scale and purpose in life ( r = 0.568; p = 0.01) and environmental mastery ( r = 0.447; p = 0.01) on the Psychological Well-Being Scale. In turn, autonomy on Ryff’s scale obtained the highest correlation ( r = 0.382; p = 0.01) with understanding context on the EDATVA scale. It was also found that the older 18–21 age group obtained higher scores than the younger 16–17 age group in all dimensions on both the EDATVA and the Psychological Well-Being Scale. Earlier studies endorse the results found in this research, especially the differences in the scores for both scales according to age groups. This opens avenues for future research to analyze the relationship between psychological well-being and autonomy as independent variables in other sectors of the population.

Introduction

Advances in positive psychology have given rise to heightened interest in psychological well-being across various disciplines ( Henn et al., 2016 ; Hides et al., 2016 ). This has led to the scientific literature taking an approach to the construct from two polarized perspectives. In the first one, psychological well-being is construed from a hedonic perspective, the result of an internal state that the individual experiences on a subjective temporal plane, associated with high levels of positive affect and life satisfaction ( Weiss et al., 2016 ; Opree et al., 2018 ). Consequently, it focuses on subjective experiences of well-being specifically relating to happiness, life satisfaction, and positive affect ( Henn et al., 2016 ). In contrast, in the second perspective, psychological well-being is construed from a eudemonic perspective as a process of self-realization through which individuals evolve over time. Subsequently, it is not associated with results but with capacities ( Díaz et al., 2015 ; Berzonsky and Cieciuch, 2016 ; Disabato et al., 2016 ; Urquijo et al., 2016 ).

In line with the second perspective, Ryff (2014 ; 2018 ; 2019 ) designed a series of indicators based on the theory of positive human functioning that are consistent with a eudemonic perspective on happiness. To this end, she configured a composite and multidimensional model, the Psychological Well-Being Scale, that has been used as the basis for this study, comprising self-acceptance , positive relations with others , autonomy , environmental mastery , personal growth , and purpose in life . These dimensions focus on the different capacities of individuals to regulate their own behavior, assume the demands of the context, develop individual potential by maintaining positive relations with others , accept their own limitations while maintaining a positive attitude, and establish meaning and direction in their own lives ( Keyes et al., 2002 ; Viejo et al., 2018 ; Gómez-López et al., 2019 ). In turn, these dimensions, and in particular environmental mastery , are closely related to the individual’s sense of autonomy and capacity for self-determination and independence ( Rosa-Rodríguez et al., 2015 ). As a result, these indicators are often referred to as “health assets” given that they affect young people’s physical and mental health and, ultimately, the development of their behavior ( Chen et al., 2019 ).

Moreover, it has been determined that sociodemographic correlates, such as age, are linked to psychological well-being in various ways. It has also been determined that psychological well-being is related to psychological constructs, such as life experiences, emotional intelligence, and personality traits, and that there is a significant positive correlation between level of education and psychological well-being—in reference to personal growth and purpose in life ( Bucchianeri et al., 2016 ; Henn et al., 2016 ; Butler-Barnes et al., 2017 ). In turn, Mayordomo et al. (2016) found a positive correlation between age and level of psychological well-being, which might be the result of successful adaptation to the social environment. In this regard, these authors specify that adaptability can be defined as the flexibility to choose how to govern one’s own behavior. In contrast, the progressive loss of psychological well-being could denote exposure to threats and challenges which the individual in question cannot resolve due to lack of adequate skills ( Bradshaw et al., 2013 ).

In the transition to adulthood, psychological well-being evolves to the extent to which the individual is capable of successfully interacting with their environment and assuming the vital challenges inherent to the different stages in life ( Vera-Villarroel et al., 2013 ; Bluth et al., 2017 ; Gómez-López et al., 2019 ). To this end, García-Moya et al. (2015) suggest that psychological well-being can be promoted through the generation of positive experiences in young people’s environments which help them perceive their purpose and direction in life and set their own goals. However, promoting psychological well-being requires identifying which variables interfere with or condition well-being.

In this context, autonomy is seen as one of the dimensions that constitute psychological well-being ( Ryff, 1989 ). Consequently, the interaction between both variables is often taken for granted, as autonomy is considered an integral construct of well-being that describes people’s positive functioning based on their ability to maintain their individuality in different contexts and situations. As a result, the study of autonomy has been approached from various disciplines, including psychoanalysis, philosophy, pedagogy, politics, psychology, and biology, inter alia . Importantly, all agree that autonomy is a complex concept in which different perspectives can be identified and grouped. One such group is the one that focuses on the study of an individual’s ability to make decisions or govern their actions according to their own criteria, which are independent from external influences ( Garberoglio et al., 2017 ). In a broad sense, this perspective emphasizes the development and construction of the criteria used by individuals to make decisions and act in consequence. Other perspectives on autonomy recognize the influence of different scenarios in which individuals construct decision-making processes. Similarly, some authors defend that within decision-making and the very construct of autonomy, the idea of interdependence between individuals takes on a leading role ( Álvarez, 2015 ; Seidl-De-Moura et al., 2017 ).

Personal autonomy as an integral part of quality of life has been studied as a process that develops throughout an individual’s lifetime. Thus, several studies in this respect show that the older a person is, the greater the degree of autonomy ( Barbosa and Wagner, 2015 ). In this regard, Campione-Barr et al. (2015) analyzed the effect of age on young people’s autonomy and the impact of siblings’ ordinal positions within the family. The authors conclude that both age and the organization of fraternal subsystems are important in the development of autonomy in individuals. In the same vein, Barbosa and Wagner (2015) found that higher levels of autonomy are found in groups of older young people. In this regard, it was determined that the desire for autonomy increases during adolescence regardless of gender ( Alonso-Stuyck and Aliaga, 2017 ). However, Mayordomo et al. (2016) conducted a study with more than 700 participants distributed in three different age groups—young people, adults, and older adults—which revealed that there were no significant differences in autonomy between adults and older adults on Ryff’s Psychological Well-Being Scale, although both groups scored higher than the group of young people.

These studies highlight the importance of research on young people’s autonomy which could lead to a better understanding of their life cycle development processes, as well as the ways in which they assume responsibility in life and for their own well-being ( Davies et al., 2015 ; Li and Hein, 2019 ). Taking into account the aforementioned literature, our study is based on the approach designed by Bernal Romero et al. (2020) , in which autonomy is considered as a wide-ranging, complex construct that involves the capacity to ask oneself questions, reflect on one’s life in relation to others, make interdependent decisions and assume the consequences, and organize oneself in relation to others and society. In consequence, Bernal Romero et al. (2019) designed a model, called the Transition to Adulthood Autonomy Scale (EDATVA), comprising four fundamental dimensions for understanding autonomy in young people: self-organization , understanding context , critical thinking , and sociopolitical engagement . This approach has been incorporated to this study with the aim of determining the potential relationships between young people’s psychological well-being and autonomy in their transition to adulthood.

Materials and Methods

Specific objectives.

This article presents selected partial results from research performed in Spain and Colombia as part of a wider study on the autonomy of young people and psychological well-being. The main objective of the study was to analyze the relationships between young people’s psychological well-being and autonomy. This responds to the hypothesis (H1) that there are statistically significant relationships between psychological well-being and autonomy for the sample participating in the study. The second objective was to examine the differences between psychological well-being and autonomy according to age by establishing two groups: young people under 18 and those 18 and over. This responds to the hypothesis (H2) that there are statistically significant differences in both the dimensions of psychological well-being and autonomy as a function of age, the assumption being that participants in the older age group will have higher scores.

For practical reasons and according to the nature of this descriptive study, a quantitative methodology and an ex post facto pre-experimental design were used.

Participants

The field work was performed from late 2018 to early 2019. An incidental non-probabilistic sampling was performed in which 1,148 young people aged 16–21 were selected ( M = 18.20; SD = 1.80). Of the total, 60.3% were female and 39.7% were male. The percentage of adolescents aged 16–17 was 39.7%, while those aged 18–21 at the time of the study represented 60.3%. The sample was divided into these two subgroups, given that the legal age is 18 in both countries. Most of the young people were Colombian (55.7%, from Bogotá), while the rest were Spanish (44.3%, from Madrid).

Most of the participants were studying in high schools and universities. Data were also collected from young people who were employed, as well as from participants who were under the tutelage of child protection services. As an exclusion criterion, it was decided not to include those individuals who had functional, physical, or mental difficulties that prevented them from participating in the study.

Two methods were used to perform the study. The first one, Ryff’s Psychological Well-Being Scale adapted in Spanish by Díaz et al. (2006) , is a multidimensional scale that assesses the factors that contribute to an individual’s psychological well-being. It has 39 items with responses from 1 (strongly disagree) to 6 (strongly agree) on a Likert-type assessment scale, with six dimensions corresponding to the positive attributes of psychological well-being established by Ryff (1989) . The first dimension is self-acceptance or fostering a positive attitude toward one’s self. This dimension presents six items (α = 0.83) and measures self-esteem and the awareness of one’s own strengths and weaknesses. The second is positive relations with others . This dimension also has six items (α = 0.81) and measures an individual’s ability to maintain trusting, stable, and intimate relationships. The third is autonomy , which has eight items (α = 0.73) that measure an individual’s capacity to maintain their individuality in different contexts and situations with determination, independence, and personal authority. The fourth is environmental mastery and has six items (α = 0.71); it explores whether individuals consider themselves to be efficient at managing and controlling their daily responsibilities. This dimension is intimately related to the locus of control, self-efficacy, and the capacity to generate favorable environments that enable the individual to satisfy their needs and desires. The fifth dimension is personal growth , which has seven items (α = 0.68) and examines an individual’s capacity to evolve, develop their potential, and continue to grow on the basis of positive learning. Finally, the sixth dimension is purpose in life which comprises six items (α = 0.83) and measures an individual’s positive psychological well-being by analyzing their capacity to set goals, establish objectives, maintain the level of motivation to achieve them, and give purpose to their life.

The second method, which was used to measure young people’s autonomy, is the Transition to Adulthood Autonomy Scale (hereinafter EDATVA) designed by Bernal Romero et al. (2020) . It has a total of 19 items composed of statements with responses on a Likert-type scale with four options (1 = strongly disagree and 4 = strongly agree). The items are grouped in four dimensions. The first dimension is self-organization , which comprises six items (α = 0.80) that examine whether young people successfully plan their time and the processes in which they participate. This capacity requires young people to make personal choices according to their priorities ( Lammers et al., 2016 ; Bernal Romero et al., 2019 ). The second dimension is understanding context , which has four items (α = 0.74) and explores young people’s interaction with their environment, which leads to them becoming more autonomous ( Reis et al., 2018 ). The third dimension is critical thinking , which has five items (α = 0.70) and aims to measure an individual’s competence in establishing their position and guaranteeing their interests in relation to different social situations that affect them and/or may interest them ( Van Petegem et al., 2015 ). Finally, the fourth dimension is sociopolitical engagement , which has four items (α = 0.77). This dimension measures young people’s commitment to the society they belong to, the processes of community participation, and the political rights of contemporary citizens ( Young, 2017 ). As a whole, the model obtained a Cronbach’s alpha of 0.84.

Procedure and Data Analysis

This study adheres to the Declaration of Helsinki (64th WMA, Brazil, October 2013) and was approved by the Human Research Ethics Committee of the universities involved in the research. The application of the models was systematic, and data were collected using a pencil and paper format mostly during school hours. Approval and informed consent were obtained from the participating centers, as well as the legal guardians and the participants themselves. Once the data had been collected, the responses were coded, arranged, and recorded in a computer database for subsequent statistical processing.

Descriptive statistics of the participants’ general characteristics were then calculated. Pearson’s correlation coefficient was also calculated for the study’s first objective, aimed at determining the relationship between the dimensions of the well-being scale and EDATVA for the sample as a whole. For the second objective, the assumptions of the statistical tests were verified using common procedures (e.g., Kolmogorov–Smirnov test, Shapiro–Wilk tests, Levene’s test, histograms, and Q-Q and P-P diagrams for normality). Mean difference analyses were performed for the two groups to determine potential differences according to age. The effect sizes were estimated using Cohen’s d . Non-parametric tests were used in those cases where assumptions of normality were not met, specifically the Mann–Whitney U test with the Bonferroni correction.

All statistical analyses were performed using the SPSS version 25.0 statistical package for Macintosh (IBM ® , SPSS ® , Statistics 25). The statistical significance level was set at <0.05.

The following are the results for the first objective, in which the relationships between the dimensions of the Psychological Well-Being Scale and EDATVA were analyzed. Table 1 shows the results of Pearson’s correlation coefficient for the different dimensions of the Psychological Well-Being Scale and EDATVA. Significant correlations with positive directionality were found between almost all dimensions on both scales. High correlations were obtained in self-organization on the EDATVA scale and purpose in life ( r = 0.568; p = 0.01) and environmental mastery ( r = 0.447; p = 0.01) on the Psychological Well-Being Scale. These results give rise to moderate correlations and show that the higher young people score in self-organization , the higher they score in purpose in life and environmental mastery .

Table 1. Correlation between autonomy and psychological wellbeing.

The dimension understanding context obtained the highest correlations with autonomy ( r = 382; p = 0.01) and personal growth ( r = 0.356; p = 0.01) on the Psychological Well-Being Scale.

Critical thinking obtained the highest correlations with personal growth ( r = 0.279; p = 0.01) and purpose in life ( r = 0.276; p = 0.01).

Sociopolitical engagement obtained the lowest overall correlations, while purpose in life obtained the highest ( r = 0.186; p = 0.01).

The overall results show that all the dimensions on the EDATVA scale correlate significantly with the dimensions on the Psychological Well-Being Scale. In this regard, self-organization obtained the highest total correlation ( r = 0.437; p = 0.01), followed by understanding context ( r = 0.426; p = 0.01). In turn, purpose in life obtained the highest correlation between the Psychological Well-Being Scale and EDATVA ( r = 0.466; p = 0.01), followed by environmental mastery ( r = 0.406; p = 0.01). Lastly, the total for both scales gave a result of 0.441 ( p = 0.01).

Comparison of Mean Values Between the Psychological Well-Being Scale and EDATVA According to Age

In order to determine the potential differences for each of the scales according to age, tests were performed to contrast central tendency scores.

The Psychological Well-Being Scale is represented in Table 2 , which shows the size of each group, mean values, and standard deviation. For both groups of young people, the highest average scores were found in autonomy and personal growth . Moreover, in all cases, the results show that those aged 18–21 obtained higher scores than those aged 16–17. However, the results of Student’s t -test show statistically significant differences in five of the six dimensions of psychological well-being and for the scale’s total score. No statistically significant differences were found in positive relations with others .

Table 2. Differences on the psychological well-being scale according to age.

In relation to EDATVA, Table 3 shows the size of each group, the mean values, the Mann–Whitney U statistic, the statistical classification and significance, and the effect size. In turn, the results show that the 18–21 group obtained the highest average scores in self-organization and critical thinking . For the under 18 group, the dimensions with the highest average scores were sociopolitical engagement and understanding context . Similarly, the effect of age on autonomy is also shown. As can be seen, the older group of young people obtained higher average scores. These differences are statistically significant in all dimensions except sociopolitical engagement .

Table 3. Differences in perceived autonomy according to age.

The results of our study confirmed the hypothesis (H1) that there are statistically significant relationships between the dimensions on the Psychological Well-Being Scale and the autonomy dimensions on the EDATVA scale. A significant positive correlation was identified between the total of the Ryff’s scale and the total of the EDATVA scale. We consider the relationship between the two scales to be highly significant, given that autonomy is only considered as one of the factors of psychological well-being and conceived as a multidimensional construct in other studies ( Panahi et al., 2013 ; Roslan et al., 2017 ; Gao and McLellan, 2018 ; Ryff, 2019 ). In our study, psychological well-being and autonomy are considered as two different constructs. In the Psychological Well-Being Scale, autonomy is construed as an individual’s capacity for self-regulation independent of others, whereas in EDATVA, it is conceived as a construct defined as a complex process of reflection and decision-making interdependent of others, constituting a relational construct ( Bernal Romero et al., 2020 ). This conceptual difference highlights the importance of establishing relationships between the two constructs, as in this study.

Earlier studies had already documented correlations between psychological well-being and autonomy, conceiving both concepts as independent processes. Thus, studies by Rivas et al. (2012) and Romero et al. (2013) correlated psychological well-being with perceived autonomy, taking into account two dimensions of the latter: choice and volitional intention. Both studies found that the greater the perceived autonomy, the greater the level of well-being, with the exception of the volitional dimension of autonomy. In turn, other studies also coincide with our study by determining that increased levels of autonomy are associated with higher levels of well-being ( Ratelle et al., 2013 ; Weiting, 2014 ; De Leersnyder and Kim, 2015 ).

In this study, we also found significant positive relationships between almost all the dimensions on the two scales. The correlations are higher between self-organization on the EDATVA scale and purpose in life and environmental mastery on the Psychological Well-Being Scale. This suggests that those individuals whose life goals and objectives are clearer and who are better able to control their environment according to their needs may also be better at organizing themselves to make better decisions, which gives them more autonomy ( Valle et al., 2011 ).

Interestingly, autonomy on the Psychological Wellness Scale presents three positive and significant correlations with the EDATVA dimensions: the highest correlations were obtained with understanding context . In this regard, the results suggest that the more younger people are concerned about their development and giving direction to their lives, the more they are able to defend their ideas and uphold their decisions. These findings are similar to those of other studies ( Morales and González, 2014 ; Rodríguez-Fernández et al., 2016 ; Valle et al., 2019 ).

Other results to be considered are that the lowest correlations in our study were found between all the dimensions on the Ryff’s scale and sociopolitical engagement on the EDATVA scale. This can be attributed to the fact that the Psychological Well-Being Scale focuses on intrasubjective aspects, while the EDATVA focuses on intersubjective aspects. Specifically, sociopolitical engagement involves a tendency to construct autonomy in relation to others, rather than to oneself. Thus, Arnett (2014) describes how young people are more focused on their processes of individuation, leaving aside the effects of their decisions on the context. In contrast, Valle et al. (2019) found that psychological well-being is related to the relationships established by individuals in public domains. Based on the difference in the results, future research needs to study this aspect further ( García-Alandete et al., 2018 ).

Our study’s second objective, namely the hypothesis (H2) of the existence of statistically significant differences both in the dimensions of psychological well-being and in the dimensions of autonomy according to age, was also confirmed. The results prove the existence of variations in psychological well-being according to age coinciding with other studies ( Ryff, 1989 , 1991 , 2014 , 2019 ; Ryff and Keyes, 1995 ; Springer et al., 2011 ). Specifically, we found that the older group scored higher than the younger group. In this regard, Bluth et al. (2017) state that during adolescence, well-being tends to decrease due to the changes experienced during that particular period, which could partly explain our findings. However, in relation to the positive correlations with other dimensions on the Psychological Well-Being Scale, no differences were found between the two age groups. These results are consistent with the studies by Roecke et al. (2009) and Carstensen et al. (2011) , who determined that affective relationships are more stable the older the individual.

On the other hand, the differences caused in autonomy as an effect of age during transition to adult life must also be taken into account. In this respect, our findings indicate that there is a significant increase in the levels of autonomy in older individuals. These results coincide with the ones obtained by Ryff (1989 , 2014) , Barbosa and Wagner (2015) ; Campione-Barr et al. (2015) , Mayordomo et al. (2016) , and Alonso-Stuyck and Aliaga (2017) . In line with the said studies, our findings show that young people over the age of 18 achieved a higher average score and that their highest average ranking was in self-organization and critical thinking. Among other factors, this result can be explained by the fact that during this period young people are making very important decisions that require looking toward the future, for example in choosing what they are going to study at university ( Kiang and Bhattacharjee, 2018 ).

It should also be noted that the results for sociopolitical engagement on the EDATVA scale were not significant. According to Ryff (1989) and Barrera et al. (2019) , autonomy involves adopting personal standards that allow the individual to take control of their decisions and discard external influences in relation to personal choices. However, in the case of EDATVA, these influences are taken into account, especially in sociopolitical engagement . Therefore, it is noteworthy that the differences according to age are not maintained in this dimension, which involves the individual reflecting on the consequences of their decisions on others. From a developmental perspective, it might be expected that this level of reflection would increase with age as in the other dimensions of autonomy, but this was not the case with the sample in this study. Likewise, Parés and Subirats (2016) research findings corroborate that young people’s political behavior is diverse, and the differences are not the result of age. Consequently, we believe that this is an area that requires future research.

Lastly, although the data in our study confirmed our hypotheses, we should not ignore its limitations. This study dealt with two particularly complex objectives within the concept of young people’s transition to adulthood. Future research should take into account other sectors of the population when exploring the relationship between psychological well-being and autonomy: different age ranges, problems, nationalities, and contexts.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by the UNED Ethical Committee; USTA Ethical Committee. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

ÁD-J coordinated the project, designed the database, completed the statistical analysis, and reviewed the final version of the article. TB and RG prepared the introduction and theoretical framework, and wrote the discussion section. RG reviewed the references section. All authors wrote the initial version of the article.

This manuscript documents the study performed by the Faculty of Psychology’s Psychology, Life Cycle and Rights Research Group at the Universidad Santo Tomás (Colombia), and TABA International Research, Social Inclusion and Human Rights, UNED (Spain), directed by TB. This study was funded through the Research Project on the Design and Validation of a Transition to Adulthood Autonomy Scale (Call 2018 FODEIN Research Development Fund Universidad Santo Tomás, Colombia, Project Code 18645020) and Project EVAP-SETVA 2015–2020 (Assessment of Personal Autonomy – Assessment in the Transition to Adulthood) UNED, funded by the Autonomous Region of Madrid General Directorate of Family and Minors, Fundación ISOS, Reina Sofia Center for Adolescence and Youth (FAD), and the Fundación Santa María.

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.

Acknowledgments

We would like to thank Miguel Melendro, Claudia Charry, Gema Campos, Isabel Martínez, Francisco Javier García-Castilla, and Ana Eva Rodríguez-Bravo, members of the TABA International Research Group, for their work in the design and validation of EDATVA.

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Keywords : autonomy, psychological well-being, transition to adulthood, young people, positive psychology, self-organization

Citation: De-Juanas Á, Bernal Romero T and Goig R (2020) The Relationship Between Psychological Well-Being and Autonomy in Young People According to Age. Front. Psychol. 11:559976. doi: 10.3389/fpsyg.2020.559976

Received: 07 May 2020; Accepted: 14 October 2020; Published: 10 December 2020.

Reviewed by:

Copyright © 2020 De-Juanas, Bernal Romero and Goig. 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: Ángel De-Juanas, [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: 21 May 2024

The bright side of sports: a systematic review on well-being, positive emotions and performance

• David Peris-Delcampo 1 ,
• Antonio Núñez 2 ,
• Paula Ortiz-Marholz 3 ,
• Aurelio Olmedilla 4 ,
• Enrique Cantón 1 ,
• Javier Ponseti 2 &
• Alejandro Garcia-Mas 2  

BMC Psychology volume  12 , Article number:  284 ( 2024 ) Cite this article

Metrics details

The objective of this study is to conduct a systematic review regarding the relationship between positive psychological factors, such as psychological well-being and pleasant emotions, and sports performance.

This study, carried out through a systematic review using PRISMA guidelines considering the Web of Science, PsycINFO, PubMed and SPORT Discus databases, seeks to highlight the relationship between other more ‘positive’ factors, such as well-being, positive emotions and sports performance.

The keywords will be decided by a Delphi Method in two rounds with sport psychology experts.

Participants

There are no participants in the present research.

The main exclusion criteria were: Non-sport thema, sample younger or older than 20–65 years old, qualitative or other methodology studies, COVID-related, journals not exclusively about Psychology.

Main outcomes measures

We obtained a first sample of 238 papers, and finally, this sample was reduced to the final sample of 11 papers.

The results obtained are intended to be a representation of the ‘bright side’ of sports practice, and as a complement or mediator of the negative variables that have an impact on athletes’ and coaches’ performance.

Conclusions

Clear recognition that acting on intrinsic motivation continues to be the best and most effective way to motivate oneself to obtain the highest levels of performance, a good perception of competence and a source of personal satisfaction.

Peer Review reports

Introduction

In recent decades, research in the psychology of sport and physical exercise has focused on the analysis of psychological variables that could have a disturbing, unfavourable or detrimental role, including emotions that are considered ‘negative’, such as anxiety/stress, sadness or anger, concentrating on their unfavourable relationship with sports performance [ 1 , 2 , 3 , 4 ], sports injuries [ 5 , 6 , 7 ] or, more generally, damage to the athlete’s health [ 8 , 9 , 10 ]. The study of ‘positive’ emotions such as happiness or, more broadly, psychological well-being, has been postponed at this time, although in recent years this has seen an increase that reveals a field of study of great interest to researchers and professionals [ 11 , 12 , 13 ] including physiological, psychological, moral and social beneficial effects of the physical activity in comic book heroes such as Tintin, a team leader, which can serve as a model for promoting healthy lifestyles, or seeking ‘eternal youth’ [ 14 ].

Emotions in relation to their effects on sports practice and performance rarely go in one direction, being either negative or positive—generally positive and negative emotions do not act alone [ 15 ]. Athletes experience different emotions simultaneously, even if they are in opposition and especially if they are of mild or moderate intensity [ 16 ]. The athlete can feel satisfied and happy and at the same time perceive a high level of stress or anxiety before a specific test or competition. Some studies [ 17 ] have shown how sports participation and the perceived value of elite sports positively affect the subjective well-being of the athlete. This also seems to be the case in non-elite sports practice. The review by Mansfield et al. [ 18 ] showed that the published literature suggests that practising sports and dance, in a group or supported by peers, can improve the subjective well-being of the participants, and also identifies negative feelings towards competence and ability, although the quantity and quality of the evidence published is low, requiring better designed studies. All these investigations are also supported by the development of the concept of eudaimonic well-being [ 19 ], which is linked to the development of intrinsic motivation, not only in its aspect of enjoyment but also in its relationship with the perception of competition and overcoming and achieving goals, even if this is accompanied by other unpleasant hedonic emotions or even physical discomfort. Shortly after a person has practised sports, he will remember those feelings of exhaustion and possibly stiffness, linked to feelings of satisfaction and even enjoyment.

Furthermore, the mediating role of parents, coaches and other psychosocial agents can be significant. In this sense, Lemelin et al. [ 20 ], with the aim of investigating the role of autonomy support from parents and coaches in the prediction of well-being and performance of athletes, found that autonomy support from parents and coaches has positive relationships with the well-being of the athlete, but that only coach autonomy support is associated with sports performance. This research suggests that parents and coaches play important but distinct roles in athlete well-being and that coach autonomy support could help athletes achieve high levels of performance.

On the other hand, an analysis of emotions in the sociocultural environment in which they arise and gain meaning is always interesting, both from an individual perspective and from a sports team perspective. Adler et al. [ 21 ] in a study with military teams showed that teams with a strong emotional culture of optimism were better positioned to recover from poor performance, suggesting that organisations that promote an optimistic culture develop more resilient teams. Pekrun et al. [ 22 ] observed with mathematics students that individual success boosts emotional well-being, while placing people in high-performance groups can undermine it, which is of great interest in investigating the effectiveness and adjustment of the individual in sports teams.

There is still little scientific literature in the field of positive emotions and their relationship with sports practice and athlete performance, although their approach has long had its clear supporters [ 23 , 24 ]. It is comforting to observe the significant increase in studies in this field, since some authors (e.g [ 25 , 26 ]). . , point out the need to overcome certain methodological and conceptual problems, paying special attention to the development of specific instruments for the evaluation of well-being in the sports field and evaluation methodologies.

As McCarthy [ 15 ] indicates, positive emotions (hedonically pleasant) can be the catalysts for excellence in sport and deserve a space in our research and in professional intervention to raise the level of athletes’ performance. From a holistic perspective, positive emotions are permanently linked to psychological well-being and research in this field is necessary: firstly because of the leading role they play in human behaviour, cognition and affection, and secondly, because after a few years of international uncertainty due to the COVID-19 pandemic and wars, it seems ‘healthy and intelligent’ to encourage positive emotions for our athletes. An additional reason is that they are known to improve motivational processes, reducing abandonment and negative emotional costs [ 11 ]. In this vein, concepts such as emotional intelligence make sense and can help to identify and properly manage emotions in the sports field and determine their relationship with performance [ 27 ] that facilitates the inclusion of emotional training programmes based on the ‘bright side’ of sports practice [ 28 ].

Based on all of the above, one might wonder how these positive emotions are related to a given event and what role each one of them plays in the athlete’s performance. Do they directly affect performance, or do they affect other psychological variables such as concentration, motivation and self-efficacy? Do they favour the availability and competent performance of the athlete in a competition? How can they be regulated, controlled for their own benefit? How can other psychosocial agents, such as parents or coaches, help to increase the well-being of their athletes?

This work aims to enhance the leading role, not the secondary, of the ‘good and pleasant side’ of sports practice, either with its own entity, or as a complement or mediator of the negative variables that have an impact on the performance of athletes and coaches. Therefore, the objective of this study is to conduct a systematic review regarding the relationship between positive psychological factors, such as psychological well-being and pleasant emotions, and sports performance. For this, the methodological criteria that constitute the systematic review procedure will be followed.

Materials and methods

This study was carried out through a systematic review using PRISMA (Preferred Reporting Items for Systematic Reviews) guidelines considering the Web of Science (WoS) and Psycinfo databases. These two databases were selected using the Delphi method [ 29 ]. It does not include a meta-analysis because there is great data dispersion due to the different methodologies used [ 30 ].

The keywords will be decided by the Delphi Method in two rounds with sport psychology experts. The results obtained are intended to be a representation of the ‘bright side’ of sports practice, and as a complement or mediator of the negative variables that have an impact on athletes’ and coaches’ performance.

It was determined that the main construct was to be psychological well-being, and that it was to be paired with optimism, healthy practice, realisation, positive mood, and performance and sport. The search period was limited to papers published between 2000 and 2023, and the final list of papers was obtained on February 13 , 2023. This research was conducted in two languages—English and Spanish—and was limited to psychological journals and specifically those articles where the sample was formed by athletes.

Each word was searched for in each database, followed by searches involving combinations of the same in pairs and then in trios. In relation to the results obtained, it was decided that the best approach was to group the words connected to positive psychology on the one hand, and on the other, those related to self-realisation/performance/health. In this way, it used parentheses to group words (psychological well-being; or optimism; or positive mood) with the Boolean ‘or’ between them (all three refer to positive psychology); and on the other hand, it grouped those related to performance/health/realisation (realisation; or healthy practice or performance), separating both sets of parentheses by the Boolean ‘and’’. To further filter the search, a keyword included in the title and in the inclusion criteria was added, which was ‘sport’ with the Boolean ‘and’’. In this way, the search achieved results that combined at least one of the three positive psychology terms and one of the other three.

Results (first phase)

The mentioned keywords were cross-matched, obtaining the combination with a sufficient number of papers. From the first research phase, the total number of papers obtained was 238. Then screening was carried out by 4 well-differentiated phases that are summarised in Fig.  1 . These phases helped to reduce the original sample to a more accurate one.

Phases of the selection process for the final sample. Four phases were carried out to select the final sample of articles. The first phase allowed the elimination of duplicates. In the second stage, those that, by title or abstract, did not fit the objectives of the article were eliminated. Previously selected exclusion criteria were applied to the remaining sample. Thus, in phase 4, the final sample of 11 selected articles was obtained

Results (second phase)

The first screening examined the title, and the abstract if needed, excluding the papers that were duplicated, contained errors or someone with formal problems, low N or case studies. This screening allowed the initial sample to be reduced to a more accurate one with 109 papers selected.

Results (third phase)

This was followed by the second screening to examine the abstract and full texts, excluding if necessary papers related to non-sports themes, samples that were too old or too young for our interests, papers using qualitative methodologies, articles related to the COVID period, or others published in non-psychological journals. Furthermore, papers related to ‘negative psychological variables’’ were also excluded.

Results (fourth phase)

At the end of this second screening the remaining number of papers was 11. In this final phase we tried to organise the main characteristics and their main conclusions/results in a comprehensible list (Table  1 ). Moreover, in order to enrich our sample of papers, we decided to include some articles from other sources, mainly those presented in the introduction to sustain the conceptual framework of the concept ‘bright side’ of sports.

The usual position of the researcher of psychological variables that affect sports performance is to look for relationships between ‘negative’ variables, first in the form of basic psychological processes, or distorting cognitive behavioural, unpleasant or evaluable as deficiencies or problems, in a psychology for the ‘risk’ society, which emphasises the rehabilitation that stems from overcoming personal and social pathologies [ 31 ], and, lately, regarding the affectation of the athlete’s mental health [ 32 ]. This fact seems to be true in many cases and situations and to openly contradict the proclaimed psychological benefits of practising sports (among others: Cantón [ 33 ], ; Froment and González [ 34 ]; Jürgens [ 35 ]).

However, it is possible to adopt another approach focused on the ‘positive’ variables, also in relation to the athlete’s performance. This has been the main objective of this systematic review of the existing literature and far from being a novel approach, although a minority one, it fits perfectly with the definition of our area of knowledge in the broad field of health, as has been pointed out for some time [ 36 , 37 ].

After carrying out the aforementioned systematic review, a relatively low number of articles were identified by experts that met the established conditions—according to the PRISMA method [ 37 , 38 , 39 , 40 ]—regarding databases, keywords, and exclusion and inclusion criteria. These precautions were taken to obtain the most accurate results possible, and thus guarantee the quality of the conclusions.

The first clear result that stands out is the great difficulty in finding articles in which sports ‘performance’ is treated as a well-defined study variable adapted to the situation and the athletes studied. In fact, among the results (11 papers), only 3 associate one or several positive psychological variables with performance (which is evaluated in very different ways, combining objective measures with other subjective ones). This result is not surprising, since in several previous studies (e.g. Nuñez et al. [ 41 ]) using a systematic review, this relationship is found to be very weak and nuanced by the role of different mediating factors, such as previous sports experience or the competitive level (e.g. Rascado, et al. [ 42 ]; Reche, Cepero & Rojas [ 43 ]), despite the belief—even among professional and academic circles—that there is a strong relationship between negative variables and poor performance, and vice versa, with respect to the positive variables.

Regarding what has been evidenced in relation to the latter, even with these restrictions in the inclusion and exclusion criteria, and the filters applied to the first findings, a true ‘galaxy’ of variables is obtained, which also belong to different categories and levels of psychological complexity.

A preliminary consideration regarding the current paradigm of sport psychology: although it is true that some recent works have already announced the swing of the pendulum on the objects of study of PD, by returning to the study of traits and dispositions, and even to the personality of athletes [ 43 , 44 , 45 , 46 ], our results fully corroborate this trend. Faced with five variables present in the studies selected at the end of the systematic review, a total of three traits/dispositions were found, which were also the most repeated—optimism being present in four articles, mental toughness present in three, and finally, perfectionism—as the representative concepts of this field of psychology, which lately, as has already been indicated, is significantly represented in the field of research in this area [ 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. In short, the psychological variables that finally appear in the selected articles are: psychological well-being (PWB) [ 53 ]; self-compassion, which has recently been gaining much relevance with respect to the positive attributional resolution of personal behaviours [ 54 ], satisfaction with life (balance between sports practice, its results, and life and personal fulfilment [ 55 ], the existence of approach-achievement goals [ 56 ], and perceived social support [ 57 ]). This last concept is maintained transversally in several theoretical frameworks, such as Sports Commitment [ 58 ].

The most relevant concept, both quantitatively and qualitatively, supported by the fact that it is found in combination with different variables and situations, is not a basic psychological process, but a high-level cognitive construct: psychological well-being, in its eudaimonic aspect, first defined in the general population by Carol Ryff [ 59 , 60 ] and introduced at the beginning of this century in sport (e.g., Romero, Brustad & García-Mas [ 13 ], ; Romero, García-Mas & Brustad [ 61 ]). It is important to note that this concept understands psychological well-being as multifactorial, including autonomy, control of the environment in which the activity takes place, social relationships, etc.), meaning personal fulfilment through a determined activity and the achievement or progress towards goals and one’s own objectives, without having any direct relationship with simpler concepts, such as vitality or fun. In the selected studies, PWB appears in five of them, and is related to several of the other variables/traits.

The most relevant result regarding this variable is its link with motivational aspects, as a central axis that relates to different concepts, hence its connection to sports performance, as a goal of constant improvement that requires resistance, perseverance, management of errors and great confidence in the possibility that achievements can be attained, that is, associated with ideas of optimism, which is reflected in expectations of effectiveness.

If we detail the relationships more specifically, we can first review this relationship with the ‘way of being’, understood as personality traits or behavioural tendencies, depending on whether more or less emphasis is placed on their possibilities for change and learning. In these cases, well-being derives from satisfaction with progress towards the desired goal, for which resistance (mental toughness) and confidence (optimism) are needed. When, in addition, the search for improvement is constant and aiming for excellence, its relationship with perfectionism is clear, although it is a factor that should be explored further due to its potential negative effect, at least in the long term.

The relationship between well-being and satisfaction with life is almost tautological, in the precise sense that what produces well-being is the perception of a relationship or positive balance between effort (or the perception of control, if we use stricter terminology) and the results thereof (or the effectiveness of such control). This direct link is especially important when assessing achievement in personally relevant activities, which, in the case of the subjects evaluated in the papers, specifically concern athletes of a certain level of performance, which makes it a more valuable objective than would surely be found in the general population. And precisely because of this effect of the value of performance for athletes of a certain level, it also allows us to understand how well-being is linked to self-compassion, since as a psychological concept it is very close to that of self-esteem, but with a lower ‘demand’ or a greater ‘generosity’, when we encounter failures, mistakes or even defeats along the way, which offers us greater protection from the risk of abandonment and therefore reinforces persistence, a key element for any successful sports career [ 62 ].

It also has a very direct relationship with approach-achievement goals, since precisely one of the central aspects characterising this eudaimonic well-being and differentiating it from hedonic well-being is specifically its relationship with self-determined and persistent progress towards goals or achievements with incentive value for the person, as is sports performance evidently [ 63 ].

Finally, it is interesting to see how we can also find a facet or link relating to the aspects that are more closely-related to the need for human affiliation, with feeling part of a group or human collective, where we can recognise others and recognise ourselves in the achievements obtained and the social reinforcement of those themselves, as indicated by their relationship with perceived social support. This construct is very labile, in fact it is common to find results in which the pressure of social support is hardly differentiated, for example, from the parents of athletes and/or their coaches [ 64 ]. However, its relevance within this set of psychological variables and traits is proof of its possible conceptual validity.

Analysing the results obtained, the first conclusion is that in no case is an integrated model based solely on ‘positive’ variables or traits obtained, since some ‘negative’ ones appear (anxiety, stress, irrational thoughts), affecting the former.

The second conclusion is that among the positive elements the variable coping strategies (their use, or the perception of their effectiveness) and the traits of optimism, perfectionism and self-compassion prevail, since mental strength or psychological well-being (which also appear as important, but with a more complex nature) are seen to be participated in by the aforementioned traits.

Finally, it must be taken into account that the generation of positive elements, such as resilience, or the learning of coping strategies, are directly affected by the educational style received, or by the culture in which the athlete is immersed. Thus, the applied potential of these findings is great, but it must be calibrated according to the educational and/or cultural features of the specific setting.

Limitations

The limitations of this study are those evident and common in SR methodology using the PRISMA system, since the selection of keywords (and their logical connections used in the search), the databases, and the inclusion/exclusion criteria bias the work in its entirety and, therefore, constrain the generalisation of the results obtained.

Likewise, the conclusions must—based on the above and the results obtained—be made with the greatest concreteness and simplicity possible. Although we have tried to reduce these limitations as much as possible through the use of experts in the first steps of the method, they remain and must be considered in terms of the use of the results.

Future developments

Undoubtedly, progress is needed in research to more precisely elucidate the role of well-being, as it has been proposed here, from a bidirectional perspective: as a motivational element to push towards improvement and the achievement of goals, and as a product or effect of the self-determined and competent behaviour of the person, in relation to different factors, such as that indicated here of ‘perfectionism’ or the potential interference of material and social rewards, which are linked to sports performance—in our case—and that could act as a risk factor so that our achievements, far from being a source of well-being and satisfaction, become an insatiable demand in the search to obtain more and more frequent rewards.

From a practical point of view, an empirical investigation should be conducted to see if these relationships hold from a statistical point of view, either in the classical (correlational) or in the probabilistic (Bayesian Networks) plane.

The results obtained in this study, exclusively researched from the desk, force the authors to develop subsequent empirical and/or experimental studies in two senses: (1) what interrelationships exist between the so called ‘positive’ and ‘negative’ psychological variables and traits in sport, and in what sense are each of them produced; and, (2) from a global, motivational point of view, can currently accepted theoretical frameworks, such as SDT, easily accommodate this duality, which is becoming increasingly evident in applied work?

Finally, these studies should lead to proposals applied to the two fields that have appeared to be relevant: educational and cultural.

Application/transfer of results

A clear application of these results is aimed at guiding the training of sports and physical exercise practitioners, directing it towards strategies for assessing achievements, improvements and failure management, which keep them in line with well-being enhancement, eudaimonic, intrinsic and self-determined, which enhances the quality of their learning and their results and also favours personal health and social relationships.

Data availability

There are no further external data.

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Psychological well-being and psychological distress: is it necessary to measure both?

• Helen R Winefield 1 ,
• Tiffany K Gill 2 ,
• Anne W Taylor 2 &
• Rhiannon M Pilkington 2  

Psychology of Well-Being: Theory, Research and Practice volume  2 , Article number:  3 ( 2012 ) Cite this article

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The objectives of the study were to explore a self-report measure for psychological well-being and to investigate the relationship between psychological well-being and psychological distress.

Telephone interviews of a representative sample of adults (N = 1933) collected information about sociodemographic variables, a standardised measure of psychological distress, and three brief existing scales to assess aspects of psychological well-being: Positive Relations with Others, Environmental Mastery, and Satisfaction with Life. The total of these three scales was also computed and explored as a measure of overall well-being.

Variables positively associated with psychological well-being were negatively associated with psychological distress and vice versa. For example low psychological well-being and high psychological distress were associated with being the only adult in the household, speaking a language other than English at home, being divorced or separated, having no educational qualifications beyond secondary school, being unable to work, having a low income, renting one’s accommodation, and receiving a pension.

Conclusions

The measure of well-being shows psychometric promise for community surveys. Psychological well-being is not exactly the opposite end of the continuum to psychological distress, but more debate is needed about whether and when, research participants need to be asked questions about both.

The positive psychology movement and corresponding social capital/new economics perspectives in public health, exemplified by the Foresight Mental Capital and Wellbeing Project ([ 2008 ]), draw attention to the desirability of developing reliable and valid standardised scales to measure psychological well-being (PW). As summarised in the WHO report (Freidli [ 2009 ], p.2): “a growing number of longitudinal studies confirm their [i.e. well-being scales] power to predict outcomes, for example, longevity, physical health, quality of life, criminality, drug and alcohol use, employment, earnings and pro-social behaviour (e.g. volunteering)”. In much the same way that we have accepted the need to measure physical health and well-being rather than being restricted to measures of illness and dysfunction, we need psychometrically sound assessment tools for psychological well-being. Unless these two attributes are perfectly negatively correlated – just opposite ends of the same continuum - attending only to distress risks leaves important gaps in our understanding of health, well-being, quality of life and resilience. This paper reports an apparently useful measure of PW and takes the opportunity in a large representative sample of adults, to contribute to the debate around the bipolar or bivariate nature of PW and psychological distress or dysfunction (PD).

Psychological well-being is usually conceptualised as some combination of positive affective states such as happiness (the hedonic perspective) and functioning with optimal effectiveness in individual and social life (the eudaimonic perspective) (Deci & Ryan [ 2008 ]). As summarised by Huppert ([ 2009 ], p.137): “Psychological well-being is about lives going well. It is the combination of feeling good and functioning effectively.” By definition therefore, people with high PW report feeling happy, capable, well-supported, satisfied with life, and so on; Huppert’s ([ 2009 ]) review also claims the consequences of PW to include better physical health, mediated possibly by brain activation patterns, neurochemical effects and genetic factors. Data in this area are necessarily correlational rather than experimental, but a report highlighting the value of assessing positive emotional states is that by Xu and Roberts ([ 2010 ]), using longitudinal data (1965 to 1993) from the Alameda County Study (N = 6856). After controlling for age, sex, education, baseline health and social networks, longevity was predicted by positive emotions but not by negative. The effect held for both younger and older age-groups and was particularly clear for healthy adults. The authors called for more focus on positive emotions as a potential promoting agent for population longevity and health. Boehm et al. ([ 2011 ]) report similarly, based on a 5-year follow-up of 7942 participants in the Whitehall II cohort, an association between psychological well-being and a modest but consistent reduction in risk of incident CHD.

Measurement of psychological well-being utilises various instruments without any having gained dominance as a “gold standard”. Life satisfaction is often a component (Diener et al. [ 1985 ]; Diener et al. [ 2000 ]; Diener et al. [ 2003 ]; Lucas et al. [ 1996 ]). Ryff’s PW scales (Ryff [ 1989 ]; Ryff and Singer [ 1996 ]) offer a richer, multidimensional view including Autonomy, Environmental Mastery, Personal Growth, Positive Relations with Others, Purpose in Life, and Self-Acceptance. The 14-item Warwick-Edinburgh Mental Well-being Scale (Tennant et al. [ 2007 ]) offers promise at the population level but its sensitivity to change requires more study. Keyes and colleagues’ Mental Health Continuum-Short Form scale of 14 items (MHC-SF: Keyes [ 2005 ]; Keyes et al. [ 2008 ]; Lamers et al. [ 2011 ]) has been developed from large South African and Dutch samples and extensively factor analysed as evidence of separate continua for mental illness and mental health. The measures used by Xu and Roberts ([ 2010 ]) were based on factor analyses of the Alameda County Study baseline psychological items (Berkman and Breslow [ 1983 ]). These were: global life satisfaction (3 items), satisfaction with important life domains (your job, your marriage, your children), and positive affect (3 items). Boehm et al. ([ 2011 ]) used five items from disparate sources measuring “emotional vitality” plus one assessing optimism. The convergence of results using related but various measuring instruments can be seen as a sign of the robustness of these findings.

While philosophical and psychological theories abound concerning the nature of happiness and a good life, a question likely to interest epidemiologists is the relationship between this experiential state and, on the one hand various socio-demographic predictors, and on the other, objective and subjective health outcomes. There is a long-standing acceptance that psychological distress (PD) in the form of anxiety, sadness, irritability, self-consciousness and emotional vulnerability is strongly correlated with physical morbidity, reduced quality and duration of life, and increased use of health services (e.g. Lahey [ 2009 ]). But what if anything does measurement of psychological well-being (PW) add to public health surveys? Are these concepts (PD and PW) sufficiently different to justify adding to respondent burden by measuring both, or will one suffice for at least some research purposes?

In order to examine the relationship between PW and PD, we surveyed a community sample using a well-accepted screening test for PD, and also tested the psychometric properties of a PW scale using 16 questions about positive emotions. The latter included life satisfaction, sense of control and satisfying social relationships. The aims of the present study were:

To assess the psychometric properties (reliability and validity) of a collection of questions relevant to psychological well-being (PW), particularly the Diener Satisfaction with Life Scale (SWLS: Diener et al. 1985) and two of the Ryff Psychological Wellbeing scales (Environmental Mastery and Personal Relations);

To apply findings from the above to examine the demographic correlates of PW;

To examine relationships between PW and PD measured using the widely-used screening measure, the K10 (ABS 2001; Kessler et al. 2002). This was done in two ways, firstly by looking at the correlations between scores on the two sorts of measure, and secondly by examining whether predictors of the PW and PD are opposite ends of the same variables. If PD and PW are just mirror reflections of each other, high levels of factors associated with high PD might predict low PW and vice versa. On the other hand if PD and PW are independent (albeit overlapping) constructs they might be associated with different predictor variables.

Participants

All households in South Australia with a telephone connected and the telephone number listed in the Electronic White Pages were eligible for selection in the sample. Telephone numbers were selected randomly from the metropolitan and country areas. Only one interview was conducted per household. Where more than one person aged 18 or over resided in the household, the respondent was the person who was last to have their birthday. There were no replacements for non-contactable persons. A sample of 4500 was drawn of which 3325 households were eligible, losses occurring due to fax/modem connections (n = 48), automated message or number not connected (n = 994), non–residential numbers (n = 113) and deceased or otherwise ineligible (n = 18). From the eligible sample of 3325, completed interviews were conducted with 1933 persons (58.1%), nonparticipation being due to refusals (n = 812), noncontact after 10 attempts (n = 279), incapacitated (n = 135), respondent unavailable (n = 91), foreign language (n = 66), or interview terminated (n = 9).

Respondents provided information about their sex, age group, the number of adults in the household and the number of children aged under 18 years, metropolitan or rural residence, country of birth and aboriginality, language spoken at home (English or other), marital status, education, work status, gross household income, and ownership or not of their dwelling. Psychological well-being was measured using the Diener Satisfaction with Life Scale (SWLS: Diener et al. [ 1985 ]), comprising five items which require respondent to indicate to what extent they agree or disagree with the statement on a seven point likert type scale with higher scores corresponding to higher life satisfaction. An example item is “In most ways my life is close to my ideal”. In addition we used two subscales from the Ryff Psychological Wellbeing Inventory (Ryff [ 1989 ]). These were environmental mastery (EM: 5 items) and positive (social) relations (PR: 6 items). These subscales were chosen to reflect the sense of control and the supportive social relationships which have been consistently identified as integral aspects of psychological wellbeing. Environmental Mastery (EM) reflects the sense of power, control and autonomy widely accepted to have stress-reducing effects (Beck [ 2007 ]), and positive Personal Relations (PR) with others have similar credence as facilitatory influences on health and wellbeing. Holt-Lunstad et al. ([ 2010 ]) showed the significant health benefits of social support in a large recent meta-analysis, while Cohen and Lemay ([ 2007 ]) have convincingly described the possible mechanisms of positive effects of social support. Response categories were on a five point scale ranging from “Strongly agree” to “Strongly disagree”. Both dimensions were measured with positively and negatively worded items, with reverse coding so that higher scores indicated higher levels of psychological wellbeing. An EM item is “I’m good at managing my many daily responsibilities” and a PR item is “I often feel lonely because I have few close friends”. The value of combining these concepts into one scale was explored by summing the scores of these three measures and conducting a factor analysis of the resultant 16 items here referred to as an Overall well-being scale.

Psychological distress was measured using the 10-item screening scale K10, as used in national and state-wide surveys in Australia (ABS [ 2001 ]; Kessler et al. [ 2002 ]). The items are based on the level of anxiety and depressive symptoms experienced in the most recent four-week period, for example: “how often did you feel nervous” and “how often did you feel hopeless”. Subjects report the frequency of each experience on a five point scale ranging from ‘all of the time’ to ‘none of the time’. The scoring system used is based on the method developed by the Clinical Research Unit for Anxiety and Depression at the University of New South Wales. In this method, five points are given for ‘all of the time’ to one point for ‘none’ of the time. This results in individual K10 scores being restricted to a range of 10–50.

This survey obtained ethics approval from the SA Health Human Research Ethics Committee and the University of Adelaide Human Research Ethics Committee. A letter introducing the study was sent to the household of each selected telephone number. The letter informed people of the purpose of the survey and indicated that they could expect a telephone call within a defined time frame. Before the conduct of the main survey, the questionnaire was pilot tested (n = 50) and where appropriate, wording was amended slightly.

To correct for disproportionality of the sample with respect to the population of interest, data were weighted by age, sex and area of residence to reflect the structure of the population in South Australia aged 18 years and over and probability of selection in the household (ABS [ 2007 ]). The probability of selection was calculated based on the number of adults in the household and the number of telephone number listings for that household. Frequencies of demographic and social characteristics were determined as were the mean scores of each of the measurement scales. Internal reliability was determined using Cronbach’s alpha and Pearson correlations determined the direction and strength of the linear association between the scales. Finally, mean scores for each demographic characteristic were determined. Significant differences were assessed using T-tests and one-way ANOVA were used to test for significant differences between groups with all post hoc comparisons conducted using the Scheffe test. Data were analysed using SPSS Version 15.0 and the conventional five per cent level of statistical significance was used.

Below we present descriptive results followed by correlations between the different scales used and comparisons of scores by sociodemographic variables. With the goal of investigating a potential composite measure of PW, scores from the three PW tests above were added and the resultant “Overall well-being” totals subjected to factor analysis.

Demographic and social characteristics of the respondent sample are shown in Table 1 . Respondents comprised approximately even numbers of men and women, well-distributed across age-groups and education and income categories and raw test scores on the measures of PW and PD in Table 2 .

Each scale had acceptable internal reliability and the well-being scales were positively correlated with each other and negatively with distress, as expected. Variances accounted for ranged from 18.5% (PR with K10), to 81% (SWLS with Overall well-being). The composite measure consisting of the total of scores for the two Ryff scales and the Diener SWLS scale had a similar correlation with the K10 as did the component scales.

A principal components analysis (PCA) was conducted on the sixteen items that formed the Overall well-being score. Enough of the bivariate correlations were higher than 0.30 therefore it was considered appropriate to proceed with PCA. The Kaiser-Meyer-Olkin measure of sampling adequacy (KMO = 0.912) was greater than 0.6 and Bartlett’s Test of Sphericity was significant ( χ ² = 9187.01, p  < .001) (Manning and Munro [ 2007 ]). Communalities ranged from 0.292 (item 5, satisfaction with life scale – “If I could live my life over I would change almost nothing) to 0.669 (item 2, satisfaction with life scale – “I am satisfied with life”). There was only one variable below 0.30 (at 0.29) suggesting that a good solution is likely.

Three components were extracted with eigenvalues greater than 1, accounting in total for 51.39% of the total variance. Component 1 accounted for 24.9% of the variance. Item 1 (Q3 Diener scale), “Am satisfied with life”, item 2 (Q1 Diener scale) “Life close to ideal” and item 3 (Q2 Diener scale) “Have excellent life conditions” all had factor loadings of > 0.7. Item 4 (Q4 Diener scale) “I have the important things in life” and item 5 (Q3 Environmental Mastery) “Able to create a lifestyle to my liking” both had factor loadings > 0.6. Item 6 (Q4 Environmental Mastery) “In charge of living situation” and item 7 (Q5 Diener scale) “If I could live my life over I would change almost nothing” had loadings of > 0.5. Item 8 (Q2 Environmental Mastery) “Difficulty arranging my life” and item 9 (Q5 Environmental Mastery) “Demands of life often get me down” had loadings of > 0.4. These last two items also have loadings on component 2 (although values are lower than those on component 1).

Component 2 explained 16.7% of the variance. Item 10 (Q2 Positive Relations) “Most people have more friends than me”, item 11 (Q1 Positive Relations) “Feel lonely because have few close friends”, item 12 (Q5 Positive Relations) “Not experienced warm and trusting relationships, and item 13 (Q4 Positive Relations) “Maintaining close relationships has been difficult” all had factor loadings >0.7. Component 3 explained 9.7% of the variance. Item 14 (Q3 Positive Relations) “People describe me as a giving person”, item 15 (Q6 Positive Relations) “Enjoy personal and mutual conversation” and item 16 (Q1 Environmental mastery) “Good at managing life responsibilities” had factor loadings of > 0.6. This last item also has a loading on component 1 (with a lower value) as does item 6 (Q4 Environmental Mastery) “In charge of living situation” (but with a higher value on component 1).

Interpreting the factor analysis, it is apparent that the rotated component 1 accounts for the highest proportion of variance. Examining the items with the highest loadings this factor seems to be representing the concept of life satisfaction. Component 2 is for the most part examining negative personal relations, and component 3 could be labelled positive personal relations, although it had fewer than four items uniquely loading on it.

Relations between PW and PD and sociodemographic variables

Table 3 presents significant differences in the mean Overall well-being scores according to the demographic characteristics of respondents. Significant differences in mean values existed between categories for all demographic characteristics except for sex, age group, area of residence and Aboriginal or Torres Strait Islander status.

Table 4 summarises the demographic variables that were significantly associated with all of the scales. Marital status, work status, income and dwelling ownership were all characteristics significantly associated with all five scores.

Generally, variables with high mean scores for distress had low mean scores for well-being and vice versa, but there were two exceptions to this. Firstly, there was usually no significant difference in mean score between genders for the PW scales, but female gender was associated with higher scores of PD. Secondly living in a household with one or more children was significantly associated with higher scores for PW but there were no significant differences in mean scores for PD.

The paper reports on a collection of 16 items from existing scales which as a total, captures the main dimensions of what the literature indicates as a consensus regarding the nature of psychological well-being: a sense of control, supportive social relations, and general satisfaction with life. The separate and the composite measures of PW are internally reliable, reasonably well correlated with each other, and negatively correlated with PD as expected.

The results indicate that if researchers wanted a briefer measure the 9-item scale formed from the factor analysis first component might be suitable, and if an even briefer measure were needed, the 5-item SWLS seems to be the best. Whether individual scales or the composite total is preferable for use may depend on specific research questions. The separate scales are not so highly correlated amongst themselves (either different components of PW, or PW against PD), as to suggest that only one factor is being investigated. The findings do not provide strong support for the need to measure PW separately from PD. However if public acceptance of positively worded items were to prove higher, it may be necessary to modify expression of EM and PR items currently phrased negatively.

An important question remains about what range of scores may be of prime interest to researchers. Well-established measures of PD have cut-off points above which scores validly suggest the need for clinical assessment and intervention, for example “high PD” is approximately the top 10% of the range of scores on the K10. It is common to use cut-off points to describe proportions of the population who for example, are at risk of a bad outcome, are disabled in everyday life (e.g. employment), or who need extra services. The national mental health survey in Australia established strong links between mental disorders such as anxiety and depression and the likelihood of being disabled in carrying out usual activities (Andrews et al. [ 2001 ]; Dear et al. [ 2002 ]). But what cut-off scores are meaningful in discussing well-being? Are researchers and public health policy-makers going to be interested only in the top 10% of PW, or, as seems more likely, will they want to know about the sociodemographic and personal attributes of scorers in the top quartile or tertile? Keyes ([ 2005 ]) used tertiles and diagnoses modelled on the DSM-IIIR approach to distinguish those “flourishing” with complete mental health, from those “languishing”, whose mental health places them between “moderately” well and those with mental illness. Huppert ([ 2009 ]) used these categories to advance the public health argument that shifting the average of the mental health distribution, even by a small change in score, would greatly increase the proportion of flourishing and decrease the languishing or mentally ill. Suggestions about how to achieve this small shift, such as school-based or worksite interventions (Huppert [ 2009 ]) or more psychotherapy (Lamers et al. [ 2011 ]) need to be seen in the context of more comprehensive prescriptions for mental health promotion (e.g. Herrman [ 2001 ]).

Suggestions for future research

Further exploration of the applicability and predictive validity of the Overall well-being measure seems warranted, and comparisons with others such as the MHC-SF (Lamers et al. [ 2011 ]) and the Warwick-Edinburgh Mental Well-being Scale (Tennant et al. [ 2007 ]), are needed to evaluate the relative merits of different instruments.

Predictors of PW need to be explored further, and candidates would include both social determinants of health such as socio-economic status, and personal history factors such as parental rearing conditions and current satisfaction with social support. As a specific example, it would be extremely useful to gain insights into how parent attitudes to their children’s achievements and failures in various areas of life, contribute to PW and resilience of those individuals in later life. Developmental changes with age also need more exploration. McMahan and Estes ([ 2011 ]) found that younger compared with older adults emphasised the experience of pleasure and self-development in their conceptions of well-being, while older people emphasised avoidance of negative experience more than younger ones, and there was no age difference in emphasis on making a contribution to others/making the world a better place. Westerhof and Keyes ([ 2010 ]) concluded that although older adults have fewer mental health problems than do younger adults, they are not in better positive mental health.

The longitudinal consequences of PW can be hypothesised as improved quality of life and life expectancy, less frequent use of health services, and adaptive coping with a range of adverse events such as acute and chronic illness and disability, relationship disruptions, single parenthood, work stress and unemployment. In fact researchers could learn more about “resilience”, by closer study of individuals who score high on PW despite living in what we know to be adverse circumstances, such as being unemployed, living alone, or having low education. Such studies are likely to require in-depth data collection in the form of interviews in order to enrich the scope of possible conclusions, by avoiding imposing preconceived ideas in the form of researcher-selected questions.

Regarding the relationship between PW and PD, numerous researchers have argued for the independence of positive and negative affect (e.g. Larsen et al. [ 2001 ]; Warr et al. [ 1983 ]), but Huppert’s ([ 2009 ]) review concluded that well-being and ill-being have many common drivers. Keyes and colleagues (Keyes [ 2005 ]; Lamers et al. [ 2011 ]) have concluded there are separate but correlated axes of mental health and mental illness. The answer is unlikely to be categorical. For example Zautra et al. ([ 2005 ]) proposed that in times of low stress, positive and negative affect are relatively uncorrelated, but that in stressful situations they collapse to a simpler bipolar dimension. And McNulty and Fincham ([ 2012 ]) have recently shown that whether forgiveness, optimism, benevolent attributions and kindness have positive outcomes or not depends on the interpersonal context; this contextual dependence means that “Just as studying dysfunction cannot tell researchers how to promote flourishing ..... studying flourishing cannot tell us how to prevent suffering” (p.107). Therefore the extent to which PW and PD are independent of each other may vary according to the external and internal environmental challenges people face, and researchers will need to make choices about the value of measuring both, according to those considerations.

The total of 16 items from exisiting scales to measure well-being shows psychometric promise for community surveys, and shorter versions have here been recommended according to researcher needs. In a representative community sample of adults, psychological well-being and psychological distress were driven by very similar sociodemographic characteristics

The issue of appropriate cut-off scores needs further investigation, to determine if there are categories of relative PW which predict successful coping with serious stress, illness or adversity. There might for example be high utility in a measure of PW where scores within a given range could reliably predict benefits from interventions such as better education, psychoeducation, or access to health care.

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Acknowledgements

This study was funded as part of the Assessment of the Determinants and Epidemiology of Psychological Distress (ADEPD) study, through the Strategic Health Research Program 2007 – 2009, SA Health.

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HW conceived of the study, prepared the literature review and drafted the report, assisted by the other authors. TG and AT participated in the design of the study and were responsible for the data collection and analyses. RP contributed substantially to the data analyses and interpretation. All authors read and approved the final manuscript.

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Winefield, H.R., Gill, T.K., Taylor, A.W. et al. Psychological well-being and psychological distress: is it necessary to measure both?. Psych Well-Being 2 , 3 (2012). https://doi.org/10.1186/2211-1522-2-3

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Assessing Psychological Well-Being: Self-Report Instruments for the NIH Toolbox

John m. salsman.

1 Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine

2 Robert H. Lurie Comprehensive Cancer Center of Northwestern University

Jin-Shei Lai

Hugh c. hendrie.

3 Center for Aging Research, Indiana University School of Medicine

Zeeshan Butt

4 Comprehensive Transplant Center, Northwestern University

5 Institute for Healthcare Studies, Northwestern University Feinberg School of Medicine

Nicholas Zill

6 Westat, Inc

Paul A. Pilkonis

7 Department of Psychiatry, University of Pittsburgh Medical Center

Christopher Peterson

8 Department of Psychology, University of Michigan

Catherine M. Stoney

9 National Heart, Lung, and Blood Institute, National Institutes of Health

Pim Brouwers

10 Division of AIDS Research, National Institute of Mental Health

David Cella

Psychological well-being (PWB) has a significant relationship with physical and mental health. As part of the NIH Toolbox for the Assessment of Neurological and Behavioral Function, we developed self-report item banks and short forms to assess PWB.

Study Design and Setting

Expert feedback and literature review informed the selection of PWB concepts and the development of item pools for Positive Affect, Life Satisfaction, and Meaning and Purpose. Items were tested with a community-dwelling U.S. internet panel sample of adults aged 18 and above (N=552). Classical and item response theory (IRT) approaches were used to evaluate unidimensionality, fit of items to the overall measure, and calibrations of those items, including differential item function (DIF).

IRT-calibrated item banks were produced for Positive Affect (34 items), Life Satisfaction (16 items), and Meaning and Purpose (18 items). Their psychometric properties were supported based on results of factor analysis, fit statistics, and DIF evaluation. All banks measured the concepts precisely (reliability ≥0.90) for more than 98% of participants.

These adult scales and item banks for PWB provide the flexibility, efficiency, and precision necessary to promote future epidemiological, observational, and intervention research on the relationship of PWB with physical and mental health.

Introduction

Research on psychological well-being (PWB) has received increasing attention over the past decade in part due to the growth of the positive psychology movement [ 1 ] and renewed interest on the relationship between positive psychology and health [ 2 ]. Research examining the relationship between PWB and health has primarily been focused on positive affect and health and has revealed links with physical, psychological, and social health. For physical health, PWB has been associated with increased longevity [ 3 ; 4 ], perceptions of good health in older adults [ 5 ], and decreased loss of functional status and mobility [ 6 ]. In a meta-analytic review, PWB was associated with reduced mortality in healthy population-based studies [ 7 ]. Links to better psychological health have been found between PWB and positive coping with life circumstances [ 8 ] and between PWB and resilience, endurance, and optimism [ 9 ]. PWB has also been associated with more and closer social contacts as evidenced by links between PWB and more diverse and closer social ties [ 10 ].

Consistent with theoretical conceptualizations of PWB, previous approaches to measuring PWB have emphasized both an experiential and an evaluative component [ 11 ]. The experiential component, known as hedonic well-being, includes positive emotions whereas the evaluative component, known as eudaimonic well-being, includes cognitive evaluations of life purpose and meaning [ 12 ; 13 ]. For the NIH Toolbox, we concentrated on both affective experiences (positive affect) and cognitive evaluations (life satisfaction, meaning and purpose) that are critical components of “living well” [ 14 ] and thus key aspects of emotional health throughout life. Positive affect has been characterized as happiness, contentment, positive energy, and interest in pleasurable or achievement-relevant activities [ 15 ]. Pressman and Cohen [ 16 ] defined positive affect as “feelings that reflect a level of pleasurable engagement with the environment such as happiness, joy, excitement, enthusiasm, and contentment.” Positive affect bears a strong relationship to overall feelings of life satisfaction but is conceptually distinct [ 17 ].

Life satisfaction is the cognitive evaluation of life experiences rather than reports of a pure affective state. Items assessing this concept are usually phrased in a general or global way, rather than having a momentary or recent recall period. Unlike measures of pure affect, life satisfaction measures are strongly influenced by expectations. Thus, individuals can report a high level of satisfaction if they genuinely experience their lives as going well, or if their expectations are low, regardless of how well their lives are going. For this and other reasons, it is helpful to assess both affect and satisfaction.

Assessments of meaning and purpose are cognitive evaluations of the extent to which people feel their life reflects goals and purposes beyond their current affect and satisfaction. These assessments contain elements of doing things viewed as “good” and being a person engaged in positive activities (cf. [ 14 ]). There is conceptual overlap with life satisfaction with correlation coefficients ranging from 0.41 [ 18 ] to as high as 0.71 [ 19 ]. However, life satisfaction focuses on whether or not people like their lives, and meaning is more specifically concerned with the extent to which people feel their life matters or makes sense [ 20 ].

The NIH Toolbox for Assessment of Neurological and Behavioral Function ( www.nihtoolbox.org ) is a project to identify, create and validate brief comprehensive assessment tools to measure outcomes in longitudinal, epidemiological and intervention studies across the lifespan in the areas of cognition, emotion, motor and sensory function. The project is one of the initiatives in the NIH Blueprint for Neuroscience Research [ 21 ]. The NIH Toolbox objectives are to provide a standard set of measures across diverse study designs and populations and maximize yield from large, expensive studies with minimal increment in subject burden and cost. In order to accomplish these goals, core batteries for cognition, emotion, motor, and sensory function were developed and will be normed for ages 3–85 years old.

Within the emotion domain, the mandate for the NIH Toolbox was to develop assessments with a broad focus, incorporating healthy emotional functioning. This process was guided by a review of the literature, an NIH Toolbox Request for Information from experts in the area of emotional health, follow-up semi-structured interviews with a subset of these experts, and discussion within the emotion domain team and among the emotional health expert consultants [ 22 ]. We identified four sub-domains of particular relevance to health outcomes – Negative Affect, PWB, Stress and Self-Efficacy, and Social Relationships. An overview of this process is discussed elsewhere [ 23 ].

This report focuses on the development of self-report measures for the PWB subdomain of the NIH Toolbox for adults aged 18 years old and above. Our data collection and analytic approach relied heavily on item response theory (IRT), a modern approach to test construction and evaluation [ 24 ]. The development of item banks (i.e., a set of carefully calibrated questions that develop, define and quantify a construct) can inform the creation of robust short forms and the application of computerized adaptive testing (CAT). By enhancing measurement efficiency, precision, and flexibility, IRT applications are particularly useful analytic strategies for the goals of the NIH Toolbox in general, and measurement of PWB specifically.

Item Pool Development

We performed extensive literature searches and received recommendations for measures assessing positive affect, life satisfaction, and meaning from 20 Ph.D. consultants and experts in measurement science who were nominated and approved by the NIH Toolbox Steering Committee to serve as contract-funded consultants and co-investigators. We drew content ideas and coverage from existing, well-validated measures of PWB. This iterative process helped to generate and refine items that covered the breadth of content included in the concepts of PWB (cf. [ 25 ; 26 ]). Comprehensive literature searches adapted the general strategies described by Klem et al. [ 27 ] and were performed using the PubMed, PsycINFO, Buros Institute Test Reviews Online, Educational Testing Service, Patient-Reported Quality of Life Instrument Database, Tests and Measures in the Social Sciences, and Health and Psychosocial Instruments databases. Cited reference searches were completed for the primary reference for each measure in order to determine its acceptance and use by the scientific community. For the Toolbox emotion domain, 554 measures were identified; 77 of them assessed concepts associated with PWB. In addition, a careful review of intellectual property issues was done for all measures. Items from proprietary measures (n=5 adult measures) were excluded. We supplemented existing measures with items from other measures to maximize the breadth of content coverage. We standardized item context (recall periods), item stems (verb tense), and response options to minimize respondent burden. Table 1 shows the number of items included for PWB and the source instrument. Item selection results for each of the PWB concepts are described below and items and response options comprising each of the three calibrated banks are listed in Appendix A .

Item Pools, Source Instrument, and Calibrated Banks for Adults by Concept

Participants

Adult subjects (ages 18 or older) were drawn from the United States general population, by Toluna ( http://www.toluna-group.com ), an internet panel company. Internet panels are increasingly used as a viable means of data collection due to the widespread availability of the internet among diverse groups and the low cost and efficient means of data collection provided by the internet [ 28 ]. Moreover, Liu et al. [ 29 ] have shown the representativeness of internet data is comparable to data from probability-based general population samples. To recruit study participants, Toluna sent emails to invite potential participants from their databases to enroll in the current study following a screening process to ensure eligibility (based on age, current English-speaking). Following initial screening, 3,648 respondents completed a demographic survey and were assigned to one of three study arms. Of those participants, 2,551 provided complete data. Participants who completed the PWB items (n=1,111) were administered identical questionnaires and eligible for incentive-based compensation through Toluna. Procedures for data quality control are described at http://us.toluna-group.com/toluna-difference/quality/ . After removing suspicious cases for “straightlining” (i.e., same response within blocks of 15 or more items), data from 522 adults (mean age=44.9; 61% female) were examined. Detailed demographic information is shown in Table 2 .

Participant Information

Data Analysis

Analyses followed general guidelines used in the PROMIS item bank development [ 25 ; 26 ] and were grouped into two phases: (1) Testing assumptions for IRT modeling -unidimensionality and local independence of items; and (2) Estimating item parameters using IRT and creating fixed-length forms for norming. Item inclusion/exclusion was decided by group consensus after reviewing analysis results and item content.

As part of phase 1, we examined items for sparse data within any rating scale category (i.e., N<5). We also identified violations of monotonicity (average scores of people across the range of item response categories should increase monotonically) for their potential impact on subsequent IRT analyses. Corrected item-total correlations were used to identify non-contributing items within each domain. Data were randomly divided into two datasets, one for exploratory factor analysis (EFA) and the other for confirmatory factor analysis (CFA), using the SAS 9.3 (SAS Institute Inc., Gary: North Carolina) and MPlus 6.1 (Muthen & Muthen, Los Angeles: CA), respectively. EFA with PROMAX rotation was used to identify potential factors among items and CFA was used to confirm final factor structure. Because the data were ordinal in nature, we used polychoric correlations in the factor analyses. In the EFAs, eigenvalues >1.0 and scree plots were used as criteria to estimate meaningful factors. To enhance the unidimensional nature of factors, items with factor loadings < 0.4 were considered for exclusion. In the CFAs we used weighted least squares and fit statistics to evaluate dimensionality of the item pool. Fit indices are influenced by multiple factors such as sample size, distribution and numbers of items [ 30 ], and we selected the commonly used indices for item banking as adopted by the Patient Reported Outcomes Measurement Information System (PROMIS): Comparative Fit Index (CFI), Tucker-Lewis index (TLI), and Root Mean Square Error of Approximation (RMSEA). Residual correlations were used to evaluate local dependency between item pairs to avoid potential secondary factors from locally dependent items.[ 31 ]

In phase 2, items that met unidimensionality assumptions were analyzed using Samejima’s Graded Response model (GRM)[ 32 ] as implemented in Multilog IRT software.[ 33 ] The GRM is one of the most commonly used IRT models in health-related quality of life research and yields difficulty and discrimination parameters. The difficulty parameter (i.e., threshold) of an item is reflected by the probability of a participant endorsing a particular response for an item depending upon his/her level of the construct relative to the location of that item on the construct continuum. The discrimination parameter (i.e., slope) describes how well an item discriminates among individuals at different points along the continuum. The IRT-based information function was used to estimate reliability and error functions at both scale and item levels, to allow examination of precision levels along the measurement continuum. The information function presents the degree of measurement precision, which varies along the continuum and can be converted into a reliability function (criterion: reliability > 0.7). In IRT models, reliability varies at different levels, as can the consequences of various precision levels. Items displaying poor IRT fit (criterion: significant S-X 2 fit statistic, p<0.01 [ 34 ]) and poorly discriminating items (i.e., those with unacceptable IRT slopes; criterion: slope < 1) were candidates for exclusion at this stage. Final inclusion/exclusion decisions were determined by the research team after review of individual item properties and content vis-à-vis the entire item bank.

In this study, we conducted differential item functioning (DIF) analyses on the basis of age (“< 50 years” versus “ ≥ 50 years”), education (“<1 year in college” versus “>1 year”) and gender for groups with a minimum of 150–200 participants per subgroup.[ 35 ] An item has significant DIF if the item exhibited different measurement properties between subgroups, which is similar to “item bias” a common term used in educational settings. DIF exists when characteristics such as age, gender, or education, which may seem extraneous to the assessment of domains of interest, have an effect on measurement. Specifically, we tested for DIF using an ordinal logistic regression procedure [ 36 ] with criteria: p< 0.01 and R 2 > 0.02. [ 37 ] Items that demonstrated DIF on more than one comparison were removed. Lastly, fixed-length forms, an intermediate measure between short forms and full item-banks, were determined in a consensus meeting where the research team (comprised of psychometricians, NIH representatives, content-expert consultants, and measurement scientists) reviewed item content across age groups, CAT simulations, and other psychometric properties. Due to constraints of responder burden, we limited PWB to 45 items across all concepts for norming testing. Fixed-length forms allowed us to obtain data on a maximum number of items from each of the three item banks in the norming data than if we administered short forms or CATs alone.

Positive Affect

Testing assumptions for irt.

Item-total correlations among 44 items being tested ranged from 0.47 (I felt interested in other people) to 0.81 (I felt happy). In the initial EFA, five factors had eigenvalues >1.0 (values = 22.1, 2.6, 1.9, 1.8 and 1.3 for the first five factors, respectively) and only one factor before the elbow in the scree plot, which explained 79% of total variance. Inter-factor correlations of these five factors ranged from 0.36 (factors 3 & 4) to 0.57 (factors 1 & 2). After reviewing item content, the research team conducted a second run of EFA excluding items having factor loadings < 0.3 on all of the first three factors – happiness, serenity, cognitive engagement. Results from the second EFA identified one dominant factor among these 38 items: one factor before the elbow in the scree plot, all items had factor loadings > 0.4 and this factor explained 20.04% of variance. Acceptable fit indices (CFI=0.93, TLI=0.985, RMSEA=0.11) were found from CFA after four more items were removed due to local dependency (residual correlations > 0.15) and content evaluation. Thus, the proposed positive affect bank is free of locally dependent items and is essentially unidimensional for purposes of scaling with IRT models.[ 38 ; 39 ]

Estimating item parameters

In IRT analysis, all 34 items had acceptable fit values (S-X 2 , p>0.01). Slope (discrimination) parameters of these items ranged from 1.0 (I was thinking creatively) to 3.3 (I felt happy) and threshold (difficulty) ranged from −3.6 (I felt determined) to 2.7 (I felt fearless). Scale information function was estimated based on these parameters. Figure 1 shows the precision of these 34 items in measuring this sample, with reliabilities all >0.95. These items measured positive affect with high precision across the continuum of the positive affect construct.

Precision Levels across the Positive Affect Measurement Continuum

Note: In these figures, the Y-axis represents information function which was then converted to reliability function; X-axis is the IRT-scaled score (theta) where reliability of each score was estimated. The area plotted in blue is mean scores with a reliability ≥ 0.95. The area plotted in yellow represents mean scores with a reliability between 0.9(inclusive) and 0.95 (exclusive). The bottom half of the figure is the participants’ positive affect scores represented in histogram and the upper part of the figure is the information function curve of items, with the cut-off lines for a reliability of 0.95 and 0.90 plotted, respectively.

Analyzing DIF

Four items had significant DIF (p<.001; “I felt excited”, “I felt at ease”, “I felt able to concentrate”, and “I felt a sense of harmony within myself”) between age groups with one item at a meaningful magnitude (“I felt excited”, R 2 =0.0231); one item showed statistically significant (p<.01) gender DIF but with a negligible magnitude (“I felt relaxed”, R 2 <.02); no items showed significant education DIF. Yet no items met the a priori exclusion criteria. Thus, all five items were retained following IRT-related analyses.

Identifying fixed forms for norming

Twenty-one items were identified for norming from among the 34 items in the positive affect bank. These items reflected both high (I felt happy) and low (I felt peaceful) activated positive affect, and they were correlated r=0.99 with the full bank and r=0.89 with the modified PANAS-positive subscale less item content overlap.

Life Satisfaction

Fifty items were included in this domain. Item-total correlations showed one item had correlation <0.3, three were between 0.3 and 0.4 and ranged from 0.43 to 0.82 for the rest of 46 items. In EFA, seven factors had eigenvalues > 1 with one factor before the elbow in the scree plot, explaining 45.6% of total variance. The second factor explained 7.7% of the total variance and the remaining factors explained < 5% of total variance. Factor loadings of items implied two potential factors among these items. The research team reviewed the results and decided to removed 10 items with low loadings on the first two factors from the item pool and group the remaining items into two factors: Life Satisfaction (item n=21) and Meaning and Purpose (item n=18). Five items were removed from life satisfaction due to content redundancy, resulting in a total of 16 items included in further analyses. CFA results confirmed the unidimensionality of these 16 items with CFI=0.99, TLI=0.98, RMSEA=0.081. No potential locally dependent item pairs were found with all residual correlations < 0.15. Thus, the proposed life satisfaction bank contains locally independent items and is essentially unidimensional for purposes of scaling with IRT models.

In IRT analyses, all items had acceptable fit (p>0.01). Slope parameters ranged from 0.9 (I am satisfied with my health) to 4.1 (I am satisfied with my life). Threshold parameters ranged from −3.0 (My life is better than most people) to 3.1 (I am satisfied with my health). Figure 2 shows 99% of participants were measured in a very precise manner (2% with reliability between 0.9 and 0.95; 97% with a reliability ≥ 0.95), indicating participants’ life satisfaction is reliably measured across the construct continuum.

Precision Levels across the Life Satisfaction Measurement Continuum

No items had significant DIF on “age”, “education”, or “gender”. Thus, we concluded the age, education, and gender of participants had no substantive impact on the measurement of life satisfaction with these items.

The items selected for norming from among the 16 items in the life satisfaction bank included twelve-items comprised of the five-item Satisfaction with Life Scale [ 40 ] and the seven-item Students’ Life Satisfaction Scale [ 41 ]. The correlations with the full-length item bank, excluding their own scale items, were r=0.83 and 0.87 for the Satisfaction with Life Scale and the Students’ Life Satisfaction Scale, respectively. The two life satisfaction scales were correlated r=0.86 with each other.

As stated above, an EFA identified a Meaning & Purpose factor comprised of 18 items. A subsequent CFA suggested acceptable fit indices (CFI=0.94 and TLI=0.98), yet RMSEA value (0.131) was higher than we expected. Borderline residual correlations were found in two item pairs: “I don't care very much about the things I do” versus “Most of what I do seems trivial and unimportant to me” (r=0.179) and versus “I value my activities a lot” (r=0.177). These items were provisionally retained for norming data collection, meaning their item-level properties would be examined in the norming sample to guide final decisions about whether to include or exclude them. The proposed meaning and purpose item bank is essentially free of locally dependent items and sufficiently unidimensional for purposes of scaling with IRT models.

All items had acceptable fit (p >0.01) Slope parameters ranged from 1.2 (I value my activities a lot) to 2.8 (My life has no clear purpose). Threshold parameters ranged from −5.4 (I value my activities a lot) to 2.0 (My daily life is full of things that are interesting to me). Figure 3 shows about 29.5% of participants had reliability associated with their theta between 0.9 (included) and 0.95, and 69% ≥ 0.95. These items measured meaning and purpose with good precision across the continuum of the meaning and purpose construct.

Precision Levels across the Meaning & Purpose Measurement Continuum

One item had significant but negligible DIF on “age” (“My life has been productive”; R 2 =0.0121). No items had significant DIF on either “education” or “gender”. Therefore, we concluded that participants’ age, education, and gender had no substantive impact on the measurement of meaning and purpose with these 18 items.

Based on a review of the information function of the individual items, eight of the most informative items across the meaning and purpose continuum were selected from the 18-item meaning and purpose bank for further testing during norming. These items were correlated r=0.97 with the full bank and r=0.81 with the Presences of Meaning subscale of the modified Meaning in Life Questionnaire [ 18 ] less item content overlap.

As a result of the NIH Toolbox process, we identified three measurable concepts of positive affect, life satisfaction, and meaning and purpose within the subdomain of PWB and developed and calibrated three item banks using IRT models. These banks showed equivalence of measurement properties across age, education, and gender and can be administered in the form of CATs or used to create static short forms to assess PWB concepts among adults to minimize response burden through more efficient assessments without compromising reliability

Most measures of positive affect assess activated emotion [ 10 ] yet the positive affect bank reflects high and low activated positive affect. The arousing nature of an emotion and not just its valence is potentially a very important distinction for improving our understanding about the relationship between PWB and health outcomes in general and between positive emotions and health outcomes more specifically. A review by Burgdorf and Panksepp [ 42 ] suggested that low and high activated positive affect are represented by separate but partially overlapping neuroanatomical substrates in the brain. Moreover, Christie and Friedman [ 43 ] found that low and high activated positive affect were associated with two different profiles of autonomic nervous system activation. In our sample, the first factor for positive affect was characterized by items describing high activation (I felt joyful, I felt delighted) and the second factor was characterized by items describing low activation (I felt peaceful, I felt at ease). A third factor was comprised of items reflecting cognitive engagement (I felt attentive, I felt interested). Despite the occurrence of three group factors, scores were sufficiently unidimensional to justify a single positive affect score.

The initial life satisfaction item pool was comprised of both general- and domain-specific life satisfaction items. Yet most domain-specific life satisfaction items were too specific (e.g., I am satisfied with my present job or work) and did not meet the measurement criteria to be included in the final item bank. This is not surprising since people can be satisfied with their life overall and yet simultaneously satisfied and dissatisfied with discrete parts of their life.[ 44 ];[ 45 ].

Meaning and purpose encompasses a range of related and somewhat distinct themes, including: a sense of comprehension, understanding, and coherence regarding life [ 46 ]; the feeling that life is worthwhile, significant, and matters [ 18 ]; and engagement in personally valued activities, a sense of purpose [ 47 ]. These themes are more abstract and somewhat esoteric terminology of meaning and purpose items relative to the more concrete and straightforward terminology of positive affect and life satisfaction items. We thus were not surprised with the less precise measurement of this item bank compared to other two. Additional work is needed to enhance the measurement precisions across the continuum.

This work had some limitations. An NIH Toolbox aim is to include concepts relevant to health and aging across the lifespan. We have not yet qualitatively tested how well these concepts resonate with community-dwelling adults. However, given the growing national and international interest in identifying and measuring well-being [ 45 ; 48 – 52 ] and our selection of items and measures that are among some of the more commonly used indices of PWB, we believe these items capture relevant dimensions of PWB among adults of diverse backgrounds. Additional data collection with a population-based sample will enhance the representativeness of this data. The NIH Toolbox normative data will allow us to more closely examine the generalizability of these results beyond this current sample. Despite these limitations, these measures provide a means to follow the evolution of emotional health concepts such as positive affect, life satisfaction, and meaning and purpose throughout adulthood. This would represent a significant advance in measurement and enhance the impact of future research on PWB and health outcomes.

In summary, the conceptualization of PWB and development of scales and item banks for positive affect, life satisfaction, and meaning and purpose has yielded robust self-report assessment tools. The scales and item banks for PWB provide the flexibility, efficiency, and precision necessary for the NIH Toolbox use in future health-related longitudinal epidemiological studies and prevention or intervention trials. It is anticipated the large general population survey already underway will provide more demographically specific normative data and permit across (Cognitive, Motor, Sensory) and within domain comparisons (Negative Affect, Stress & Self-Efficacy, and Social Relationships), yielding additional, informative data about the utility of these new measures and providing promise of greater standardization of measurement for these important, health-relevant concepts.

Acknowledgments

This project is funded in whole or in part with Federal funds from the Blueprint for Neuroscience Research and the Office of Behavioral and Social Sciences Research, National Institutes of Health, under Contract No. HHS-N-260-2006-00007-C. Preparation of this manuscript was supported in part by NIH grants KL2RR025740 from the National Center for Research Resources and 5K07CA158008-01A1 from the National Cancer Institute. The authors would like to thank the subdomain consultants, Felicia Huppert, PhD, Alice Carter, PhD, Marianne Brady, PhD, Dilip Jeste, MD, Colin Depp, PhD, Bruce Cuthbert, PhD, and members of the NIH project team, Gitanjali Taneja, Ph.D., and Sarah Knox, Ph.D., who provided critical and constructive expertise during the development of the NIH Toolbox Emotion measurement battery.

Study Funding: Supported by federal funds from the Blueprint for Neuroscience Research, National Institutes of Health, under Contract No. HHS-N-260-2006-00007-C.

Appendix A: Toolbox Psychological Well-Being Adult Item Banks

Response options for the positive affect item bank were: “1 = Not at all, 2 = A little bit, 3 = Somewhat, 4 = Quite a bit, 5 = Very much”

Response options for the life satisfaction item bank were: “1 = Strongly disagree, 2 = Disagree, 3 = Slightly disagree, 4 = Neither agree nor disagree, 5 = Slightly agree, 6 = Agree, 7 = Strongly agree” for items 1–5 and “1 = Strongly disagree, 2 = Disagree, 3 = Neither agree nor disagree, 4 = Agree, 5 = Strongly agree” for items 6–16.

Response options for the meaning and purpose item bank were: “1 = Strongly disagree, 2 = Disagree, 3 = Neither agree nor disagree, 4 = Agree, 5 = Strongly agree” for items 1–14, and “1 = Not at all, 2 = A little bit, 3 = Somewhat, 4 = Quite a bit, 5 = Very much” for items 15–18.

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• 12 May 2024

Is the Internet bad for you? Huge study reveals surprise effect on well-being

• Carissa Wong

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People who had access to the Internet scored higher on measures of life satisfaction in a global survey. Credit: Ute Grabowsky/Photothek via Getty

A global, 16-year study 1 of 2.4 million people has found that Internet use might boost measures of well-being, such as life satisfaction and sense of purpose — challenging the commonly held idea that Internet use has negative effects on people’s welfare.

US TikTok ban: how the looming restriction is affecting scientists on the app

“It’s an important piece of the puzzle on digital-media use and mental health,” says psychologist Markus Appel at the University of Würzburg in Germany. “If social media and Internet and mobile-phone use is really such a devastating force in our society, we should see it on this bird’s-eye view [study] — but we don’t.” Such concerns are typically related to behaviours linked to social-media use, such as cyberbullying, social-media addiction and body-image issues. But the best studies have so far shown small negative effects, if any 2 , 3 , of Internet use on well-being, says Appel.

The authors of the latest study, published on 13 May in Technology, Mind and Behaviour , sought to capture a more global picture of the Internet’s effects than did previous research. “While the Internet is global, the study of it is not,” said Andrew Przybylski, a researcher at the University of Oxford, UK, who studies how technology affects well-being, in a press briefing on 9 May. “More than 90% of data sets come from a handful of English-speaking countries” that are mostly in the global north, he said. Previous studies have also focused on young people, he added.

To address this research gap, Pryzbylski and his colleagues analysed data on how Internet access was related to eight measures of well-being from the Gallup World Poll , conducted by analytics company Gallup, based in Washington DC. The data were collected annually from 2006 to 2021 from 1,000 people, aged 15 and above, in 168 countries, through phone or in-person interviews. The researchers controlled for factors that might affect Internet use and welfare, including income level, employment status, education level and health problems.

Like a walk in nature

The team found that, on average, people who had access to the Internet scored 8% higher on measures of life satisfaction, positive experiences and contentment with their social life, compared with people who lacked web access. Online activities can help people to learn new things and make friends, and this could contribute to the beneficial effects, suggests Appel.

The positive effect is similar to the well-being benefit associated with taking a walk in nature, says Przybylski.

However, women aged 15–24 who reported having used the Internet in the past week were, on average, less happy with the place they live, compared with people who didn’t use the web. This could be because people who do not feel welcome in their community spend more time online, said Przybylski. Further studies are needed to determine whether links between Internet use and well-being are causal or merely associations, he added.

The study comes at a time of discussion around the regulation of Internet and social-media use , especially among young people. “The study cannot contribute to the recent debate on whether or not social-media use is harmful, or whether or not smartphones should be banned at schools,” because the study was not designed to answer these questions, says Tobias Dienlin, who studies how social media affects well-being at the University of Vienna. “Different channels and uses of the Internet have vastly different effects on well-being outcomes,” he says.

doi: https://doi.org/10.1038/d41586-024-01410-z

Vuorre, M. & Przybylski, A. K. Technol. Mind Behav . https://doi.org/10.1037/tmb0000127 (2024).

Article   Google Scholar  

Heffer, T. et al. Clin. Psychol. Sci. 7 , 462–470 (2018).

Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L. & Booth, M. Comput. Hum. Behav . 104 , 106160 (2020).

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The effect of physical fitness on psychological health: evidence from Chinese university students

• Shuzhen Ma   ORCID: orcid.org/0009-0009-9325-4539 1 , 2 ,
• Yanqi Xu 3 ,
• Simao Xu 4 &
• Zhicheng Guo 5  

BMC Public Health volume  24 , Article number:  1365 ( 2024 ) Cite this article

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Despite frequent discussions on the link between physical and mental health, the specific impact of physical fitness on mental well-being is yet to be fully established.

This study, carried out between January 2022 and August 2023, involved 4,484 Chinese University students from eight universities located in various regions of China. It aimed to examine the association between physical fitness on psychological well-being. Descriptive statistics, t-tests, and logistic regression were used to analyze the association between physical fitness indicators (e.g., Body Mass Index (BMI), vital capacity, and endurance running) and mental health, assessed using Symptom Checklist-90 (SCL-90). All procedures were ethically approved, and participants consented to take part in.

Our analysis revealed that BMI, vital capacity, and endurance running scores significantly influence mental health indicators. Specifically, a 1-point increase in BMI increases the likelihood of an abnormal psychological state by 10.9%, while a similar increase in vital capacity and endurance running decreases the risk by 2.1% and 4.1%, respectively. In contrast, reaction time, lower limb explosiveness, flexibility, and muscle strength showed no significant effects on psychological states ( p  > 0.05).

Improvements in BMI, vital capacity, and endurance running capabilities are associated with better mental health outcomes, highlighting their potential importance in enhancing overall well-being.

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In China, while University students were traditionally perceived as being ‘blessed by fortune’ and less prone to mental distress or disorders, the rapid expansion of universities and universities during significant socio-economic transitions has brought unprecedented attention to mental health issues among this demographic in recent decades [ 1 , 2 ]. Both male and female university students commonly experience psychological challenges stemming from environmental changes, academic pressures, emotional setbacks, and health issues [ 3 ]. Mental disorders during this period can lead to significant adverse outcomes, including university dropout, academic underachievement, strained relationships, and diminished emotional well-being, ultimately compromising physical health and future career prospects [ 4 ]. The Symptom Checklist-90 (SCL-90) is frequently employed in China for assessing the mental health status of university students [ 5 ]. According to Ren (2009), the SCL-90 was utilized in 63.8% of the published articles addressing mental health among university students [ 6 ].

Wang introduced the SCL-90 to China in 1984 [ 7 ]. Since its translation by Wang from English to Chinese, the scale has gained widespread usage in China [ 8 ]. It comprises 90 self-report items, with each question utilizing a 5-point Likert scale, ranging from 0 (not at all) to 4 (extremely). The SCL-90 effectively identifies individuals with existing psychiatric symptoms, screens for potential symptoms, determines their type and severity, and highlights the urgency for personalized intervention based on higher total scores [ 9 ].

Given that the World Health Organization (WHO) regards mental and physical dimensions as fundamental components of overall health and well-being [ 10 ], a robust correlation has been identified between mental and physical health [ 11 , 12 , 13 , 14 ]. Dr. Kishore, in an article published in the Bulletin of the WTO, notably asserted that “true physical health cannot exist without mental health” [ 15 ]. Ohrnberger (2017) discovered robust cross-effects between physical and mental health, even when adjusting for confounding variables [ 16 ]. In prior research, several cross-sectional studies have identified mental health as a significant correlate of physical health [ 17 , 18 , 19 ], however, there is a lack of studies investigating the dynamic association between the two [ 16 ].

Physical health, encompassing cardiorespiratory endurance, muscular strength endurance, flexibility, and body composition, serves as a critical indicator of health [ 20 , 21 ]. The Ministry of Education of China released the National Physical Health Standards for Students (revised in 2014, NPHSS) to assess the physical health status of young individuals, including university students, thus reflecting their overall physical fitness level. These standards are evaluated annually, with fitness measures assessed according to the 2014 revised Chinese National Student Physical Fitness Standard (CNSPFS), covering various aspects such as aerobic capacity, upper body strength, flexibility, body mass index (BMI), abdominal strength, and trunk strength [ 21 ]. The national standards aid educators in establishing the desired objectives for students to accomplish by the conclusion of their academic endeavors [ 22 ]. Since its initial introduction, the NPHSS has been instrumental in shaping physical education policies in China. Studies have demonstrated that structured physical fitness programs, aligned with these standards, not only enhance physical health but also contribute to academic performance and psychological resilience among students. Furthermore, longitudinal data suggest that continuous engagement with NPHSS-guided activities significantly improves health outcomes over time [ 23 ].

Our study employs the 2014 revised NPHSS to assess the physical fitness level of Chinese university students, alongside the use of the SCL-90 as a screening tool for evaluating their mental health status. We analyze the discrepancy in average scores of the sports quality index between students with regular and abnormal mental health statuses and examine the impact of sports quality index scores on students’ psychological well-being using a binary logistic regression model. To provide some suggestions on how to improve the mental health status of students with abnormal mental health status in university through some physical exercises.

Participants

Between January 2022 and August 2023, a cross-sectional study was conducted in three regions of China—North and Northeast, Northwest, and Southwest—focusing on the psychological and physical health of university students. This study was approved by the Ethics Committee of Guangxi Normal University and involved eight universities. Within each of the four academic levels at every university, 150 students were selected, totaling 4800 participants. Students were recruited on a voluntary basis from various departments within each university. A random sampling technique was applied across the different majors to ensure a representative sample, reflecting the diversity of academic disciplines. Recruitment was facilitated through university instructors in physical education and mental health, and participants were offered academic credit as an incentive, a practice approved by the ethics committee for its educational value. Recruitment and data collection were carried out from January 2022 to August 2023. Recruitment was initiated in January 2022 and completed by April 2022. Each university conducted its recruitment independently, allowing for data collection to proceed until August 2023. The participants were primarily first through fourth-year undergraduate students, typically aged 18 to 22. After excluding participants with incomplete physical fitness tests or questionnaires, data from the remaining participants were analyzed. Specifically, if a participant left any item blank or provided evidently non-serious responses (such as marking the same answer across multiple items without considering the content), we considered the data incomplete or the responses erroneous, thus excluding them from our analysis. To address the ethical consideration of incentivizing participation with academic credit, it is important to note that this practice was carefully reviewed and approved by the ethics committee. The incentive was deemed appropriate given its direct relevance to the educational outcomes of the students involved in the study. Moreover, the use of academic credit as an incentive aligns with the educational goals of the participants, enhancing their engagement in activities that contribute to their academic and personal development.

The research involved the evaluation of participants through a combination of physical fitness assessments and questionnaire surveys. The physical fitness evaluations adhered to the criteria outlined in the NPHSS provided by the Ministry of Education of China. These standards encompass various parameters such as BMI, lung capacity, 50-meter sprint, sit-and-reach flexibility, standing long jump, pull-ups (for men) or 1-minute sit-ups (for women), and either a 1000-meter run (for men) or an 800-meter run (for women). The physical fitness assessment greatly benefits students by assisting them in achieving higher academic credits and preparing them for the workforce with improved physical conditions.

The Symptom SCL-90 is a widely recognized tool in psychiatric assessment [ 24 ]. Its reliability has been confirmed by previous studies [ 25 ]. The scale comprises nine subscale dimensions: Somatization, Obsessive-Compulsive, Interpersonal-Sensitivity, Depression, Anxiety, Hostility, Phobic-Anxiety, Paranoid Ideation, and Psychoticism [ 9 ]. In this study, teachers from eight universities utilized mobile phones to distribute the questionnaire to their students, who were instructed to complete it accurately. The questionnaire exhibited a 100% response rate and a 94% effectiveness rate, demonstrating its utility in capturing relevant data.

Variable table

This study integrates a thorough assessment of physical fitness encompassing seven primary dimensions. Due to inherent differences in physical characteristics between genders, variations in indicator selection are observed. The overall physical quality test comprises seven aspects: BMI, vital capacity, 50m run, standing long jump, sitting forward bend, 1000m run (male)/800m run (female), and pull-up (male)/one-minute sit-up (female). The implications of these seven indicators are delineated in Table  1 below. The ratings of ‘Excellent’, ‘Good’, ‘Pass’, and ‘Failure’ in this study are determined based on the specific scoring criteria set by the NPHSS, which adjust scoring thresholds for different physical indicators according to gender.

The SCL-90 scale comprises 90 items divided into 10 distinct factors. Each factor and its corresponding items are as follows:

Somatization (items 1, 4, 12, 27, 40, 42, 48, 49, 52, 53, 56, and 58, totaling 12 items): Reflects distress arising from perceptions of bodily dysfunction.

Obsessive-compulsive (items 3, 9, 10, 28, 38, 45, 46, 51, 55, and 65, totaling 10 items): Indicates an inclination towards repetitive thoughts and compulsive behaviors.

Interpersonal Sensitivity (items 6, 21, 34, 36, 37, 41, 61, 69, and 73, totaling 9 items): Measures feelings of inadequacy and inferiority, particularly in comparison to others.

Depression (items 5, 14, 15, 20, 22, 26, 29, 30, 31, 32, 54, 71, and 79, totaling 13 items): Assesses symptoms associated with mood disturbance, including melancholy, hopelessness, and the lack of interest in life.

Anxiety (items 2, 17, 23, 33, 39, 57, 72, 78, 80, and 86, totaling 10 items): Evaluates general signs of anxiety such as nervousness, tension, and tremulousness.

Hostility (items 11, 24, 63, 67, 74, and 81, totaling 6 items): Concerns feelings of anger and irritability.

Phobic Anxiety (items 13, 25, 47, 50, 70, 75, and 82, totaling 7 items): Represents persistent and irrational fears about specific objects, people, or situations.

Paranoid Ideation (items 8, 18, 43, 68, 76, and 83, totaling 6 items): Involves thoughts or beliefs of being harmed by others.

Psychoticism (items 7, 16, 35, 62, 77, 84, 85, 87, 88, and 90, totaling 10 items): Encompasses a range of symptoms suggestive of psychosis, including isolation and withdrawal.

Other (items 19, 44, 59, 60, 64, 66, and 89, totaling 7 items): Items that do not neatly fall into the above categories.

Each item employs a 5-level scoring system with the following instructions: None (1 point), Very mild (2 points), Moderate (3 points), Severe (4 points), and very severe (5 points). Factor scores are computed by summing the scores of each item within the factor and dividing by the number of items within that factor. A score of 1–2 indicates a normal result, while a score greater than 2 indicates an abnormal result.

Equity, diversity and inclusion

This research targeted university students across China, with recruitment strategies meticulously designed to accommodate the accessibility requirements, geographical diversity, educational attainment, and socioeconomic statuses of participants. To achieve a balanced and diverse sample, recruitment efforts spanned multiple provinces, deliberately including individuals of varying genders and ethnic backgrounds. The composition of the author team reflects a commitment to diversity, evidenced by a gender balance and a wide array of research disciplines represented.

Data analysis

This study conducted descriptive statistics analysis on the total score of Sport Quality test and SCL-90 scale test results of the total sample, respectively; And according to gender differences, descriptive statistics analysis was also conducted for the total score of Sport Quality test and SCL-90 scale test results. This study uses statistical software SPSS for Independent sample t-test and logistic regression analysis. Specifically, independent sample t test was used to compare differences between psychological state among seven Sport Quality indicators. Logistic regression analysis was used to assess the impacts of scores of sport quality indicators on students’ psychological state. In the logistic regression analysis, this study used the 10 factors (somatization, obsessive-compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, psychoticism and others) in the SCL-90 scale as the dependent variables and the scores of the seven aspects (BMI, vital capacity, reaction rate, lower limb explosive force, flexibility, endurance running and muscle strength) of physical testing as the independent variables for regression analysis. The dependent variable is divided into two categories: normal (record as 1) and abnormal (record as 2), this study uses the binary logistic regression model to explore the impacts of scores of sport quality indicators on students’ psychological state. Making adjustments in logistic regression involves several crucial steps, including variable selection, model diagnostics, and validation to ensure robustness and relevance. The variable selection method of the manual selection based on theoretical understanding was used in the current study. Variables with a high p-value (above a threshold, typically 0.05) are considered insignificant and can be excluded. This study uses tests and plots to check for the adequacy of the model fit.

In the physical fitness assessments, adjustments were made to account for potential confounders such as weather conditions and venue characteristics. These variables were selected due to their potential impact on the physical performance outcomes measured in the study. By controlling for these environmental and situational variables during the testing phase, we aimed to ensure that the data on sport quality indicators accurately reflect the students’ physical capabilities, minimizing any external influences that could affect the outcomes. In this study, our initial target was to include 4,800 university students. However, a total of 4,484 students participated in the physical and psychological assessments, leading to a participation rate of 9%. The primary reasons for non-participation included absenteeism on the scheduled days of testing and incomplete survey responses. We estimated the required sample size using G*Power software, based on an effect size of 0.138 derived from previous studies [ 27 ]. The analysis indicated that a minimum sample size of 1832 was necessary. Our actual sample size of 4484 significantly exceeds this threshold, confirming the statistical robustness of our findings.

Descriptive statistics of total score of sport quality test results

In 2023, a total of 4,484 university students in China participated in this study, comprising 3,565 males and 919 females, who underwent physical and psychological testing. The results of the sports quality test are presented in Table  2 . It shows that only five students achieved an ‘excellent’ total score, representing a mere 0.1% of participants. Additionally, 268 students scored ‘good,’ accounting for 6.0% of the total; 3,731 students received a ‘pass,’ making up 83.2% and representing the largest proportion; and 480 students were categorized as ‘fail,’ comprising 10.7% of the total.

Descriptive statistics of SCL-90 scale test results

Table  3 displays the frequency distribution of SCL-90 scale test results, indicating that a higher number of students exhibited normal psychological outcomes compared to those with abnormal results, representing 8.7% of the total sample. Among the students, 8.5% of males and 9.5% of females showed abnormal psychological test outcomes. The predominant dimensions observed in the overall sample were obsessive-compulsive disorder (13.4%), interpersonal sensitivity (9.3%), and depression (8.3%), with somatization presenting the lowest incidence at 4.6%. For males, the primary issues were obsessive-compulsive disorder (13.0%), interpersonal sensitivity (9.3%), and depression (7.8%), with somatization at 4.5%. Among females, the main dimensions identified were obsessive-compulsive disorder (14.8%), interpersonal sensitivity (9.5%), and depression (10.3%), with somatization at 5.0%.

Differences of sport quality indicators between normal and abnormal state of psychological test ( N  = 4484)

Table  4 displays the differences in sport quality indicators between students with normal and abnormal psychological test states. Across the overall sample, as well as separately within the male and female groups, the mean BMI was significantly lower in those with a normal psychological state compared to those with an abnormal state ( p  < 0.01). Additionally, the mean scores for vital capacity and endurance running were significantly higher in the normal psychological state than in the abnormal state ( p  < 0.01). However, no significant differences were observed in the sport quality indicators of reaction rate, lower limb explosive force, flexibility, and muscle strength between the two groups ( p  > 0.05).

Logistic regression analysis

In order to explore the impact of physical fitness on psychological health of university students from a quantitative perspective. This study used the 10 factors (somatization, obsessive-compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, psychoticism and others) in the SCL-90 scale as the dependent variables and the scores of the seven aspects (BMI, vital capacity, reaction rate, lower limb explosive force, flexibility, endurance running and muscle strength) of physical testing as the independent variables for regression analysis. In this study, because the dependent variables (overall psychological state, somatization, obsessive-compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, psychoticism and others) are divided into two categories: normal (record as 1) and abnormal (record as 2), this study uses the binary logistic regression models to explore the impacts of scores of sport quality indicators (BMI, vital capacity, reaction rate, lower limb explosive force, flexibility, endurance running and muscle strength) on students’ psychological state. The results are shown in Table  5 , 6 and 7 .

Firstly, in binary logistic regression, the overall psychological state is taken as the dependent variable. It can be seen from Table  5 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ psychological state in the overall sample, namely, BMI, vital capacity, and endurance running. From Table  5 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ psychological state being abnormal increases by 10.9%; when the vital capacity score increases by 1 point, the risk of a student’s psychological state being abnormal decreases by 2.1%; when the endurance running score increases by 1 point, the risk of a student’s psychological state being abnormal decreases by 4.1%.

Next, in binary logistic regression, the factor of somatization is taken as the dependent variable. From Table  5 , it is observed that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ somatization in the overall sample, namely, BMI, vital capacity and endurance running. From Table  5 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ somatization being abnormal increases by 8.8%; when the vital capacity score increases by 1 point, the risk of a student’s somatization being abnormal decreases by 1.8%; when the endurance running score increases by 1 point, the risk of a student’s somatization being abnormal decreases by 2.7%.

In the binary logistic regression, the factor of obsessive-compulsive is taken as the dependent variable. It can be seen from Table  5 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ obsessive-compulsive in the overall sample, namely, BMI, vital capacity, and endurance running. From Table  5 , it can be further seen that for every 1-point increase in BMI score, the risk of students’ obsessive-compulsive being abnormal increases by 7.8%; when the vital capacity score increases by 1 point, the risk of a student’s obsessive-compulsive being abnormal decreases by 2.0%; when the endurance running score increases by 1 point, the risk of a student’s obsessive-compulsive being abnormal decreases by 3.0%.

In the binary logistic regression, the factor of interpersonal sensitivity is taken as the dependent variable. It can be seen from Table  5 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ interpersonal sensitivity in the overall sample, namely, BMI, vital capacity, and endurance running. From Table  5 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ interpersonal sensitivity being abnormal increases by 9.2%; when the vital capacity score increases by 1 point, the risk of a student’s interpersonal sensitivity being abnormal decreases by 1.6%; when the endurance running score increases by 1 point, the risk of a student’s interpersonal sensitivity being abnormal decreases by 3.1%.

In the binary logistic regression, the factor of depression is taken as the dependent variable. It can be seen from Table  6 that the regression coefficients of the independent variables, vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other five independent variables BMI ( p  > 0.05), reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are two factors that affect students’ depression in the overall sample, namely, vital capacity and endurance running. From Table  6 , it can be further seen that when the vital capacity score increases by 1 point, the risk of a student’s depression being abnormal decreases by 5.6%; when the endurance running score increases by 1 point, the risk of a student’s depression being abnormal decreases by 16.5%.

In the binary logistic regression, the factor of anxiety is taken as the dependent variable. It can be seen from Table  6 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01), lower limb explosive force ( p  < 0.05) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other three independent variables reaction rate ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are four factors that affect students’ anxiety in the overall sample, namely, BMI, vital capacity, lower limb explosive force and endurance running. From Table  6 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ anxiety being abnormal increases by 8.3%; when the vital capacity score increases by 1 point, the risk of a student’s anxiety being abnormal decreases by 1.9%; when the lower limb explosive force score increases by 1 point, the risk of a student’s anxiety being abnormal increases by 2.0%;when the endurance running score increases by 1 point, the risk of a student’s anxiety being abnormal decreases by 1.7%.

In the binary logistic regression, the factor of hostility is taken as the dependent variable. It can be seen from Table  6 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ hostility in the overall sample, namely, BMI, vital capacity and endurance running. From Table  6 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ hostility being abnormal increases by 8.8%; when the vital capacity score increases by 1 point, the risk of a student’s hostility being abnormal decreases by 1.5%; when the endurance running score increases by 1 point, the risk of a student’s hostility being abnormal decreases by 2.3%.

In the binary logistic regression, the factor of phobic anxiety is taken as the dependent variable. It can be seen from Table  6 that the regression coefficients of the independent variables, BMI ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other five independent variables vital capacity ( p  > 0.05), reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are two factors that affect students’ phobic anxiety in the overall sample, namely, BMI and endurance running. From Table  6 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ phobic anxiety being abnormal increases by 9.5%; when the endurance running score increases by 1 point, the risk of a student’s phobic anxiety being abnormal decreases by 1.7%.

In the binary logistic regression, the factor of paranoid ideation is taken as the dependent variable. It can be seen from Table  7 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ paranoid ideation in the overall sample, namely, BMI, vital capacity and endurance running. From Table  7 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ paranoid ideation being abnormal increases by 10.4%; when the vital capacity score increases by 1 point, the risk of a student’s paranoid ideation being abnormal decreases by 1.4%; when the endurance running score increases by 1 point, the risk of a student’s paranoid ideation being abnormal decreases by 1.9%.

In the binary logistic regression, the factor of psychoticism is taken as the dependent variable. It can be seen from Table  7 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ psychoticism in the overall sample, namely, BMI, vital capacity and endurance running. From Table  7 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ psychoticism being abnormal increases by 11.0%; when the vital capacity score increases by 1 point, the risk of a student’s psychoticism being abnormal decreases by 1.9%; when the endurance running score increases by 1 point, the risk of a student’s psychoticism being abnormal decreases by 2.0%.

In the binary logistic regression, the factor of Others is taken as the dependent variable. It can be seen from Table  7 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ Others in the overall sample, namely, BMI, vital capacity and endurance running. From Table  7 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ Others being abnormal increases by 8.3%; when the vital capacity score increases by 1 point, the risk of a student’s Others being abnormal decreases by 1.6%; when the endurance running score increases by 1 point, the risk of a student’s Others being abnormal decreases by 2.7%.

This study reveals a potential correlation between physical fitness and mental health, highlighting the beneficial effects of lowering BMI, enhancing lung capacity, and engaging in endurance running on various aspects of mental well-being, such as somatization, obsessive tendencies, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychosis. These findings offer valuable insights into strategies for enhancing overall health and well-being.

In our study, we found that for every one-point increase in BMI, students face an 8.8% higher risk of abnormal somatization, a 7.8% higher risk of obsessive-compulsive disorder, a 9.2% higher risk of abnormal interpersonal sensitivity, an 8.3% higher risk of abnormal anxiety, an 8.8% higher risk of abnormal hostility, a 9.5% higher risk of abnormal phobic anxiety, a 10.4% higher risk of abnormal paranoid ideation, and an 11.0% higher risk of abnormal psychoticism. Extensive documentation exists on the physical health implications of obesity, indicating a consistent association between elevated BMI and heightened risks of chronic diseases and mortality [ 28 , 29 , 30 ]. Nevertheless, a growing body of research examining the psychological effects of obesity produces inconsistent results. Most studies indicate an inverse correlation between body weight and psychological well-being [ 31 , 32 , 33 , 34 ]. However, some studies suggest a positive association instead [ 35 ], while others find either a neutral or insignificant correlation [ 36 ]. In our study, we provided a more precise depiction of the association of BMI with individuals’ mental well-being. Engaging in regular exercise and making dietary changes to lower BMI could potentially alleviate somatic symptoms linked to psychological distress. Those with lower BMI typically report fewer physical complaints and demonstrate enhanced coping mechanisms for stressors.

Our research revealed that for every one-point increase in lung capacity, the risk of abnormal somatization decreases by 1.8%, the risk of abnormal obsessive-compulsive disorder decreases by 2.0%, the risk of abnormal interpersonal sensitivity decreases by 1.6%, the risk of abnormal depression decreases by 5.6%, the risk of abnormal anxiety decreases by 1.9%, the risk of abnormal hostility decreases by 1.5%, the risk of abnormal paranoid ideation decreases by 1.4%, and the risk of abnormal psychoticism decreases by 1.9%. Increasing evidence in current research suggests a close association between obstructive pulmonary diseases such as asthma, chronic bronchitis, and emphysema, and psychological health issues like depression and anxiety [ 37 , 38 , 39 , 40 , 41 ]. Previous research conducted among adult clinical and general practice populations has revealed elevated rates of anxiety and mood disorders, particularly major depression [ 42 , 43 , 44 , 45 , 46 , 47 ]. Community-based studies have confirmed and expanded upon the general validity of the association between asthma, chronic obstructive pulmonary disease, and mental disorders [ 39 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. Our study aligns with previous research but provides a more nuanced examination of the association between lung capacity and psychological well-being.

Our study indicates that for every one-point increase in endurance running, the risk of abnormal somatization decreases by 2.7%, the risk of abnormal obsessive-compulsive disorder decreases by 3.0%, the risk of abnormal interpersonal sensitivity decreases by 3.1%, the risk of abnormal depression decreases by 16.5%, the risk of abnormal anxiety decreases by 1.7%, the risk of abnormal hostility decreases by 2.3%, the risk of abnormal phobic anxiety decreases by 1.7%, the risk of abnormal paranoid ideation decreases by 1.9%, and the risk of abnormal psychoticism decreases by 2.0%. Additionally, we found that for every point increase in lower limb explosive force, there is a 2.0% increase in the risk of abnormal anxiety among students, indicating an adverse effect on mental health. There is considerable evidence substantiating the link between physical activity and different mental health results throughout all stages of life [ 55 , 56 , 57 ]. Long-term running interventions frequently enhance mental health metrics, particularly depression indicators, among individuals with psychosis [ 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ]. Some prior research suggests that running enhances mood, especially when conducted outdoors, across various intensities, except for an intensity significantly above the lactate threshold [ 67 , 68 , 69 ]. Our study supplements previous research by emphasizing the impact of endurance running scores on psychological resilience and validating the significant role of endurance running in various psychological indicators, with particular effectiveness observed in depression indicators.

The study delves into the complex association between physical fitness and mental health, revealing a potential correlation between these two domains. It demonstrates the substantial impact of interventions aimed at lowering BMI, enhancing lung capacity, and engaging in endurance running on various facets of mental well-being, including somatization, obsessive tendencies, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychosis. These findings underscore the pivotal role of physical activity in promoting mental health. This potential correlation between physical fitness and mental health can be elucidated through various underlying mechanisms [ 70 ]. Firstly, interventions targeting BMI reduction through regular exercise and dietary adjustments show promise in alleviating somatic symptoms associated with psychological distress. These effects may be attributed to exercise-induced physiological changes, such as improved cardiovascular health and hormonal regulation, which positively impact mood and emotional well-being. Similarly, enhancements in lung capacity and engagement in endurance running have been associated with reduced obsessive-compulsive tendencies, possibly mediated by neurotransmitter modulation in the brain. Furthermore, the release of endorphins and serotonin during exercise may further contribute to the amelioration of depressive and anxious symptoms, thereby enhancing mental well-being. Moreover, endurance running has been shown to facilitate emotional regulation and empathy by fostering social bonding and communication skills, thereby enhancing interpersonal sensitivity. Physical activity also serves as a constructive outlet for managing stress and aggression, leading to decreased hostility and improved emotional well-being. Additionally, interventions aimed at enhancing lung capacity and engaging in endurance running may mitigate symptoms of phobic-anxiety disorders by promoting relaxation and stress relief. Furthermore, strategies targeting BMI reduction and regular physical activity maintenance can contribute to reduced paranoid ideation by bolstering self-esteem and self-efficacy.

In summary, these findings offer valuable insights into strategies for enhancing overall health and well-being, emphasizing the importance of integrating physical activity into mental health management approaches. By understanding the potential correlation between physical fitness and mental health and implementing appropriate interventions, individuals can take proactive steps towards improving their mental well-being and achieving a better quality of life.

Limitations

While our study offers valuable insights into the correlation between physical fitness and mental health, it is not without limitations. The cross-sectional design limits our ability to determine causality, and selection bias may arise since participants likely have higher physical activity levels than the general population, potentially skewing mental health outcomes positively. Additionally, the use of self-reported mental health measures might introduce reporting bias, affecting the accuracy of associations. The generalizability of our findings could also be influenced by the demographic and geographic characteristics of our sample. Furthermore, this study did not employ multilevel modeling, despite the hierarchical nature of the data, due to complexity and sample size constraints, which might limit the added value of this approach for our specific research questions. Addressing these biases and limitations in future longitudinal studies, and considering multilevel models, could strengthen the validity of our findings and enable a more comprehensive interpretation of interactions across different levels of data. Future research involving diverse populations across various settings is essential to validate and expand our conclusions on a global scale.

In summary, lowering BMI, increasing lung capacity, and improving endurance running have shown promising benefits for various dimensions of mental health. Incorporating regular physical activity into lifestyle interventions may serve as an effective strategy for promoting holistic well-being and reducing the burden of mental health disorders. Further research is warranted to explore the mechanisms underlying these associations and to develop targeted interventions for improving mental health outcomes through physical fitness interventions.

Data availability

No datasets were generated or analysed during the current study.

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S. contributed to the conceptualization, data curation, investigation, and methodology of the study, and wrote the original draft. Z. assisted with data curation. Y. provided supervision and contributed to writing the original draft as well as reviewing and editing the manuscript. S. also provided supervision.

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Ma, S., Xu, Y., Xu, S. et al. The effect of physical fitness on psychological health: evidence from Chinese university students. BMC Public Health 24 , 1365 (2024). https://doi.org/10.1186/s12889-024-18841-y

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Psychological distress, well-being, resilience, posttraumatic growth, and turnover intention of mental health nurses during COVID-19: A cross-sectional study

Affiliations.

• 1 School of Nursing, Midwifery and Paramedicine, Australian Catholic University, Fitzroy, Australia.
• 2 Faculty of Health, School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia.
• 3 Monash Rural Health, Monash University, Warragul, Australia.
• 4 School of Allied Health, Australian Catholic University, Banyo, Australia.
• 5 Nursing Research and Practice Development Centre, The Prince Charles Hospital, Chermside, Queensland, Australia.
• 6 School of Nursing, Midwifery and Public Health, University of Canberra, Bruce, Australian Capital Territory, Australia.
• 7 ACT Government Health Directorate, Philip, Australian Capital Territory, Australia.
• 8 School of Nursing and Midwifery, University of Technology Sydney, Ultimo, New South Wales, Australia.
• PMID: 38747675
• DOI: 10.1111/inm.13354

Mental health nurses (MHNs) experience a range of stressors as part of their work, which can impact their well-being and turnover intention. There is no prior evidence, however, on MHNs' mental health, well-being, resilience, and turnover intention during the COVID-19 pandemic. The aims of this online survey-based cross-sectional study, conducted during the pandemic, were to explore the psychological distress, well-being, emotional intelligence, coping self-efficacy, resilience, posttraumatic growth, sense of workplace belonging, and turnover intention of n = 144 Australian mental health registered and enrolled nurses; and explore relationships between these variables, in particular, psychological distress, well-being, and turnover intention. There was a higher percentage of MHNs with high (27.78%) and very high psychological distress (9.72%) compared to population norms as measured by the K10. Emotional intelligence behaviours were significantly lower than the population mean (GENOS-EI Short). Coping self-efficacy was mid-range (CSES-Short). Resilience was moderate overall (Brief Resilience Scale), and posttraumatic growth was mid-range (Posttraumatic Growth Inventory; PTGI). Sense of workplace belonging was moderate, and turnover intention was low. Higher levels of psychological distress were associated with higher turnover intention, and lower workplace belonging, coping self-efficacy, well-being, resilience, and emotional intelligence behaviours. Despite the levels of psychological distress, nearly half the sample (n = 71) was 'flourishing' in terms of well-being (Mental Health Continuum Short-Form). To help prevent staff distress in the post-pandemic period, organisations need to proactively offer support and professional development to strengthen staff's psychological well-being, emotional intelligence, and resilience skills. These strategies and group clinical supervision may also support lower turnover.

Keywords: COVID‐19; mental health nursing; posttraumatic growth; resilience; turnover intention; well‐being.

© 2024 The Authors. International Journal of Mental Health Nursing published by John Wiley & Sons Australia, Ltd.

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Birdwatching Can Help Students Improve Mental Health, Reduce Distress

A new study finds people who have nature-based experiences report better well-being and lower psychological distress than those who do not. Birdwatching in particular yielded promising results, with higher gains in subjective well-being and more reduction in distress than more generic nature exposure, such as walks. Because birdwatching is an easily accessible activity, the results are encouraging for college students – who are among those most likely to suffer from mental health problems.

“There has been a lot of research about well-being coming out through the pandemic that suggests adolescents and college-aged kids are struggling the most,” said Nils Peterson, corresponding author of the study and a professor of forestry and environmental resources at North Carolina State University. “Especially when you think about students and grad students, it seems like those are groups that are struggling in terms of access to nature and getting those benefits.

“Bird watching is among the most ubiquitous ways that human beings interact with wildlife globally, and college campuses provide a pocket where there’s access to that activity even in more urban settings.”

To quantitatively measure subjective well-being, researchers used a five-question survey known as the World Health Organization-Five Well-Being Index (WHO-5). This tool asks participants to assign a rating of zero through five to statements about well-being, depending on how often they have felt that way in the last two weeks. For example, given the prompt “I have felt calm and relaxed,” a participant would mark a zero for “at no time” or a five for “all of the time.” Researchers can calculate a raw well-being score by simply adding the five responses, with zero being the worst possible and 25 the best possible quality of life.

Researchers split the participants into three groups: a control group, a group assigned five nature walks and a group assigned five 30-minute birdwatching sessions. While all three groups had improved WHO-5 scores, the birdwatching group started lower and ended higher than the other two. Using STOP-D, a similar questionnaire designed to measure psychological distress, researchers also found that nature engagement performed better than the control, with participants in both birdwatching and nature walks showing declines in distress.

This study differed from some previous research, Peterson said, in that it compared the effects of birdwatching and nature engagement to a control group rather than a group experiencing more actively negative circumstances.

“One of the studies that we reviewed in our paper compared people who listen to birds to people who listened to the sounds of traffic, and that’s not really a neutral comparison,” Peterson said. “We had a neutral control where we just left people alone and compared that to something positive.”

The study supports the idea that birdwatching helps improve mental health and opens up many avenues for future research. For example, future study could examine why birdwatching helps people feel better or the moderating effects of race, gender and other factors.

The paper, “ Birdwatching linked to increased psychological well-being on college campuses: A pilot-scale experimental study ,” is published in Environmental Psychology. Co-authors include Lincoln Larson, Aaron Hipp, Justin M. Beall, Catherine Lerose, Hannah Desrochers, Summer Lauder, Sophia Torres, Nathan A. Tarr, Kayla Stukes, Kathryn Stevenson and Katherine L. Martin, all from NC State.

-pitchford-

Note to Editors: The study abstract follows.

“Birdwatching linked to increased psychological well-being on college campuses: A pilot-scale experimental study”

Authors: Nils Peterson, Lincoln Larson, Aaron Hipp, Justin Beall, Catherine Lerose, Hannah Desrochers, Summer Lauder, Sophia Torres, Nathan Tarr, Kayla Stukes, Kathryn Stevenson, Katherine Martin

Published: April 26, 2024 in Environmental Psychology

DOI: 10.1016/j.jenvp.2024.102306

Abstract: Exposure to nature is known to improve human health, but little is known about how one of the most common forms of nature engagement, birdwatching, impacts psychological well-being – especially among campus populations at great risk for experiencing mental health challenges. This study engaged 112 campus participants in a stepped design experiment evaluating the degree to which five >30 min weekly birdwatching (n = 62) and nature walk (n = 77) exposures impacted self-reported subjective well-being (WHO-5) and psychological distress (STOP-D) levels relative to a control group (n = 81). The directions of all relationships supported hypotheses that nature-based experiences, and birdwatching in particular, would increase well-being and reduce distress. These results build on preliminary evidence of a causal relationship between birdwatching and well-being and highlight the value of considering well-being impacts for specific types of activities in nature, underscoring the need for future research with larger and more diverse samples.

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Understanding the Unique Challenges of Autistic Mothers

Insights from recent research on autism and motherhood..

Posted May 20, 2024 | Reviewed by Michelle Quirk

• What Is Autism?
• Find counselling to help with autism
• Autistic mothers often discover their diagnosis later in life, adding to their parenting confusion and stress.
• Pregnancy and parenthood increase sensory demands, leading to chronic overstimulation for autistic mothers.
• Parenting's sensory demands disrupt sleep, self-care, and social interactions, affecting well-being.

This post was written by Dr. Kiley Hanish .

As a later-in-life-diagnosed autistic woman, I started connecting the dots to make sense of my thoughts, behaviors, and life experiences. One area of my life where I gained particular clarity was my experience as a mother. Being a parent has been extremely difficult for me, as I feel constantly overwhelmed and chronically exhausted. I then read this article about the sensory experiences of autistic mothers , which highlighted that autistic mothers experience higher stress responses and more challenges adapting to their daily lives as parents.

The researchers believe this is likely due to the heightened sensory sensitivities of autistic people combined with increased sensory demands during parenting, including sound, touch, and visual input. This article gave me a deeper understanding of why parenting has been challenging in my experience. The constant demands of motherhood push my nervous system into a state of constant hyper-arousal, and the strategies that I used in the past for self-regulation became unavailable once I had a living child.

I became curious to explore this topic further and decided to dive deeper with my research team of occupational therapy students. We developed an online survey to learn if what the researchers discovered in their qualitative research study was relevant to a larger sample size. We explored autistic mothers’ sensory experiences and the impact of these on their daily lives as a parent, including habits, routines, physical health, and mental health. I am excited to share what we discovered.

Autism Diagnosis in Women

Most autistic mothers are not aware of their diagnosis at the time they enter their new parenting role. They find motherhood challenging and overwhelming but do not know why. Due to fear of being perceived as a “bad mother,” they rarely share these feelings with others and feel isolated.

Due to the high rates of masking (hiding or suppressing autistic traits) in women, there is a great deal of misdiagnosis or no diagnosis at all. This is compounded by the lack of awareness from health care providers of the presentation of adult autism, especially in women, resulting in limited access to receiving a proper diagnosis.

Many autistic mothers begin exploring their own neurotype once their child receives an autism diagnosis. They recognize traits in themselves that they see in their child and wonder if they, too, might be autistic.

Sensory Demands During Parenting

When an individual becomes pregnant and enters parenthood , sensory demands increase. Autistic mothers may experience significantly more sensitivities through multiple sensory systems. For example, increased auditory (hearing) reactivity can result from the baby crying, noises from toys, and too much talking as the child grows older. Being a mother also involves increased tactile (touching) input. This is seen with breastfeeding, which is sometimes painful and overwhelming, as well as a need to soothe the baby, often requiring physical touch and rocking.

As autistic mothers care for their children, they are at a higher risk of their nervous system being dysregulated because of pre-existing sensory sensitivities, increased environmental sensory demands of their day-to-day activities, and constant caregiving . In addition, babies are not able to self-regulate and rely on their parents’ nervous system to achieve regulation, which is called co-regulation. Therefore, when the mother is dysregulated, it often means the baby is as well. This can often lead to a spiral of co-dysregulation that leaves the mother confused, overwhelmed, and feeling as if she is failing as a parent.

Impact of Sensory Sensitivities on Daily Living

The heightened sensory demands of parenting affect autistic mothers' ability to engage in their daily activities in the same ways they did before having a child. This is particularly observed in the following areas:

Socializing

• Emotional wellness

It is common for autistic people to experience challenges with sleep, and, when a baby comes into their life, sleep is further disrupted—either due to their child not sleeping through the night or not being able to fall back asleep after being woken by their child. This lack of sleep and exhaustion further impacts their mental and physical well-being as well as their ability to be present in their role as a mother and soothing their child.

When autistic mothers experience periods of sensory overstimulation, their self-care practices and self-soothing strategies are disrupted. Before having a child, these individuals developed and utilized strategies to regulate their nervous systems, even if they weren’t aware that this is what they were doing. Examples include spending time alone, engaging in physical activity, deeply focusing on areas of interest, and participating in leisure activities. When autistic mothers become unable to engage in self-care practices, they find it more challenging to self-regulate when overstimulated, negatively impacting their behavioral and emotional state.

Mothers noted that their sensory needs aligned with their child only some of the time, meaning there exists some level of disconnect or misalignment between the parent’s and child’s needs, potentially leading to a higher likelihood and longer periods of overstimulation. For example, the parent could have a hyper-arousal stress response and the child could have a hypo-arousal stress response. We can also say that one (parent or child) is sensory-seeking and the other is sensory-avoiding.

When experiencing sensory overstimulation, many mothers stated that they rarely had someone to watch their children to give them time alone to self-regulate. This statement is particularly important because if the mother is not able to take the time to regulate their nervous system, they may be unable to tend to their child’s needs effectively. That said, autistic mothers find it difficult to ask for help for fear of judgment about their ability to parent. However, women feel more comfortable seeking help from other autistic mothers because they do not feel judged. This underscores the potential benefits of establishing more supportive networks where autistic mothers can interact, nurturing their well-being throughout the child-rearing period.

Socialization with other parents as an autistic mother can be challenging due to communication differences, feeling different, and various sensory needs. There is an increased pressure to participate in activities related to their child—for example, attending birthday parties, school parent nights, and children’s playdates. If they do participate, they experience overwhelm and exhaustion. However, if they choose not to engage, they feel guilty for not doing so. Mothers reported feeling a struggle between participating and feeling overstimulated, and not participating and feeling isolated and guilty.

Emotional Wellness

Many autistic people also experience co-occurring mental and physical health conditions. Common co-occurring mental health diagnoses include anxiety , depression , obsessive-compulsive disorder (OCD), and attention -deficit/hyperactivity disorder ( ADHD ).

After becoming a parent, there is often a decrease in mental well-being for autistic mothers. Women with an autism or ADHD diagnosis are at a significantly higher risk of developing a postpartum mood and anxiety disorder. The experience of postpartum depression , anxiety, or OCD combined with sensory overload often leads to chronic fatigue, exhaustion, and burnout . The convergence of these findings highlights the critical need for enhanced screening protocols for ADHD and autism in parents presenting with postpartum depression or anxiety.

Other factors contributing to poor mental health are feelings of stress, anxiety, and depression, as well as the inability to self-regulate, sensory overstimulation, and lack of knowledge surrounding their autism diagnosis. Given these compounding factors, autistic mothers often grapple with confusion and a tendency to internalize these struggles as personal failings. The coexistence of these factors, along with their autism and co-occurring diagnoses, adds an extra layer of challenge to the parenting experience of autistic mothers.

Several areas of daily living are impacted in the lives of autistic mothers, including sleep, self-care, socialization, and attachment with their children. As sensory demands increase during parenthood, the ability to access self-regulation strategies decreases, leading to chronic fatigue, burnout, and decreased mental well-being. This is further complicated by the fact that most autistic mothers do not discover their autism diagnosis until later in life, leading them to blame themselves because they are struggling and are reluctant to reach out for help.

Despite all of these challenges, the mothers we surveyed loved being a parent. The hope is that there is an increase in education and support of health professionals working in spaces with birthing people and/or neurodivergent children to provide family-centered and neurodivergent-affirming care. These providers can assist their patients or parents of their patients with autism identification and access to services. Increasing education about high sensory demands during parenting and offering strategies to parents on managing sensory overload will be helpful for all parents.

Andersson, A., Garcia-Argibay, M., Viktorin, A., Ghirardi, A., Butwicka, A., Skoglund, C., Bang Madsen, K., D’onofrio, B. M., Lichtenstein, P., Tuvblad, C., & Larsson, H. (2023). Depression and anxiety disorders during the postpartum period in women diagnosed with attention deficit hyperactivity disorder. Journal of Affective Disorders . https://doi.org/10.1016/j.jad.2023.01.069

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Talcer, M. C., Duffy, O., & Pedlow, K. (2023). A qualitative exploration into the sensory experiences of autistic mothers. Journal of Autism and Developmental Disorders, 53 (2), 834–849. https://doi.org/10.1007/s10803-021-05188-1

Megan Anna Neff, Psy.D., (She/They) is a neurodivergent clinical psychologist (autistic-ADHD) and founder of Neurodivergent Insights where she creates education and wellness resource for neurodivergent adults.

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What is Psychological Well-Being, Really? A Grassroots Approach from the Organizational Sciences

• Research Paper
• Published: 16 July 2011
• Volume 13 , pages 659–684, ( 2012 )

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• Véronique Dagenais-Desmarais 1 &
• André Savoie 2  

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Research on psychological well-being (PWB) in organizational settings is now facing two major challenges. First, conceptual confusion surrounds PWB (Danna and Griffin 1999 ; Diener 1994 ; Ryff 1989 ), and the debate about the superiority of concurrent hedonic, eudaimonic, and integrative approaches is still open. Second, researchers in the organizational sciences mainly study context-free PWB while measuring organizational correlates, despite contextualized non-cognitive measures having proven their incremental validity over context-free measures (e.g. English 2001 ; Hunthausen et al. 2003 ). In an attempt to address these issues from a new perspective, an inductive approach was proposed. To confer good content validity to our model of PWB at work (PWBW), a preliminary bottom-up qualitative phase was carried out. On this basis, a quantitative study was conducted. From the 80 manifestations of PWBW obtained, a new instrument was generated and administered to 1,080 workers, supplemented by measures of context-free PWB and distress, of positive and negative affect, and of life satisfaction. Exploratory factor analyses revealed that PWBW can be conceptualized through 5 dimensions, namely, Interpersonal Fit at Work, Thriving at Work, Feeling of Competency at Work, Desire for Involvement at Work, and Perceived Recognition at Work. The questionnaire showed satisfactory internal consistency. Correlational analyses support the “related but distinct” nature of PWBW with regard to context-free hedonic and eudaimonic PWB dimensions and psychological distress indicators. In sum, the study led to the development of a grounded conceptualization of PWBW based on a work frame-of-reference and tied to a reliable and valid measure.

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Well-being: the Ultimate Criterion for Organizational Sciences

Complementary variable- and person-centered approaches to the dimensionality of psychometric constructs: application to psychological wellbeing at work.

Well-Being at Work: A Balanced Approach to Positive Organizational Studies

The distinction between top-down (deduction) and bottom-up (induction) methodology for theory construction (Trochim 2006 ) is not to be confused with the top-down versus bottom-up theories of PWB, as described more extensively by Diener ( 1984 ), or with top-down/bottom-up investigations of PWB causal direction (Brief et al. 1993 ).

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Acknowledgments

Preparation of this article was made possible by a grant to the first author from the Fonds Québécois de la Recherche sur la Société et la Culture, as well as by a seed grant from Université de Sherbrooke. The authors would like to extend special thanks to Timothy A. Judge and Robert Liden, Karine Savaria, as well as to two anonymous reviewers for their useful comments on a previous version of this paper.

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Véronique Dagenais-Desmarais

Department of Psychology, Université de Montréal, C.P. 6128, succursale Centre-Ville, Montréal, QC, H3C 3J7, Canada

André Savoie

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Correspondence to Véronique Dagenais-Desmarais .

1.1 Index of Psychological Well-Being at Work

This questionnaire presents a list of statements describing how people may feel at work. Considering your job over the past 4 weeks, please indicate to what extent you agree with each statement.

• OFV = original French version. All items were administered in French. English translations for communication purpose

1.2 Correction Key

Interpersonal Fit at Work: Items 1, 6, 11, 16, 21

Thriving at Work: Items 2, 7, 12, 17, 22

Feeling of Competency at Work: Items 3, 8, 13, 18, 23

Perceived Recognition at Work: Items 4, 9, 14, 19, 24

Desire for Involvement at Work: Items 5, 10, 15, 20, 25

1.3 Cotation Procedure

Scores by dimension and/or total score may be used. Dimensional or total scores are obtained by averaging the scores for dimensional or total questionnaire items.

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Dagenais-Desmarais, V., Savoie, A. What is Psychological Well-Being, Really? A Grassroots Approach from the Organizational Sciences. J Happiness Stud 13 , 659–684 (2012). https://doi.org/10.1007/s10902-011-9285-3

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Published : 16 July 2011

Issue Date : August 2012

DOI : https://doi.org/10.1007/s10902-011-9285-3

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