The best evidence about what matters most for well-being

By BenefitOfTheDoubt @ 2025-05-30T16:10 (+16)

The best evidence about what matters most for well-being

Mental health matters most

Mental health (anxiety and depression) appears to be the strongest modifiable predictor of overall well-being. In Clark et al.’s (2018) extraordinarily data rich model, it has the largest standardized effect on increasing life satisfaction (β = 0.19) and on reducing what they call “misery” (β = -0.16). However, it’s important to note that “misery” in their model refers to being in the lowest category of life satisfaction, not affective well-being (i.e., emotional experience). So while Clark’s findings confirm mental health’s role in cognitive well-being, they don’t directly speak to its effect on day-to-day emotions. Although Clark et al.’s model does not assess affective states, a wealth of clinical and experimental research shows that improving mental health typically leads to improvements in emotional experience. It is therefore highly plausible that mental health remains the most important modifiable predictor of affective well-being.

Relationships are next most important

Because there is already high standard of evidence in literature accessible to our community on addressing anxiety and depression, such as clinical practice guidelines in psychiatry, I’ll focus on the next strongest predictor in Clark et al.’s (2018) model which appears to be relationship satisfaction. Aksu et al. (2023) found a moderate bivariate correlation between relationship satisfaction and life satisfaction (r = 0.46). More direct evidence comes from Hudson et al. (2020), who showed that people in satisfying relationships reported higher emotional well-being in daily life. Those in unsatisfying relationships had lower emotional well-being than those who were unpartnered, underscoring that it’s not being in a relationship that matters, but the emotional quality of that relationship.

Happy relationships stay happy. Unhappy relationships stay unhappy.

Relationship satisfaction was found by Joel et al 2020 to be higher for longer duration relationships. Although other studies like Bühler and Orth found that relationship satisfaction tends to decline during the first 10 years of the relationship, increase between 10 and 20 years, and then decline slightly again after 20 years (Bühler & Orth, in press, p. 4). Although the study does not provide raw mean changes in satisfaction scores, it reports that satisfaction “never falls below 77% of the maximum possible” and that 70–90% of couples maintain relatively high satisfaction across time, with only 10–30% experiencing substantial declines (Bühler & Orth, in press, p. 39). These findings suggest that any increase in satisfaction is modest and concentrated in the second decade of long-term relationships.

Commitment and harmony predict happy relationships

The grass is greenest where you water it: The largest and most integrative data analytic effort in the study of romantic relationships (Joel et al., 2020) shows that it's not the person you choose but the relationship you build. The top predictor of relationship satisfaction and commitment, excluding variables about one's own feelings like sexual satisfaction, were perception of your partner's commitment (e.g., “My partner wants our relationship to last forever”), perceived partner satisfaction (e.g., “Our relationship makes my partner very happy”), and conflict (e.g., “How often do you have fights with your partner?”).

Sex feels good

Time-use data from the World Happiness Report (2020) based on a large-scale study originally conducted by George MacKerron and Susana Mourato called Mappiness, which collected over 3 million data points from tens of thousands of participants in the UK, shows that the single most positively associated activity with life satisfaction was by a very large margin, making love (11.40), followed by playing sport or exercising (6.902). The metric used in this context is "net affect", as defined by the World Happiness Report as average positive affect (e.g., happiness, enjoyment) minus negative affect (e.g., stress, anger) during a particular activity.

For commitment, consider sexual history

Based on the strongest quantitative evidence available from three major studies, the most powerful predictors of relationship dissolution are relational quality variables, particularly love, commitment, and relationship satisfaction - followed closely by the more objective factor of premarital sexual history, specifically the number of premarital sexual partners.

In a meta-analysis of 137 studies covering 37,761 participants in nonmarital relationships, Le et al. (2010) found that love (Cohen’s d = -0.85) and commitment (d = -0.83) were the most protective factors against breakup. These are large effect sizes by conventional standards (Cohen, 1992), indicating that individuals who feel emotionally bonded and devoted to their partner are significantly more likely to remain together. Similarly, dependence (d = -0.75) and satisfaction (d = -0.73) also showed strong protective effects. In contrast, higher perceived alternatives (d = +0.57) and lower social network support (d = -0.56) were moderate risk factors for dissolution. These findings establish that emotional investment and interpersonal interdependence are the most consistently predictive relational factors for relationship stability.

Turning to marital relationships, Smith and Wolfinger (2023) conducted a longitudinal study using U.S. data with a sample of 10’s of thousands to examine the link between premarital sexual behavior and divorce. They found that individuals with nine or more premarital sexual partners faced a substantially higher risk of divorce, with a hazard ratio over 2.0 even after adjusting for religiosity, mental health, adolescent values, and family background. This risk translates to an estimated Cohen’s d of approximately 0.80, placing it on par with love and commitment in predictive strength. Importantly, the risk increase was nonlinear: minimal for 1–2 partners but markedly higher above eight, suggesting that more premarital partners is not just a proxy for liberal values, but may itself be a strong behavioral risk factor for marital instability.

Complementing these findings, a third study using machine learning on German data (Wolfinger et al., 2020s) found that low satisfaction and frequent conflict were the top predictors of cohabitation dissolution, while younger age at marriage and lower education levels were the most important for marital dissolution. Though this study did not provide effect sizes in traditional terms, feature importance rankings imply that these demographic variables had moderate effects, roughly equivalent to d = 0.4–0.5. That makes them clearly relevant, but not as predictive as love, commitment, or sexual history.

Taken together, these three studies indicate that the most powerful known predictors of relationship dissolution are:

Love – d = -0.85 (Le et al., 2010)
Commitment – d = -0.83 (Le et al., 2010)
Premarital sex (9+ partners) – d ≈ 0.80 (Smith & Wolfinger, 2023)
Dependence – d = -0.75, and
Satisfaction – d = -0.73 (Le et al., 2010)

Other factors such as alternatives, arguing, age at marriage, and education rank lower in effect size and predictive power.

In Smith & Wolfinger's event history models, having any premarital sexual partners results in 2.5 times the odds of divorce compared to having none (OR = 2.50–2.52). For those with nine or more premarital sexual partners, the odds of divorce are even higher—between 2.65 and 3.20 times that of individuals with no prior partners. These odds translate roughly into Cohen’s d values between 0.80 and 0.90, indicating a very large effect size in the context of social science.

In contrast, the effect of past divorce (i.e., the risk of divorce in a second or subsequent marriage) is not measured in Smith & Wolfinger’s models, because their sample is restricted to first marriages. Prior research, particularly by Teachman (2002), shows that being previously divorced only increases the risk of another divorce by 1.5 to 2.0 times (OR = 1.5–2.0), corresponding to a Cohen’s d of approximately 0.55–0.70. This is still a large effect, but it falls below the estimated impact of having many premarital sexual partners on divorce risk.

Compatibility is otherwise unpredictable and personality may not matter

Joel et al (2020) also found that depression and personality were less important for your relationship satisfaction, and even less for your partner’s relationship satisfaction. Demographics (income, age, ethnicity) were unimportant).

Some variables in Joel et al that intuitively seem critical like love, trust, and intimacy were intentionally removed from the main predictive models. The reason was that these variables are conceptually and statistically almost identical to the outcome being predicted: relationship satisfaction. Including them would have artificially inflated the models’ performance, because they may not be independent causes of satisfaction but simply different ways of expressing it.

Other traits like empathy and affection were included in some analyses but did not perform as strongly as expected. While these traits did show some predictive power, they ranked significantly lower than top predictors like perceived partner commitment, appreciation, sexual satisfaction, and perceived partner satisfaction. For instance, empathy and affection had success rates of around 43–44%, whereas appreciation and perceived partner commitment were closer to 72–85%. The researchers also tested different model versions—moderate and stringent—where they progressively removed more variables. Interestingly, the overall predictive power of the models dropped only slightly (about 2%) when these lower-performing or conceptually overlapping traits were excluded, suggesting they added relatively little unique information.

Despite these findings, the author’s advice to single people in a Reddit AMA (Joel, 2020, Reddit AMA) interestingly was to look for a partner who seems genuinely interested in you, who is good at perspective-taking with you (empathy), and who seems to be responsive to your needs’. Joel had previously written about responsiveness before which may be informing her views rather than this particularly data rich study.

Despite the predictive power of some relationship-specific traits, the study found that models could only explain about 45% of variance in baseline relationship quality, and much less (around 18%) at follow-up. Even more strikingly, they were almost entirely unable to predict change in relationship quality over time. This highlights the limits of self-report-based models in fully capturing the complexity of human relationships. Individual differences and partner-reported traits did not significantly improve predictions once a person's own perceptions of the relationship were accounted for. This is why, even with detailed data, researchers and systems cannot claim to match people with high certainty - we can identify general trends, but not make reliably precise predictions about individual compatibility.

Breakups hurt forever

While most large-scale statistical models focus on cognitive well-being, Kettlewell et al. (2020) is a rare and rigorous exception that does focus on affective well-being. Using data from the nationally representative HILDA survey and fixed-effects regression models, they directly measured affective well-being using a multi-item emotional health index. Kettlewell et al. (2020) found that separation led to a significant and lasting drop in affective well-being: nearly 0.30 standard deviations with no evidence of hedonic adaptation. By contrast, marriage produced only a brief emotional boost that faded within two years. These findings highlight how fragile affective well-being is to relationship loss or dissatisfaction.

Infidelity is unpredictable

Infidelity is often associated with relationship dissolution. The study Is Infidelity Predictable? Using Explainable Machine Learning to Identify the Most Important Predictors of Infidelity by Vowels, Vowels, and Mark (2021) provides some of the most robust and reliable evidence regarding the factors associated with both in-person and online infidelity. Infidelity is relatively common, with up to 50% of individuals in relationships reporting engagement in some form of it. While numerous predictors have previously been identified, findings across studies have often been inconsistent. This study stands out for its methodological strength, using explainable machine learning to analyze a large dataset of over 150 variables including interactions that traditional statistical techniques often cannot capture.

Interestingly, although infidelity was found to be somewhat predictable overall, each individual variable contributed only a small amount to the total prediction accuracy. This implies that no single factor is highly predictive on its own; particularly after excluding the most obvious and strongest factors like relationship satisfaction, leaving factors like frequency of sex with your current partner and conservative attitudes to sex in general. Rather, a combination of many interpersonal and demographic variables is necessary to make accurate predictions, or that we should just accept the risk as unpredictable.

Physical health matters most next after health and relationships

Similarly, physical health is a notable contributor to well-being. In Clark et al.’s model, physical health had a moderate effect on life satisfaction (β = 0.10), and likely contributes to emotional experience through its influence on vitality and day-to-day functioning. Supporting this, Kettlewell et al. found that serious illness or injury led to lasting declines in both affective and cognitive well-being, with little evidence of recovery over time. These findings suggest that chronic or disruptive health issues are among the most emotionally burdensome life conditions.

Income and employment are next most important but way less

Other factors such as income (β = 0.09) and employment (β = 0.06) contribute modestly to life satisfaction and appear to matter less for affective well-being. For example, Kahneman and Deaton (2010) found that while income improves how people evaluate their lives, it does not meaningfully increase positive emotion or reduce negative emotion. Education (β = 0.02) and non-criminality (β = 0.06) have minimal effects and are unlikely to influence emotional states substantially.

The rest don’t matter as much

Among life events, financial loss also caused long-term declines in both cognitive and affective well-being in Kettlewell et al.’s analysis. Positive events such as childbirth, retirement, and financial windfalls offered only temporary affective benefits. Notably, retirement and financial gains continued to support life satisfaction long after their emotional effects faded, further illustrating the divergence between affective and cognitive well-being.

Some notes on methodology

Interestingly, Kettlewell et al. reported that the correlation between cognitive and affective well-being was just r = 0.23, meaning the two constructs share less than a quarter of their variance. This reinforces the importance of treating them as distinct and not as interchangeable outcomes.

While there may be other studies or anecdotal claims that suggest different outcomes, I've aimed to summarize the highest-quality, rigorously conducted research available, particularly in terms of generalization. All the usual caveats apply like about locations data was collected and a myriad other things.

Like anyone summarizing complex research, I may have missed something or misunderstood, If you notice any gaps, misinterpretations, or have alternative readings of the evidence, I’d appreciate hearing so in the comments.

Smaller or less comprehensive studies may still offer valuable insights, especially in specific contexts or under certain constraints. However, unless those findings are supported by equally rigorous methodology or clearly defined limitations, they do not override the broader conclusions drawn from well-conducted, generalizable studies. It is important to distinguish between emerging or context-bound results and those grounded in strong, replicable empirical evidence.

Even when the modifiable factors I have focused on are accounted for, much of the variation in well-being remains unexplained. This likely reflects unmodifiable influences such as genetic predispositions (estimated to account for 30–40% of well-being; Røysamb et al., 2018) and early-life experiences. As Brown and Rohrer (2019) argue in their critique of the "happiness pie chart," identifying the full set of well-being determinants will require data collected across a wide range of timescales, from fleeting daily experiences to major life events and long-term developmental changes. Only by integrating such time-sensitive data can we fully understand and support human flourishing.

To the extent I have attempted to do that for the modifiable factors, we’re still limited by different studies using different types of statistical measures, which shapes how we interpret and compare their findings. Correlation coefficients (r) reflect raw associations between two variables; standardized regression coefficients (β) show the strength of a variable’s predictive power while controlling for others; R² indicates the share of variance explained by a set of variables; and fixed-effects estimates, like those in Kettlewell et al., track within-person changes over time, offering stronger causal inferences.

References

Aksu, G., Eser, T., & Emekli, H. (2023). The relationship between marital satisfaction and life satisfaction: A meta-analysis study. Adnan Menderes University.

Brown, N. J. L., & Rohrer, J. M. (2019). Re-slicing the happiness pie: How big are the pieces? Journal of Happiness Studies.

Bühler, J. L., & Orth, U. (in press). Rank-order stability of relationship satisfaction: A meta-analysis of longitudinal studies. Journal of Personality and Social Psychology.

Clark, A. E., Flèche, S., Layard, R., Powdthavee, N., & Ward, G. (2018). The origins of happiness: The science of well-being over the life course. Princeton University Press.

Hudson, N. W., Lucas, R. E., & Donnellan, M. B. (2020). Day-to-day fluctuations in relationship satisfaction: Predicting well-being over time. Journal of Research in Personality.

Kahneman, D., & Deaton, A. (2010). High income improves evaluation of life but not emotional well-being. Proceedings of the National Academy of Sciences.

Kettlewell, N., Morris, R. W., Ho, N., Cobb-Clark, D. A., Cripps, S., & Glozier, N. (2020). The differential impact of major life events on cognitive and affective wellbeing. SSM - Population Health.

Joel, S., Eastwick, P. W., Allison, C. J., Arriaga, X. B., Baker, Z. G., Bar-Kalifa, E., Bergeron, S., Birnbaum, G. E., Brock, R. L., Brumbaugh, C. C., Carmichael, C. L., Chen, S., Clarke, J., Cobb, R. J., Coolsen, M. K., Davis, J., de Jong, D. C., Debrot, A., DeHaas, E. C., ... Wolf, S. (2020). Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies. Proceedings of the National Academy of Sciences

Joel, S. [u/samanthajoel]. (2020, August 4). I’m Dr. Samantha Joel. My team and I use AI to study romantic relationships. Ask me anything! [Online forum post]. Reddit. https://www.reddit.com/r/IAmA/comments/i5dtml/im_dr_samantha_joel_my_team_and_i_use_ai_to/

Robins, R. W., Caspi, A., & Moffitt, T. E. (2002). It's not just who you're with, it's who you are: Personality and relationship experiences across multiple relationships. Journal of Personality

Røysamb, E., Nes, R. B., & Veenhoven, R. (2018). Genetic and environmental influences on well-being: A model for the study of human flourishing. Behavior Genetics.

World Happiness Report. (2020). World Happiness Report 2020. Sustainable Development Solutions Network.

Le, B., Dove, N. L., Agnew, C. R., Korn, M. S., & Mutso, A. A. (2010). Predicting Nonmarital Romantic Relationship Dissolution: A Meta-Analytic Synthesis. Personal Relationships, 17, 377–390. https://doi.org/10.1111/j.1475-6811.2010.01285.x

Smith, J., & Wolfinger, N. H. (2023). Re-Examining the Link Between Premarital Sex and Divorce. Journal of Family Issues, 45(3), 674–696.

Vowels, L. M., Vowels, M. J., & Mark, K. P. (2021). Is infidelity predictable? Using explainable machine learning to identify the most important predictors of infidelity. The Journal of Sex Research, 59(1), 1–14. https://doi.org/10.1080/00224499.2021.1967846

Wolfinger, N., et al. (2020s). What Tears Couples Apart: A Machine Learning Analysis of Union Dissolution in Germany. Demography, Duke University Press.

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.