Positive affect during adolescence and health and well-being in adulthood: An outcome-wide longitudinal approach
Bibliographic record
Abstract
BACKGROUND: Several intergovernmental organizations, including the World Health Organization and United Nations, are urging countries to use well-being indicators for policymaking. This trend, coupled with increasing recognition that positive affect is beneficial for health/well-being, opens new avenues for intervening on positive affect to improve outcomes. However, it remains unclear if positive affect in adolescence shapes health/well-being in adulthood. We examined if increases in positive affect during adolescence were associated with better health/well-being in adulthood across 41 outcomes. METHODS AND FINDINGS: We conducted a longitudinal cohort study using data from Add Health-a prospective and nationally representative cohort of community-dwelling U.S. adolescents. Using regression models, we evaluated if increases in positive affect over 1 year (between Wave I; 1994 to 1995 and Wave II; 1995 to 1996) were associated with better health/well-being 11.37 years later (in Wave IV; 2008; N = 11,040) or 20.64 years later (in Wave V; 2016 to 2018; N = 9,003). Participants were aged 15.28 years at study onset, and aged 28.17 or 37.20 years-during the final assessment. Participants with the highest (versus lowest) positive affect had better outcomes on 3 (of 13) physical health outcomes (e.g., higher cognition (β = 0·12, 95% CI = 0·05, 0·19, p = 0.002)), 3 (of 9) health behavior outcomes (e.g., lower physical inactivity (RR = 0·80, CI = 0·66, 0·98, p = 0.029)), 6 (of 7) mental health outcomes (e.g., lower anxiety (RR = 0·81, CI = 0·71, 0·93, p = 0.003)), 2 (of 3) psychological well-being (e.g., higher optimism (β = 0·20, 95% CI = 0·12, 0·28, p < 0.001)), 4 (of 7) social outcomes (e.g., lower loneliness (β = -0·09, 95% CI = -0·16, -0·02, p = 0.015)), and 1 (of 2) civic/prosocial outcomes (e.g., more voting (RR = 1·25, 95% CI = 1·16, 1·36, p < 0.001)). Study limitations include potential unmeasured confounding and reverse causality. CONCLUSIONS: Enhanced positive affect during adolescence is linked with a range of improved health/well-being outcomes in adulthood. These findings suggest the promise of testing scalable positive affect interventions and policies to more definitively assess their impact on outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".