Associations between positive affect and physical activity from young adulthood to midlife: A 25-year prospective study.
Bibliographic record
Abstract
OBJECTIVE: Positive affect may influence health by promoting physical activity, but evidence evaluating this association is mostly cross-sectional and cannot discern directionality. This study used a counterfactual-based framework to estimate the causal effect of positive affect on physical activity patterns over 25 years, accounting for potential reverse associations. METHOD: Data were from 3,352 participants in the Coronary Artery Risk Development in Young Adults study. Repeated assessments of positive affect and physical activity were collected from 1990 to 2016. Longitudinal associations were evaluated in two ways: (a) using baseline positive affect in traditional linear mixed models that accounted for reverse causal associations by adjusting for baseline physical activity, and (b) using marginal structural models that treated positive affect as a time-varying exposure, thus accounting for dynamic reverse causal associations due to bidirectional relationships. RESULTS: Fully adjusted traditional models found no association with physical activity at the first follow-up assessment, but positive affect was related to a slower decline in physical activity over time. Marginal structural models similarly found that positive affect was unrelated to physical activity at the first follow-up assessment but robustly associated with a slower decline in activity levels (5-year change: β = -3.33, 95% confidence interval [CI] = -5.80, -0.86; difference in 5-year change per 1 - SD positive affect: β = 4.99, 95% CI = 2.52, 7.46). CONCLUSIONS: Positive affect may play a causal role in slowing the decline in physical activity adults generally experience during through midlife. Efforts to enhance positive affect at the population level may be a promising new approach to help individuals stay active as they age. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".