An examination of older adults’ social experiences predicting physical activity and psychological well-being trajectories during the COVID-19 pandemic
Bibliographic record
Abstract
Many older adults’ social experiences, physical activity (PA), and psychological well-being (PWB) were negatively impacted during COVID-19. We examined associations between social experiences and trajectories of PA and PWB during a period when public health restrictions were changing. 890 older adults (Mage = 65 years) responded to six monthly questionnaires. Latent growth curve models demonstrated good fit. Consistent with hypotheses, results indicated a significant (p ≤ .05) decline in stress, with higher descriptive norms associated with greater declines. Negative affect declined, but unexpectedly higher social participation was associated with slower declines. Positive affect and light PA had a quadratic trajectory where they declined, the rate of decline slowed, and they then increased again. Unexpectedly, higher social participation was associated with greater declines in positive affect. There were no predictors of change in light PA. MVPA declined from time 1-3 then increased, with a quadratic effect where the rate of increase slowed over time. The slowing rate of increase in MVPA was negatively predicted by social network size: those with larger social networks slowed their rate of increase of MVPA from time 4-6 more quickly. Findings indicate that while some social experiences were associated with adaptive outcomes, others may have presented vulnerabilities (e.g., greater social participation prior to periods of program closure may have been associated with less desirable changes in affect because they had more to lose). Further research on how the trajectories of social experiences and outcomes, PA, and PWB are associated may further elucidate these dynamic relationships.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".