Physical Activity, Social Support, and Mood in Older Adults during the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Research shows that increased physical activity is associated with improved mood and reduced symptoms of depression in older adults. Prior research has also found that loneliness and social isolation have a significant impact on the mental and physical well-being of older adults, with higher amounts of social connectedness and social activity associated with more frequent positive mood states. Overall social isolation is increased due to the COVID-19 pandemic and this could have a large impact on the physical and mental health of older adults. A group of 36 community dwelling older adults (Mean age = 70.5) completed questionnaires measuring physical activity, social activity, and social support, during the COVID-19 pandemic. Analyses found that perceived social support and average social network size significantly predicted positive mood states (F(2,33)=3.32, p<0.05) accounting for 16.7% of the variance, with a large effect. After adding average number of hours of sedentary activity the model was not significant. Perceived social support was more predictive of positive mood (β=0.32) compared to network size (β=0.17). There was a trend for the same three variables to predict negative mood (F(3,32)=2.76, p=0.06) accounting for 22% of the variance. Sedentary behaviour was the most predictive (t=2.68, p<0.05, β= 0.49). This suggests that perceived social support is most predictive of positive mood, and sedentary behaviour is predictive of negative mood during the COVID-19 pandemic.
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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 | 0.001 |
| 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".