"You got a friend in me": The effects of an exercise intervention on peer and expert social support in older adults
Bibliographic record
Abstract
Social support is a key component in facilitating initiation and adherence to physical activity, as it may provide increased motivation (Eyler et al., 1999). The purpose of this study was to investigate the effect of a 12-week exercise and balance training program on social support in older adults. Participants (women = 212, men = 81; Mage = 68.3 ±6.42 years) were community dwelling older adults free from neuromuscular conditions. They were randomly assigned to either an exercise group or control group. The exercise group completed a 12-week exercise program, consisting of 3 weekly exercise sessions in accordance with Canadian physical activity guidelines; the control group was instructed to continue their normal activities. Social support was assessed at the beginning and end of 12 weeks. The exercise program was based on social cognitive theory, designed to foster self-efficacy and social support through both peers and student trainers. Two separate repeated-measures ANOVAs were conducted to determine if there were group differences in social support from baseline to follow-up testing. There was a significant group-by-time interaction for both peer social support, F (1, 292)= 11.87, p<0.01 and expert social support, F (1, 292) = 12.65, p<0.01. Paired sample t-tests showed that both peer and expert social support significantly increased in the exercise group, with no change in the control group. These findings indicate that both peers and trainers can be effective at fostering social support in older adults, which may be a simple and inexpensive way to positively impact exercise behaviours in seniors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".