Body image concerns for older adult men and women. can we identify correlates of exercise adherence
Bibliographic record
Abstract
Body image research has focused predominantly on younger populations; less is known about older adults. Given the health benefits of exercise and the declining rates of physical activity levels with aging, it is important to identify correlates of adherence to exercise. The present study (1) examined gender differences in body image in older adults, and (2) determined if body image could predict adherence to an exercise program in older adults. Participants were community dwelling men (n = 80) and women (n = 216) aged 55 years or older who were independent walkers with no neural impairments. There were significant gender differences reported for appearance and fitness evaluation (ps < .001), with men reporting feeling more positive and satisfied with their physical appearance and fitness compared to women. Exercise adherence data was examined for a subset of women (n = 133) and men (n = 51) who participated in the 12-week structured exercise program. Two hierarchal regressions were conducted to determine if satisfaction with fitness, health, appearance and body functioning could predict adherence. For women, controlling for BMI, satisfaction variables accounted for significant variance in adherence, F (5, 127) = 3.00, p = .014, R2adj. = .07; satisfaction with body functioning was the only significant predictor (ß = .241, p = .01). For men, the overall regression was not significant. Future research should focus on designing exercise programs for older adults that emphasize body function as opposed to appearance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".