Lapses and Psychosocial Factors Related to Physical Activity in Early Postmenopause
Bibliographic record
Abstract
PURPOSE: After menopause, leisure physical activity (PA) levels seem to decline for reasons that are not completely understood. This study examines the associations between PA, lapses in PA, and psychosocial factors in early postmenopausal women. METHODS: This cross-sectional analysis included 497 women from the Women on the Move through Activity and Nutrition study. PA was assessed with a past-year, interviewer-administered Modifiable Activity Questionnaire. Measures of activity lapses of >or= 2 wk in the past 6 months, exercise decision making, processes of change, and self-efficacy were collected along with Beck Depression Inventory, State-Trait Anxiety Inventory, Cohen Perceived Stress Scale, and Short Form-36. RESULTS: Mean age of participants was 56.9 yr. Compared with less active women, women with significantly higher activity levels reported greater exercise self-efficacy (r = 0.31), more frequent use of behavioral exercise processes of change (r = 0.31), greater perceived benefits for PA (r = 0.22), and better physical quality of life (r = 0.16) (all P < 0.001). Women reporting no activity lapses had higher reported activity levels than regularly active women with lapses or occasionally active women with lapses (P < 0.0001 for trend). Of the women who reported lapses, 24% reported low self-confidence, 43% reported difficulty controlling their weight, and 55% reported difficulty maintaining their diet when they lapsed from PA. Thirty-nine percent of women reporting lapses did not resume PA (i.e., relapsed to inactivity). Higher anxiety and depressive symptoms, and less frequent use of behavioral exercise processes of change, were associated with relapse to inactivity. CONCLUSIONS: Future interventions for early postmenopausal women should consider psychosocial factors when attempting to encourage and maintain higher levels of PA. Addressing and preventing PA lapses may help to achieve PA goals in this population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".