Correlates of exercise motivation and behavior in a population-based sample of endometrial cancer survivors: an application of the Theory of Planned Behavior
Bibliographic record
Abstract
BACKGROUND: Despite evidence of the benefits of exercise in cancer survivors, exercise participation rates tend to decline after treatments. Few studies have examined the determinants of exercise in less common cancer sites. In this study, we examined medical, demographic, and social cognitive correlates of exercise in endometrial cancer survivors using the Theory of Planned Behavior (TPB). METHODS: A mailed survey was completed by 354 endometrial cancer survivors (1 to 10 years postdiagnosis) residing in Alberta, Canada. The study was cross-sectional. Exercise behavior was assessed using the Godin Leisure Time Exercise Questionnaire and the TPB constructs were assessed with standard self-report scales. Multiple regression analyses were used to determine the independent associations of the TPB constructs with intention and behavior. RESULTS: Chi-square analyses indicated that marital status (p = .003), income level (p = .013), and body mass index (BMI) (p = .020) were associated with exercise. The TPB explained 34.1% of the variance in exercise behavior with intention (beta = .38, p < .001) and self-efficacy (beta = .18, p = .029) being independent correlates. For intention, 38.3% of the variance was explained by the TPB with self-efficacy (beta = .34, p < .001) and affective attitude (beta = .30, p < .001) being the independent correlates. The TPB mediated the associations of marital status and BMI with exercise but not income level. Age and BMI moderated the associations of the TPB with intention and behavior. CONCLUSION: The TPB may be a useful framework for understanding exercise in endometrial cancer survivors. Exercise behavior change interventions based on the TPB should be tested in this growing 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".