Predictors of Adherence to Supervised Exercise Training During Breast Cancer Chemotherapy
Bibliographic record
Abstract
Exercise adherence is difficult during cancer treatments but few studies have examined the predictors of such exercise. PURPOSE: To examine predictors of adherence to supervised exercise training during breast cancer chemotherapy. METHODS: Breast cancer patients (N=242) initiating adjuvant chemotherapy in Edmonton, Ottawa, and Vancouver, Canada were randomly assigned to usual care (n=82), supervised resistance exercise (n=82), or supervised aerobic exercise (n=78) for the duration of their chemotherapy (Mdn=17 weeks; 95% CI=9 to 24). Baseline data were collected on standard demographic, medical, behavioral, fitness, and psychosocial variables as well as motivational variables from the Theory of Planned Behavior. Adherence was assessed by objective attendance records. RESULTS: Adherence to supervised exercise training during breast cancer chemotherapy was 70.2%. Univariate analyses indicated significant or borderline significant associations between exercise adherence and location/center (r=.30; p<.001), VO2peak (r=.21; p=.008), muscular strength (r=.21; p=.008), % body fat (r=−.21; p=.012), disease stage (r=.17; p=.031), education (r=.15; p=.053), depression (r=−.14; p=.073), and smoking (r=−.14; p=.081). In multivariate analysis, location/center (β=.28; p=.001), VO2peak (β=.19; p=.016), disease stage (β=.18; p=.015), and depression (β=−.16; p=.033) explained 21% of the variance in exercise adherence. Participants in Vancouver, with higher aerobic fitness, more advanced disease stage, and lower depression scores achieved better adherence. CONCLUSION: Adherence to supervised exercise training was predicted by unique aspects of the location/centers, disease stage, and baseline aerobic fitness and depression but not by motivational variables. These findings may have implications for improving adherence during breast cancer chemotherapy. Funded by the Canadian Breast Cancer Research Alliance
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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 | 0.000 | 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".