Barriers to Supervised Exercise Training in a Randomized Controlled Trial of Breast Cancer Patients Receiving Chemotherapy
Bibliographic record
Abstract
BACKGROUND: Exercise adherence is a challenge for breast cancer patients receiving chemotherapy but few studies have identified the key barriers. PURPOSE: In this paper, we report the barriers to supervised exercise in breast cancer patients participating in a randomized controlled trial. METHODS: Breast cancer patients initiating adjuvant chemotherapy (N = 242) were randomly assigned to usual care (n = 82) or supervised resistance (n = 82) or aerobic (n = 78) exercise. Participants randomized to the two exercise groups (n = 160) were asked to provide a reason for each missed exercise session. RESULTS: The two exercise groups attended 70.2% (5,495/7,829) of their supervised exercise sessions and provided a reason for missing 89.5% (2,090/2,334) of their unattended sessions. The 2,090 reasons represented 36 different barriers. Feeling sick (12%), fatigue (11%), loss of interest (9%), vacation (7%), and nausea/vomiting (5%) accounted for the most missed exercise sessions. Disease/treatment-related barriers (19 of the 36 barriers) accounted for 53% (1,102/2,090) of all missed exercise sessions. Demographic and medical variables did not predict the types of exercise barriers reported. CONCLUSIONS: Barriers to supervised exercise in breast cancer patients receiving chemotherapy are varied but over half can be directly attributed to the disease and its treatments. Behavioral support programs need to focus on strategies to maintain exercise in the face of difficult treatment side effects.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".