Associations between Symptoms and Exercise Barriers in Breast Cancer Survivors
Bibliographic record
Abstract
Despite exercise benefits for cancer survivor health, most breast cancer survivors do not meet exercise recommendations. Few studies have examined associations between psychosocial symptoms and exercise barriers in this population. To improve physician exercise counseling by identifying survivors with high barriers in a clinical setting, associations between breast cancer symptoms (fatigue, mood, sleep quality) and exercise barriers were investigated. Physically inactive survivors (N = 320; average age 55 ± 8 years, 81% White, 77% cancer stage I or II) completed a baseline survey for a randomized physical activity trial and secondary analyses were performed. Potential covariates, exercise barriers interference score, Fatigue Symptom Inventory, Hospital Anxiety and Depression Scale (HADS), and Pittsburgh Sleep Quality Index were assessed. Based on multiple linear regression analyses, only HADS Global (B = 0.463, p < 0.001) and number of comorbidities (B = 0.992, p = 0.01) were independently associated with total exercise barriers interference score, explaining 8.8% of the variance (R2 = 0.088, F(2,317) = 15.286, p < 0.001). The most frequent barriers to exercise for survivors above the HADS clinically important cut point included procrastination, routine, and self-discipline. These results indicate greater anxiety levels, depression levels, and comorbidities may be independently associated with specific exercise barriers. Health professionals should consider mood and comorbidities when evaluating survivors for exercise barriers, and tailoring exercise counseling.
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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 | 0.000 | 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".