Physical Activity and Sleep Quality in Breast Cancer Survivors
Bibliographic record
Abstract
PURPOSE: Data from large randomized controlled trials confirming sleep quality improvements with aerobic physical activity have heretofore been lacking for post-primary treatment breast cancer survivors. Our primary purpose for this report was to determine the effects of a physical activity behavior change intervention, previously reported to significantly increase physical activity behavior, on sleep quality in post-primary treatment breast cancer survivors. METHODS: Post-primary treatment breast cancer survivors (n = 222) were randomized to a 3-month physical activity behavior change intervention (Better Exercise Adherence after Treatment for Cancer [BEAT Cancer]) or usual care. Self-report (Pittsburgh Sleep Quality Index [PSQI]) and actigraphy (latency and efficiency) sleep outcomes were measured at baseline, 3 months (M3), and 6 months (M6). RESULTS: After adjusting for covariates, BEAT Cancer significantly improved PSQI global sleep quality when compared with usual care at M3 (mean between-group difference [M] = -1.4, 95% confidence interval [CI] = -2.1 to -0.7, P < 0.001) and M6 (M = -1.0, 95% CI = -1.7 to -0.2, P = 0.01). BEAT Cancer improved several PSQI subscales at M3 (sleep quality M = -0.3, 95% CI = -0.4 to -0.1, P = 0.002; sleep disturbances M = -0.2, 95% CI = -0.3 to -0.03, P = 0.016; daytime dysfunction M = -0.2, 95% CI = -0.4 to -0.02, P = 0.027) but not M6. A nonsignificant increase in percent of participants classified as good sleepers occurred. No significant between-group difference was noted for accelerometer latency or efficiency. CONCLUSION: A physical activity intervention significantly reduced perceived global sleep dysfunction at 3 and 6 months, primarily because of improvements in sleep quality aspects not detected with accelerometer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".