Effects of a multicomponent physical activity behavior change intervention on fatigue, anxiety, and depressive symptomatology in breast cancer survivors: randomized trial
Bibliographic record
Abstract
OBJECTIVES: To determine the effects of the 3-month multicomponent Better Exercise Adherence after Treatment for Cancer (BEAT Cancer) physical activity behavior change intervention on fatigue, depressive symptomatology, and anxiety. METHODS: Postprimary treatment breast cancer survivors (n = 222) were randomized to BEAT Cancer or usual care. Fatigue Symptom Inventory and Hospital Anxiety and Depression Scale were assessed at baseline, postintervention (month 3; M3), and follow-up (month 6; M6). RESULTS: Adjusted linear mixed-model analyses demonstrated significant effects of BEAT Cancer vs usual care on fatigue intensity (M3 mean between group difference [M] = -0.6; 95% confidence interval [CI] = -1.0 to -0.2; effect size [d] = -0.32; P = .004), fatigue interference (M3 M = -0.8; CI = -1.3 to -0.4; d = -0.40; P < .001), depressive symptomatology (M3 M = -1.3; CI = -2.0 to -0.6; d = -0.38; P < .001), and anxiety (M3 M = -1.3; CI = -2.0 to -0.5; d = -0.33; P < .001). BEAT Cancer effects remained significant at M6 for all outcomes (all P values <.05; d = -0.21 to -.35). Clinically meaningful effects were noted for fatigue intensity, fatigue interference, and depressive symptomatology. CONCLUSIONS: BEAT Cancer reduces fatigue, depressive symptomatology, and anxiety up to 3 months postintervention in postprimary treatment breast cancer survivors. Further study is needed to determine sustainable methods for disseminating and implementing the beneficial intervention components.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".