Exercise Effects on Depressive Symptoms in Cancer Survivors: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Depression is a distressing side effect of cancer and its treatment. In the general population, exercise is an effective antidepressant. OBJECTIVE: We conducted a systematic review and meta-analysis to determine the antidepressant effect of exercise in cancer survivors. DATA SOURCES: In May 2011, we searched MEDLINE, PsycInfo, EMBASE, CINAHL, CDSR, CENTRAL, AMED, Biosis Previews, and Sport Discus and citations from relevant articles and reviews. STUDY ELIGIBILITY CRITERIA: We included randomized controlled trials (RCT) comparing exercise interventions with usual care in cancer survivors, using a self-report inventory or clinician rating to assess depressive symptoms, and reporting symptoms pre- and postintervention. STUDY APPRAISAL: Around 7,042 study titles were identified and screened, with 15 RCTs included. SYNTHESIS METHODS: Effect sizes (ES) were reported as mean change scores. The Q test was conducted to evaluate heterogeneity of ES. Potential moderator variables were evaluated with examination of scatter plots and Wilcoxon rank-sum or Kruskal-Wallis tests. RESULTS: The overall ES, under a random-effects model, was -0.22 (confidence interval, -0.43 to -0.09; P = 0.04). Significant moderating variables (ps < 0.05) were exercise location, exercise supervision, and exercise duration. LIMITATIONS: Only one study identified depression as the primary endpoint. CONCLUSIONS: Exercise has modest positive effects on depressive symptoms with larger effects for programs that were supervised or partially supervised, not conducted at home, and at least 30 minutes in duration. IMPACT: Our results complement other studies showing that exercise is associated with reduced pain and fatigue and with improvements in quality of life among cancer survivors.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".