Physical Activity and Mortality in Cancer Survivors: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Recommendations for improved survival after cancer through physical activity (PA) exist, although the evidence is still emerging. Our primary objective was to conduct a systematic review and meta-analysis of the association between prediagnosis and postdiagnosis PA and survival (cancer-specific, all-cause, and cardiovascular disease mortality) for all cancers and by tumor site. Secondary objectives were to examine the associations within population subgroups, by PA domain, and to determine the optimal dose of PA related to survival. METHODS: PubMed, EMBASE, and SportsDiscus databases were searched from inception to November 1, 2018. DerSimonian-Laird random-effects models were used to estimate the summary hazard ratios (HRs) and 95% confidence intervals (CI) for primary and secondary analyses and to conduct dose-response analyses. RESULTS: Evidence from 136 studies showed improved survival outcomes with highest vs lowest levels of prediagnosis or postdiagnosis total or recreational PA for all-cancers combined (cancer specific mortality: HR = 0.82, 95% CI = 0.79 to 0.86, and HR = 0.63, 95% CI = 0.53 to 0.75, respectively) as well as for 11 specific cancer sites. For breast and colorectal cancers, greater reductions were observed for postdiagnosis PA (HR = 0.58-0.63) compared with prediagnosis PA (HR = 0.80-0.86) for cancer-specific and all-cause mortality. Survival benefits through PA were observed in most subgroups (within sex, body mass index, menopausal status, colorectal subtypes, and PA domain) examined. Inverse dose-response relationships between PA and breast cancer-specific and all-cause mortality were observed, with steep reductions in hazards to 10-15 metabolic equivalent hours per week. CONCLUSION: Higher prediagnosis and postdiagnosis levels of PA were associated with improved survival outcomes for at least 11 cancer types, providing support for global promotion of PA guidelines following cancer.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".