Effects of virtual exercise interventions on physical function in individuals with a current or previous diagnosis of cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Purpose Evaluate the effects of virtual exercise interventions compared to no intervention or usual care on physical function in individuals with a current or previous diagnosis of cancer.Methods Six electronic databases (MEDLINE, EMBASE, CINAHL, EMCARE, PsycINFO, AMED) were searched from inception to February 5th, 2025. Eligible studies evaluated virtual exercise interventions, compared to usual care or no intervention on physical function outcomes in adults (≥18 years) with current or previous cancer. Random-effects meta-analyses were conducted. Cochrane Risk of Bias 2 and the GRADE tools were used to assess certainty of evidence.Results Fifteen trials (n = 843) were included; 13 studies (n = 668) were synthesized quantitatively. Meta-analyses showed benefits of virtual exercise on upper body strength (SMD = 0.53, 95%CI [0.11, 0.94], p = 0.01), lower body strength (SMD = 0.53, 95%CI [0.14, 0.93], p = 0.009), gait-based measures (SMD = 0.65, 95%CI [0.06, 1.24], p = 0.03), chair-stand tests (SMD = 1.07, 95%CI [0.13, 2.01], p = 0.03), pain (SMD = 0.50, 95%CI [0.09, 0.91], p = 0.02), and fatigue (SMD = 0.70, 95% CI [0.44, 0.96], p < 0.001). No effect was observed on Short Physical Performance Battery scores (MD = 0.71, 95% CI [−0.52, 1.94], p = 0.26). Certainty of evidence of all outcomes were very low.Conclusion Virtual exercise interventions may improve physical function and reduce barriers to exercise participation in individuals with cancer.Registration PROSPERO:CRD42025639046
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.006 | 0.005 |
| 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) | 0.004 | 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".