Virtually Supervised Exercise Programs for People With Cancer
Bibliographic record
Abstract
BACKGROUND: Exercise has been shown to improve physical function and quality of life for individuals with cancer. However, low rates of exercise adoption and commonly reported barriers to accessing exercise programming have demonstrated a need for virtual exercise programming in lieu of traditional in-person formats. OBJECTIVE: The aim of this study was to summarize the existing research on supervised exercise interventions delivered virtually for individuals living with and beyond cancer. METHODS: We conducted a scoping review of randomized controlled trials, pilot studies, or feasibility studies investigating virtually supervised exercise interventions for adults either during or after treatment of cancer. The search included EMBASE, MEDLINE, CINAHL, SPORTDiscus, Cochrane Library, and conference abstracts. RESULTS: Fifteen studies were included. The interventions were delivered mostly over Zoom in a group format, with various combinations of aerobic and resistance exercises. Attendance ranged from 78% to 100%, attrition ranged from 0% to 29%, and satisfaction ranged from 94% to 100%. No major adverse events were reported, and only 3 studies reported minor adverse events. Significant improvements were seen in upper and lower body strength, endurance, pain, fatigue, and emotional well-being. CONCLUSION: Supervised exercise interventions delivered virtually are feasible and may improve physical function for individuals with cancer. The supervision included in these virtual programs promoted similar safety as seen with in-person programming. More randomized controlled trials with large cohorts are needed to validate these findings. IMPLICATIONS FOR PRACTICE: Individuals living with and beyond cancer can be encouraged to join virtually supervised exercise programs because they are safe, well enjoyed, and may improve physical function and quality of life.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".