The online delivery of exercise oncology classes supported with health coaching: A pilot randomized controlled trial
Bibliographic record
Abstract
Abstract Purpose The primary objective was to investigate the feasibility of a synchronous, online delivered, group-based, supervised, exercise oncology maintenance program supported with health coaching. Methods All participants had previously completed a 12-week group-based exercise study. In the current study, participants were randomized to a 12-week exercise oncology maintenance class with or without health coaching. The primary outcome was feasibility, assessed as intervention attendance, safety and fidelity, study recruitment, attrition and outcome assessment completion. Additionally, semi-structured interviews at the end of the intervention provided participants’ perspectives on intervention feasibility. Results Forty participants (n 8WK =25; n 12WK =15) enrolled in the study. Feasibility was confirmed for recruitment rate (42.6%), attrition rate (2.5%), safety (no adverse events), health coaching attendance (97%), health coaching fidelity (96.7%), class attendance (91.2%), class fidelity (92.6%), and assessment completion (questionnaire=98.8%; physical functioning=97.5%). Based on the qualitative feedback, feasibility was facilitated by the convenience, while the diminished ability to connect with other participants online was a drawback compared to in-person delivery. Conclusion The synchronous online delivery of an exercise oncology maintenance class, the additional health coaching support, and the tools used to measure the intervention effectiveness were feasible for individuals living with and beyond 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".