The online delivery of exercise oncology classes supported with health coaching: a parallel pilot randomized controlled trial
Bibliographic record
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: Participants had previously completed a 12-week group-based exercise program. All participants received synchronous online delivered exercise maintenance classes, and half were block randomized to receive additional weekly health coaching calls. A class attendance rate of ≥ 70%, a health coaching completion rate of ≥ 80%, and an assessment completion rate of ≥ 70% were set as markers of feasibility. Additionally, recruitment rate, safety, and fidelity of the classes and health coaching calls were reported. Post-intervention interviews were performed to further understand the quantitative feasibility data. Two waves were conducted - as a result of initial COVID-19 delays, the first wave was 8 weeks long, and the second wave was 12 weeks long, as intended. RESULTS: = 15) enrolled in the study with 19 randomized to the health coaching group and 21 to the exercise only group. The recruitment rate (42.6%), attrition (2.5%), safety (no adverse events), and feasibility were confirmed for 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%; Garmin wear-time = 83.4%). Interviews highlighted that convenience contributed to participant attendance, while the diminished ability to connect with other participants was voiced as a drawback compared to in-person delivery. CONCLUSION: The synchronous online delivery and assessment of an exercise oncology maintenance class with health coaching support was feasible for individuals living with and beyond cancer. Providing feasible, safe, and effective exercise online to individuals living with cancer may support increased accessibility. For example, online may provide an accessible alternative for those living in rural/remote locations as well as for those who may be immunocompromised and cannot attend in-person classes. Health coaching may additionally support individuals' behavior change to a healthier lifestyle. TRIAL REGISTRATION: The trial was retrospectively registered (NCT04751305) due to the rapidly evolving COVID-19 situation that precipitated the rapid switch to online programming.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".