Telerehabilitation’s Safety, Feasibility, and Exercise Uptake in Cancer Survivors: Process Evaluation
Bibliographic record
Abstract
BACKGROUND: Access to exercise for cancer survivors is poor despite global recognition of its benefits. Telerehabilitation may overcome barriers to exercise for cancer survivors but is not routinely offered. OBJECTIVE: Following the rapid implementation of an exercise-based telerehabilitation program in response to COVID-19, a process evaluation was conducted to understand the impact on patients, staff, and the health service with the aim of informing future program development. METHODS: A mixed methods evaluation was completed for a telerehabilitation program for cancer survivors admitted between March and December 2020. Interviews were conducted with patients and staff involved in implementation. Routinely collected hospital data (adverse events, referrals, admissions, wait time, attendance, physical activity, and quality of life) were also assessed. Patients received an 8-week telerehabilitation intervention including one-on-one health coaching via telehealth, online group exercise and education, information portal, and home exercise prescription. Quantitative data were reported descriptively, and qualitative interview data were coded and mapped to the Proctor model for implementation research. RESULTS: The telerehabilitation program received 175 new referrals over 8 months. Of those eligible, 123 of 150 (82%) commenced the study. There were no major adverse events. Adherence to health coaching was high (674/843, 80% of scheduled sessions), but participation in online group exercise classes was low (n=36, 29%). Patients improved their self-reported physical activity levels by a median of 110 minutes per week (IQR 90-401) by program completion. Patients were satisfied with telerehabilitation, but clinicians reported a mixed experience of pride in rapid care delivery contrasting with loss of personal connections. The average health service cost per patient was Aus $1104 (US $790). CONCLUSIONS: Telerehabilitation is safe, feasible, and improved outcomes for cancer survivors. Learnings from this study may inform the ongoing implementation of cancer telerehabilitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".