Feasibility, User Acceptance, and Outcomes of Using a Cancer Prehabilitation App for Exercise: Pilot Cohort Study
Bibliographic record
Abstract
Background: The efficacy of cancer prehabilitation programs is supported by international reviews and meta-analyses. Technology has been deployed in cancer prehabilitation to address challenges such as access or limited resources. This study evaluated the feasibility, user acceptance, safety, and program outcomes of a newly developed mobile app for cancer prehabilitation. The app integrates with Singapore's existing health care mobile app, Health Buddy, and provides instructional videos for prescribed exercises. Objective: The objectives of this study were to investigate the feasibility, user experience, safety, and outcomes of a mobile app for cancer prehabilitation within a hospital-associated, home-based, multimodal cancer prehabilitation program. Methods: This retrospective study analyzed the records of patients enrolled in the cancer prehabilitation program from September 1, 2022, to March 30, 2023. Patients who participated in the prehabilitation program (n=63) were categorized into 2 groups: those prescribed the app (n=41) and those who were not (n=22). There was further subgroup analysis of those who were prescribed: app users (n=25) versus those who were non-app users (n=16). Demographics, Fried Frailty Phenotype, prehabilitation duration, app use, and functional outcome measures (6-minute walk test [6MWT], 30-second sit-to-stand test [STS], timed up and go test [TUG], and Hospital Anxiety and Depression Scale [HADS]) were collected. Compliance was determined by the completion of prescribed exercises and the accuracy of executing these exercises, with a high compliance rate considered to be at 80% or more. Baseline characteristics and preoperative outcomes were compared between the groups. User satisfaction was assessed through surveys among app users (n=25). Results: Among 63 patients, 41 (65.1%) patients were prescribed the app, of which 22 (34.9%) patients were users. No significant differences in preoperative functional improvements were observed between app users and nonusers (6MWT: P=.60; STS: P=.81; TUG: P=.53; HADS: P=.36), or between those prescribed and not prescribed the app (6MWT: P=.94; STS: P=.26; TUG: P=.39; HADS: P=.62). However, high compliance rates (80%) were observed among app users. Patient satisfaction with the app was high (>90%), with positive feedback on ease of use and technical reliability. Baseline measures revealed significantly lower functional scores and higher mean frailty scores in the nonprescribed group. Conclusions: This preliminary study demonstrates the acceptability, feasibility, and safety of Singapore's first smartphone app for exercise prescription in cancer prehabilitation. Lower baseline functional outcome measures and a higher mean frailty score in the unprescribed group have implications for the selection process and patient participation. Further studies should include strategies to enhance patients' readiness for technology, sustainability, and effectiveness in older patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".