A Mobile App With Multimodality Prehabilitation Programs for Patients Awaiting Elective Surgery: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Complying with a prehabilitation program is difficult for patients who will undergo surgery, owing to transportation challenges and a limited intervention time window. Mobile health (mHealth) using smartphone apps has the potential to remove barriers and improve the effectiveness of prehabilitation. OBJECTIVE: This study aimed to develop a mobile app as a tool for facilitating a multidisciplinary prehabilitation protocol involving blood flow restriction training and sport nutrition supplementation. METHODS: The app was developed using "Appy Pie," a noncoding app development platform. The development process included three stages: (1) determination of principles and requirements of the app through prehabilitation research team meetings; (2) app prototype design using the Appy Pie platform; and (3) app evaluation by clinicians and exercise and fitness specialists, technical professionals from Appy Pie, and non-team-member users. RESULTS: We developed a prototype of the app with the core focus on a multidisciplinary prehabilitation program with accessory features to improve engagement and adherence to the mHealth intervention as well as research-focused features to evaluate the effects of the program on frailty status, health-related quality of life, and anxiety level among patients awaiting elective surgery. Evaluations by research members and random users (n=8) were consistently positive. CONCLUSIONS: This mobile app has great potential for improving and evaluating the effectiveness of the multidisciplinary prehabilitation intervention in the format of mHealth in future.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".