A Modular Mobile Health App for Personalized Rehabilitation Throughout the Breast Cancer Care Continuum: Development Study
Bibliographic record
Abstract
BACKGROUND: Although many mobile health (mHealth) apps have evolved as support tools for self-management of breast cancer, limited studies have developed a comprehensive app and described the algorithms for personalized rehabilitation throughout the breast cancer care continuum. OBJECTIVE: This study aimed to develop a comprehensive mobile app and to describe an algorithm that adjusts personalized content to facilitate self-management throughout the breast cancer care continuum. METHODS: The development process of the modular mHealth app included the following 4 steps: (1) organizing expert teams, (2) defining evidence-based fundamental content and modules, (3) classifying user information for algorithms to personalize the content, and (4) creating the app platform and connectivity to digital health care devices. RESULTS: We developed a modular mHealth app service, which took 18 months, including a review of related literature and guidelines and the development of the app and connectivity to digital health care devices. A total of 11 functionalities were defined in the app with weekly analysis. The user information classification was formulated for personalized rehabilitation according to 5 key criteria: general user information, breast operation type, lymph node surgery type, chemotherapy and hormonal therapy use, and change in treatment after surgery. The main modules for personalized content included a self-monitoring screen, personalized health information, personalized exercise, and diet management. CONCLUSIONS: The strength of this study was the development of a comprehensive mHealth app and algorithms to adjust content based on user medical information for personalized rehabilitation during the breast cancer care continuum.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".