Cocreation of a Mobile App (AYABytes) by Physicians and Adolescents and Young Adults With Cancer to Improve Access to Cancer-Related Resources and Reduce Distress: Protocol for a Single-Arm Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Adolescents and young adults with cancer require dedicated and tailored management that bridges adult and pediatric oncology services. At the National Cancer Centre Singapore, 40% of newly diagnosed adolescents and young adults report significant distress due to uncertainty about prognosis, treatment, and disruption of life milestones. A major unmet need is access to reliable, age-appropriate information. Prior studies demonstrate that digital technology can effectively deliver such support. OBJECTIVE: This study describes the protocol for evaluating AYABytes (Adolescent and Young Adult Building Youths, a Technology for Education and Sharing), a mobile app cocreated by patients and health care professionals to improve health-related quality of life for adolescent and young adult oncology patients. An iterative information-gathering process was conducted, including semistructured interviews with 2 clinicians, 3 cancer survivors, and 3 care partners to cocreate this mobile app. AYABytes is an interactive, phone-based intervention designed to engage adolescent and young adult oncology patients with personalized education, mood, and symptom self-management resources with an inbuilt algorithm that responds to patient-reported questionnaires. METHODS: The app will be evaluated in 2 phases-a pilot test and an implementation test. In the pilot test, the app will be launched to a test group of 20 adolescent and young adult oncology patients aged between 16 and 45 years, selected for representation among the age group and their malignancies. Patients will be allowed to use the app for 1 month. Feasibility and acceptability were assessed via a semistructured survey. In the implementation stage, 200 patients will be allowed to use the app over 6 months and will complete an EQ-5D-5L questionnaire at baseline and at the 1- and 6-month marks. Evaluation of the mobile app was performed via the mHealth App Usability Questionnaire at similar intervals. RESULTS: Funding for the development and trial of AYABytes was awarded in October 2020 through a National Cancer Centre Singapore research grant. Pilot testing was completed in May 2024. The implementation phase began in June 2024 and is currently ongoing. CONCLUSIONS: We believe that AYABytes, a novel eHealth mobile app, will be both beneficial and easily used by adolescent and young adult oncology patients. Evaluating the app and its quantifiable impact on improving the quality of life of adolescent and young adult oncology patients will help enrich the evidence for mobile health interventions. It will also validate new digital approaches to help adolescent and young adult oncology patients reduce their distress and address unmet needs and concerns. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69453.
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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 | 0.026 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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".