Designing an App to Facilitate Self-Management in Young Adult Survivors of Childhood Cancer: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Young adult survivors of childhood cancer are at risk for late and long-term effects from their treatment, and less than 1 in 5 obtain risk-based care in adulthood. Transitioning young adult survivors from pediatric, parent-driven care to adult, self-driven care is a challenging process during which young adults face multiple barriers. Intervening during this period may facilitate better transition readiness. For this purpose, we previously developed the Managing Your Health (MYH) web-based intervention, which showed initial feasibility and acceptability; however, young adult participants wanted to access the intervention through a mobile app. OBJECTIVE: We used an iterative, cocreation design process to translate, build, and evaluate the usability of the MYH web-based intervention into a mobile app to be used in a future peer-mentoring educational intervention. METHODS: In phase 1, we conducted key informant workshops with 3 stakeholder groups to understand target users' needs and expectations related to the content and design of the mobile app. In phase 2, we conducted usability testing with young adult survivors of childhood cancer to evaluate the app's usability and subjective appeal. RESULTS: Participants in the key informant workshops (n=13) agreed that the content of the proposed app matched the barriers faced by young adult survivors of childhood cancer. Participants provided suggestions about the design of the app, including content and features, although there were mixed views about the inclusion of gamification features. Usability testing participants (n=25) rated the app highly on measures of technology acceptance, usability, and aesthetic appeal. Participants' qualitative comments suggested that they found the app to be useful, easy to use, and likable or familiar relative to other existing apps. Participants suggested a variety of features to enhance the app, including adding features to enhance usability and reformatting certain aspects of the app to enhance interactivity and feedback to the user. Suggestions with uniformly positive reports were used to refine the app, while suggestions with mixed enthusiasm were not prioritized in refining the app. CONCLUSIONS: We engaged target users of an educational app in an iterative app design process to create a product that would meet the needs and expectations of those users. Results suggested that the app was generally viewed as acceptable, useful, and visually appealing. Common suggestions for improvement, such as reformatting quizzes to enhance interactivity and provide feedback regarding correct answers, were used to refine the app. The refined app will be used in the future intervention efficacy trial. TRIAL REGISTRATION: ClinicalTrials NCT06763770; https://clinicaltrials.gov/study/NCT06763770.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".