A Mobile Phone App to Support Self-Management and Transition to Adult Health Services in Young People With a Chronic Illness: Single-Arm Pilot Intervention Study
Bibliographic record
Abstract
Background: Pediatric to adult health service (AHS) transition is a challenging time for many adolescents and young adults with chronic illness. As the responsibility of illness management shifts from parents to the young person themselves, many young people fail to transition in a timely manner, which has important health consequences. Mobile apps show potential in assisting young people to self-manage their condition during this vulnerable time, but empirical data on app uptake as well as efficacy with respect to transition outcomes and illness control are lacking. Objective: This study aimed to evaluate the usage of a mobile app called "TransitionMate" (TMApp) by adolescents and young adults, which was purpose-built to support chronic illness self-management for youth undergoing transition. Secondary aims were to assess AHS attendance and changes in illness control over the first 12 months post transition. Upon demonstration of TMApp feasibility (49/70, ≥70% of participants using TMApp at least once a month), a randomized controlled trial was planned to test app efficacy in relation to transition and illness control outcomes. Methods: Young people aged 16-19 years who were transitioning out of 2 major pediatric hospitals in Sydney, New South Wales, Australia, were recruited. Just before transition, participants' demographic and clinical details were collected, and TMApp was installed on their mobile devices. Participants were contacted by telephone at 6 and 12 months for information on self-reported usage, app usefulness, and other feedback. Quantitative cohort-level usage data, including the number of times specific app features were accessed, were tracked via mobile analytics. At 12 months, electronic medical records from participants' designated AHS were accessed for data on AHS attendances, measures of illness control, and unplanned hospitalizations. Results: In total, 73 adolescents and young adults (30 male; median age 18, IQR 17-18 y) were recruited, with 1 withdrawing participation in the first month. Participants were primarily from 3 chronic illness subgroups-diabetes mellitus (n=23), inflammatory bowel disease (n=19), and cystic fibrosis (n=9). Of the total, 50% (36/72) of participants reported using TMApp at 1 month post transition. Self-reported usage rates fell to 25% and 11% at 6 and 12 months, respectively. Mobile analytics data broadly corroborated self-reported usage. Furthermore, 8 participants who continued to use TMApp for 12 months gave a median usefulness rating of 8/10. Over two-thirds (48/72, 68%) of participants successfully transitioned to their designated AHS by 12 months. Among the successful transitioners who had illness control data available on electronic medical records, over 80% (24/30) maintained a stable or improved illness status at 12 months. Conclusions: TMApp was not used regularly enough by our young people to demonstrate feasibility and justify progression to a randomized controlled trial. Despite low app uptake, most participants successfully transitioned, suggesting that TMApp had minimal influence on transition outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".