Exploring the Influence of a Smartphone App (Young with Diabetes) on Young People’s Self-Management: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Adequate self-management is the cornerstone of preventing type 1 diabetes mellitus (T1DM) complications. However, T1DM self-management is challenging for young people, who often struggle during the transition from childhood to adulthood. The mobile health (mHealth) app Young with Diabetes (YWD) was developed in collaboration with young people to enhance their T1DM self-management during this transition. OBJECTIVE: The purpose of this study was to explore the influence of YWD on young people's self-management during a 12-month period. METHODS: A qualitative explorative approach was used, comprising a purposive sample of 20 young people (11 females and 9 males, ages 15 to 23 years, with app use of 3 to 64 days) from 3 pediatric and 3 adult departments. Participants were interviewed individually using a semistructured interview guide. Data were collected from January to March 2017 and analyzed using thematic analysis. RESULTS: A total of 5 themes were identified: (1) not feeling alone anymore ("we are in this together"); (2) gaining competence by sharing experiences and practical knowledge ("they know what they are talking about"); (3) feeling safer ("it's just a click away"); (4) breaking the ice by starting to share thoughts and feelings and asking for help ("it is an outstretched hand"); and (5) lack of motivating factors ("done with the app"). Young people reported that YWD promoted self-management by peer-to-peer social support, exchanging messages with health care providers, and sharing YWD with parents. Participants recommended YWD as a supplement to self-management for newly diagnosed young people with T1DM and suggested improvements in app content and functionality. CONCLUSIONS: The mHealth app YWD has the potential to support self-management. In particular, peer-to-peer support reduced feelings of loneliness and helped young people to gain knowledge and skills for managing T1DM. A need exists for alternative ways to train health care providers in using YWD and to support collaboration between young people and their parents to further improve young people's self-management of T1DM.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".