Testing a Smartphone App (Young with Diabetes) to Improve Self-Management of Diabetes Over 12 Months: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Young people often struggle to self-manage type 1 diabetes during the transition from childhood to adulthood. Mobile health (mHealth) apps may have the potential to support self-management, but evidence is limited and randomized controlled trials are needed. Objective: We assessed whether the mHealth app “Young with Diabetes” improved young people’s self-management measured by glycated hemoglobin (HbA1c) and three self-reported psychometric scales. Methods: Young people (14-22 years) with inadequate glycemic control and their parents were enrolled in a randomized controlled trial and assigned either to Young with Diabetes and usual care (Young with Diabetes group) or to usual care alone (control). Young with Diabetes use was monitored; functions included a chat room, contact the health care provider, reminders, tips, information about the diabetes department and type 1 diabetes topics, carbohydrate counting, and a parents’ section. Outcomes included HbA1c and three self-reported psychometric scales: Perceived Competence in Diabetes Scale; Health Care Climate Questionnaire; and Problem Areas In Diabetes care survey. Data were collected at baseline and at 2, 7, and 12 months. Results: A total of 151 young people were randomized (Young with Diabetes group=76, control=75) and 49 parents agreed to participate. At 12 months, HbA1c was significantly higher (4.1 mmol/mol; 0.4 %) in the Young with Diabetes group, compared to the control group (P=.04); this finding did not occur when comparing app users (Young with Diabetes use ≥5 days) with nonusers. Young people used Young with Diabetes on a mean of 10.5 days. They spent the most time chatting about alcohol and searching for information about sex. Most young people and half of the parents reported that Young with Diabetes helped them. More than 80% would recommend Young with Diabetes to peers. Conclusions: Young with Diabetes did not improve HbA1c, but it may be a useful complement to self-management. Qualitative evaluation is needed to explore benefits and shortcomings of Young with Diabetes. Health care providers should address young peoples’ knowledge about sensitive topics, provide them with peer support, and be aware of parents’ need for information about how to support Trial Registration: ClinicalTrials.gov NCT02632383; https://clinicaltrials.gov/ct2/show/NCT02632383 (Archived by WebCite at http://www.webcitation.org/6zCK2u7xM)
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".