Effects of Using a Digital Peer-Supported App on Glycated Hemoglobin Changes Among Patients With Type 2 Diabetes: Prospective Single-Arm Pilot Study
Bibliographic record
Abstract
Background: Controlling glycated hemoglobin (HbA1c) levels can be challenging for patients with type 2 diabetes mellitus (T2DM). Peer support promotes HbA1c control, and a digital peer-supported app designed for group interactions may enable patients with T2DM to encourage one another to achieve better HbA1c outcomes. However, no studies have investigated the use of digital peer-supported apps to control HbA1c levels in patients with T2DM. Objective: This pilot study aimed to explore the effects of a digital peer-supported app on HbA1c control in patients with T2DM. Methods: This prospective single-arm pilot study enrolled patients with T2DM who owned smartphones and visited medical institutions in Japan. During the 3-month intervention, participants used a digital peer-supported app in addition to receiving standard care. This app allowed participants to share activity logs and concerns via a chat function to improve HbA1c levels through mutual engagement and encouragement. The primary outcome was the change in HbA1c levels, measured at health care facilities at baseline and after 3 months. The secondary outcomes were body weight and blood pressure, with the most recent data obtained from hospitals and clinics. Physical activity (≥1 hour/day) was assessed at the same time points using a self-reported questionnaire. Results: The study included 21 participants with a median age of 56 (IQR 51-61) years, of which 13 (61.9%) were female. After using the digital peer-supported app for 3 months, the participants' HbA1c levels significantly decreased from 7.1% (SD 0.6%) at baseline to 6.9% (SD 0.1%) (P=.04). Similarly, participants' body weight decreased from 70.7 (SD 12.7) kg to 69.9 (SD 12.4) kg (P =.004) through app use. Although blood pressure decreased slightly from 128.2 (SD 12.5) mm Hg to 126.0 (SD 12.9) mm Hg, this change was not statistically significant (P=.20). Additionally, the proportion of participants engaged in ≥1 hour of daily physical activity significantly increased from 23.5% (n=4) to 58.5% (n=10) (P=.03). Conclusions: In addition to receiving standard clinical care, the use of a digital peer-supported app may significantly lower HbA1c levels in patients with T2DM by promoting healthy behaviors.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".