Comparing a Social and Communication App, Telephone Intervention, and Usual Care for Diabetes Self-Management: 3-Arm Quasiexperimental Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Many technology-assisted innovations have been used to manage disease. However, most of these innovations are not broadly used by older adults due to their cost. Additionally, disease management through technology-assisted innovations has not been compared with other interventions. OBJECTIVE: In this study, we tested the employment of a free and widely used social and communication app to help older adults with diabetes manage their distress and glycemic control. We also compared the effectiveness of the app with 2 other methods, namely telephone and conventional health education, and determined which subgroup experiences the most effects within each intervention. METHODS: Adults aged ≥50 years with type 2 diabetes were recruited from Southern Taiwan (N=231) and were allocated to different 3-month interventions. Informed consent was obtained at the Ministry of Science and Technology and approved by the National Cheng Kung University Hospital Institutional Review Board (No. A-ER-102-425). RESULTS: Participants in the mobile-based group had significant reductions in hemoglobin A1c compared with the telephone-based and usual care groups (mean changes of -0.4%, 0.1%, and 0.03%, respectively; P=.02). Diabetes-specific distress decreased to a greater extent in the mobile-based group compared to the other 2 groups (mean changes of -5.16, -3.49, and -2.44, respectively, P=.02). Subgroup analyses further revealed that the effects on reducing blood glucose levels in the social and communication app groups were especially evident in patients with lower distress scores, and diabetes-related distress was especially evident in participants who were younger than 60 years or had higher educational levels. CONCLUSIONS: The findings of this study inform more flexible use of social and communication apps with in-person diabetes education and counselling.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".