A Culturally Tailored Diabetes Self-Management Education Program With Mobile Health Integration for Chinese Americans With Type 2 Diabetes: Development and Pilot Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Although progress has been made in improving the efficacy of Diabetes Self-Management Education (DSME) programs, there remains a dearth of research on culturally adapted, evidence-based DSMEs for Chinese Americans (CAs) with type 2 diabetes. OBJECTIVE: Through collaborative partnerships with 2 large community recreation centers and the AHMC Hospital Network in San Gabriel Valley, California, we developed and pilot-tested a culturally tailored DSME program with integrated mobile health (mHealth) technology, entitled Culturally Appropriate Strategies for Chinese Americans with Diabetes (CASCADe). METHODS: The CASCADe program utilized a combined, theoretically driven, and community-participatory approach and was developed based on information gleaned from focus groups, semistructured interviews, and a questionnaire survey conducted among CA patients with diabetes, physicians, and nurses, as well as from extensive literature reviews of evidence-based program curricula. A single-group pre- and posttest design with a 3-month study period was then employed to assess the program's preliminary efficacy. The study protocols were registered on ClinicalTrials.gov. RESULTS: The CASCADe program consisted of (1) a home visit in the first month for training in monitoring device use and WeChat app (a mobile instant-messaging platform widely used in the Chinese population) usage, as well as for acquiring family support; (2) 8 weekly sessions over the following 2 months, delivered in a combined format of group classes, games, group exercises, videos, and discussions; and (3) WeChat follow-up involving education tips, monitoring data summaries, and group discussions after each of the 8 weekly sessions. Topics covered in the weekly sessions included recognition of diabetes and its complications, risk factors, nutrition knowledge, dietary practices, exercise, behavioral self-monitoring, medication adherence, and stress management. The monitoring system used a smartphone to coordinate cloud-based data transmission from a set of wireless devices to capture daily monitoring data on physical activity, body weight, blood pressure, and blood glucose levels. Behavioral self-monitoring was further facilitated by the WeChat app, which provided daily messages related to the diabetes education curriculum; weekly summary reports of monitoring data; feedback; bidirectional 1-on-1 communication between intervention providers and participants; and group discussions among participants regarding readings and the implications of monitoring results. The pre- and postcomparison from the 3-month pilot trial showed a significant reduction in glycated hemoglobin (HbA1c; 7.48 vs 7.09, P=.03), with all but 1 participant demonstrating a reduction and 7 out of 12 (58%) achieving a >0.5 decrease in HbA1c. Significant improvements were also observed in self-efficacy in diabetes management (6.59 vs 8.01, P=.003), quality of life (3.21 vs 3.69, P=.005), and stress-coping skills (3.18 vs 3.74, P=.01) at 3 months after baseline among CA patients with type 2 diabetes. CONCLUSIONS: Our pilot study demonstrated the feasibility of implementing the CASCADe program among CAs to improve diabetes self-management skills and yielded promising results, warranting further evaluation in a larger randomized trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT04737499; https://clinicaltrials.gov/ct2/show/NCT04737499.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".