Web-Based TangPlan and WeChat Combination to Support Self-management for Patients With Type 2 Diabetes: Randomized Controlled Trial
Bibliographic record
Abstract
Background China has the largest number of patients with type 2 diabetes mellitus (T2DM) in the world. However, owing to insufficient knowledge of self-management in patients with diabetes, blood glucose (BG) control is poor. Most diabetes-related self-management applications fail to bring significant benefits to patients with T2DM because of the low use rate and difficult operation. Objective This study aims to examine the effectiveness of the combination of the self-designed web-based T2DM management software TangPlan and WeChat on fasting BG (FBG), glycated hemoglobin (HbA1c), body weight, blood pressure (BP), and lipid profiles in patients with T2DM over a 6-month period. Methods Participants were recruited and randomized into the TangPlan and WeChat or control groups. Participants in the control group received usual care, whereas the TangPlan and WeChat participants received self-management guidance with the help of TangPlan and WeChat from health care professionals, including BG self-monitoring; healthy eating; active physical exercise; increasing medication compliance; and health education during follow-ups, lectures, or web-based communication. They were also asked to record and send self-management data to the health care professionals via WeChat to obtain timely and effective guidance on diabetes self-management. Results In this study, 76.9% (120/156) of participants completed the 6-month follow-up visit. After the intervention, FBG (mean 6.51, SD 1.66 mmol/L; P=.048), HbA1c (mean 6.87%, SD 1.11%; P<.001), body weight (mean 66.50, SD 9.51 kg; P=.006), systolic BP (mean 127.03, SD 8.00 mm Hg; P=.005), diastolic BP (mean 75.25, SD 5.88 mm Hg; P=.03), serum low-density lipoprotein cholesterol (mean 2.50, SD 0.61 mmol/L; P=.006), and total cholesterol (mean 4.01, SD 0.83 mmol/L; P=.02) in the TangPlan and WeChat group were all significantly lower, whereas serum high-density lipoprotein cholesterol (mean 1.20, SD 0.25 mmol/L; P=.01) was remarkably higher than in those in the control group. Compared with the baseline data, significance was found in the mean change in FBG (95% CI −0.83 to −0.20; P=.002), HbA1c (95% CI −1.92 to −1.28; P<.001), body weight (95% CI −3.13 to −1.68; P<.001), BMI (95% CI −1.10 to −0.60; P<.001), systolic BP (95% CI −7.37 to −3.94; P<.001), diastolic BP (95% CI −4.52 to −2.33; P<.001), triglycerides (95% CI −0.16 to −0.03; P=.004), serum low-density lipoprotein cholesterol (95% CI −0.54 to −0.30; P<.001), and total cholesterol (95% CI −0.60 to −0.34; P<.001) in the TangPlan and WeChat group but not in the control group (P=.08-.88). Conclusions Compared with usual care for patients with T2DM, the combination of TangPlan and WeChat was effective in improving glycemic control (decrease in HbA1c and BG levels) and serum lipid profiles as well as reducing body weight in patients with T2DM after 6 months. Trial Registration Chinese Clinical Trial Registry ChiCTR2000028843; https://tinyurl.com/559kuve6
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".