eHealth Applications Improve Glycemic Control in Patients With Diabetes: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The eHealth app introduced a chronic disease management module to enhance the efficiency of clinical consultations and facilitate self-health management. However, there was limited information on health outcomes after using the module. OBJECTIVE: This study aimed to examine the health outcomes of individuals who used the health management module of an individualized electronic application compared to those who did not. METHODS: A randomized controlled trial was conducted with 165 participants, with 82 participants assigned to the control group and 83 participants assigned to the intervention group. Randomization was done via a computer randomizer to evaluate the impact of the eHealth chronic disease management module installation on clinical outcomes such as blood pressure, hemoglobin A1c (HbA1c), renal function tests, estimated glomerular filtration rate, and urine albumin/creatinine ratio. Data were collected at baseline and at follow-up visits at 4 and 8 months. Student t tests and chi-square tests were performed to analyze the difference between the intervention and control groups and examined the potential impact of the use of the eHealth chronic disease management module on various health outcomes. RESULTS: In total, 161 participants were included in the analysis, with an average age of 66.58 (SD 9.75) and 66.49 (SD 8.45) years in control and intervention group respectively. After 4 months, the intervention group showed better glycemic control, with significantly lower mean HbA1c levels (mean 6.76%, SD 0.64%) compared to the control group (mean 7.09%, SD 0.82%, P=.007). Also, more participants in intervention group achieved optimal HbA1c levels (n=58, 73.4%; P=.004) compared to the control group (n=36, 49.3%) in month 4. App usage had significantly decreased when comparing the usage after 4 months (mean 1.88 points, SD 0.81 points) and month 8 (mean 1.39 points, SD 0.72 points; P<.001). The results indicated better glycemic control for participants using the module in a relatively shorter period of time, and app adherence was the key for the continuous optimal glycemic control. CONCLUSIONS: These findings support the potential of the module for clinical application in patients with suboptimal glycemic control. The long-term benefit of the module may be affected by the compliance of participants to the module. TRIAL REGISTRATION: Chinese Clinical Trial Register ChiCTR2500108895; https://www.chictr.org.cn/showprojEN.html?proj=214865.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| 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".