Clinical Utility of a Digital Therapeutic Intervention in Indian Patients With Type 2 Diabetes Mellitus: 12-Week Prospective Single-Arm Intervention Study
Bibliographic record
Abstract
Background Patients with type 2 diabetes mellitus (T2DM) having elevated levels of blood glucose and glycated hemoglobin (HbA1c) are at higher risk of macro- and microvascular complications. Nonetheless, the goal of achieving glycemic control cannot be met with the use of pharmacotherapy alone. The recent emergence of digital therapeutic tools has shown the possibility of improving the modifiable risk factors and self-management of diabetes. Objective The aim of this study was to examine the clinical utility of a digital therapeutic intervention as an add-on therapy to achieve glycemic control in patients with T2DM. Methods This was a 12-week prospective, single-arm digital intervention study in patients with T2DM receiving regular antidiabetic treatment. The eligibility criteria included male and female patients with HbA1c≥6.5%, functional English literacy, and a mobile phone capable of running the intervention app. Outcome measures of the study were mean changes in HbA1c, fasting blood glucose (FBG), postprandial blood glucose (PPBG), BMI, and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) index at the end of 12 weeks. Results A total of 128 participants completed the study period of 12 weeks. There were 54.7% (70/128) men and 45.3% (58/128) women with a mean age of 48.48 years (SD 10.27). At the end of 12 weeks, the mean change in HbA1c, FBG, PPBG, and BMI for the overall study population was –0.84% (P<.001), –8.39 mg/dl (P=.02), –14.97 mg/dl (P<.001), and –0.24 kg/m2 (P=.06), respectively. Among the participants showing improvement in the HbA1c value at the end of 12 weeks (responders), the mean change in HbA1c, FBG, PPBG, and BMI was –1.24% (P<.001), –12.42 mg/dl (P=.003), –21.45 mg/dl (P<.001), and –0.34 kg/m2 (P=.007), respectively. There was an increase in HOMA-IR values for the overall study population (0.54, P=.29). HbA1c response showed a significant association with a baseline HbA1c level ≥7.5%, no prior history of smoking, and no prior COVID-19 infection, as well as with higher levels of program engagement. Conclusions A digital therapeutic intervention when used alongside standard medications significantly reduces HbA1c, FBG, and PPBG levels in patients with T2DM.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".