Real-World Effectiveness of My Dose Coach™-Assisted Basal Insulin Titration in People with Type 2 Diabetes in Saudi Arabia and Kuwait
Bibliographic record
Abstract
My Dose Coach (MDC) is a digital smartphone application approved in multiple countries, including Saudi Arabia and Kuwait, to help people with type 2 diabetes (T2D) titrate their basal insulin as per their clinician-guided, individualized diabetes care plan. A retrospective, observational cohort analysis was conducted on MDC user data collected from 1 January 2021 to 1 June 2023 in Saudi Arabia and Kuwait. Primary outcome was change in fasting blood glucose (FBG). Key secondary outcomes included time to achieve FBG and HbA 1c targets, and time to first hypoglycemia event. Outcomes were analyzed by FBG target status and frequency of MDC usage (high: > 3 days per week; moderate: > 1– ≤ 3 days per week; low: ≤ 1 day per week). Among all users ( N = 494), mean ± SD FBG decrease was −44.4 ± 72.5 mg/dL. Mean ± SD time to achieve FBG target was 14.8 ± 20.9 days and 12.8 ± 18.8, 29.1 ± 28.0, and 43.5 ± 41.7 days for high-, moderate-, and low-frequency MDC users, respectively. Individualized FBG targets were achieved by 276 (55.9%) users, and high-frequency of MDC use was associated with better target achievement ( p < 0.01). Mean ± SD time to achieve HbA 1c target was 48.0 ± 40.5 days. Reduction in HbA 1c was more in high-frequency MDC users (18.3%) than low-frequency MDC users (6.3%). Mean ± SD time to the first hypoglycemia event was 4.86 ± 4.8 days. Hypoglycemia events were reported in only seven (1.4%) participants and not significantly correlated with MDC use frequency ( p = 0.1431). Current findings show that using MDC is associated with improved glycemic control in people with T2D in Saudi Arabia and Kuwait, with greater benefits observed with higher frequency MDC usage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".