Diabetes App-Related Text Messages From Health Care Professionals in Conjunction With a New Wireless Glucose Meter With a Color Range Indicator Improves Glycemic Control in Patients With Type 1 and Type 2 Diabetes: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Mobile diabetes apps enable health care professionals (HCPs) to monitor patient progress, offer remote consultations, and allow more effective and informed treatment decisions between patients and HCPs. The OneTouch Reveal app aggregates data from a blood glucose meter and provides analytics to help patients and HCPs visualize glycemic trends and patterns, enabling more informed treatment and lifestyle decisions. The app also allows patients and HCPs to keep connected by exchanging text messages (short message service [SMS]) or progress reports via email. Objective: The primary objective of our study was to assess changes in glycemic control and overall experiences of patients and HCPs using the app in conjunction with the wireless OneTouch Verio Flex blood glucose meter. Methods: We randomly assigned 137 adults with type 1 (T1DM) or type 2 diabetes mellitus (T2DM) and a glycated hemoglobin (HbA1c) level of ≥7.5% and ≤11.0% to use the glucose meter alone or glucose meter plus the app for 24 weeks. The meter + app group were scheduled to receive diabetes-related text messages from their HCP every 2 weeks (total of 12 texts). Clinical measures and self-reported outcomes were assessed during face-to-face clinic visits between the participant and a diabetes nurse at baseline, week 12, and week 24. Results: In 128 completed participants, HbA1c decreased after 12 and 24 weeks in both the meter-only (n=66) (0.56% and 0.55%, respectively) and meter + app groups (n=62) (0.78% and 0.67%, respectively) compared with baseline (each P<.001). The difference in HbA1c reduction between the 2 groups was not statistically significant at 12 or 24 weeks (P=.12 and P=.45, respectively). However, the decrease in HbA1c was greater in T2DM participants using the meter + app after 12 weeks (1.04%) than in T2DM participants using the meter alone (0.58%; P=.09). In addition, decrease in HbA1c in participants using the meter + app who received at least 10 diabetes-related text messages (1.05%) was significantly greater than in meter-only participants (P<.01). Conclusions: Use of the OneTouch Verio Flex glucose meter alone or in combination with the OneTouch Reveal diabetes app was associated with significant improvements in glycemic control after 12 and 24 weeks. Improvements using the app were greatest in participants with T2DM and those participants who received the highest number of HCP text messages. This study suggests that real-time availability of patient data and the ability to send personalized diabetes-related text messages can assist HCPs to improve glycemic control in patients between scheduled visits. Trial Registration: Clinicaltrials.gov NCT02429024; https://clinicaltrials.gov/ct2/show/NCT02429024 (Archived by WebCite at http://www.webcitation.org/6sCTDRa1l)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| 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 | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".