ArticleJMIR diabetes2023
Impact of a Combined Continuous Glucose Monitoring-Digital Health Solution on Glucose Metrics and Self-Management Behavior for Adults With Type 2 Diabetes: Real-World, Observational Study.
Article in JMIR diabetes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 23 citations in OpenAlex.
- Article
- App-Based Digital Therapeutics Integrating Continuous Glucose Monitoring for Glycemic Control in Type 2 Diabetes: Prospective Observational Cohort Study.JMIR diabetes · 2026Article
- Exploring provider attitudes towards digital therapeutics for diabetes and depression.Scientific reports · 2026Article
- Continuous Glucose Monitoring for Personalized Nutrition in Real-World Vively App Users: Retrospective Observational Study.JMIR human factors · 2026Observational
- A digital-health multidomain lifestyle management framework and its associations with cardiometabolic health: a real-world observational study.BMC medicine · 2026Observational
- The application of AI-based interventions in diabetes personalized management: a systematic review and meta-analysis.Diabetology & metabolic syndrome · 2026Review
- 7. Diabetes Technology: Standards of Care in Diabetes-2026.Diabetes care · 2026Review
- Nurse-led digital therapeutics for type 2 diabetes in routine care: a retrospective cohort study of adherence, glycemic outcomes, and care continuity.Frontiers in public health · 2026Article
- Digital Bioimpedance for Physical Activity Detection in Type-2 Diabetes: Quasi-Experimental Validation Study.JMIR diabetes · 2025Article
- Therapeutic Patient Education in the Digital Era: Opportunities and Challenges in Diabetes Care.Mayo Clinic proceedings. Digital health · 2025Review
- Systematic review of dynamically tailored eHealth interventions targeting physical activity and healthy diet in chronic disease.NPJ digital medicine · 2025Article
- Real-World and Clinical Implications of Patient Education, Lifestyle and Treatment Adherence in Romanian Diabetes Care: An Observational Study.Journal of clinical medicine · 2025Article
- Initiation of Continuous Glucose Monitoring and Mortality in Type 2 Diabetes.Diabetes technology & therapeutics · 2025Article
- The Association of Previous Day Carbohydrate Consumption With Fasted, Exhaled Carbon Dioxide in Lumen Users: Retrospective Real-World Study.JMIR mHealth and uHealth · 2025Observational
- A Pilot Study Examining If the Additional Use of a Continuous Glucose Monitoring Is Helpful for Glucose Control in Older Adults.Yonsei medical journal · 2025Article
- Temporal Glycemic Patterns in Type 1 and Type 2 Diabetes: Insights From Extended Continuous Glucose Monitoring.Journal of diabetes science and technology · 2025Article
- AI Applications for Chronic Condition Self-Management: Scoping Review.Journal of medical Internet research · 2025Article
- An interventional study of digital therapy on blood glucose and weight management in Chinese patients with type 2 diabetes.Journal of diabetes investigation · 2025Article
- Personalized cardiometabolic care powered by artificial intelligence.Frontiers in endocrinology · 2025Article
- 7. Diabetes Technology: Standards of Care in Diabetes-2025.Diabetes care · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe BlueStar (Welldoc) digital health solution for people with diabetes incorporates data from multiple devices and generates coaching messages using artificial intelligence. The BlueStar app syncs glucose data from the G6 (Dexcom) real-time continuous glucose monitoring (RT-CGM) system, which provides a glucose measurement every 5 minutes.
objectiveThe objective of this real-world study of people with type 2 diabetes (T2D) using the digital health solution and RT-CGM was to evaluate change in glycemic control and engagement with the program over 3 months.
methodsParticipants were current or former enrollees in an employer-sponsored health plan, were aged 18 years or older, had a T2D diagnosis, and were not using prandial insulin. Outcomes included CGM-based glycemic metrics and engagement with the BlueStar app, including logging medications taken, exercise, food details, blood pressure, weight, and hours of sleep.
resultsParticipants in the program that met our analysis criteria (n=52) were aged a mean of 53 (SD 9) years; 37% (19/52) were female and approximately 50% (25/52) were taking diabetes medications. The RT-CGM system was worn 90% (SD 8%) of the time over 3 months. Among individuals with suboptimal glycemic control at baseline, defined as mean glucose >180 mg/dL, clinically meaningful improvements in glycemic control were observed, including reductions in a glucose management indicator (-0.8 percentage points), time above range 181-250 mg/dL (-4.4 percentage points) and time above range >250 mg/dL (-14 percentage points; all P<.05). Time in range 70-180 mg/dL also increased by 15 percentage points (P=.016) in this population, which corresponds to an increase of approximately 3.5 hours per day in the target range. Over the 3-month study, 29% (15/52) of participants completed at least one engagement activity per week. Medication logging was completed most often by participants (23/52, 44%) at a rate of 12.1 (SD 0.8) events/week, and this was closely followed by exercise and food logging.
conclusionsThe combination of an artificial intelligence-powered digital health solution and RT-CGM helped people with T2D improve their glycemic outcomes and diabetes self-management behaviors.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.