ArticleJMIR diabetes2017
One Drop | Mobile: An Evaluation of Hemoglobin A1c Improvement Linked to App Engagement.
Article in JMIR diabetes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- User Engagement With mHealth Interventions to Promote Treatment Adherence and Self-Management in People With Chronic Health Conditions: Systematic Review.Journal of medical Internet research · 2024Pooled it
- The Impact of Digital Self-Monitoring of Weight on Improving Diabetes Clinical Outcomes: Quasi-Randomized Study.Journal of medical Internet research · 2024Trial
- One Drop App With an Activity Tracker for Adults With Type 1 Diabetes: Randomized Controlled Trial.JMIR mHealth and uHealth · 2020Trial
- Association Between Treatment Adherence and Continuous Glucose Monitoring Outcomes in People With Diabetes Using Smart Insulin Pens in a Real-World Setting.Diabetes care · 2024Article
- Evaluation of Mobile Applications for Patients with Diabetes Mellitus: A Scoping Review.Healthcare (Basel, Switzerland) · 2024Article
- Diabetes management in the era of artificial intelligence.Archives of medical sciences. Atherosclerotic diseases · 2024Article
- Sustained Improvements in Readings in-Range Using an Advanced BluetoothDiabetes therapy : research, treatment and education of diabetes and related disorders · 2023Article
- Adherence and Persistence to Insulin Therapy in People with Diabetes: Impact of Connected Insulin Pen Delivery Ecosystem.Journal of diabetes science and technology · 2022Review
- A Nudge-Inspired AI-Driven Health Platform for Self-Management of Diabetes.Sensors (Basel, Switzerland) · 2022Article
- Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults With Type 2 Diabetes Using One Drop: Retrospective Cohort Study.JMIR diabetes · 2022Article
- Managing Diabetes Using Mobiab: Long-Term Case Study of the Impact of a Mobile App on Self-management.JMIR diabetes · 2022Article
- Standardized evaluation of the quality and persuasiveness of mobile health applications for diabetes management.Scientific reports · 2022Article
- Blood Pressure Monitoring as a Digital Health Tool for Improving Diabetes Clinical Outcomes: Retrospective Real-world Study.Journal of medical Internet research · 2022Article
- Estimating the impact of engagement with digital health interventions on patient outcomes in randomized trials.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- The Effects of Continuous Usage of a Diabetes Management App on Glycemic Control in Real-world Clinical Practice: Retrospective Analysis.Journal of medical Internet research · 2021Article
- Role of Digital Engagement in Diabetes Care Beyond Measurement: Retrospective Cohort Study.JMIR diabetes · 2021Article
- International ResearchKit App for Women with Menstrual Pain: Development, Access, and Engagement.JMIR mHealth and uHealth · 2020Article
- A Mobile Lifestyle Management Program (GlycoLeap) for People With Type 2 Diabetes: Single-Arm Feasibility Study.JMIR mHealth and uHealth · 2019Article
- Connecting Smartphone and Wearable Fitness Tracker Data with a Nationally Used Electronic Health Record System for Diabetes Education to Facilitate Behavioral Goal Monitoring in Diabetes Care: Protocol for a Pragmatic Multi-Site Randomized Trial.JMIR research protocols · 2018Article
- Seamless recording of glucometer measurements among older experienced diabetic patients - A study of perception and usability.PloS one · 2018Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThree recent reviews evaluated 19 studies testing the hemoglobin A1c (HbA1c) benefit of 16 diabetes apps, including 5 publicly available apps. Most studies relied on small samples and did not link app engagement with outcomes.
objectiveThis study assessed both HbA1c change in a large sample of people using the One Drop | Mobile app and associations between app engagement and changes in HbA1c.
methodsThe One Drop | Mobile app for iOS and Android is designed to manually and passively (via Apple HealthKit, Google Fit, and the One Drop | Chrome blood glucose meter) store, track, and share data. Users can schedule medication reminders, view statistics, set goals, track health outcomes, and get data-driven insights. In June 2017, we queried data on people with diabetes using the app who had entered at least 2 HbA1c values in the app >60 and ≤365 days apart. Multiple imputation corrected for missing data. Unadjusted and adjusted mixed effects repeated measures models tested mean HbA1c change by time, diabetes type, and their interaction. Multiple regression models assessed relationships between using the app to track food, activity, blood glucose, and medications and HbA1c change.
resultsThe sample (N=1288) included people with type 1 diabetes (T1D) (n=367) or type 2 diabetes (T2D) (n=921) who were 35% female, diagnosed with diabetes for a mean 9.4 (SD 9.9) years, and tracked an average 1646.1 (SD 3621.9) self-care activities in One Drop | Mobile between their first (mean 8.14% [SD 2.06%]) and second HbA1c entry (mean 6.98% [SD 1.1%]). HbA1c values were significantly associated with user-entered average blood glucose 90 days before the second HbA1c entry (rho=.73 to .75, P<.001). HbA1c decreased by an absolute 1.07% (unadjusted and adjusted F=292.03, P<.001) from first to second HbA1c entry. There was a significant interaction between diabetes type and HbA1c. Both groups significantly improved, but users with T2D had a greater HbA1c decrease over time than users with T1D (F=10.54, P<.001). For users with T2D (n=921), HbA1c decreased by an absolute 1.27% (F=364.50, P<.001) from first to second HbA1c entry. Finally, using One Drop | Mobile to record food was associated with greater HbA1c reductions even after adjusting for covariates and after also adjusting for insulin use for users with T2D (all P<.05).
conclusionsPeople with T1D and T2D reported a 1.07% to 1.27% absolute reduction in HbA1c during a median 4 months of using the One Drop | Mobile app. Using the app to track self-care was associated with improved HbA1c. More research is needed on the health benefits of publicly available diabetes apps, particularly studies associating app engagement with short- and long-term effects.
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.