ArticleJMIR mHealth and uHealth2019
Impact of Use Frequency of a Mobile Diabetes Management App on Blood Glucose Control: Evaluation Study.
Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07591415 (Validation of an App in the Elderly Population With Diabetes Mellitus.), which is not on this map. Cited by 12 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.
Validation of an App in the Elderly Population With Diabetes Mellitus.
Who cites it
12 citing papers in PubMed, 25 citations in OpenAlex.
- Association between various blood glucose variability-related indicators during early ICU admission and 28-day mortality in non-diabetic patients with sepsis.Diabetology & metabolic syndrome · 2025Article
- Exploring mHealth app utilization for diabetes self-management: survey insights from a northern district in Malaysia.BMC public health · 2024Article
- Evaluation of Mobile Applications for Patients with Diabetes Mellitus: A Scoping Review.Healthcare (Basel, Switzerland) · 2024Article
- High engagement in mobile peer support is associated with better glycemic control in type 1 diabetes: A real-world study.Journal of diabetes investigation · 2022Article
- Mobile applications for the sport and exercise nutritionist: a narrative review.BMC sports science, medicine & rehabilitation · 2022Review
- A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin.Sensors (Basel, Switzerland) · 2022Article
- Mobile Health, Disease Knowledge, and Self-Care Behavior in Chronic Kidney Disease: A Prospective Cohort Study.Journal of personalized medicine · 2021Article
- User Engagement With Smartphone Apps and Cardiovascular Disease Risk Factor Outcomes: Systematic Review.JMIR cardio · 2021Review
- An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning.Sensors (Basel, Switzerland) · 2020Article
- User Retention and Engagement With a Mobile App Intervention to Support Self-Management in Australians With Type 1 or Type 2 Diabetes (My Care Hub): Mixed Methods Study.JMIR mHealth and uHealth · 2020Article
- Comparing a Social and Communication App, Telephone Intervention, and Usual Care for Diabetes Self-Management: 3-Arm Quasiexperimental Evaluation Study.JMIR mHealth and uHealth · 2020Article
- Effectiveness of Lilly Connected Care Program (LCCP) App-Based Diabetes Education for Patients With Type 2 Diabetes Treated With Insulin: Retrospective Real-World Study.JMIR mHealth and uHealth · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTechnology has long been used to carry out self-management as well as to improve adherence to treatment in people with diabetes. However, most technology-based apps do not meet the basic requirements for engaging patients.
objectiveThis study aimed to evaluate the effect of use frequency of a diabetes management app on glycemic control.
methodsOverall, 2 analyses were performed. The first consisted of an examination of the reduction of blood glucose (BG) mean, using a randomly selected group of 211 users of the SocialDiabetes app (SDA). BG levels at baseline, month 3, and month 6 were calculated using the intercept of a regression model based on data from months 1, 4, and 7, respectively. In the second analysis, the impact of low and high BG risk was examined. A total of 2692 users logging SDA ≥5 days/month for ≥6 months were analyzed. The highest quartile regarding low blood glucose index (LBGI) and high blood glucose index (HBGI) at baseline (t1) was selected (n=74 for group A; n=440 for group B). Changes in HBGI and LBGI at month 6 (t2) were analyzed.
resultsFor analysis 1, baseline BG results for type 1 diabetes mellitus (T1DM) groups A and B were 213.61 (SD 31.57) mg/dL and 206.43 (SD 18.65) mg/dL, respectively, which decreased at month 6 to 175.15 (SD 37.88) mg/dL and 180.6 (SD 40.47) mg/dL, respectively. For type 2 diabetes mellitus (T2DM), baseline BG was 218.77 (SD 40.18) mg/dL and 232.55 (SD 46.78) mg/dL, respectively, which decreased at month 6 to 160.51 (SD 39.32) mg/dL and 173.14 (SD 52.81) mg/dL for groups A and B, respectively. This represents a reduction of estimated A
conclusionsA significant reduction in BG was found in all groups, independent of the use frequency of the app. Better outcomes were found for T2DM patients. A significant reduction in LBGI and HBGI was found in all groups, regardless of the use frequency of the app. LBGI and HBGI indices of both groups tend to have similar values after 6 months of app use.
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.