ArticleJAMA network open2021
Feasibility of Electronic Health Record Assessment of 6 Pediatric Type 1 Diabetes Self-management Habits and Their Association With Glycemic Outcomes.
Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Equity of Continuous Glucose Monitoring in Children and Young People With Type 1 Diabetes: A Systematic Review.Pediatric diabetes · 2025Pooled it
- Establishment of a Diabetes-Tailored Data Intelligence Platform Enhances Clinical Care, Enables Risk-Based Monitoring, and Facilitates Population-Health-Based Approaches at a Pediatric Diabetes Network.Journal of diabetes science and technology · 2025Article
- Toward a Clinically Actionable, Electronic Health Record-Based Machine Learning Model to Forecast 90-Day Change in Hemoglobin AJMIR diabetes · 2025Article
- The Influence of a Christian, Seventh-Day Adventist Lifestyle Based on Religious Principles on the Risk of Developing Type 2 Diabetes Mellitus.Healthcare (Basel, Switzerland) · 2025Article
- Predicting and Ranking Diabetic Ketoacidosis Risk Among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model.Diabetes technology & therapeutics · 2025Article
- ROCKET T1D Remote Patient Monitoring Program: Launching Diabetes Management Habits in New-Onset Diabetes.Clinical diabetes : a publication of the American Diabetes Association · 2025Article
- Augmenting clinicians' analytical workflow through task-based integration of data visualizations and algorithmic insights: a user-centered design study.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Simplified integration of optimal self-management behaviors is associated with improved HbA1c in patients with type 1 diabetes.Journal of endocrinological investigation · 2024Observational
- Learning control-ready forecasters for Blood Glucose Management.Computers in biology and medicine · 2024Article
- The Glycemia Risk Index Predicts Performance of Diabetes Self-Management Habits in Youth With Type 1 Diabetes Mellitus.Journal of diabetes science and technology · 2024Article
- Screening for diabetes distress and depression in routine clinical care for youth with type 1 diabetes.Journal of pediatric psychology · 2024Article
- Technology and health inequities in diabetes care: How do we widen access to underserved populations and utilize technology to improve outcomes for all?Diabetes, obesity & metabolism · 2024Article
- Shining the Spotlight on Multiple Daily Insulin Therapy: Real-World Evidence of the InPen Smart Insulin Pen.Diabetes technology & therapeutics · 2024Article
- The Launch of the iCoDE-2 Standard Project: Integration of Connected Diabetes Device Data Into the Electronic Health Record.Journal of diabetes science and technology · 2024Article
- The Design of the Electronic Health Record in Type 1 Diabetes Centers: Implications for Metrics and Data Availability for a Quality Collaborative.Journal of diabetes science and technology · 2024Article
- Blue Circle Health: A Novel Patient-Centered Model of Health Care Delivery for Low-Income Patients With Type 1 Diabetes.Journal of diabetes science and technology · 2023Article
- Roadmap to Achieving Continuous Glucose Monitoring Equity: Insights From the T1D Exchange Quality Improvement Collaborative.Diabetes spectrum : a publication of the American Diabetes Association · 2023Article
- Disparities in Glycemic Outcomes Persist in Youth with Type 1 Diabetes and High-Technology Use.Pediatric diabetes · 2023Article
- Language Matters in Diabetes and in Diabetes Science and Research.Journal of diabetes science and technology · 2022Article
- Understanding the needs of people aging with type 1 diabetes: a user-focused research study with prototype diabetes advance directive and setting the stage for future research.Journal of diabetes and metabolic disorders · 2022Article
Corrections and comments
- Commented on by
- Erratum issued
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
Importance: A low-burden electronic health record (EHR) workflow has been devised to systematize the collection and validation of 6 key diabetes self-management habits: (1) checks glucose at least 4 times/day or uses continuous glucose monitor (CGM); (2) gives at least 3 rapid-acting insulin boluses per day; (3) uses insulin pump; (4) delivers boluses before meals; (5) reviewed glucose data since last clinic visit, and (6) has changed insulin doses since the last clinic visit. Objective: To describe the performance of these habits and examine their association with hemoglobin A1c (HbA1c) levels and time in range (TIR). Design, Setting, and Participants: This cross-sectional study included individuals with known type 1 diabetes who were seen in a US pediatric diabetes clinic in 2019. Main Outcomes and Measures: Habit performance, total habit score (sum of 6 habits per person), HbA1c levels, and TIR. Results: Of 1344 patients, 1212 (609 [50.2%] males; 66 [5.4%] non-Hispanic Black; 1030 [85.0%] non-Hispanic White; mean [SD] age, 15.5 [4.5] years) were included, of whom 654 (54.0%) were using CGM and had a TIR. Only 105 patients (8.7%) performed all 6 habits. Habit performance was lower among older vs younger patients (age ≥18 years vs ≤12 years: 17 of 411 [4.1%] vs 57 of 330 [17.3%]; P < .001), Black vs White patients (3 [4.5%] vs 95 [9.2%]; P < .001), those with public vs private insurance (14 of 271 [5.2%] vs 91 of 941 [9.7%]; P < .001), and those with lower vs higher parental education levels (<college degree vs ≥college degree: 35 of 443 [7.9%] vs 66 of 574 [11.5%]; P < .001). After adjustment for demographic characteristics and disease duration, for every 1-unit increase in total habit score, we found a mean (SE) 0.6% (0.05) decrease in HbA1c among all participants and a mean (SE) 2.86% (0.71) increase in TIR among those who used CGMs. Multiple regression models revealed that performing each habit was associated with a significantly lower HbA1c level (habit 1: -0.16% [95% CI, -1.91% to -1.37%]; habit 2: -1.01% [-1.34% to -0.69%]; habit 3: -0.71% [95% CI, -0.93% to -0.49%]; habit 4: -0.97% [95% CI, -1.21% to -0.73%]; habit 5: -0.44% [95% CI, -0.71% to -0.17%]; habit 6: -0.75% [95% CI, -0.96% to -0.53%]; all P < .001). There were differences in HbA1c according to race, insurance, and parental education, but these associations were attenuated with the inclusion of the 6 habits, which had more robust associations with HbA1c levels than the demographic characteristics. Conclusions and Relevance: These findings suggest that a focus on increasing adherence to the 6 habits could be critical for improving disparities in glycemic outcomes; these metrics have been adopted by the Type 1 Diabetes Exchange Quality Improvement Collaborative for continuous quality improvement.
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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.