ArticleJournal of diabetes science and technology2024
Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App.
Article in Journal of diabetes science and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Evaluation of a Prediction-Based Bedtime Intervention in Reducing Nocturnal Low Glucose in Adults With Type 1 Diabetes: The DailyDose Bedtime Smart Snack Crossover Study.Diabetes care · 2025Trial
- Prediction of Nocturnal Hypoglycemia Following Exercise in Type 1 Diabetes Using Temporally Structured CGM-Derived Digital Biomarkers.Sensors (Basel, Switzerland) · 2026Article
- Integrating Pharmacists into CGM-Enabled Digital Diabetes Care: Advancing Personalized and Data-Driven Management.Healthcare (Basel, Switzerland) · 2026Review
- Digital physiological biomarkers predict within-person symptom changes in complex chronic illness.NPJ digital medicine · 2026Article
- Optimizing Continuous Glucose Monitoring Adoption in India: From Current Challenges to Future Solutions.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2026Review
- Continuous Glucose Monitoring Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications.Journal of diabetes science and technology · 2025Article
- Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review.Annals of translational medicine · 2025Review
- And the Dog Was Barking: Transforming Quality of Life in Diabetes Through Innovative Hypoglycemia Detection.Journal of diabetes · 2025Article
- Towards a decision support system for post bariatric hypoglycaemia: development of forecasting algorithms in unrestricted daily-life conditions.BMC medical informatics and decision making · 2025Article
- Concept and Implementation of a Novel Continuous Glucose Monitoring Solution With Glucose Predictions on Board.Journal of diabetes science and technology · 2024Article
- Fear of Hypoglycemia and Diabetes Distress: Expected Reduction by Glucose Prediction.Journal of diabetes science and technology · 2024Article
- The Promise of Hypoglycemia Risk Prediction.Journal of diabetes science and technology · 2024Article
- Clinical Usage and Potential Benefits of a Continuous Glucose Monitoring Predict App.Journal of diabetes science and technology · 2024Article
- Predicting Glucose Values: A New Era for Continuous Glucose Monitoring.Journal of diabetes science and technology · 2024Article
- Continuous Glucose Monitoring: A Transformative Approach to the Detection of Prediabetes.Journal of multidisciplinary healthcare · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite abundant evidence demonstrating the benefits of continuous glucose monitoring (CGM) in diabetes management, a significant proportion of people using this technology still struggle to achieve glycemic targets. To address this challenge, we propose the Accu-Chek
methodsThe app's functionalities, powered by three machine learning models, include a two-hour glucose forecast, a 30-minute low glucose detection, and a nighttime low glucose prediction for bedtime interventions. Evaluation of the models' performance included three data sets, comprising subjects with T1D on MDI (n = 21), subjects with type 2 diabetes (T2D) on MDI (n = 59), and subjects with T1D on insulin pump therapy (n = 226).
resultsOn an aggregated data set, the two-hour glucose prediction model, at a forecasting horizon of 30, 45, 60, and 120 minutes, achieved a percentage of data points in zones A and B of Consensus Error Grid of: 99.8%, 99.3%, 98.7%, and 96.3%, respectively. The 30-minute low glucose prediction model achieved an accuracy, sensitivity, specificity, mean lead time, and area under the receiver operating characteristic curve (ROC AUC) of: 98.9%, 95.2%, 98.9%, 16.2 minutes, and 0.958, respectively. The nighttime low glucose prediction model achieved an accuracy, sensitivity, specificity, and ROC AUC of: 86.5%, 55.3%, 91.6%, and 0.859, respectively.
conclusionsThe consistency of the performance of the three predictive models when evaluated on different cohorts of subjects with T1D and T2D on different insulin therapies, including real-world data, offers reassurance for real-world efficacy.
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