ArticleJournal of the American Medical Informatics Association : JAMIA2019
Automated meal detection from continuous glucose monitor data through simulation and explanation.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 43 citations in OpenAlex.
- On the road to fully automated insulin delivery: A systematic review of meal announcement free algorithms.PLOS digital health · 2026Article
- Metabolic Responses to Exercise and Nutritional Strategies in Type 1 Diabetes Using Automated Insulin Delivery Systems: A Narrative Review.Metabolites · 2026Review
- Blood-glucose Profile Evaluation with a Model-based Approach using Continuous Glucose Monitoring Data.The AAPS journal · 2026Article
- A personalized and automated real-time meal detection algorithm based on continuous glucose monitoring and heart rate data for individuals with post-bariatric hypoglycemia.Frontiers in digital health · 2026Article
- New Technologies for Blood Glucose Management in Elderly Diabetics: An Interpretation of theAging medicine (Milton (N.S.W)) · 2025Review
- Continuous glucose monitoring as an objective measure of meal consumption in individuals with binge-spectrum eating disorders: A proof-of-concept study.European eating disorders review : the journal of the Eating Disorders Association · 2024Article
- Objective Determination of Eating Occasion Timing: Combining Self-Report, Wrist Motion, and Continuous Glucose Monitoring to Detect Eating Occasions in Adults With Prediabetes and Obesity.Journal of diabetes science and technology · 2024Article
- Multimodal digital phenotyping of diet, physical activity, and glycemia in Hispanic/Latino adults with or at risk of type 2 diabetes.NPJ digital medicine · 2024Article
- Data-driven meal events detection using blood glucose response patterns.BMC medical informatics and decision making · 2023Article
- Detection of Meals and Physical Activity Events From Free-Living Data of People With Diabetes.Journal of diabetes science and technology · 2023Article
- Multivariable Automated Insulin Delivery System for Handling Planned and Spontaneous Physical Activities.Journal of diabetes science and technology · 2023Article
- DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions.Scientific data · 2023Article
- Insulin Delivery Hardware: Pumps and Pens.Diabetes technology & therapeutics · 2023Article
- Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review.Frontiers in nutrition · 2023Article
- Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies.Nutrients · 2022Article
- A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems.Sensors (Basel, Switzerland) · 2022Article
- A New Meal Absorption Model for Artificial Pancreas Systems.Journal of diabetes science and technology · 2022Article
- Dual-hormone artificial pancreas for management of type 1 diabetes: Recent progress and future directions.Artificial organs · 2021Review
- Data-Driven Insights on Behavioral Factors that Affect Diabetes Management.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
- Future Directions for Integrative Objective Assessment of Eating Using Wearable Sensing Technology.Frontiers in nutrition · 2020Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundArtificial pancreas systems aim to reduce the burden of type 1 diabetes by automating insulin dosing. These systems link a continuous glucose monitor (CGM) and insulin pump with a control algorithm, but require users to announce meals, without which the system can only react to the rise in blood glucose.
objectiveWe investigate whether CGM data can be used to automatically infer meals in daily life even in the presence of physical activity, which can raise or lower blood glucose. MATERIALS AND
methodsWe propose a novel meal detection algorithm that combines simulations with CGM, insulin pump, and heart rate monitor data. When observed and predicted glucose differ, our algorithm uses simulations to test whether a meal may explain this difference. We evaluated our method on simulated data and real-world data from individuals with type 1 diabetes.
resultsIn simulated data, we detected meals earlier and with higher accuracy than was found in prior work (25.7 minutes, 1.2 g error; compared with 48.3 minutes, 17.2 g error). In real-world data, we discovered a larger number of plausible meals than was found in prior work (30 meals, 76.7% accepted; compared with 33 meals, 39.4% accepted). DISCUSSION: Prior research attempted meal detection from CGM, but had delays and lower accuracy in real data or did not allow for physical activity. Our approach can be used to improve insulin dosing in an artificial pancreas and trigger reminders for missed meal boluses.
conclusionsWe demonstrate that meal information can be robustly inferred from CGM and body-worn sensor data, even in challenging environments of daily life.
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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.