Trial reportJournal of diabetes science and technology2019
Prediction of Hypoglycemia During Aerobic Exercise in Adults With Type 1 Diabetes.
Trial report in Journal of diabetes science and technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
What it found
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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
32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 73 citations in OpenAlex.
- Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.Frontiers in public health · 2023Pooled it
- Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments.Diabetes/metabolism research and reviews · 2021Pooled it
- Prediction of Nocturnal Hypoglycemia Following Exercise in Type 1 Diabetes Using Temporally Structured CGM-Derived Digital Biomarkers.Sensors (Basel, Switzerland) · 2026Article
- GlucoseGo: a simple tool to predict hypoglycaemia during exercise in type 1 diabetes.Diabetologia · 2026Observational
- Transforming hypoglycemia prediction in adult type 1 diabetes: a systematic review and meta-analysis for precision care.Open life sciences · 2026Article
- Temporal gradient analysis of blood glucose responses to non-standard physical activity: a free-living study in type 1 diabetes.Frontiers in sports and active living · 2026Article
- Managing Exercise-Related Glycemic Events in Type 1 Diabetes: Development and Validation of Predictive Models for a Practical Decision Support Tool.JMIR diabetes · 2025Article
- Artificial Intelligence as a Tool for Self-Care in Patients with Type 1 and Type 2 Diabetes-An Integrative Literature Review.Healthcare (Basel, Switzerland) · 2025Review
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
- Impact of structure and formulation changes on the function of insulin products.Frontiers in endocrinology · 2025Review
- Design of a Real-Time Physical Activity Detection and Classification Framework for Individuals With Type 1 Diabetes.Journal of diabetes science and technology · 2024Article
- Explainable hypoglycemia prediction models through dynamic structured grammatical evolution.Scientific reports · 2024Article
- Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis.JMIR medical informatics · 2023Article
- The Type 1 Diabetes and EXercise Initiative: Predicting Hypoglycemia Risk During Exercise for Participants with Type 1 Diabetes Using Repeated Measures Random Forest.Diabetes technology & therapeutics · 2023Article
- Hypoglycemia risk with physical activity in type 1 diabetes: a data-driven approach.Frontiers in digital health · 2023Article
- Effects of Aerobic Exercise on Systemic Insulin Degludec Concentrations in People with Type 1 Diabetes.Journal of diabetes science and technology · 2023Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
- A hypoglycemia early alarm method for patients with type 1 diabetes based on multi-dimensional sequential pattern mining.Heliyon · 2022Article
- The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey.Journal of medical Internet research · 2022Review
- Quantifying the impact of physical activity on future glucose trends using machine learning.iScience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundFear of exercise related hypoglycemia is a major reason why people with type 1 diabetes (T1D) do not exercise. There is no validated prediction algorithm that can predict hypoglycemia at the start of aerobic exercise.
methodsWe have developed and evaluated two separate algorithms to predict hypoglycemia at the start of exercise. Model 1 is a decision tree and model 2 is a random forest model. Both models were trained using a meta-data set based on 154 observations of in-clinic aerobic exercise in 43 adults with T1D from 3 different studies that included participants using sensor augmented pump therapy, automated insulin delivery therapy, and automated insulin and glucagon therapy. Both models were validated using an entirely new validation data set with 90 exercise observations collected from 12 new adults with T1D.
resultsModel 1 identified two critical features predictive of hypoglycemia during exercise: heart rate and glucose at the start of exercise. If heart rate was greater than 121 bpm during the first 5 min of exercise and glucose at the start of exercise was less than 182 mg/dL, it predicted hypoglycemia with 79.55% accuracy. Model 2 achieved a higher accuracy of 86.7% using additional features and higher complexity.
conclusionsModels presented here can assist people with T1D to avoid exercise related hypoglycemia. The simple model 1 heuristic can be easily remembered (the 180/120 rule) and model 2 is more complex requiring computational resources, making it suitable for automated artificial pancreas or decision support systems.
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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.