Trial reportJournal of diabetes science and technology2015
Combining information of autonomic modulation and CGM measurements enables prediction and improves detection of spontaneous hypoglycemic events.
Trial report in Journal of diabetes science and technology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Sugar slay: a gamified decision support ecosystem for type 1 diabetes.Frontiers in digital health · 2026Article
- Early Detection of Elevated Ketone Bodies in Type 1 Diabetes Using Insulin and Glucose Dynamics Across Age Groups: Model Development Study.JMIR diabetes · 2025Article
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
- Development and Validation of a Nocturnal Hypoglycaemia Risk Model for Patients With Type 2 Diabetes Mellitus.Nursing open · 2024Article
- Predicting risk for nocturnal hypoglycemia after physical activity in children with type 1 diabetes.Frontiers in medicine · 2024Article
- Classification of Gastroparesis from Glycemic Variability in Type 1 Diabetes: A Proof-of-Concept Study.Journal of diabetes science and technology · 2022Article
- A Conditional Generative Adversarial Network for Synthesis of Continuous Glucose Monitoring Signals.Journal of diabetes science and technology · 2022Article
- Hypoglycemia event prediction from CGM using ensemble learning.Frontiers in clinical diabetes and healthcare · 2022Article
- Autonomic cardiac regulation during spontaneous nocturnal hypoglycemia in children with type 1 diabetes.Pediatric diabetes · 2021Observational
- Validation of an Algorithm for Predicting Hypoglycemia From Continuous Glucose Measurements and Heart Rate Variability Data.Journal of diabetes science and technology · 2019Article
- Concise Review: Canine Diabetes Mellitus as a Translational Model for Innovative Regenerative Medicine Approaches.Stem cells translational medicine · 2019Review
- Prediction of In-Hospital Pressure Ulcer Development.Advances in wound care · 2019Article
- Use of Wearable Sensors and Biometric Variables in an Artificial Pancreas System.Sensors (Basel, Switzerland) · 2017Article
- Moving Toward a Unified Platform for Insulin Delivery and Sensing of Inputs Relevant to an Artificial Pancreas.Journal of diabetes science and technology · 2017Review
- Heart-Rate Variability-More than Heart Beats?Frontiers in public health · 2017Review
- Long-Term Prediction of Severe Hypoglycemia in Type 1 Diabetes: Is It Really Possible?Journal of diabetes science and technology · 2016Article
- Sensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review.Sensors (Basel, Switzerland) · 2016Review
- Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications.Journal of diabetes science and technology · 2015Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We have previously tested, in a laboratory setting, a novel algorithm that enables prediction of hypoglycemia. The algorithm integrates information of autonomic modulation, based on heart rate variability (HRV), and data based on a continuous glucose monitoring (CGM) device. Now, we investigate whether the algorithm is suitable for prediction of hypoglycemia and for improvement of hypoglycemic detection during normal daily activities. Twenty-one adults (13 men) with T1D prone to hypoglycemia were recruited and monitored with CGM and a Holter device while they performed normal daily activities. We used our developed algorithm (a pattern classification method) to predict spontaneous hypoglycemia based on CGM and HRV. We compared 3 different models; (i) a model containing raw data from the CGM device; (ii) a CGM* model containing data derived from the CGM device signal; and (iii) a CGM+HRV model-combining model (ii) with HRV data. A total of 12 hypoglycemic events (glucose levels < 3.9 mmol/L, 70 mg/dL) and 237 euglycemic measurements were included. For a 20-minute prediction, model (i) resulted in a ROC AUC of 0.69. If a high sensitivity of 100% was chosen, the corresponding specificity was 69%. (ii) The CGM* model yielded a ROC AUC of 0.92 with a corresponding sensitivity of 100% and specificity of 71%. (iii) The CGM+HRV model yielded a ROC AUC of 0.96 with a corresponding sensitivity of 100% and specificity of 91%. Data shows that adding information of autonomic modulation to CGM measurements enables prediction and improves the detection of hypoglycemia.
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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.