ArticleIEEE transactions on bio-medical engineering2021
Physical Activity and Psychological Stress Detection and Assessment of Their Effects on Glucose Concentration Predictions in Diabetes Management.
Article in IEEE transactions on bio-medical engineering, 2021. 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, 70 citations in OpenAlex.
- Poor Glycemic Control in East Africa: Prevalence, Risk Factors and Public Health Implications in Diabetes Management.Endocrinology, diabetes & metabolism · 2026Review
- Work-Related Stress and Glucose Regulation in Air Traffic Control Officers: Implications for Medical Certification.Biomedicines · 2025Review
- Metabolic Models, in Silico Trials, and Algorithms.Diabetes technology & therapeutics · 2025Review
- Metabolic Models, in Silico Trials, and Algorithms.Journal of diabetes science and technology · 2025Review
- Mindfulness-based interventions for adults with type 2 diabetes mellitus: A systematic review and meta-analysis.Integrative medicine research · 2025Review
- Wearable Physiological Signals under Acute Stress and Exercise Conditions.Scientific data · 2025Article
- Association of Blood Glucose Data With Physiological and Nutritional Data From Dietary Surveys and Wearable Devices: Database Analysis.JMIR diabetes · 2024Article
- Perceived stress and associations between physical activity, sedentary time, and interstitial glucose in healthy adolescents.Physiology & behavior · 2024Article
- Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App.Journal of diabetes science and technology · 2024Article
- Recent advances in the precision control strategy of artificial pancreas.Medical & biological engineering & computing · 2024Review
- Participatory Development of an Integrated, eHealth-Supported, Educational Care Pathway (Diabetes Box) for People With Type 2 Diabetes: Development and Usability Study.JMIR human factors · 2024Article
- Why Should Diabetic Women Be Active?-The Role of Personality, Self-Esteem, Body-Esteem, and Imagery.Healthcare (Basel, Switzerland) · 2024Article
- NIH Fifth Artificial Pancreas Workshop 2023: Meeting Report: The Fifth Artificial Pancreas Workshop: Enabling Fully Automation, Access, and Adoption.Journal of diabetes science and technology · 2024Article
- Strengths and Challenges of Closed-Loop Insulin Delivery During Exercise in People With Type 1 Diabetes: Potential Future Directions.Journal of diabetes science and technology · 2023Article
- Adaptive Personalized Prior-Knowledge-Informed Model Predictive Control for Type 1 Diabetes.Control engineering practice · 2023Article
- Blood glucose forecasting from temporal and static information in children with T1D.Frontiers in pediatrics · 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
- Review
- MsWH: A Multi-Sensory Hardware Platform for Capturing and Analyzing Physiological Emotional Signals.Sensors (Basel, Switzerland) · 2022Article
- Diabetes Technology Meeting 2021.Journal of diabetes science and technology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
objectiveContinuous glucose monitoring (CGM) enables prediction of the future glucose concentration (GC) trajectory for making informed diabetes management decisions. The glucose concentration values are affected by various physiological and metabolic variations, such as physical activity (PA) and acute psychological stress (APS), in addition to meals and insulin. In this work, we extend our adaptive glucose modeling framework to incorporate the effects of PA and APS on the GC predictions.
methodsA wristband conducive of use by free-living ambulatory people is used. The measured physiological variables are analyzed to generate new quantifiable input features for PA and APS. Machine learning techniques estimate the type and intensity of the PA and APS when they occur individually and concurrently. Variables quantifying the characteristics of both PA and APS are integrated as exogenous inputs in an adaptive system identification technique for enhancing the accuracy of GC predictions. Data from clinical experiments illustrate the improvement in GC prediction accuracy.
resultsThe average mean absolute error (MAE) of one-hour-ahead GC predictions with testing data decreases from 35.1 to 31.9 mg/dL (p-value = 0.01) with the inclusion of PA information, and it decreases from 16.9 to 14.2 mg/dL (p-value = 0.006) with the inclusion of PA and APS information.
conclusionThe first-ever glucose prediction model is developed that incorporates measures of physical activity and acute psychological stress to improve GC prediction accuracy. SIGNIFICANCE: Modeling the effects of physical activity and acute psychological stress on glucose concentration values will improve diabetes management and enable informed meal, activity and insulin dosing decisions.
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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.