ArticleNPJ digital medicine2021
Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
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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
35 citing papers in PubMed.
- Multimodal In-Vehicle Hypoglycemia Warning for Drivers With Type 1 Diabetes: Design and Evaluation in Simulated and Real-World Driving.JMIR human factors · 2024Trial
- Noninvasive Interstitial Glucose Estimation Using Wearables and Machine Learning in Healthy Individuals and Individuals With Obesity: Observational Cohort Study.JMIR mHealth and uHealth · 2026Observational
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Self-powered intelligence for personalized healthcare.National science review · 2026Review
- Digital physiological biomarkers predict within-person symptom changes in complex chronic illness.NPJ digital medicine · 2026Article
- Patient-specific deep offline artificial pancreas for blood glucose regulation in type 1 diabetes.Smart health (Amsterdam, Netherlands) · 2026Article
- The accuracy of Apple Watch measurements: a living systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Review
- The accuracy of VO2 max, heart rate and energy expenditure measurements from Google Pixel Watch 3.PloS one · 2026Article
- Within-person modeling of postprandial glucose using multimodal wearable data.Frontiers in digital health · 2026Article
- A deep learning-derived digital biomarker of dysglycemia and its association with genetic risk of type 2 diabetes.npj metabolic health and disease · 2025Article
- Artificial Intelligence in Diabetes Care: Applications, Challenges, and Opportunities Ahead.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2025Review
- Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes.NPJ digital medicine · 2025Article
- Digital biomarkers for interstitial glucose prediction in healthy individuals using wearables and machine learning.Scientific reports · 2025Article
- Predicting postprandial glucose excursions to personalize dietary interventions for type-2 diabetes management.Scientific reports · 2025Article
- Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.Journal of diabetes science and technology · 2025Review
- Variability of the Skin Temperature from Wrist-Worn Device for Definition of Novel Digital Biomarkers of Glycemia.Sensors (Basel, Switzerland) · 2025Article
- Development of Non-Invasive Continuous Glucose Prediction Models Using Multi-Modal Wearable Sensors in Free-Living Conditions.Sensors (Basel, Switzerland) · 2025Article
- Photoplethysmography based non-invasive blood glucose estimation using systolic-diastolic framing MFCC features and machine learning regression.BioImpacts : BI · 2025Article
- Association of Blood Glucose Data With Physiological and Nutritional Data From Dietary Surveys and Wearable Devices: Database Analysis.JMIR diabetes · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes. There is a critical need for innovative, practical strategies to improve monitoring and management of glycemic health. In this study, using a dataset of 25,000 simultaneous interstitial glucose and noninvasive wearable smartwatch measurements, we demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively. We also establish methods for designing variables using data-driven and domain-driven methods from noninvasive wearables toward interstitial glucose prediction.
Identifiers
What OpenQuestion holds
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.