ArticleBMJ open diabetes research & care2021
Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept.
Article in BMJ open diabetes research & care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Population differences in wearable device wear time: Rescuing data to address biases and advance health equity.medRxiv : the preprint server for health sciences · 2026Article
- Sugar slay: a gamified decision support ecosystem for type 1 diabetes.Frontiers in digital health · 2026Article
- Electrochemical and Nanomaterial-Based Strategies for Nonenzymatic Glucose Detection: A Review.ChemistryOpen · 2025Review
- Variability of the Skin Temperature from Wrist-Worn Device for Definition of Novel Digital Biomarkers of Glycemia.Sensors (Basel, Switzerland) · 2025Article
- Association of Blood Glucose Data With Physiological and Nutritional Data From Dietary Surveys and Wearable Devices: Database Analysis.JMIR diabetes · 2024Article
- AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review.Journal of medical Internet research · 2024Article
- Blood glucose monitoring devices for type 1 diabetes: a journey from the food and drug administration approval to market availability.Frontiers in endocrinology · 2024Review
- Continuous Glucose Monitoring: A Transformative Approach to the Detection of Prediabetes.Journal of multidisciplinary healthcare · 2024Article
- Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies.Nutrients · 2022Article
- Overview of Artificial Intelligence-Driven Wearable Devices for Diabetes: Scoping Review.Journal of medical Internet research · 2022Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDiabetes prevalence continues to grow and there remains a significant diagnostic gap in one-third of the US population that has pre-diabetes. Innovative, practical strategies to improve monitoring of glycemic health are desperately needed. In this proof-of-concept study, we explore the relationship between non-invasive wearables and glycemic metrics and demonstrate the feasibility of using non-invasive wearables to estimate glycemic metrics, including hemoglobin A1c (HbA1c) and glucose variability metrics. RESEARCH DESIGN AND
methodsWe recorded over 25 000 measurements from a continuous glucose monitor (CGM) with simultaneous wrist-worn wearable (skin temperature, electrodermal activity, heart rate, and accelerometry sensors) data over 8-10 days in 16 participants with normal glycemic state and pre-diabetes (HbA1c 5.2-6.4). We used data from the wearable to develop machine learning models to predict HbA1c recorded on day 0 and glucose variability calculated from the CGM. We tested the accuracy of the HbA1c model on a retrospective, external validation cohort of 10 additional participants and compared results against CGM-based HbA1c estimation models.
resultsA total of 250 days of data from 26 participants were collected. Out of the 27 models of glucose variability metrics that we developed using non-invasive wearables, 11 of the models achieved high accuracy (<10% mean average per cent error, MAPE). Our HbA1c estimation model using non-invasive wearables data achieved MAPE of 5.1% on an external validation cohort. The ranking of wearable sensor's importance in estimating HbA1c was skin temperature (33%), electrodermal activity (28%), accelerometry (25%), and heart rate (14%).
conclusionsThis study demonstrates the feasibility of using non-invasive wearables to estimate glucose variability metrics and HbA1c for glycemic monitoring and investigates the relationship between non-invasive wearables and the glycemic metrics of glucose variability and HbA1c. The methods used in this study can be used to inform future studies confirming the results of this proof-of-concept study.
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