Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. Issue 1 (15th June 2021)

• Record Type: Journal Article
• Authors: Bent, Brinnae; Cho, Peter J; Wittmann, April; Thacker, Connie; Muppidi, Srikanth; Snyder, Michael; Crowley, Matthew J; Feinglos, Mark; Dunn, Jessilyn P
• Is Part Of: BMJ open diabetes research and care. Volume 9:Issue 1(2021)
• Publication Date: 2021-06-15
• Subjects: algorithms -- biomedical technology -- pre-diabetic state -- diabetes mellitus -- type 2; Diabetes -- Periodicals; 616.462005
• DOI: 10.1136/bmjdrc-2020-002027
• Languages: English
• ISSNs: 2052-4897

Abstract

Abstract : Introduction: Diabetes 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 methods: We 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. Results: A 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%). Conclusions: This 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.