Browsing by Author "Dunn, Jessilyn P"

Prediabetes is a progressive, chronic condition characterized by abnormal glucose control that affects over one third of people in the United States. While prediabetes is highly prevalent and has serious consequences, it is also seriously under-diagnosed— only ten percent of those with prediabetes are aware that they have the disease and for those who have been diagnosed, prediabetes is often poorly managed. Innovative, practical strategies to improve monitoring and management of glycemic health are desperately needed.

Non-invasive wrist-worn biometric sensors, often referred to as ‘wearables,’ are becoming nearly ubiquitous in the United States, with 117 million currently in use and an expected 100% growth in the next three years. Because of this widespread use, wearables have important potential to aid in the development of digital biomarkers which will facilitate detection and monitoring of chronic diseases. Digital biomarkers are digitally collected data (e.g. heart rate measurements from a wearable) that may be used as indicators of health outcomes (e.g. prediabetes). Important contributors to prediabetes, glucose control and variability, are physiologically linked to the autonomic nervous system (ANS) and wearable sensors have the capability to noninvasively measure metrics of the ANS, suggesting the feasibility of utilizing non-invasive, wrist-worn wearable sensors to monitor glycemic health and improve monitoring of prediabetes. The primary objective of this dissertation is to explore the development of digital biomarkers from wearable sensors to assess glycemic health for remote diagnosis, monitoring, and management of prediabetes.

Digital biomarker development is a rapidly growing field facing numerous challenges, including validation and optimization of wearable sensor data and a lack of standards for wearable sensor validation and digital biomarker development. In this dissertation, we address these challenges in order to develop a platform to assess the feasibility of developing digital biomarkers of glycemic health, which would aid in the early detection of prediabetes and the management of prediabetes.

In this work we present a validation and verification framework for wearable sensor data and we use this framework to investigate sources of inaccuracy in wearable optical heart rate sensors. We determined activity levels, device type, and device to be significant contributors to inaccuracy of the sensors but showed that accuracy was not affected by skin tone.

A problem with digital biomarker discovery is the need for data to be high resolution, which is at odds with the storage costs of data and battery power consumption. In order to optimize wearable sensor data for digital biomarker discovery, we determined the optimal sampling rate for optical blood volume pulse and found the optimal sampling rate for nearly all heart rate and heart rate variability metrics to be 21-64Hz. We then built and open-sourced a wearables data compression toolbox, testing five data compression methods on five different wearable sensor data types. We incorporated this toolbox in the Digital Biomarker Discovery Pipeline, an open source platform for the development of digital biomarkers to establish best practices for digital biomarker development that we launched as part of this work.

Building upon the frameworks we developed for digital biomarker discovery, we showed the feasibility of using noninvasive wearables to estimate glucose variability metrics and hemoglobin A1c (HbA1c). We developed 11 glucose variability estimation models using non-invasive wearables data that achieved high accuracy (<10% mean average percent error, MAPE). Our HbA1c estimation model using wearables data achieved MAPE of 5.1% on an external data test set and performed comparably to the American Diabetes Association estimated HbA1c model from continuous glucose monitors and our own continuous glucose monitor-based HbA1c estimation model. This shows the feasibility of using noninvasive wearables for HbA1c estimation, although limitations of our study include a narrow HbA1c range, resulting in our models not being significantly different from the mean model. Combining estimation of glucose variability and HbA1c, we could greatly improve screening for prediabetes.

We incorporated all of the previous work into the final component of this work, engineering putative digital biomarkers for intraday interstitial glucose prediction. In order to manage glucose fluctuations, it is important for patients to understand how their lifestyle habits may influence their blood glucose levels so that they can begin to appropriately manage their disease. There is a critical need for innovative, practical strategies to improve monitoring and management of glycemic health. In the final component of this dissertation, we demonstrated the feasibility of using noninvasive and widely accessible methods to classify glucose excursions and predict interstitial glucose values. We also show robust methods for both data-driven and domain-driven feature engineering from noninvasive wearables. Furthermore, we compared population approach machine learning and personalized approach machine learning for the prediction of glucose and demonstrated the existence of a “crossover point” at which the personalized model accuracy exceeds the traditional population approach to modeling glucose.

Overall, this dissertation addresses challenges to digital biomarker development, including validation and optimization of wearable sensor data, an absence of open-source methodologies, and a lack of standards for wearable sensor validation and digital biomarker development, in order to establish a platform for discovering digital biomarkers of glycemic health. We show feasibility of estimating metrics of glycemic health using non-invasive wearable sensors. Finally, we show the utility of digital biomarkers in the classification and prediction of interstitial glucose for intraday glycemic health monitoring and management. Because wearables are prevalent in the general population, leveraging them for glycemic health monitoring could represent a major advancement in early detection of prediabetes and improved monitoring and self-management of prediabetes.