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<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.</p><p>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. </p><p>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. </p><p>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. </p><p>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. </p><p>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. </p><p>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.</p><p>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.</p>
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