Abstract
The use of consumer-grade wearables for purposes beyond fitness tracking has not been comprehensively explored. We generated and analyzed multidimensional data from 233 normal volunteers, integrating wearable data, lifestyle questionnaires, cardiac imaging, sphingolipid profiling, and multiple clinical-grade cardiovascular and metabolic disease markers. We show that subjects can be stratified into distinct clusters based on daily activity patterns and that these clusters are marked by distinct demographic and behavioral patterns. While resting heart rates (RHRs) performed better than step counts in being associated with cardiovascular and metabolic disease markers, step counts identified relationships between physical activity and cardiac remodeling, suggesting that wearable data may play a role in reducing overdiagnosis of cardiac hypertrophy or dilatation in active individuals. Wearable-derived activity levels can be used to identify known and novel activity-modulated sphingolipids that are in turn associated with insulin sensitivity. Our findings demonstrate the potential for wearables in biomedical research and personalized health.
Highlights
Public adoption of consumer-grade wearable activity trackers (“wearables”) has been steadily increasing in recent years [1,2], and it is estimated that the global market for wearables will exceed $34 billion US$ by 2020 [3]
The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
We compared wearable-derived resting heart rate (RHR) with in-clinic measurements obtained from two sources, namely RHR measured by an automatic blood pressure monitor (ABPM_HR) and during an electrocardiogram test (ECG_HR)
Summary
Public adoption of consumer-grade wearable activity trackers (“wearables”) has been steadily increasing in recent years [1,2], and it is estimated that the global market for wearables will exceed $34 billion US$ by 2020 [3]. Basic activity trackers provide accelerometer-based activity data, whereas more sophisticated models are capable of monitoring heart rate (HR) Together, these indicators have the potential to provide deep insights into an individual’s cardiovascular health and fitness. Resting heart rate (RHR) is an important indicator of cardiovascular health [4,5,6], whereas step counts can be used to infer patterns and levels of physical activity. Both metrics play roles in the modulation and prediction of risk of cardiovascular and metabolic disorders (CVMDs) [7]. Deep neural networks (DNNs) trained on HR and step count data obtained from the Apple Watch (Apple, www.apple.com) were able to detect AF, SA, and hypertension at accuracies of 97%, 90%, and 82%, respectively [13,14]
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