Wearables

Circadian Rhythm Analysis Using Wearable-Based Accelerometry as a Digital Biomarker of Aging and Healthspan
Recognizing the pivotal role of circadian rhythm in the human aging process and its scalability through wearables, we introduce …
DOI
Circadian Rhythm Analysis Using Wearable-Based Accelerometry as a Digital Biomarker of Aging and Healthspan
1.Digital biomarker for longevity & Healthspan
Applying AI/ML and statistical methods for predicting biological age and healthspan using wearable data
May 3, 2024
1.Digital biomarker for longevity & Healthspan
The Bitemporal Lens Model—toward a holistic approach to chronic disease prevention with digital biomarkers
We introduce the Bitemporal Lens Model, a comprehensive methodology for chronic disease prevention using digital biomarkers. The …
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The Bitemporal Lens Model—toward a holistic approach to chronic disease prevention with digital biomarkers
Comparative Efficacy of Commercial Wearables for Circadian Rhythm Home Monitoring from Activity, Heart Rate, and Core Body Temperature
Circadian rhythms govern biological patterns that follow a 24-hour cycle. Dysfunctions in circadian rhythms can contribute to various …
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Comparative Efficacy of Commercial Wearables for Circadian Rhythm Home Monitoring from Activity, Heart Rate, and Core Body Temperature
Wearable-based accelerometer activity profile as digital biomarker of inflammation, biological age, and mortality using hierarchical clustering analysis in NHANES 2011–2014
Repeated disruptions in circadian rhythms are associated with implications for health outcomes and longevity. The utilization of …
DOI
Wearable-based accelerometer activity profile as digital biomarker of inflammation, biological age, and mortality using hierarchical clustering analysis in NHANES 2011–2014
Precise Segmentation of U.S. Adults from 24-Hour Wearable-based Physical Activity Profiles Using Machine Learning Clustering
Growing evidence shows that wearable-based physical activity data (e.g., accelerometer) could be used as digital biomarkers to identify …
DOI
Precise Segmentation of U.S. Adults from 24-Hour Wearable-based Physical Activity Profiles Using Machine Learning Clustering