Research Paper Volume 13, Issue 6 pp 7900—7913
Deep longitudinal phenotyping of wearable sensor data reveals independent markers of longevity, stress, and resilience
- 1 Gero PTE. LTD., Singapore 409051, Singapore
- 2 Moscow Institute of Physics and Technology, Moscow Region 141700, Russia
Received: January 6, 2021 Accepted: March 3, 2021 Published: March 14, 2021https://doi.org/10.18632/aging.202816
How to Cite
Copyright: © 2021 Pyrkov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License(CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Biological age acceleration (BAA) models based on blood tests or DNA methylation emerge as a de facto standard for quantitative characterizations of the aging process. We demonstrate that deep neural networks trained to predict morbidity risk from wearable sensor data can provide a high-quality and cheap alternative for BAA determination. The GeroSense BAA model was trained and validated using steps per minute recordings from 103,830 one-week long and 2,599 of up to 2 years-long longitudinal samples and exhibited a superior association with life-expectancy over the average number of steps per day in, e.g., groups stratified by professional occupations. The association between the BAA and effects of lifestyles, the prevalence of future incidence of diseases was comparable to that of BAA from models based on blood test results. Wearable sensors let sampling of BAA fluctuations at time scales corresponding to days and weeks and revealed the divergence of organism state recovery time (resilience) as a function of chronological age. The number of individuals suffering from the lack of resilience increased exponentially with age at a rate compatible with Gompertz mortality law. We speculate that due to the stochastic character of BAA fluctuations, its mean and auto-correlation properties together comprise the minimum set of biomarkers of aging in humans.