Fig. 1: Study overview.
From: A wearable-based aging clock associates with disease and behavior
Methods summary. (i) Following Abbaspourazad et al.42, we use a contrastive loss to learn PPG segment representations, a 256-dimensional vector summarizing features of a 60-s PPG segment, on a subset of 172,318 participants. (ii) PPG representations from a self-reported healthy subpopulation are used to fit a linear regression model, targeting chronological age. For a new subject, predicted age (PpgAge) is computed, and we examine the gap between PpgAge and chronological age and longitudinal changes in PpgAge time series that may co-occur with physiological changes. (iii) We analyze held out “healthy” and “general” participants, assessing accuracy of PpgAge, cross-sectional associations with disease and behavior, and longitudinal analyses of participant-level PpgAge time series.
