Document Type

Article

Publication Date

2025

Abstract

Autonomic dysfunction has long been implicated in schizophrenia, yet objective physiological markers remain underutilized in psychiatric research. Heart rate variability (HRV), a noninvasive measure of autonomic regulation, is conventionally derived from short electrocardiographic recordings. In this study, we tested whether continuous, wearable-derived HRV can identify physiologic alterations in schizophrenia-spectrum disorders. Using the NIH All of Us database, we analyzed second-level Fitbit heart rate data from 26 individuals with schizophrenia-spectrum diagnoses and 26 matched controls. Frequency-domain analyses focused on ultra-low frequency (ULF), very low frequency (VLF), and truncated low frequency (tLF) bands, given sampling constraints. Schizophrenia participants showed significantly reduced power across all bands (ULF p = 0.009; VLF p = 0.041; tLF p = 0.017), consistent with impaired autonomic regulation. These findings demonstrate that consumer wearables can detect dysautonomia in schizophrenia and highlight the potential of scalable digital biomarkers to advance psychiatric research and clinical care.

Comments

Preprint © 2025 the authors.

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