Ambulatory physiological state dynamics predict proximal behavioral markers of affect regulation in everyday life.

Abstract

Human physiology reflects the body's capacity for self-regulation that is crucial for flexible adaptation to changing environmental demands. Leveraging wearable sensors and machine learning, we aimed to uncover latent physiological states from ambulatory recordings of cardiac, respiratory, and activity signals that correspond with self-reported momentary affective processes, with implications for informing just-in-time adaptive interventions. Fifty-one participants with remitted major depressive disorder and 42 healthy controls completed 7-day ecological momentary assessments of affect, affect regulation, and momentary impulsivity while their heart rate variability, respiration, and movement were passively monitored. Using Hidden Markov models for state decoding, we found that frequency, dwell time, and transitions of physiological states predicted self-reported momentary affect, affect regulation, and impulsivity, with depression history moderating some of the associations. Findings underscore the feasibility of passive physiological phenotyping for tracking momentary affective processes that would otherwise be difficult to actively sample via self-report, but that may be crucial to informing timing and targets for intervention. (PsycInfo Database Record (c) 2025 APA, all rights reserved).