Autonomic Inertia as a Proximal Risk Marker for Moments of Perseverative Cognition in Everyday Life in Remitted Depression

Background: Trait perseverative cognition (PC) is associated with inflexible autonomic activity and risk for depressive recurrence. However, the identification of dynamic psychophysiological markers of PC that fluctuate within individuals over time could facilitate the passive detection of moments when PC occurs in daily life.

Methods: Using intensively sampled data across 1 week (3x/day) in adults with remitted major depressive disorder (rMDD) and never-depressed controls (CONs), we investigated the utility of monitoring ambulatory autonomic complexity to predict moments of PC engagement in everyday life. Autonomic complexity metrics, including the root mean square of successive difference (RMSSD), indexing vagal control, and sample entropy, indexing signal complexity, were calculated in the 30 min before each measurement of PC to enable time-lagged analyses. Multilevel models examined proximal fluctuations in the mean level and inertia of complexity metrics as predictors of subsequent PC engagement.

Results: Momentary increases in the inertia of sample entropy, but not other metrics, predicted higher levels of subsequent PC in the rMDD group, but not among never-depressed CONs.

Conclusions: The inertia of sample entropy could index autonomic rigidity and serve as a dynamic risk marker for real-world PC in individuals with a history of depression. This could inform the development of technologies to passively detect fluctuations in risk for PC, facilitating real-time interventions to prevent PC and reduce the risk for depressive recurrence.