Key result
Momentary increases in the inertia of sample entropy predicted higher levels of subsequent perseverative cognition in adults with remitted major depressive disorder, but not in controls.
Why the study?
Identifying dynamic psychophysiological markers of perseverative cognition fluctuating over time could facilitate the passive detection of moments when perseverative cognition occurs in daily life.
Does monitoring ambulatory autonomic complexity predict moments of perseverative cognition in adults with remitted major depressive disorder?
Observational
Does monitoring ambulatory autonomic complexity predict moments of perseverative cognition in adults with remitted major depressive disorder?
The inertia of sample entropy may serve as a dynamic risk marker for real-world perseverative cognition in individuals with a history of depression.
Autonomic inertia may flag proximal PC moments in remitted depression; leaves open utility for passive detection or recurrence prevention.
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.
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Zapetis et al. (2024) conducted an observational in Remitted major depressive disorder. Momentary increases in the inertia of sample entropy vs. Never-depressed controls was evaluated on Subsequent perseverative cognition (PC) engagement. Momentary increases in the inertia of sample entropy predicted higher levels of subsequent perseverative cognition in adults with remitted major depressive disorder, but not in controls.
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