Accurate Prediction of Momentary Cognition From Intensive Longitudinal Data

Abstract

Background: Deficits in cognitive performance are implicated in the development and maintenance of psychopathology. Emerging evidence further suggests that within-person fluctuations in cognitive performance may represent sensitive early markers of neuropsychiatric decline. Incorporating routine cognitive assessments into standard clinical care-to identify between-person differences and monitor within-person fluctuations-has the potential to improve diagnostic screening and treatment planning. In support of these goals, it is critical to understand to what extent cognitive performance varies under routine, remote assessment conditions (i.e., momentary cognition) in relation to a wide range of possible predictors.

Methods: Using data-driven, high-dimensional methods, we ranked strong predictors of momentary cognition and evaluated out-of-sample predictive accuracy. Our approach leveraged innovations in digital technology, including ambulatory assessment of cognition and behavior 1) at scale (n = 122 participants, n = 94 females), 2) in naturalistic environments, and 3) within an intensive longitudinal study design (mean = 25.5 assessments/participant).

Results: Reaction time (R² > 0.70) and accuracy (0.56 >R² > 0.35) were strongly predicted by age, between-person differences in mean performance, and time of day. Effects of self-reported, intraindividual fluctuations in environmental (e.g., noise) and internal (e.g., stress) states were also observed.

Conclusions: Our results provide robust estimates of effect size to characterize sources of cognitive variability, to support the identification of optimal windows for psychosocial interventions, and to possibly inform clinical evaluation under remote neuropsychological assessment conditions.

Keywords: Assessment; Cognition; Data-driven; Digital health; Interindividual; Intraindividual.