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Accurate prediction of momentary cognition from intensive longitudinal data

Published:December 14, 2022DOI:https://doi.org/10.1016/j.bpsc.2022.12.002

      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 (
      • Rutter L.A.
      • Vahia I.V.
      • Forester B.P.
      • Ressler K.J.
      • Germine L.
      Heterogeneous Indicators of Cognitive Performance and Performance Variability Across the Lifespan.
      ) at scale (n = 122, n = 94 female), (
      • Singh S.
      • Strong R.W.
      • Jung L.
      • Li F.H.
      • Grinspoon L.
      • Scheuer L.S.
      • et al.
      The TestMyBrain Digital Neuropsychology Toolkit: Development and Psychometric Characteristics.
      ) in naturalistic environments, and (

      Kreitler S, Weissler K, Barak F. Physical health and cognition. In: Cognition and motivation: Forging an interdisciplinary perspective. New York, NY, US: Cambridge University Press; 2013. p. 238–269.

      ) within an intensive longitudinal study design (mean = 25.5 assessments/participant).

      Results

      Reaction time (R2 > .70) and accuracy (.56 > R2 > .35) were strongly predicted by age, between-person differences in mean performance, and time of day. Effects of self-reported, intra-individual fluctuations in environmental (e.g., noise) and internal (e.g., stress) states were also observed.

      Conclusions

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