Precisely quantifying the dynamic impact of sleep quality on cognitive fluctuations

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PROJECT SUMMARY Amid the ever-growing number of empirically-supported behavioral treatments, a principal challenge for clinicians is identifying when and for whom a given treatment will be effective (1–4). Extant evidence suggests the utility of sleep interventions to improve cognitive functioning in adults diagnosed with a variety of psychiatric disorders (5–7). Cognitive improvements, in turn, predict psychosocial and adaptive improvements (8–10). However, given that the strength of the association between sleep and cognitive functioning varies across individuals and over time (11–13), it is unlikely that all individuals with impaired cognition will benefit equally from sleep-based cognitive interventions. The long-term goal of this work is to develop and test comprehensive, evidence-based guidelines that will be useful in optimizing cognitive functioning in adults at risk for adverse psychiatric outcomes. The immediate objective of this F32 proposal is to quantify the dynamic impact of sleep on cognitive variation in the context of between- and within-person moderators (e.g., psychiatric traits and states), enabling clinicians to evaluate how patient sleep habits impact cognitive performance and tailor sleep recommendations accordingly. Until recently, technological limitations precluded assessment of the dynamic association between sleep and cognition. To date, most research on the topic has recruited non-clinical samples, imposed non-naturalistic sleep manipulations, and/or evaluated sleep in relation to a single cognitive outcome (most commonly, vigilance) (14). To overcome these barriers to clinical translation, the present study will analyze passive sensing and ecological momentary assessment (EMA) data from large, multisite U01 (AURORA; n=2626) and R01 (total n=600) studies. These studies densely sample sleep (actigraphy-derived), cognitive functioning (processing speed, memory, and vigilance), and psychological state (self-reported) variables in diverse clinical and community samples, affording unprecedented power to develop a generalizable model describing the impact of sleep on cognitive fluctuations, accounting for inter- and intra-individual variability that has heretofore confounded precision approaches to intervention. Specific aims are threefold: (1) to specify the robust impact of sleep on cognitive variation in the context of posttraumatic exposure; (2) to quantify inter- and intra-individual psychopathological factors that influence the strength of the relationship between sleep and cognition; and (3) to examine the generalizability of resultant models in independent clinical and community samples. This contribution is significant because it will facilitate individualized psychotherapeutic interventions, empowering clinicians to customize sleep guidelines to maximally benefit cognition. This proposal is innovative because it entails analysis of data from smartphone- based cognitive measures that participants complete under typical conditions, enhancing ecological validity. Finally, this proposal is instructive because it occasions advanced trainings in precision psychiatry, digital phenotyping, and reproducibility from renowned leaders at McLean’s Institute for Technology in Psychiatry, providing critical knowledge and skills to assist the candidate as she launches an independent research career.