Identifying Factors linking Stress Exposure with Adolescent Internalizing Psychopathology using High-Frequency Longitudinal Designs

Understanding the mechanisms linking stressful life events (SLEs) to psychopathology is critical. We use an intensive 1-year longitudinal study of adolescents to reveal how sleep variability, social communication, and emotion differentiation prospectively explain relationships between SLEs and symptoms of anxiety and depression

Year-Long Digital Phenotyping and Natural Language Processing of Daily Voice Diaries Reveal Affective and Behavioral Signatures of Real-World Life Stress

Experiencing stressful life events has been associated with poor mental health outcomes, but less is known about daily stressful events’ more proximate impacts on daily behavior and affect. Here we leverage mobile and wearable technology and recent advances in natural language processing for a fine-grained examination of first-year college students’ daily life stress, with a focus on academic and social domains, over a full academic year (8,000+ total daily observations). Experiences of stress were characterized from participants’ daily voice diaries narrating the main events of the day, using a combination of expert human labeling and large language models fine-tuned for sentiment analysis and topic modeling. Bayesian hierarchical models assessed within-person associations between diary-derived instances of academic and social stressful events and same-day sleep, physical activity, social activity, and negative affect measured with actigraphy wristbands and phone surveys. Days with academic stressful events were associated with shorter sleep duration, decreased physical activity, reduced desire to be around others, and modestly increased negative affect. Additionally, days with academic stressful events had significantly reduced social interaction and increased time spent on schoolwork relative to days with social stressful events. Meanwhile, days with social stressful events stood out by especially heightened negative affect, above and beyond the effect of academic stressful events. Our results suggest that academic and social dimensions of life stress may present distinct signatures in daily affect and behavior, with potential implications for long-term wellbeing

Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation

BackgroundWearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep. ObjectiveThis study aims to introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify the relationships between derived sleep metrics and other variables of interest. MethodsThe pipeline released here for the deep phenotyping of sleep, as the DPSleep software package, uses a stepwise algorithm to detect missing data; within-individual, minute-based, spectral power percentiles of activity; and iterative, forward-and-backward–sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of the derived sleep features and correction for time zone changes. In this paper, we have illustrated the pipeline with data from participants studied for more than 200 days each. ResultsActigraphy-based measures of sleep duration were associated with self-reported sleep quality ratings. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data. ConclusionsWe discuss the use of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (eg, smartphones and tablets) to measure individual differences across a wide range of behavioral variations in health and disease. A new open-source pipeline for deep phenotyping of sleep, DPSleep, analyzes raw accelerometer data from wearable devices and estimates sleep onset and offset while allowing for manual quality control adjustments

A year in the social life of a teenager: Within-person fluctuations in stress, phone communication, and anxiety and depression

Stressful life events (SLEs) are strongly associated with the emergence of adolescent anxiety and depression, but the underlying mechanisms remain poorly understood, especially at the within-person level. We investigated how adolescent social communication (i.e., frequency of calls and texts) following SLEs relates to changes in internalizing symptoms in a multi-timescale intensive year-long study (N=30; n=355 monthly observations; n=~5,000 experience-sampling observations). Within-person increases in SLEs were associated with receiving more calls than usual at both monthly- and momentary-levels, and making more calls at the monthly-level. Increased calls were prospectively associated with worsening internalizing symptoms at the monthly-level only, suggesting that SLEs rapidly influences phone communication patterns, but these communication changes may have a more protracted, cumulative influence on internalizing symptoms. Finally, increased incoming calls prospectively mediated the association between SLEs and anxiety at the monthly-level. We identify adolescent social communication fluctuations as a potential mechanism conferring risk for stress-related internalizing psychopathology