Pediatric precision sleep network: a study protocol for identifying sleep signatures of mental health risk in peri-adolescents.
Article
Baker, Amanda E, Cooper, Rebecca E, Buysse, Daniel J et al. (2026). Pediatric precision sleep network: a study protocol for identifying sleep signatures of mental health risk in peri-adolescents.
. BMC Pediatrics, 10.1186/s12887-026-07253-z
Baker, Amanda E, Cooper, Rebecca E, Buysse, Daniel J et al. (2026). Pediatric precision sleep network: a study protocol for identifying sleep signatures of mental health risk in peri-adolescents.
. BMC Pediatrics, 10.1186/s12887-026-07253-z
BACKGROUND: Peri-adolescence (ages 10-13) is a sensitive-and clinically critical-developmental window for the emergence of psychiatric symptoms, yet scalable strategies for early risk detection in pediatric primary care (PPC) remain limited. Sleep disturbances are among the most prevalent, predictive, and modifiable indicators of youth mental health, but current pediatric assessments rely heavily on subjective, single-source reports that fail to capture the multidimensional nature of sleep health. The Pediatric Precision Sleep Network (PPSN) is a longitudinal, multi-site study designed to integrate multimodal sleep data with longitudinal clinical outcomes to improve early identification of psychiatric risk during peri-adolescence-a period marked by rapid changes in sleep-circadian biology and vulnerability to psychopathology. METHODS: PPSN will enroll 1,200 youth ages 10-13 across three metropolitan areas (Pittsburgh, Boston, Miami). Over three years, participants will complete multimodal sleep assessments-including self-report, daily sleep logs, actigraphy, ambulatory electroencephalography (EEG), and passive smartphone-based monitoring-administered primarily at home to maximize ecological validity and reduce burden. Psychiatric symptoms and functioning will be assessed biannually via online surveys and electronic health records (EHRs), including structured fields and unstructured notes processed with natural language processing to extract sleep-related information. Scalable, open-source pipelines will automatically derive sleep features. Analyses will: (1) identify multivariable "sleep signatures"-defined as empirically derived patterns of sleep features across modalities-using factor and cluster methods; (2) evaluate their predictive utility for transdiagnostic mental health outcomes using stepwise machine learning approaches; and (3) examine developmental and contextual moderators (e.g., puberty and sociocultural factors). PPSN will also partner with the NIMH Individually Measured Phenotypes to Advance Computational Translation in Mental Health (IMPACT-MH) Data Coordinating Center to standardize procedures, ensure rigorous quality control, and disseminate open-source analytic tools for the broader research community. DISCUSSION: By integrating ecologically valid, longitudinal sleep monitoring with EHR-based and survey outcomes, PPSN aims to identify developmentally sensitive sleep signatures that could be translated into scalable screening tools for PPC. Embedding sleep-informed algorithms into primary care could improve precision and equity of early risk detection and inform future efforts aimed at earlier identification and monitoring of mental health risk in peri-adolescence.
