# The Pediatric Precision Sleep Network

> **NIH NIH U01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $3,675,428

## Abstract

ABSTRACT
Peri-adolescence (ages 10-13) heralds an alarming escalation in pediatric mental health (MH) risk, making this
transitionary time between childhood and adolescence a crucial window for early detection and intervention.
Predictive models built on electronic health record (EHR) data are emerging as practical tools to automate MH
risk stratification in pediatric primary care (PPC); however, to date these models have only modest predictive
value. Common, causal, and modifiable transdiagnostic MH risk factors are needed to improve on EHR models
and pave the way for deployment of preventive care during peri-adolescence. Sleep is one such risk factor: more
than 50% of peri-adolescents experience unhealthy sleep patterns, and sleep problems during peri-adolescence
prospectively predict negative MH outcomes more strongly than at other times in development. Moreover, youth
from minoritized racial and ethnic backgrounds are even more severely impacted by poor sleep health than non-
minoritized youth. However, several barriers have impeded the incorporation of sleep into PPC screening and
risk stratification tools: (1) the heterogeneity of sleep characteristics hinders the differentiation of normative
versus at-risk sleep patterns; (2) the multidimensionality and multimodality of sleep data have led to
inconsistencies regarding which particular features and modalities are needed for screening; and (3) until very
recently, it has been impossible to capture crucial behavioral and physiological measures of habitual sleep in
sufficiently large samples. To address these challenges, we propose the Pediatric Precision Sleep Network:
a multi-site observational study of unprecedented scope and scale, focused on establishing sleep signatures
that predict transdiagnostic MH problems among youth in PPC. Across three sites, we will recruit a diverse
sample of 1,200 youth (10-13yr) with a range of sleep disturbances from PPC settings. We will develop and
validate multidimensional and multimodal sleep signatures (Aim 1) and practical and scalable transdiagnostic
MH risk stratification algorithms (Aim 2). Aims 1-2 will employ a sequential, multimodal machine learning
workflow to establish the extent to which each data modality can incrementally inform signatures and identify
youth at greatest transdiagnostic MH risk. These findings will inform screening, risk stratification, and future
developmentally-appropriate effectiveness and/or experimental therapeutics trials. In Aim 3, we will coordinate
with the IMPACT-MH Data Coordinating Center to efficiently recruit participants, collect data, hone and share
innovative data processing pipelines, and securely transfer data. We are committed to bringing sleep and MH
data to our broader scientific community, along with the accompanying tools and resources needed to process
sleep data efficiently. Our long-term goal is to inform clinical decision-making algorithms for PPC that leverage
sleep to improve MH outcomes and reduc...

## Key facts

- **NIH application ID:** 10866727
- **Project number:** 1U01MH136020-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** MARIA JALBRZIKOWSKI
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $3,675,428
- **Award type:** 1
- **Project period:** 2024-05-01 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10866727

## Citation

> US National Institutes of Health, RePORTER application 10866727, The Pediatric Precision Sleep Network (1U01MH136020-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10866727. Licensed CC0.

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