0251 How do Nonlinear Age Trajectories of Sleep Differ Between Youth at Familial Risk for Mood Disorders and Low-Risk Youth?

Abstract Introduction Familial risk for mood disorders is associated with early vulnerability, with sleep dysregulation emerging as a key feature that often precedes clinical onset. However, few studies have assessed sleep in high-risk youth using objective methods, and it remains unknown whether objectively measured sleep develops differently across age in this population. Because sleep changes dynamically rather than linearly across childhood and adolescence, models that allow for nonlinear developmental trajectories may be particularly well-suited to detect emerging group differences. We hypothesized that high-risk youth would exhibit more extreme sleep duration, later sleep midpoint timing, and poorer sleep regularity in late adolescence and young adulthood. Methods A total of 177 youth at familial risk for mood disorders (mean age = 14.9 years) and 113 control youth (mean age = 13.8 years) wore wrist actigraphs continuously for two weeks. Sleep duration, sleep midpoint timing, and sleep regularity were derived from the actigraphy data. Generalized additive mixed models were used to estimate age-related trajectories and to allow nonlinear relationships between age and each sleep parameter. Model fit was evaluated by comparing linear and nonlinear models using Akaike and Bayesian information criteria. Results All nonlinear models demonstrated superior fit relative to linear models based on AIC and BIC. High-risk youth showed a significantly different development trajectory in total sleep time (F = 5.5, pBH = 0.009), with greater sleep duration from 18-22 years. Sleep midpoint significantly differed by risk status (F = 2.4, pBH = 0.028), such that high-risk youth slept later from 11-14 years and earlier from 16-19 years. Sleep regularity significantly differed by risk status (F = 7.0, pBH = < 0.001), with high-risk youth sleeping more irregularly from 12-15 years and more regularly from 17-22 years. Conclusion High-risk youth exhibited poorer sleep than controls around puberty but, contrary to expectations, better sleep by late adolescence. This developmental reversal may reflect pubertal-related disruptions in sleep regulation and shifting sleep priorities in young adulthood. These findings underscore the importance of age-by-family-risk interactions and closer examination of late-adolescent sleep in high-risk youth. Support (if any)

Authors

Institutions

Publication Details

Journal
SLEEP
Published
2026-05-01
DOI
https://doi.org/10.1093/sleep/zsag091.0251
Primary Topic
Sleep and related disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

0251 How do Nonlinear Age Trajectories of Sleep Differ Between Youth at Familial Risk for Mood Disorders and Low-Risk Youth?

Nitya Adepalli, Анна Назарова, Zachary Hubshman, Jason d’Eon et al.
SLEEP
Sleep and related disorders
article

0251 How do Nonlinear Age Trajectories of Sleep Differ Between Youth at Familial Risk for Mood Disorders and Low-Risk Youth?

Nitya Adepalli, Анна Назарова, Zachary Hubshman, Jason d’Eon, Rudolf Uher, Mayank Ramchandani, Kathryn Freeman, Max Wang
article en

Abstract

Abstract Introduction Familial risk for mood disorders is associated with early vulnerability, with sleep dysregulation emerging as a key feature that often precedes clinical onset. However, few studies have assessed sleep in high-risk youth using objective methods, and it remains unknown whether objectively measured sleep develops differently across age in this population. Because sleep changes dynamically rather than linearly across childhood and adolescence, models that allow for nonlinear developmental trajectories may be particularly well-suited to detect emerging group differences. We hypothesized that high-risk youth would exhibit more extreme sleep duration, later sleep midpoint timing, and poorer sleep regularity in late adolescence and young adulthood. Methods A total of 177 youth at familial risk for mood disorders (mean age = 14.9 years) and 113 control youth (mean age = 13.8 years) wore wrist actigraphs continuously for two weeks. Sleep duration, sleep midpoint timing, and sleep regularity were derived from the actigraphy data. Generalized additive mixed models were used to estimate age-related trajectories and to allow nonlinear relationships between age and each sleep parameter. Model fit was evaluated by comparing linear and nonlinear models using Akaike and Bayesian information criteria. Results All nonlinear models demonstrated superior fit relative to linear models based on AIC and BIC. High-risk youth showed a significantly different development trajectory in total sleep time (F = 5.5, pBH = 0.009), with greater sleep duration from 18-22 years. Sleep midpoint significantly differed by risk status (F = 2.4, pBH = 0.028), such that high-risk youth slept later from 11-14 years and earlier from 16-19 years. Sleep regularity significantly differed by risk status (F = 7.0, pBH = < 0.001), with high-risk youth sleeping more irregularly from 12-15 years and more regularly from 17-22 years. Conclusion High-risk youth exhibited poorer sleep than controls around puberty but, contrary to expectations, better sleep by late adolescence. This developmental reversal may reflect pubertal-related disruptions in sleep regulation and shifting sleep priorities in young adulthood. These findings underscore the importance of age-by-family-risk interactions and closer examination of late-adolescent sleep in high-risk youth. Support (if any)

SLEEPVol. 49(Supplement_1)
Dalhousie University (CA), Nova Scotia Health Authority (CA)
No poverty
Openalex Percentile: Top 5%
Sleep and related disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.