ArticleSleep2026
Performance of an automated sleep scoring approach for actigraphy data in children and adolescents.
Article in Sleep, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Developmental trajectories of sleep in offspring of parents with mood disorders.Psychological medicine · 2026Article
- Association of Stress and Neighborhood Social Context With Actigraphy-Measured and Self-Reported Adolescent Sleep Outcomes.The Journal of adolescent health : official publication of the Society for Adolescent Medicine · 2026Article
- Meal Timing Patterns and Associations with Fat Mass in Adolescents.medRxiv : the preprint server for health sciences · 2026Article
- Universal approach to actigraphic sleep/wake scoring, verified against 5 classic algorithms on 3 datasets.Scientific reports · 2026Article
- The Modular Actigraphy Platform: A Data Science Solution for Processing High-Resolution Time Series Sensor Data for Sleep and Physical Activity Assessment.medRxiv : the preprint server for health sciences · 2025Article
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Authors and funding
17 authors.
Funding
Abstract
STUDY
objectivesGGIR is an R package for processing raw acceleration data to estimate sleep health parameters. We aimed to (1) assess the performance of three sleep algorithms within GGIR against PSG for detecting sleep/wake in clinically referred, typically-developing children (criterion validity); and (2) describe GGIR-derived sleep estimates from typically developing children enrolled in multiple cohort studies (face validity).
methodsFor criterion evaluation, children (8-16 years, N = 30) wore an actigraphy device for one night during in-lab polysomnography with performance assessed using epoch-by-epoch analyses. For face validity evaluation, four community/free living datasets were used: (1) Bone Mineral Accretion in Young Children (3-5 years, N = 310), (2) School Summer Sleep (5-8 years, N = 118), (3) Sleep and Growth Study 2 (12-13 years; N = 291), and (4) Early Life Exposure to Environmental Toxicants (9-18 years; N = 543). All raw acceleration data were processed using GGIR (v.3.0-0) with the Cole-Kripke (CK), Sadeh (S), and van Hees (vH) algorithm settings.
resultsFollowing the in-lab test, 60 per cent of children were diagnosed with mild to severe obstructive sleep apnea (OSA). For criterion evaluation, the 30-s epoch-by-epoch analyses revealed that average balanced accuracies were 0.80 (Sensitivity = 0.80; Specificity = 0.79), 0.76 (Sensitivity = 0.86; Specificity = 0.65), and 0.67 (Sensitivity = 0.95, Specificity = 0.39) for GGIR-CK, GGIR-vH, and GGIR-S, respectively. For face validity evaluation, sleep estimates mirrored the in-lab performance metrics (e.g. sleep duration estimates were similar when using GGIR-CK and GGIR-VH but approximately 1 h longer when using GGIR-S).
conclusionsThe in-lab performance metrics from typically developing children with and without OSA and cohort-based descriptive statistics from samples of typically developing children provide benchmark data to guide investigators on the suitability of GGIR for automated processing of raw acceleration data for pediatric sleep estimation.
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