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Measuring Neural Mechanisms Underlying Sleep-Dependent Memory Consolidation During Naps in Early Childhood
Published on: October 2, 2019
Performance of an automated sleep scoring approach for actigraphy data in children and adolescents
Pin-Wei Chen1, Erica C Jansen2, Christopher M Cielo3,4
1Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, United States.
Study Objectives:
GGIR 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).
Methods:
For 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.
Results:
Following 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).
Conclusions:
The 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.
