Performance of an Automated Sleep Scoring Approach for Actigraphy Data in Children and Adolescents.

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 (8-16y, 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) BMAYC (3-5y, N=310), 2) SSS (5-8y, N=118), 3) S-Grow2 (12-13y; N=291) and 4) ELEMENT (9-18y; 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% 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 one hour 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.