Abstract 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.
Chen et al. (Thu,) studied this question.