Application of a novel actigraphy algorithm to detect movement and sleep/wake patterns in children with autism spectrum disorder

Abstract

ObjectiveActigraphy is a non-intrusive method of recording rest/activity cycles as well as a surrogate for sleep/wake activity. Standard actigraphy analysis is limited in ascribing discrete movement events to wake status during sleep. We applied a novel algorithm to overnight actigraphy data recorded simultaneously with video polysomnography-electroencephalography (video PSG-EEG) to determine its ability to define movement and sleep/wake patterns in children with autism spectrum disorder (ASD) and age-comparable typically developing (TD) controls. MethodsA previously published novel algorithm uses mathematical endpoints to analyze actigraphy data without assumptions about sleep/wake status, and smooths data using moving windows of increasing length. Nighttime activity level “S” events (S1–S5) determined by this algorithm (n = 273) were identified in 15 children ages 3–10 years (nine with ASD and six TD) who wore an AW2 Spectrum Actiwatch (Philips Respironics) while undergoing simultaneous video PSG-EEG. Data were analyzed to identify the time each activity level “S” event occurred, video movement events (movements captured by video and scored based on level of severity), and sleep/wake status defined by PSG-EEG. The relationships among activity level “S” events, video movement events, and sleep/wake status were analyzed statistically. ResultsActivity level “S” events, the presence and severity of video movement events, and sleep-wake status, were significantly associated. These associations were present in both participants with ASD and those who were typically developing. ConclusionThis actigraphy algorithm shows promise for detecting nighttime movements and sleep/wake status and warrants further study in larger datasets of neurotypical children and those with neurodevelopmental disorders.