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

Objective: Actigraphy 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.

Methods: A 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.

Results: Activity 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.

Conclusion: This 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.