Sleep patterns predictive of daytime challenging behavior in individuals with low-functioning autism
Abstract
Increased severity of problematic daytime behavior has been associated with poorer
sleep quality in individuals with autism spectrum disorder. In this work, we
investigate whether this relationship holds in a real-time setting, such that an
individual’s prior sleep can be used to predict their subsequent daytime behavior. We
analyzed an extensive real-world dataset containing over 20,000 nightly sleep
observations matched to subsequent challenging daytime behaviors (aggression, selfinjury, tantrums, property destruction and a challenging behavior index) across 67
individuals with low-functioning autism living in two U.S. residential facilities. Using
support vector machine classifiers, a statistically significant predictive relationship
was found in 81% of individuals studied (P < 0.05). For all five behaviors examined,
prediction accuracy increased up to approximately eight nights of prior sleep used to
make the prediction, indicating that the behavioral effects of sleep may manifest on
extended timescales. Accurate prediction was most strongly driven by sleep
variability measures, highlighting the importance of regular sleep patterns. Our
findings constitute an initial step towards the development of a real-time monitoring
tool to pre-empt behavioral episodes and guide prophylactic treatment for individuals
with autism.
Lay Summary:
We analyzed over 20,000 nights of sleep from 67 individuals with autism to
investigate whether daytime behaviors can be predicted from prior sleep patterns.
Better-than-chance accuracy was obtained for 81% of individuals, with measures of
night-to-night variation in sleep timing and duration most relevant for accurate
prediction. Our results highlight the importance of regular sleep patterns for better daytime functioning, and represent a step towards the development of ‘smart sleep technologies’ to pre-empt behavior in individuals with autism.