Sleep patterns predictive of daytime challenging behavior in individuals with low-functioning autism

Simonne Cohen, Ben D. Fulcher, Shantha M. W. Rajaratnam, Russell Conduit, Jason P. Sullivan, Melissa A. St Hilaire, Andrew J. K. Phillips, Tobias Loddenkemper, Sanjeev V. Kothare, Kelly McConnell, Paula Braga-Kenyon, William Ahearn, Andrew Shlesinger, Jacqueline Potter, Frank Bird, Kim M. Cornish, Steven W. Lockley

Research output: Contribution to journalArticleResearchpeer-review

63 Citations (Scopus)


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, self-injury, 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. Autism Res 2018, 11: 391–403.

Original languageEnglish
Pages (from-to)391-403
Number of pages13
JournalAutism Research
Issue number2
Publication statusPublished - 1 Feb 2018


  • autism spectrum disorder
  • challenging behavior
  • intellectual disability
  • machine learning
  • sleep

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