Exploratory study examining the at-home feasibility of a wearable tool for social-affective learning in children with autism.

Journal: NPJ digital medicine
Published Date: Aug 2, 2018

Abstract

Although standard behavioral interventions for autism spectrum disorder (ASD) are effective therapies for social deficits, they face criticism for being time-intensive and overdependent on specialists. Earlier starting age of therapy is a strong predictor of later success, but waitlists for therapies can be 18 months long. To address these complications, we developed Superpower Glass, a machine-learning-assisted software system that runs on Google Glass and an Android smartphone, designed for use during social interactions. This pilot exploratory study examines our prototype tool's potential for social-affective learning for children with autism. We sent our tool home with 14 families and assessed changes from intake to conclusion through the Social Responsiveness Scale (SRS-2), a facial affect recognition task (EGG), and qualitative parent reports. A repeated-measures one-way ANOVA demonstrated a decrease in SRS-2 total scores by an average 7.14 points ((1,13) = 33.20,  = <.001, higher scores indicate higher ASD severity). EGG scores also increased by an average 9.55 correct responses ((1,10) = 11.89,  = <.01). Parents reported increased eye contact and greater social acuity. This feasibility study supports using mobile technologies for potential therapeutic purposes.

Authors

  • Jena Daniels
    1Division of Systems Medicine, Department of Pediatrics, Stanford University, Palo Alto, CA USA.
  • Jessey N Schwartz
    1Division of Systems Medicine, Department of Pediatrics, Stanford University, Palo Alto, CA USA.
  • Catalin Voss
    Department of Computer Science, Stanford University, Stanford, California.
  • Nick Haber
    School of Education, Stanford University, Stanford, California.
  • Azar Fazel
    1Division of Systems Medicine, Department of Pediatrics, Stanford University, Palo Alto, CA USA.
  • Aaron Kline
    Department of Pediatrics (Systems Medicine), Stanford University, Stanford, California; Department of Biomedical Data Science, Stanford University, Stanford, California.
  • Peter Washington
    Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA.
  • Carl Feinstein
    3Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA USA.
  • Terry Winograd
    2Department of Computer Science, Stanford University, Palo Alto, CA USA.
  • Dennis P Wall
    Department of Pediatrics (Systems Medicine), Stanford University, Stanford, California; Department of Biomedical Data Science, Stanford University, Stanford, California; Department of Psychiatry and Behavioral Sciences (by courtesy), Stanford University, Stanford, California. Electronic address: dpwall@stanford.edu.

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