Exploratory study examining the at-home feasibility of a wearable tool for social-affective learning in children with autism.
Jena DanielsJessey N SchwartzCatalin VossNick HaberAzar FazelAaron KlinePeter WashingtonCarl FeinsteinTerry WinogradDennis Paul WallPublished in: NPJ digital medicine (2018)
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 (F(1,13) = 33.20, p = <.001, higher scores indicate higher ASD severity). EGG scores also increased by an average 9.55 correct responses (F(1,10) = 11.89, p = <.01). Parents reported increased eye contact and greater social acuity. This feasibility study supports using mobile technologies for potential therapeutic purposes.
Keyphrases
- autism spectrum disorder
- healthcare
- mental health
- intellectual disability
- machine learning
- attention deficit hyperactivity disorder
- young adults
- bipolar disorder
- traumatic brain injury
- systematic review
- emergency department
- blood pressure
- risk factors
- randomized controlled trial
- study protocol
- body mass index
- climate change
- deep learning