Literature DB >> 30907929

Effect of Wearable Digital Intervention for Improving Socialization in Children With Autism Spectrum Disorder: A Randomized Clinical Trial.

Catalin Voss1, Jessey Schwartz2, Jena Daniels2, Aaron Kline2, Nick Haber2, Peter Washington2, Qandeel Tariq2, Thomas N Robinson2,3, Manisha Desai2, Jennifer M Phillips3, Carl Feinstein3, Terry Winograd1, Dennis P Wall2,4,5.   

Abstract

Importance: Autism behavioral therapy is effective but expensive and difficult to access. While mobile technology-based therapy can alleviate wait-lists and scale for increasing demand, few clinical trials exist to support its use for autism spectrum disorder (ASD) care. Objective: To evaluate the efficacy of Superpower Glass, an artificial intelligence-driven wearable behavioral intervention for improving social outcomes of children with ASD. Design, Setting, and Participants: A randomized clinical trial in which participants received the Superpower Glass intervention plus standard of care applied behavioral analysis therapy and control participants received only applied behavioral analysis therapy. Assessments were completed at the Stanford University Medical School, and enrolled participants used the Superpower Glass intervention in their homes. Children aged 6 to 12 years with a formal ASD diagnosis who were currently receiving applied behavioral analysis therapy were included. Families were recruited between June 2016 and December 2017. The first participant was enrolled on November 1, 2016, and the last appointment was completed on April 11, 2018. Data analysis was conducted between April and October 2018. Interventions: The Superpower Glass intervention, deployed via Google Glass (worn by the child) and a smartphone app, promotes facial engagement and emotion recognition by detecting facial expressions and providing reinforcing social cues. Families were asked to conduct 20-minute sessions at home 4 times per week for 6 weeks. Main Outcomes and Measures: Four socialization measures were assessed using an intention-to-treat analysis with a Bonferroni test correction.
Results: Overall, 71 children (63 boys [89%]; mean [SD] age, 8.38 [2.46] years) diagnosed with ASD were enrolled (40 [56.3%] were randomized to treatment, and 31 (43.7%) were randomized to control). Children receiving the intervention showed significant improvements on the Vineland Adaptive Behaviors Scale socialization subscale compared with treatment as usual controls (mean [SD] treatment impact, 4.58 [1.62]; P = .005). Positive mean treatment effects were also found for the other 3 primary measures but not to a significance threshold of P = .0125. Conclusions and Relevance: The observed 4.58-point average gain on the Vineland Adaptive Behaviors Scale socialization subscale is comparable with gains observed with standard of care therapy. To our knowledge, this is the first randomized clinical trial to demonstrate efficacy of a wearable digital intervention to improve social behavior of children with ASD. The intervention reinforces facial engagement and emotion recognition, suggesting either or both could be a mechanism of action driving the observed improvement. This study underscores the potential of digital home therapy to augment the standard of care. Trial Registration: ClinicalTrials.gov identifier: NCT03569176.

Entities:  

Mesh:

Year:  2019        PMID: 30907929      PMCID: PMC6503634          DOI: 10.1001/jamapediatrics.2019.0285

Source DB:  PubMed          Journal:  JAMA Pediatr        ISSN: 2168-6203            Impact factor:   16.193


  37 in total

1.  Presidential Address, 2020-Using Technology to Deliver Services and Supports in Homes, Neighborhoods, and Communities: Evidence and Promise.

Authors:  Leonard Abbeduto
Journal:  Intellect Dev Disabil       Date:  2020-12-01

2.  Digital Interventions for Autism Spectrum Disorder: A Meta-analysis.

Authors:  Helena Sandgreen; Line Hofmann Frederiksen; Niels Bilenberg
Journal:  J Autism Dev Disord       Date:  2020-11-10

3.  Beyond artificial intelligence: exploring artificial wisdom.

Authors:  Dilip V Jeste; Sarah A Graham; Ellen E Lee; Ho-Cheol Kim; Tanya T Nguyen; Colin A Depp
Journal:  Int Psychogeriatr       Date:  2020-06-25       Impact factor: 3.878

4.  Digital health should augment (not replace) autism treatment providers.

Authors:  Heather J Nuske; David S Mandell
Journal:  Autism       Date:  2021-08-27

5.  Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels.

Authors:  Peter Washington; Haik Kalantarian; Jack Kent; Arman Husic; Aaron Kline; Emilie Leblanc; Cathy Hou; Cezmi Mutlu; Kaitlyn Dunlap; Yordan Penev; Nate Stockham; Brianna Chrisman; Kelley Paskov; Jae-Yoon Jung; Catalin Voss; Nick Haber; Dennis P Wall
Journal:  Cognit Comput       Date:  2021-09-27       Impact factor: 4.890

Review 6.  Data-Driven Diagnostics and the Potential of Mobile Artificial Intelligence for Digital Therapeutic Phenotyping in Computational Psychiatry.

Authors:  Peter Washington; Natalie Park; Parishkrita Srivastava; Catalin Voss; Aaron Kline; Maya Varma; Qandeel Tariq; Haik Kalantarian; Jessey Schwartz; Ritik Patnaik; Brianna Chrisman; Nathaniel Stockham; Kelley Paskov; Nick Haber; Dennis P Wall
Journal:  Biol Psychiatry Cogn Neurosci Neuroimaging       Date:  2019-12-13

Review 7.  Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension.

Authors:  Xiaoxuan Liu; Samantha Cruz Rivera; David Moher; Melanie J Calvert; Alastair K Denniston
Journal:  Lancet Digit Health       Date:  2020-09-09

Review 8.  Structural, Functional, and Molecular Imaging of Autism Spectrum Disorder.

Authors:  Xiaoyi Li; Kai Zhang; Xiao He; Jinyun Zhou; Chentao Jin; Lesang Shen; Yuanxue Gao; Mei Tian; Hong Zhang
Journal:  Neurosci Bull       Date:  2021-03-29       Impact factor: 5.271

Review 9.  Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension.

Authors:  Samantha Cruz Rivera; Xiaoxuan Liu; An-Wen Chan; Alastair K Denniston; Melanie J Calvert
Journal:  Lancet Digit Health       Date:  2020-09-09

10.  Meta-Analysis of RCTs of Technology-Assisted Parent-Mediated Interventions for Children with ASD.

Authors:  Hong Ji Pi; Kannan Kallapiran; Shashidhara Munivenkatappa; Preeti Kandasamy; Richard Kirubakaran; Paul Russell; Valsamma Eapen
Journal:  J Autism Dev Disord       Date:  2021-07-27
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