Phase 3 diagnostic evaluation of a smart tablet serious game to identify autism in 760 children 3–5 years old in Sweden and the United Kingdom

Lindsay Millar, Alex McConnachie, Helen Minnis, Philip Wilson, Lucy Thompson, Anna Anzulewicz, Krzysztof Sobota, Philip Rowe, Christopher Gillberg, Jonathan Delafield-Butt (Corresponding Author)

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Abstract

Introduction Recent evidence suggests an underlying movement disruption may be a core component of autism spectrum disorder (ASD) and a new, accessible early biomarker. Mobile smart technologies such as iPads contain inertial movement and touch screen sensors capable of recording subsecond movement patterns during gameplay. A previous pilot study employed machine learning analysis of motor patterns recorded from children 3–5 years old. It identified those with ASD from age-matched and gender-matched controls with 93% accuracy, presenting an attractive assessment method suitable for use in the home, clinic or classroom.

Methods and analysis This is a phase III prospective, diagnostic classification study designed according to the Standards for Reporting Diagnostic Accuracy Studies guidelines. Three cohorts are investigated: children typically developing (TD); children with a clinical diagnosis of ASD and children with a diagnosis of another neurodevelopmental disorder (OND) that is not ASD. The study will be completed in Glasgow, UK and Gothenburg, Sweden. The recruitment target is 760 children (280 TD, 280 ASD and 200 OND). Children play two games on the iPad then a third party data acquisition and analysis algorithm (Play.Care, Harimata) will classify the data as positively or negatively associated with ASD. The results are blind until data collection is complete, when the algorithm’s classification will be compared against medical diagnosis. Furthermore, parents of participants in the ASD and OND groups will complete three questionnaires: Strengths and Difficulties Questionnaire; Early Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examinations Questionnaire and the Adaptive Behavioural Assessment System-3 or Vineland Adaptive Behavior Scales-II. The primary outcome measure is sensitivity and specificity of Play.Care to differentiate ASD children from TD children. Secondary outcomes measures include the accuracy of Play.Care to differentiate ASD children from OND children.

Ethics and dissemination This study was approved by the West of Scotland Research Ethics Service Committee 3 and the University of Strathclyde Ethics Committee. Results will be disseminated in peer-reviewed publications and at international scientific conferences.

Trial registration number NCT03438994; Pre-results.
Original languageEnglish
Article numbere026226
Number of pages7
JournalBMJ Open
Volume9
Issue number7
Early online date16 Jul 2019
DOIs
Publication statusPublished - 2019

Bibliographical note

Acknowledgments
We are grateful to the children, their parents, teachers and clinicians who have worked so hard to make this study possible, both in its design and in its implementation.

Funding This work was subcontracted to the University of Strathclyde by Harimata sp. z o.o. as an integral part of a Horizon 2020 SME Instrument, grant number 756079.

Prepublication history for this paper is available online. To view these files, please visit the journal online (http://dx.doi.org/10.1136/bmjopen-2018-026226).

Keywords

  • autism
  • diagnosis
  • digital health
  • machine learning
  • motor control
  • smart technology

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