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

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.

Keywords: autism; diagnosis; digital health; machine learning; motor control; smart technology.

Conflict of interest statement

Competing interests: The academic authors LM, HM, PW, AM, PR, CG and JD-B are members of the trial steering and management committees and declare no financial interest in this product or the funding company, Harimata sp. z o.o. Coauthors AA and KS are board members of the Harimata sp. z o.o. that intends to commercialise the Play.Care assessment technology. AA and KS have options vesting in the company. AA is a voting member of the trial steering committee.

© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1

Play.Care assessment serious games. (A) ‘Sharing’. The child taps the fruit to divide it into four pieces for sharing among the game characters. When all four characters have a slice of fruit, they express happiness for three seconds before the fruit is replaced with another food and the characters return to their original positions. (B) ‘Creativity’. The child is free to choose an object or animal they wish to trace and then colour in freely by choosing a colour form the colour wheel. If the child is satisfied with their colouring, a new shape can be chosen by selecting the green button at the top of the screen, and the process repeats. Reproduced from Anzulewicz et al. (2016).

Figure 2

Movement data acquisition. (A) A child engages freely with the serious game. The tablet is protected by a bumper and placed firmly on a table. Movement data are acquired from (B) the touch screen and (C) the inertial movement unit sensor that detect the kinematics and contact forces of a gesture, respectively. Adapted from Anzulewicz et al. (2016).