Picture of boy being examining by doctor at a tuberculosis sanatorium

Understanding our future through Open Access research about our past...

Strathprints makes available scholarly Open Access content by researchers in the Centre for the Social History of Health & Healthcare (CSHHH), based within the School of Humanities, and considered Scotland's leading centre for the history of health and medicine.

Research at CSHHH explores the modern world since 1800 in locations as diverse as the UK, Asia, Africa, North America, and Europe. Areas of specialism include contraception and sexuality; family health and medical services; occupational health and medicine; disability; the history of psychiatry; conflict and warfare; and, drugs, pharmaceuticals and intoxicants.

Explore the Open Access research of the Centre for the Social History of Health and Healthcare. Or explore all of Strathclyde's Open Access research...

Image: Heart of England NHS Foundation Trust. Wellcome Collection - CC-BY.

A diagnostic evaluation of tablet serious games for the assessment of autism spectrum disorder in young children

Millar, Lindsay and McConnachie, Alex and Minnis, Helen and Wilson, Phil and Thompson, Lucy and Anzulewicz, Anna and Sobota, Krzysztof and Rowe, Philip and Gillberg, Christopher and Delafield-Butt, Jonathan (2018) A diagnostic evaluation of tablet serious games for the assessment of autism spectrum disorder in young children. PsyArXiv Preprints. pp. 1-17.

[img]
Preview
Text (Millar-etal-PsyArXiv2018-A-diagnostic-evaluation-of-tablet-serious-games-for-the-assessment)
Millar_etal_PsyArXiv2018_A_diagnostic_evaluation_of_tablet_serious_games_for_the_assessment.pdf
Preprint
License: Creative Commons Attribution 4.0 logo

Download (276kB) | Preview

Abstract

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 sub second 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- and gender-matched controls with 93% accuracy, presenting an attractive assessment method suitable for use in the home, clinic or classroom. This is a Phase III prospective, diagnostic classification study designed according to the Standards for Reporting Diagnostic Accuracy Studies (STARD) guidelines. Three cohorts are investigated: children developing typically (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, U.K., 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 sp. z o.o., Poland) 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 will complete three questionnaires: Strengths and Difficulties Questionnaire; ESSENCE Questionnaire; and the Adaptive Behavioural Assessment System. The primary outcome measure is sensitivity and specificity of Play.Care to detect ASD. Secondary outcomes include the ability of Play.Care to differentiate ASD from OND.