Abstract
| Language | English |
|---|---|
| Pages | 1-17 |
| Number of pages | 17 |
| Journal | PsyArXiv Preprints |
| DOIs | |
| Publication status | Published - 31 Oct 2018 |
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Keywords
- autism
- diagnosis
- digital health
- machine learning
- motor control
- smart technology
Cite this
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A diagnostic evaluation of tablet serious games for the assessment of autism spectrum disorder in young children. / Millar, Lindsay; McConnachie, Alex; Minnis, Helen; Wilson, Phil; Thompson, Lucy ; Anzulewicz, Anna; Sobota, Krzysztof; Rowe, Philip; Gillberg, Christopher; Delafield-Butt, Jonathan.
In: PsyArXiv Preprints, 31.10.2018, p. 1-17.Research output: Contribution to journal › Article
TY - JOUR
T1 - A diagnostic evaluation of tablet serious games for the assessment of autism spectrum disorder in young children
AU - Millar, Lindsay
AU - McConnachie, Alex
AU - Minnis, Helen
AU - Wilson, Phil
AU - Thompson, Lucy
AU - Anzulewicz, Anna
AU - Sobota, Krzysztof
AU - Rowe, Philip
AU - Gillberg, Christopher
AU - Delafield-Butt, Jonathan
PY - 2018/10/31
Y1 - 2018/10/31
N2 - 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.
AB - 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.
KW - autism
KW - diagnosis
KW - digital health
KW - machine learning
KW - motor control
KW - smart technology
U2 - 10.31234/osf.io/hdjwe
DO - 10.31234/osf.io/hdjwe
M3 - Article
SP - 1
EP - 17
JO - PsyArXiv Preprints
T2 - PsyArXiv Preprints
JF - PsyArXiv Preprints
ER -