Screening in toddlers and preschoolers at risk for autism spectrum disorder: Evaluating a novel mobile-health screening tool

AbstractProdromal symptoms of Autism Spectrum Disorder (ASD) have been detected within the first year of life. This review evaluated evidence from randomized controlled trials (RCTs) of parent-mediated interventions for infants under 24 months who are at risk for ASD. Electronic databases, including grey literature, were searched up till November 2019. Seven RCTs were identified. There was substantial heterogeneity in recruitment, outcome measures and effect size calculations. Interventions did not reduce the risk of later ASD diagnosis and post-intervention effects on infant outcomes were inconsistent, with five studies reporting significant improvements across both treatment and control groups. Moderate level of evidence of intervention effects on parental interaction skills and the small number of RCTs, and significant limitations restrict generalizability across studies.

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The mediating role of joint attention in the relationship between motor skills and receptive and expressive language in siblings at risk for autism spectrum disorder

Infant Behavior and Development ◽

10.1016/j.infbeh.2019.101377 ◽

2019 ◽

Vol 57 ◽

pp. 101377

Author(s):

E. Bruyneel ◽

E. Demurie ◽

P. Warreyn ◽

H. Roeyers

Keyword(s):

Autism Spectrum Disorder ◽

At Risk ◽

Joint Attention ◽

Motor Skills ◽

Expressive Language ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Mediating Role ◽

The Relationship

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Exploring receptive and expressive language components at the age of 36 months in siblings at risk for autism spectrum disorder

Research in Autism Spectrum Disorders ◽

10.1016/j.rasd.2019.101419 ◽

2019 ◽

Vol 66 ◽

pp. 101419 ◽

Cited By ~ 1

Author(s):

Eva Bruyneel ◽

Ellen Demurie ◽

Inge Zink ◽

Petra Warreyn ◽

Herbert Roeyers

Keyword(s):

Autism Spectrum Disorder ◽

At Risk ◽

Expressive Language ◽

Autism Spectrum ◽

Spectrum Disorder

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Parent-Mediated Intervention for One-Year-Olds Screened as At-Risk for Autism Spectrum Disorder: A Randomized Controlled Trial

Journal of Autism and Developmental Disorders ◽

10.1007/s10803-017-3268-0 ◽

2017 ◽

Vol 47 (11) ◽

pp. 3520-3540 ◽

Cited By ~ 23

Author(s):

Linda R. Watson ◽

Elizabeth R. Crais ◽

Grace T. Baranek ◽

Lauren Turner-Brown ◽

John Sideris ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

At Risk ◽

Randomized Controlled Trial ◽

Controlled Trial ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Randomized Controlled ◽

One Year

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Identification of social engagement indicators associated with autism spectrum disorder using a game-based mobile application

10.1101/2021.06.20.21259187 ◽

2021 ◽

Author(s):

Maya Varma ◽

Peter Washington ◽

Brianna Chrisman ◽

Aaron Kline ◽

Emilie Leblanc ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Mobile Health ◽

Social Engagement ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Computational Techniques ◽

Visual Scanning ◽

Gaze Fixation ◽

Healthcare Needs ◽

Children With Asd

Autism spectrum disorder (ASD) is a widespread neurodevelopmental condition with a range of potential causes and symptoms. Children with ASD exhibit behavioral and social impairments, giving rise to the possibility of utilizing computational techniques to evaluate a child's social phenotype from home videos. Here, we use a mobile health application to collect over 11 hours of video footage depicting 95 children engaged in gameplay in a natural home environment. We utilize automated dataset annotations to analyze two social indicators that have previously been shown to differ between children with ASD and their neurotypical (NT) peers: (1) gaze fixation patterns and (2) visual scanning methods. We compare the gaze fixation and visual scanning methods utilized by children during a 90-second gameplay video in order to identify statistically-significant differences between the two cohorts; we then train an LSTM neural network in order to determine if gaze indicators could be predictive of ASD. Our work identifies one statistically significant region of fixation and one significant gaze transition pattern that differ between our two cohorts during gameplay. In addition, our deep learning model demonstrates mild predictive power in identifying ASD based on coarse annotations of gaze fixations. Ultimately, our results demonstrate the utility of game-based mobile health platforms in quantifying visual patterns and providing insights into ASD. We also show the importance of automated labeling techniques in generating large-scale datasets while simultaneously preserving the privacy of participants. Our approaches can generalize to other healthcare needs.

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