Literature DB >> 29734507

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

Stephen M Kanne1, Laura Arnstein Carpenter2, Zachary Warren3.   

Abstract

There are many available tools with varying levels of accuracy designed to screen for Autism Spectrum Disorder (ASD) in young children, both in the general population and specifically among those referred for developmental concerns. With burgeoning waitlists for comprehensive diagnostic ASD assessments, finding accurate methods and tools for advancing diagnostic triage becomes increasingly important. The current study compares the efficacy of four oft used paper and pencil measures, the Modified Checklist for Autism in Toddlers Revised with Follow-up, the Social Responsiveness Scale, Second Edition, and the Social Communication Questionnaire, and the Child Behavior Checklist to a novel mobile-health screening tool developed by Cognoa, Inc. (Cognoa) in a group of children 18-72 months of age. The Cognoa tool may have potential benefits as it integrates a series of parent-report questions with remote clinical ratings of brief video segments uploaded via parent's smartphones to calculate level of ASD risk. Participants were referred to one of three tertiary care diagnostic centers for ASD-related concerns (n = 230) and received a best estimate ASD diagnosis. Analysis and comparison of psychometric properties indicated potential advantages for Cognoa within this clinical sample across age ranges not often covered by another single measure/tool. Autism Res 2018, 11: 1038-1049.
© 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: With the wait times getting longer for comprehensive Autism Spectrum Disorder (ASD) diagnostic assessments, it is becoming increasingly important to find accurate tools to screen for ASD. The current study compares four screening measures that have been in use for some time to a novel mobile-health screening tool, called Cognoa. The Cognoa tool is novel because it integrates parent-report questions with clinical ratings of brief video segments uploaded via parent's smartphones to calculate ASD risk. Two hundred thirty children who were referred to one of three ASD specialty diagnostic centers to see if they had ASD participated in the study. A direct comparison indicated potential advantages for Cognoa not often covered by another single measure/tool. © 2018 International Society for Autism Research, Wiley Periodicals, Inc.

Entities:  

Keywords:  Autism; screening measure; sensitivity and specificity

Mesh:

Year:  2018        PMID: 29734507     DOI: 10.1002/aur.1959

Source DB:  PubMed          Journal:  Autism Res        ISSN: 1939-3806            Impact factor:   5.216


  11 in total

Review 1.  Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements.

Authors:  Troy Vargason; Genevieve Grivas; Kathryn L Hollowood-Jones; Juergen Hahn
Journal:  Semin Pediatr Neurol       Date:  2020-03-05       Impact factor: 1.636

2.  The feasibility of a crowd-based early developmental milestone tracking application.

Authors:  Ayelet Ben-Sasson; Kayla Jacobs; Eli Ben-Sasson
Journal:  PLoS One       Date:  2022-05-26       Impact factor: 3.752

3.  Provider and Caregiver Satisfaction with Telehealth Evaluation of Autism Spectrum Disorder in Young Children During the COVID-19 Pandemic.

Authors:  Debra L Reisinger; Elesia Hines; Christine Raches; Qing Tang; Cristina James; Rebecca McNally Keehn
Journal:  J Autism Dev Disord       Date:  2022-05-17

Review 4.  Data-Driven Diagnostics and the Potential of Mobile Artificial Intelligence for Digital Therapeutic Phenotyping in Computational Psychiatry.

Authors:  Peter Washington; Natalie Park; Parishkrita Srivastava; Catalin Voss; Aaron Kline; Maya Varma; Qandeel Tariq; Haik Kalantarian; Jessey Schwartz; Ritik Patnaik; Brianna Chrisman; Nathaniel Stockham; Kelley Paskov; Nick Haber; Dennis P Wall
Journal:  Biol Psychiatry Cogn Neurosci Neuroimaging       Date:  2019-12-13

Review 5.  The Acceptability and Effectiveness of Web-Based Developmental Surveillance Programs: Rapid Review.

Authors:  Jess Baker; Jane Kohlhoff; Se-Inyenede Onobrakpor; Sue Woolfenden; Rebecca Smith; Constanze Knebel; Valsamma Eapen
Journal:  JMIR Mhealth Uhealth       Date:  2020-04-23       Impact factor: 4.773

6.  Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection.

Authors:  Peter Washington; Qandeel Tariq; Emilie Leblanc; Brianna Chrisman; Kaitlyn Dunlap; Aaron Kline; Haik Kalantarian; Yordan Penev; Kelley Paskov; Catalin Voss; Nathaniel Stockham; Maya Varma; Arman Husic; Jack Kent; Nick Haber; Terry Winograd; Dennis P Wall
Journal:  Sci Rep       Date:  2021-04-07       Impact factor: 4.379

Review 7.  Information and Communication Technologies to Support Early Screening of Autism Spectrum Disorder: A Systematic Review.

Authors:  Lorenzo Desideri; Patricia Pérez-Fuster; Gerardo Herrera
Journal:  Children (Basel)       Date:  2021-02-01

8.  The Digital Divide in Technologies for Autism: Feasibility Considerations for Low- and Middle-Income Countries.

Authors:  Aubrey J Kumm; Marisa Viljoen; Petrus J de Vries
Journal:  J Autism Dev Disord       Date:  2021-06-13

9.  Parent Perceptions of Caregiver-Mediated Telemedicine Tools for Assessing Autism Risk in Toddlers.

Authors:  Laura L Corona; Amy S Weitlauf; Jeffrey Hine; Anna Berman; Alexandra Miceli; Amy Nicholson; Caitlin Stone; Neill Broderick; Sara Francis; A Pablo Juárez; Alison Vehorn; Liliana Wagner; Zachary Warren
Journal:  J Autism Dev Disord       Date:  2021-02

10.  Screening for Autism Spectrum Disorder in Premature Subjects Hospitalized in a Neonatal Intensive Care Unit.

Authors:  Norrara Scarlytt de Oliveira Holanda; Lidiane Delgado Oliveira da Costa; Sabrinne Suelen Santos Sampaio; Gentil Gomes da Fonseca Filho; Ruth Batista Bezerra; Ingrid Guerra Azevedo; Silvana Alves Pereira
Journal:  Int J Environ Res Public Health       Date:  2020-10-21       Impact factor: 3.390

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