Measurement properties of instruments that assess participation in young people with autism spectrum disorder: a systematic review

BackgroundThe needs of children with autism spectrum disorder (ASD) are complex and this is reflected in the number and diversity of outcomes assessed and measurement tools used to collect evidence about children’s progress. Relevant outcomes include improvement in core ASD impairments, such as communication, social awareness, sensory sensitivities and repetitiveness; skills such as social functioning and play; participation outcomes such as social inclusion; and parent and family impact.ObjectivesTo examine the measurement properties of tools used to measure progress and outcomes in children with ASD up to the age of 6 years. To identify outcome areas regarded as important by people with ASD and parents.MethodsThe MeASURe (Measurement in Autism Spectrum disorder Under Review) research collaboration included ASD experts and review methodologists. We undertook systematic review of tools used in ASD early intervention and observational studies from 1992 to 2013; systematic review, using the COSMIN checklist (Consensus-based Standards for the selection of health Measurement Instruments) of papers addressing the measurement properties of identified tools in children with ASD; and synthesis of evidence and gaps. The review design and process was informed throughout by consultation with stakeholders including parents, young people with ASD, clinicians and researchers.ResultsThe conceptual framework developed for the review was drawn from the International Classification of Functioning, Disability and Health, including the domains ‘Impairments’, ‘Activity Level Indicators’, ‘Participation’, and ‘Family Measures’. In review 1, 10,154 papers were sifted – 3091 by full text – and data extracted from 184; in total, 131 tools were identified, excluding observational coding, study-specific measures and those not in English. In review 2, 2665 papers were sifted and data concerning measurement properties of 57 (43%) tools were extracted from 128 papers. Evidence for the measurement properties of the reviewed tools was combined with information about their accessibility and presentation. Twelve tools were identified as having the strongest supporting evidence, the majority measuring autism characteristics and problem behaviour. The patchy evidence and limited scope of outcomes measured mean these tools do not constitute a ‘recommended battery’ for use. In particular, there is little evidence that the identified tools would be good at detecting change in intervention studies. The obvious gaps in available outcome measurement include well-being and participation outcomes for children, and family quality-of-life outcomes, domains particularly valued by our informants (young people with ASD and parents).ConclusionsThis is the first systematic review of the quality and appropriateness of tools designed to monitor progress and outcomes of young children with ASD. Although it was not possible to recommend fully robust tools at this stage, the review consolidates what is known about the field and will act as a benchmark for future developments. With input from parents and other stakeholders, recommendations are made about priority targets for research.Future workPriorities include development of a tool to measure child quality of life in ASD, and validation of a potential primary outcome tool for trials of early social communication intervention.Study registrationThis study is registered as PROSPERO CRD42012002223.FundingThe National Institute for Health Research Health Technology Assessment programme.

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The measurement properties of the spence children's anxiety scale-parent version in a large international pooled sample of young people with autism spectrum disorder

Autism Research ◽

10.1002/aur.1809 ◽

2017 ◽

Vol 10 (10) ◽

pp. 1629-1652 ◽

Cited By ~ 18

Author(s):

Iliana Magiati ◽

Jian Wei Lerh ◽

Matthew J. Hollocks ◽

Mirko Uljarevic ◽

Jacqui Rodgers ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Young People ◽

Autism Spectrum ◽

Anxiety Scale ◽

Spectrum Disorder ◽

Measurement Properties ◽

Children’S Anxiety ◽

Spence Children’S Anxiety Scale ◽

Pooled Sample ◽

Parent Version

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Caregiver- and Child-Reported Anxiety Using an Autism-Specific Measure: Measurement Properties and Correlates of the Anxiety Scale for Children with Autism Spectrum Disorder (ASC-ASD) in Verbal Young People with ASD

Journal of Autism and Developmental Disorders ◽

10.1007/s10803-020-04739-2 ◽

2020 ◽

Author(s):

Chui Pin Soh ◽

Tze Jui Goh ◽

Iliana Magiati ◽

Min Sung

Keyword(s):

Autism Spectrum Disorder ◽

Young People ◽

Children With Autism ◽

Autism Spectrum ◽

Anxiety Scale ◽

Spectrum Disorder ◽

Measurement Properties ◽

Specific Measure

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Parent Education in Studies With Nonverbal and Minimally Verbal Participants With Autism Spectrum Disorder: A Systematic Review

American Journal of Speech-Language Pathology ◽

10.1044/2019_ajslp-19-00007 ◽

2020 ◽

Vol 29 (2) ◽

pp. 890-902

Author(s):

Lynn Kern Koegel ◽

Katherine M. Bryan ◽

Pumpki Lei Su ◽

Mohini Vaidya ◽

Stephen Camarata

Keyword(s):

Systematic Review ◽

Autism Spectrum Disorder ◽

Parent Education ◽

Autism Spectrum ◽

Verbal Communication ◽

Fidelity Of Implementation ◽

Spectrum Disorder ◽

Education Programs ◽

Minimally Verbal ◽

Parent Education Programs

Purpose The purpose of this systematic review was to identify parent education procedures implemented in intervention studies focused on expressive verbal communication for nonverbal (NV) or minimally verbal (MV) children with autism spectrum disorder (ASD). Parent education has been shown to be an essential component in the habilitation of individuals with ASD. Parents of individuals with ASD who are NV or MV may particularly benefit from parent education in order to provide opportunities for communication and to support their children across the life span. Method ProQuest databases were searched between the years of 1960 and 2018 to identify articles that targeted verbal communication in MV and NV individuals with ASD. A total of 1,231 were evaluated to assess whether parent education was implemented. We found 36 studies that included a parent education component. These were reviewed with regard to (a) the number of participants and participants' ages, (b) the parent education program provided, (c) the format of the parent education, (d) the duration of the parent education, (e) the measurement of parent education, and (f) the parent fidelity of implementation scores. Results The results of this analysis showed that very few studies have included a parent education component, descriptions of the parent education programs are unclear in most studies, and few studies have scored the parents' implementation of the intervention. Conclusions Currently, there is great variability in parent education programs in regard to participant age, hours provided, fidelity of implementation, format of parent education, and type of treatment used. Suggestions are made to provide both a more comprehensive description and consistent measurement of parent education programs.

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Herbal Medicine as an Alternative Treatment in Autism Spectrum Disorder: A Systematic Review

Current Drug Metabolism ◽

10.2174/1389200219666171227202332 ◽

2018 ◽

Vol 19 (5) ◽

pp. 454-459 ◽

Cited By ~ 2

Author(s):

Francielly Mourao Gasparotto ◽

Francislaine Aparecida dos Reis Lívero ◽

Sara Emilia Lima Tolouei Menegati ◽

Arquimedes Gasparotto Junior

Keyword(s):

Systematic Review ◽

Autism Spectrum Disorder ◽

Herbal Medicine ◽

Alternative Treatment ◽

Autism Spectrum ◽

Spectrum Disorder

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Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

10.2196/preprints.14108 ◽

2019 ◽

Author(s):

Sun Jae Moon ◽

Jin Seub Hwang ◽

Rajesh Kana ◽

John Torous ◽

Jung Won Kim

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Autism Spectrum Disorder ◽

Meta Analysis ◽

Learning Algorithms ◽

Structural Mri ◽

Autism Spectrum ◽

Machine Learning Algorithms ◽

Spectrum Disorder ◽

Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

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