Dataset Information

Application of Latent Class Analysis to Identify Subgroups of Children with Autism Spectrum Disorders who Benefit from Social Skills Training.

ABSTRACT: With Latent Class Analysis applied on data of 98 children with autism spectrum disorder (ASD) (9-12 years; 17 girls) participating in social skills training (SST) in a randomized controlled trial (Dekker et al. 2019), four subgroups were detected, based on social-communicative skills before, and response patterns to training. Two subgroups improved after SST. Characterizing the subgroups based on participant and intervention characteristics showed that improvement was related to lower parent-reported perceived difficulty of social-communicative skills at start, higher verbal ability, younger age and milder symptoms of ASD and anxiety. The lowest performing non-improving subgroup participated more often in SST without parent/teacher involvement, compared to all other subgroups. Response to SST in ASD seems to vary depending on participant characteristics.

SUBMITTER: Dekker V

PROVIDER: S-EPMC8124042 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Application of Latent Class Analysis to Identify Subgroups of Children with Autism Spectrum Disorders who Benefit from Social Skills Training.

Dekker Vera V Nauta Maaike H MH Timmerman Marieke E ME Mulder Erik J EJ Hoekstra Pieter J PJ de Bildt Annelies A

Journal of autism and developmental disorders 20210601 6

With Latent Class Analysis applied on data of 98 children with autism spectrum disorder (ASD) (9-12 years; 17 girls) participating in social skills training (SST) in a randomized controlled trial (Dekker et al. 2019), four subgroups were detected, based on social-communicative skills before, and response patterns to training. Two subgroups improved after SST. Characterizing the subgroups based on participant and intervention characteristics showed that improvement was related to lower parent-rep ...[more]

PMID: 32892235

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Project description:BackgroundThe identification of reproducible subtypes within autistic populations is a priority research area in the context of neurodevelopment, to pave the way for identification of biomarkers and targeted treatment recommendations. Few previous studies have considered medical comorbidity alongside behavioural, cognitive, and psychiatric data in subgrouping analyses. This study sought to determine whether differing behavioural, cognitive, medical, and psychiatric profiles could be used to distinguish subgroups of children on the autism spectrum in the Australian Autism Biobank (AAB).MethodsLatent profile analysis was used to identify subgroups of children on the autism spectrum within the AAB (n = 1151), utilising data on social communication profiles and restricted, repetitive, and stereotyped behaviours (RRBs), in addition to their cognitive, medical, and psychiatric profiles.ResultsOur study identified four subgroups of children on the autism spectrum with differing profiles of autism traits and associated comorbidities. Two subgroups had more severe clinical and cognitive phenotype, suggesting higher support needs. For the 'Higher Support Needs with Prominent Language and Cognitive Challenges' subgroup, social communication, language and cognitive challenges were prominent, with prominent sensory seeking behaviours. The 'Higher Support Needs with Prominent Medical and Psychiatric and Comorbidity' subgroup had the highest mean scores of challenges relating to social communication and RRBs, with the highest probability of medical and psychiatric comorbidity, and cognitive scores similar to the overall group mean. Individuals within the 'Moderate Support Needs with Emotional Challenges' subgroup, had moderate mean scores of core traits of autism, and the highest probability of depression and/or suicidality. A fourth subgroup contained individuals with fewer challenges across domains (the 'Fewer Support Needs Group').LimitationsData utilised to identify subgroups within this study was cross-sectional as longitudinal data was not available.ConclusionsOur findings support the holistic appraisal of support needs for children on the autism spectrum, with assessment of the impact of co-occurring medical and psychiatric conditions in addition to core autism traits, adaptive functioning, and cognitive functioning. Replication of our analysis in other cohorts of children on the autism spectrum is warranted, to assess whether the subgroup structure we identified is applicable in a broader context beyond our specific dataset.

Project description:IntroductionSocial skills training (SST) is used to help individuals with autism spectrum conditions (ASC) better understand the perspectives of others and social interactions, develop empathy skills, and learn how to engage with others socially. However, many individuals with ASC cannot easily sustain high motivation and concentration during such an intervention when it is administered by humans. We developed a social skills training program using multiple humanoid robots (STUH), including an android robot, that aimed to enable individuals with ASC to become familiar with the perspectives of others and improve their sociability and empathy skills. The objective of the present study was to investigate the effectiveness of STUH for these individuals.MethodsIn STUH, we prepared 50 social exercises that consisted of conversations and behavioral interactions between an android robot and a simple humanoid robot. We prepared another humanoid robot that featured a cartoon-like and mechanical design, which played the role of host. In the first half-session of STUH, participants worked on the exercise from the perspective of an outsider. In the second half-session of STUH, they simulated experience by using robots as their avatars. The intervention associated with STUH was conducted for five days in total. We conducted an analysis of variance (ANOVA) featuring the intervention time point as the independent variable to examine changes in each score on the sociability index items.ResultsIn total, 14 individuals with ASC participated in the study. The results of multiple comparison tests using the Bonferroni method indicated that all sociability index items improved between preintervention and follow-up. Our program enabled the participants to become familiar with the perspectives of others and improve their sociability.DiscussionGiven the promising results of this study, future studies featuring long-term follow-up should be conducted to draw definitive conclusions about the efficacy of our training system.

Dataset Information

Application of Latent Class Analysis to Identify Subgroups of Children with Autism Spectrum Disorders who Benefit from Social Skills Training.

Publications

Application of Latent Class Analysis to Identify Subgroups of Children with Autism Spectrum Disorders who Benefit from Social Skills Training.

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