Evidence map›Paper›PMID 42097826›Full record

ArticleBMJ paediatrics open2026

Sensory processing differences and behavioural problems in children with autism: a retrospective study using statistical modelling and multi-output machine learning.

Chenyu Yan, Jialu Xu, Kexin Duan, Yifei Xiang, Haifeng Li

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Article in BMJ paediatrics open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Chenyu YanDepartment of Rehabilitation, Children's Hospital, National Clinical Research Center for Child and Adolescents' Health and Diseases, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.ORCID http://orcid.org/0009-0009-8924-9112
Jialu XuDepartment of Rehabilitation, Children's Hospital, National Clinical Research Center for Child and Adolescents' Health and Diseases, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China dew2020@zju.edu.cn.
Kexin DuanZhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Yifei XiangDepartment of Rehabilitation, Children's Hospital, National Clinical Research Center for Child and Adolescents' Health and Diseases, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Haifeng LiDepartment of Rehabilitation, Children's Hospital, National Clinical Research Center for Child and Adolescents' Health and Diseases, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.ORCID http://orcid.org/0000-0002-3972-5573

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSensory processing differences (SPDs) are common in children with autism, yet their specific contributions to behavioural problems remain insufficiently explored. To examine how SPDs relate to behavioural problems in children with autism and to identify key sensory predictors and sensory-based subtypes associated with behavioural risk.

methodsThe retrospective study included 127 children with autism (1-7 years) who received rehabilitation training at a tertiary children's hospital between 2020 and 2024. Fifteen SPD features across visual, auditory, tactile, gustatory/olfactory and vestibular/proprioceptive modalities (covering sensitivity, hyporesponsivity and seeking behaviours) and six behavioural problems were coded as binary variables based on parent-reported questionnaires. Associations were analysed using Phi correlations and logistic regression. A multi-output random forest with Shapley Additive Explanations (SHAP) evaluated the joint predictive value of SPDs. Hierarchical clustering using Jaccard distance was used to identify sensory subtypes and compare behavioural profiles.

resultsVestibular/proprioceptive seeking was positively associated with frequent tantrums (φ=0.30) and emerged as an independent predictor (OR=3.83, 95% CI 1.65 to 8.90). Visual seeking was negatively associated with difficulty adapting to routine changes (φ=-0.23; OR=0.34, 95% CI 0.15 to 0.78). The multi-output random forest showed moderate performance. SHAP analysis highlighted auditory sensitivity and multiple seeking behaviours as major contributors. Clustering revealed a 'multi-sensory seeking' subtype with higher rates of tantrums.

conclusionsSPDs are meaningfully linked to behavioural problems in autism. Vestibular/proprioceptive seeking and auditory sensitivity are key behavioural risk indicators, while visual seeking may support adaptability. Sensory-based profiling has the potential to identify children at elevated behavioural risk and guide personalised intervention planning.

Indexed as

Autistic DisorderChild Behavior DisordersMachine LearningProblem BehaviorSensation DisordersChildChild, PreschoolFemaleHumansInfantMaleModels, StatisticalRandom ForestRetrospective StudiesChildMachine Learning

Identifiers

PMID42097826
PMCPMC13157742

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.