Scharoun Benson Sara M, Salters Danielle, Benson Alex J
Department of Kinesiology, University of Windsor, 401 Sunset Ave, Windsor, ON, N9B3P4, Canada.
Department of Psychology, Western University, 1151 Richmond St, London, ON, N6A3K7, Canada.
J Autism Dev Disord. 2025 Mar 13. doi: 10.1007/s10803-025-06780-5.
We used a person-centered approach to: (1) elucidate distinct configurations of social and motor skills across subgroups of children, (2) determine how profiles map to autistic traits and behavioral tendencies, and (3) identify how children with and without different diagnoses are categorized within profiles. Parents/guardians (N = 538) of 5- to 15-year-olds reported on their children's autistic traits, social skills, motor skills, and behavioral tendencies. Factor scores were extracted as indicators for latent profile analysis and a series of profile solutions were generated. After selecting the final profile solution, mean-level differences were examined across each profile, pairing for key measures of interest. Frequency distribution analysis was used to identify the number of children with and without formal diagnoses in each profile. A 6-profile solution was identified, drawing attention to how social and motor competencies combine in qualitatively distinct ways across subpopulations. Whereas several profiles had similar levels of social and motor skills (Profile 2: weak social/motor, Profile 4: average social/motor, Profile 6: exceptional social/motor), other profiles showed divergent levels of social and motor competencies (Profile 1: weak motor but average social, Profile 3: above average social and strong motor, Profile 5: strong social and above average motor). These subpopulations differed in terms of their proportions of diagnoses (i.e., of autism specifically and co-occurring with other diagnoses), autistic traits, and behavioral difficulties. Findings support a person-centered approach that considers the relationships, interactions, and shared mechanisms of multiple developmental domains to better understand child development, optimize interventions, and improve outcomes.
(1) 阐明不同儿童亚组的社交和运动技能的不同配置,(2) 确定这些特征模式如何映射到自闭症特征和行为倾向,以及 (3) 确定有不同诊断和无不同诊断的儿童在这些特征模式中是如何分类的。5至15岁儿童的父母/监护人(N = 538)报告了他们孩子的自闭症特征、社交技能、运动技能和行为倾向。提取因子得分作为潜在特征分析的指标,并生成一系列特征模式解决方案。在选择最终的特征模式解决方案后,对每个特征模式的平均水平差异进行了检验,并对关键的感兴趣指标进行了配对。频率分布分析用于确定每个特征模式中有无正式诊断的儿童数量。确定了一个六特征模式解决方案,该方案关注了不同亚群体中社交和运动能力如何以质的不同方式结合。虽然有几个特征模式的社交和运动技能水平相似(特征模式2:社交/运动能力弱,特征模式4:社交/运动能力中等,特征模式6:社交/运动能力卓越),但其他特征模式显示出社交和运动能力水平不同(特征模式1:运动能力弱但社交能力中等,特征模式3:社交能力高于平均水平且运动能力强,特征模式5:社交能力强且运动能力高于平均水平)。这些亚群体在诊断比例(即自闭症以及与其他诊断同时出现的情况)、自闭症特征和行为困难方面存在差异。研究结果支持一种以个体为中心的方法,该方法考虑多个发育领域的关系、相互作用和共同机制,以更好地理解儿童发育、优化干预措施并改善结果。