Significance statement By providing reliable, interval-level measurement, the VBQ offers a promising tool for identifying visual symptoms in children with ASD; its utility for screening, monitoring, and clinical management will need to be confirmed in future externally validated and longitudinal studies. Background Atypical visual behaviors are highly prevalent in children with autism spectrum disorder (ASD), yet validated instruments to quantify these symptoms remain limited. This study aimed to develop and validate the Visual Behavior Questionnaire (VBQ) for the assessment of visual symptoms associated with ASD. Methods Parents of 93 children with clinically confirmed ASD (aged 4–12 years) and 100 typically developing controls completed a 15-item pilot Visual Behavior Questionnaire. The questionnaire was refined and psychometrically evaluated using Rasch analysis. Discriminative ability, validity, and test–retest reliability were subsequently assessed. Results Rasch refinement yielded an 8-item instrument with ordered response categories, satisfactory model fit, and no gender-based DIF. Person reliability was 0.83 and item reliability 0.93, supporting adequate discrimination and stable item hierarchy. Unidimensionality was supported by residual principal component analysis. Targeting revealed a floor effect (−1.68 logits), reflecting limited representation of high symptom severity. ROC analysis showed excellent discrimination between the ASD and control groups within this sample (AUC = 0.951), with a preliminary optimal cutoff score of ≥ 7. The VBQ correlated significantly with MEM and NPC values and showed excellent repeatability (ICC = 0.980). Conclusion The VBQ demonstrates strong psychometric performance and provides a brief tool for identifying visual symptoms associated with ASD. External validation, including longitudinal studies, is needed before its utility for monitoring and clinical management can be established.
Hypermobile Ehlers-Danlos syndrome (hEDS), a multi-systemic connective tissue disorder with extensive biopsychosocial impacts, occurs with greater than expected frequency in autistic people, yet the specific impacts on their daily lives and healthcare experiences are largely undocumented. In particular, there is limited information regarding ways in which each condition (autism, hEDS) might complicate or ameliorate the needs associated with the other one. We conducted semi-structured interviews with five women diagnosed with both autism and hEDS, which were analysed using Interpretative Phenomenological Analysis (IPA). While journeys to diagnosis were lengthy and distressing, obtaining diagnoses yielded the benefits of self-knowledge and self-compassion, improved self-management of their health, and better negotiation of practical and emotional support. Participants also noted that while autism facilitated some aspects of hEDS self-management, hEDS could exacerbate the medical challenges already disproportionately faced by autistic individuals. Overall, our preliminary findings suggest that the unique healthcare needs of this poorly understood and vulnerable subgroup may be unmet by current healthcare provision; moreover, potentially adaptive autistic strengths for illness management may not be commonly facilitated. To better accommodate these needs, communication training for healthcare professionals, adaptation of patient care environments and adoption of a holistic management approach are recommended.
Developing accurate test norms is crucial to developmental disability practice but requires substantial resources. Continuous norms show promise relative to traditional norms, yet there is limited guidance on optimal methods and minimum sample sizes. This simulation study compared traditional norming with continuous norming models across 96 data conditions varying in sample size (500−1500), age trajectories (ages 2–22), variance patterns, and skewness. Generalized Additive Models for Location, Scale, and Shape (GAMLSS) that approximated the data generating conditions tended to be the best fitting models. Best fitting GAMLSS models showed closer fit to the data at N = 500 than traditional windowing norms at N = 1500 with the largest GAMLSS performance gains occurring by N = 750. Real-world neurobehavioral data supported the need for an iterative model selection process when developing continuous norms. Test developers can achieve high norming accuracy with smaller samples using a GAMLSS model selection process, potentially reducing costs while improving precision.
Background Wearable devices have been utilized to detect real-time physiological changes, allowing for a better understanding of the mechanisms underlying behavioral and cognitive processes in humans. However, little is known about the efficacy of wearable devices in measuring emotional processes in youth with autism, who may experience challenges with social communication and emotion dysregulation. Method This systematic review analyzed 14 peer-reviewed journal articles published over the past 10 years. Search terms were categorized into “wearable,” “emotion,” “autism,” and “youth.” Body-worn wearable devices, such as smartwatches and wristbands, were the most frequently used, while cardiovascular signals were the most measured. Results Findings from the reviewed articles indicate the potential of interventions using wearable technology to improve emotion recognition, decrease emotional outbursts, and reduce stress or anxiety levels. Based on wearable data, machine learning approaches were also utilized to predict challenging behaviors or aggressive episodes before they occurred in autistic youth. Conclusion This review discusses clinical implications and ethical considerations for improving socio-emotional development and emotion regulation in autistic youth. Future research should include more studies using wearable technology in naturalistic settings, such as homes or schools, to better understand emotion regulation strategies across diverse autistic populations.
Autism is a neurodevelopmental condition for which timely and accurate identification remains an important clinical priority. Early and reliable identification can facilitate access to assessment, diagnosis, and appropriate support; however, current diagnostic pathways still rely largely on behavioural evaluation and clinical judgement. In this context, machine learning (ML) approaches have attracted growing interest because they can identify subtle and complex patterns in speech data that may not be readily captured through conventional methods. The current study investigated the potential of ML models to distinguish vowel productions from autistic and neurotypical adults based on acoustic speech features. Acoustic measures included fundamental frequency (F0), the first three formants (F1, F2, F3), duration, jitter, shimmer, harmonics-to-noise ratio (HNR), and intensity, elicited through a controlled production task. Four supervised ML models were evaluated: LightGBM, Random Forest, Support Vector Machine, and XGBoost. Under random token-level splitting, all models demonstrated good classification performance, with the best-performing model achieving an area under the curve (AUC) of approximately 0.89. However, performance decreased substantially under speaker-independent cross-validation, with AUCs approximately 0.60 and wider confidence intervals, indicating more limited generalization to vowel productions from unseen speakers. SHAP analyses nevertheless showed a broadly consistent feature-importance pattern across validation schemes, with F0 emerging as the strongest predictor, followed by intensity, while F3 and other acoustic measures made smaller contributions. These findings indicate that vowel acoustics contain information relevant to distinguishing autistic and neurotypical speech within a controlled dataset, while also demonstrating that classification performance is substantially attenuated under speaker-independent validation. The results highlight both the potential of interpretable speech-based ML for investigating acoustic markers of autism and the importance of rigorous speaker-independent evaluation.
Autistic children frequently experience motor coordination difficulties that, together with environmental barriers such as limited availability of trained professionals and inclusive physical activity settings, restrict participation in physical activities and reduce opportunities for social interaction. Despite the importance of motor skill development in this population, few studies have examined teaching strategies that optimise long-term skill acquisition and generalisation.Few studies have examined motor learning strategies that optimize long-term skill acquisition and generalization in this population. This study investigated whether an easy-to-difficult transfer framework would enhance the acquisition, retention, and transfer of a complex motor skill- two-wheel bicycle riding- compared with a traditional training-wheels approach. Fifty children aged 5–8 years (24 with autism, 26 typically developing [TD]) were randomly assigned within their diagnostic groups to either the easy-to-difficult transfer condition (balance bike to assisted wheels to independent riding) or the traditional condition (training wheels progression only). Participants completed six acquisition sessions over two weeks, followed by retention and transfer tests two weeks later. Riding performance was measured using a validated 15-item questionnaire (maximum score = 75). During acquisition, both TD groups performed equivalently and superior to both autism groups. Within the autism groups, the conventional approach produced significantly higher scores from Session 2 onward. However, the easy-to-difficult transfer group showed markedly superior retention and transfer performance across both populations. For autistic children, the easy-to-difficult group outperformed the traditional group in retention (M = 62.17, SD = 7.17 vs. M = 48.50, SD = 13.88) and transfer (M = 62.25, SD = 5.85 vs. M = 40.50, SD = 11.38). Parallel advantages were observed in the TD sample. Although traditional practice accelerated short-term gains during acquisition of complex skills, the easy-to-difficult transfer framework promoted more robust long-term retention and generalization, particularly in autistic children. This theoretically grounded approach offers a promising strategy for motor skill interventions in developmental disabilities.
Purpose This study aimed to characterise the health, social, and demographic circumstances of autistic adults in the UK using data from the Adult Autism Spectrum Cohort-UK (ASC-UK), a large, cross-sectional, population-based cohort. It sought to identify age- and gender-related differences in mental and physical health, healthcare access, employment, social relationships, and support needs. Methods Between 2015 and 2021, 2129 autistic adults (diagnosed or self-identified), aged 16 years and older, were recruited via diverse routes including NHS services and community organisations. Data were collected using a 78-item registration questionnaire covering ten domains of daily life and the Social Responsiveness Scale-2 (SRS-2). Responders were grouped into four age categories and compared using appropriate statistical tests including ANOVA, Kruskal-Wallis, Chi-square, and effect size measures. Results Autistic adults reported high rates of physical (e.g., gastrointestinal, obesity, arthritis) and mental health conditions (e.g., anxiety, depression), with notable gender and age variation. Females reported significantly more mental and physical health issues than males. Employment was lowest in the youngest and oldest groups and unemployment was associated with poor mental health. A substantial gap was found between needed and received support, particularly among females and those with complex health needs. Social deprivation was not significantly associated with health diagnoses. Conclusion Autistic adults across the UK experience substantial health disparities and unmet support needs. Gender and age influence outcomes, and healthcare services must adapt to improve access and equity. This study offers critical, lifespan-wide insights to inform policy, practice, and future longitudinal research.
Purpose Sensory reactivity differences are a core feature of autism, but the experiences of autistic individuals with co-occurring intellectual and developmental disabilities (IDD) remain underrepresented in the literature. Methods This systematic review synthesizes findings from 29 studies that examined sensory reactivity in this population, including 15 studies of idiopathic autism and 14 studies of genetic syndromes associated with autism and IDD. Results Across both idiopathic and syndromic autism, sensory hyporeactivity emerged as a dominant phenotype, often co-occurring with elevated sensory seeking. Hyperreactivity, in contrast, was less prominent in this population compared to autistic individuals without IDD. However, contradictory findings were identified, particularly in studies comparing overall sensory reactivity across cognitive levels in idiopathic autism. Methodological factors likely contribute to these inconsistencies, as many studies relied on proxy-report questionnaires with aggregated sensory scores, while studies employing observational or multimethod approaches were more sensitive to differences in sensory reactivity profiles. Conclusion Together, these findings highlight the need for more inclusive, reliable measurement strategies, including adapted self-report tools and psychophysiological methods, to advance understanding of sensory reactivity in autistic individuals with IDD and ensure that research reflects the full spectrum of autistic experiences.
Cognitive empathy difficulties are often observed in autism, but research on the factors associated with these difficulties remains limited. This study investigated whether social and non-social contexts are associated with contextualized cognitive empathy performance for different emotions (happiness, sadness, anger, and fear) in autistic children. Thirty autistic children (mean age = 9.00 +/- 0.70 years, range = 8-10 years) and 30 non-autistic children watched a series of videobased emotional situations depicting everyday experiences in either social (involving peer interactions) or non-social contexts. Afterward, they answered questions designed to assess their contextualized cognitive empathy performance. Compared to non-autistic children, autistic children had lower cognitive empathy task scores across all types of emotion, though their scores were relatively higher for happiness than for other emotions. Additionally, among autistic children, cognitive empathy task scores were lower in social contexts than in non-social contexts for happiness, anger, and fear, but not for sadness. These findings provide preliminary evidence that contextualized cognitive empathy performance may vary across social and non-social contexts in autistic children. Stronger conclusions about emotion-specific contextual effects require further investigation.
Objectives As the number of students with autism continues to rise, more educators are expected to address their needs. This qualitative study examined teachers’ strengths and limitations across academic, behavioral, social, and functional domains, and what they perceive as facilitators and barriers in supporting these students. Methods Focus groups were conducted with 17 teachers from an urban Southwestern U.S. region. A preliminary codebook was developed based on prior literature and iteratively refined through team coding of transcripts and identification of emergent themes. Results Teachers most frequently identified academics as a strength, citing familiarity with the content and teaching methods. Addressing behavioral issues was most frequently reported as the most challenging area, due to significant student needs, limited training, and resource gaps. Top facilitators included collaboration with families or staff, availability of support personnel, and teacher confidence; top barriers included inadequate training, staff support, and administrative support. Conclusions Study findings suggest the need for more frequent and relevant training for teachers across general and special education, more opportunities for professional collaboration, and administrative actions to strengthen staff capacity to serve students with autism across domains.
Previous research has found that law enforcement officers (LEOs) have limited knowledge of autism, and most have not received formal training. Researchers aimed to replicate and extend previous findings on the impact on and experiences of LEOs participating in an autism training that included virtual reality (VR) simulation. Training components included an interactive presentation, VR simulation, and think-pair-share activities. LEOs’ perceived confidence when responding to calls involving autistic individuals was the primary outcome. A total of 401 sworn LEOs attended one of 20 training sessions. Participants completed pre- and post-training surveys assessing perceived confidence in responding to related calls. LEOs also provided positive feedback on their experiences with the VR simulation, though a small number shared negative experiences such as reported motion sickness. LEOs reported significant increases in perceived confidence in responding to calls. The vast majority indicated that the training would positively influence their professional interactions with autistic individuals. Notably, LEOs without prior autism training and those without a personal relationship with an autistic individual demonstrated the greatest gains in confidence. Future research should examine the long-term effects of such training on officer behavior and call outcomes.
Background Children with Autism Spectrum Disorder (ASD) often exhibit atypical oculomotor behavior and deficits in executive functioning, particularly in tasks involving inhibition and working memory. Eye-tracking paradigms provide objective markers for assessing these functions. Objective To assess whether a brief memory-based cognitive training can modulate saccadic eye movements and improve executive functioning in children with ASD. Method Fifty-four children with ASD were randomly assigned to a training group (G1) or a control group (G2). All participants completed oculomotor tasks before and after the intervention. These tasks included prosaccades (reflexive responses to peripheral stimuli) and memory-guided saccades (requiring inhibition of reflexive responses and spatial memory). G1 underwent 10 min of visuospatial memory training, while G2 had a 10-minute rest period. The main outcome measures included saccadic latency, anticipatory saccades (indicative of impulsivity), and error rate in memory-guided saccades (indicative of working memory performance). Results No significant change in prosaccadic latency was observed in the two groups. In contrast, the occurrence of anticipatory saccades and error rates in memory-guided saccades decreased significantly in G1 only, while G2 showed no change. Conclusion A brief memory training significantly improved oculomotor markers of inhibition and working memory in children with ASD. These findings suggest that memory training may enhance attentional engagement and inhibitory control in this population.
Autistic adults vary in how they identify with being autistic, which may influence their quality of life and flourishing. We examined the association of autistic identity, characterized by the Autism Spectrum Identity Scale dimensions of positive difference and changeability, with the wellbeing, daily life, and relationships of autistic adults. Among 1139 participants, 893 were formally diagnosed and 246 were self-diagnosed. Participants who more strongly endorsed positive difference and changeability reported higher degrees of wellbeing, daily living skills, and relationship success. Linear regression analyses indicated that these identity dimensions exhibited distinct patterns, with positive difference primarily relating to psychological health and wellbeing markers, and changeability relating to daily functioning and relationship success. These findings suggest how differences in autistic identity relate to flourishing and may inform the development of therapeutic approaches and structures that enhance wellbeing and quality of life among autistic adults.
Background: Autism spectrum disorder (ASD) and genetically defined conditions such as Fragile X syndrome (FXS) and Rett syndrome (RTT) are characterized by substantial neurobehavioral heterogeneity. Although sensory and executive dysfunction are well established, the relative contribution of epilepsy burden and pharmacological treatment to autism severity remains unclear. Methods: This study included 263 children with ASD (n = 96), FXS (n = 84), and RTT (n = 83). Sensory reactivity, executive functioning, and autism severity were assessed using standardized caregiver-reported scales. Group comparisons, correlation analyses, and multiple regression models were conducted to examine multidomain relationships. Results: A consistent gradient of impairment was observed across groups (ASD < FXS < RTT). While sensory and executive domains were significantly associated with autism severity, epilepsy-related variables emerged as the strongest predictors, particularly in RTT. Seizure type and antiepileptic drug use showed the largest effects, with regression analyses indicating that these factors accounted for a substantial proportion of variance in autism severity. In contrast, environmental and intervention-related factors had stronger effects in ASD and FXS, suggesting greater neurodevelopmental plasticity. Correlation analyses further revealed distinct network patterns, with ASD showing a flexible multidomain structure and RTT a more biologically constrained profile. Conclusions: These findings support an epilepsy-driven model of neurobehavioral severity in genetically defined autism and highlight the differential balance between biological constraint and environmental modulation across diagnostic groups. Integrating seizure phenotype and treatment burden into clinical assessment may improve individualized intervention strategies and advance precision medicine approaches in neurodevelopmental disorders.
Caregivers are increasingly using artificial intelligence (AI) platforms to obtain information about autism spectrum disorder (ASD), yet the consistency and usability of these responses remain unclear. This study examined the quality and temporal stability of responses generated by ten widely used, freely accessible AI platforms (Brave, ChatGPT, Claude, DeepSeek, Gemini, Grok, Meta AI, Microsoft Copilot, Perplexity, and Poe) when asked 15 autism-related questions. A descriptive research design was used. Responses were collected at two time points (August 2025 and February 2026) and evaluated across six dimensions including accuracy, readability, language framing, actionability, reference presence and format, and safety indicators. All responses reflected single-turn, first-pass outputs without follow-up prompting. Findings revealed considerable differences in performance across platforms, alongside consistent patterns over time. AI platforms generally provided accurate responses to autism-related questions; however, the readability of responses exceeded recommended guidelines. Most responses were presented using medicalized language, with limited use of neurodiversity-affirming wording. Actionable guidance was also limited, as only a small proportion of responses included clear next steps for families. Reference practices showed considerable variation, with some platforms including multiple sources (e.g., Gemini, Brave, Perplexity) while others offered few or none (e.g., ChatGPT, Meta AI). Across all platforms and both time points, no explicit misinformation, inappropriate reassurance, discouragement of evaluation or intervention, or unsafe recommendations were identified. Comparisons across time points showed that platform-level performance patterns were largely stable, with only minor fluctuations in scores across evaluation dimensions. These findings suggest that differences across AI platforms reflect consistent platform-level behaviors under default conditions rather than short-term variation in outputs. Overall, while AI platforms can provide broadly accurate information about ASD, they differ considerably in clarity, tone, usability, and transparency. The results highlight the need for cautious interpretation of AI-generated autism information and suggest that families may benefit from guidance when using these platforms.
This study models teachers' reflections on school transition practices for students with autism spectrum disorder (ASD) and/or Intellectual Disabilities as a hierarchical cognitive-metacognitive ability and evaluates its latent structure using structural equation modeling (SEM) and bifactor item response theory (IRT). Drawing on hierarchical models of intelligence, we examined whether reflective professional reasoning comprises a general factor alongside domain-specific components. A sample of 237 special education teachers working with students with ASD and/ or Intellectual Disabilities completed measures of reflective transition practices and mathematics teaching efficacy. Results from SEM supported a correlated multidimensional structure, while bifactor modeling indicated the presence of a statistically identifiable general reflective reasoning dimension coexisting with substantial domain-specific variance. The general factor demonstrated high reliability, although explained common variance suggested meaningful differentiation across domains. Reflective reasoning was strongly associated with mathematics teaching efficacy (r = .63), and structural modeling indicated that teachers' reflections on school transition practices positively predicted mathematics teaching efficacy beliefs (beta =.30). These findings position reflections on transition practices for students with ASD and/or Intellectual Disabilities as a hierarchical cognitive construct linked to performance-related beliefs in a reasoning-intensive domain. Implications for modeling professional cognition and teacher development in autismrelated educational contexts are discussed.
Family accommodation (FA) of restricted and repetitive behaviors (RRBs) is common in autism and associated with child and family outcomes, yet its relationship to emotion regulation (ER) in both children and parents remains unexamined. This study investigated associations between FA, RRBs, child temperament traits (surgency, negative affect, effortful control), and parental ER strategies (cognitive reappraisal, expressive suppression) across autistic (n = 146), developmentally delayed (DD; n = 44), and non-diagnosed (ND; n = 98) groups of young children (23–60 months). All the children were referred for suspected autism prior to evaluation, and underwent standardized diagnostic assessments (ADOS-2, Mullen/WPPSI). Parents completed measures of FA-RRBs, child temperament (CBQ), and parental ER strategies (ERQ). FA-RRBs were positively associated with child negative affect across all three groups (ρ =.37–.54, all p ≤ .021; four of six parent-group combinations survived FDR correction). These associations were observed across a spectrum of autistic, DD, and ND groups, establishing a broadly relevant link between child emotional distress and parental accommodation of RRBs. Parental ER strategies were not significantly associated with FA-RRBs in any group after FDR correction, and this null result was confirmed under multiple imputation. This is the first study to situate family accommodation of RRBs within the emotion-regulation ecology of the family, and the first to test whether the construct is specific to autism or extends to developmentally delayed and non-diagnosed children referred for the same concerns. These findings establish FA-RRBs as a construct that is not reducible to level of autism characteristics, and identify child negative affect as its most consistent correlate across diagnostic boundaries.
