ASD symptoms in toddlers and preschoolers: An examination of sex differences
Rosmary Ros‐Demarize, Catherine Bradley, Stephen M. Kanne, Zachary Warren, Andrea Boan, Clara Lajonchere, Justine Park, Laura Arnstein Carpenter Abstract Although considerable work has documented higher prevalence rates of autism spectrum disorder (ASD) in boys, fewer studies have focused on sex differences within samples of young children at‐risk for ASD. This study examined sex differences in ASD symptom domains and ASD screening outcomes among toddlers (18–35 months) and preschoolers (36–72 months) with ASD‐related concerns. Participants included 480 children between 18 and 72 months evaluated by university‐based ASD specialty clinics. Results revealed significant sex differences in severity of social communication (SC) deficits across age groups. Within the toddler group, girls diagnosed with ASD displayed greater SC deficits according to standardized observation and clinician severity ratings. Within the preschool group, girls diagnosed with ASD were rated by parents as having more severe SC deficits, but these differences were not corroborated by standardized observations or clinician ratings. No sex differences emerged for severity of restricted repetitive behaviors (RRBs) for either age group. Across the entire referred sample, boys and girls did not differ in terms of scores on commonly used screening instruments. Importantly, results suggest that two of the most commonly used ASD screeners (i.e., Modified‐Checklist for Autism in Toddlers‐Revised with Follow‐up and Social Communication Questionnaire ) may underidentify RRBs in toddler and preschool‐aged girls as screening scores were only influenced by severity of SC deficits. Greater SC deficits in young girls with ASD along with its impact on screening status suggests greater attention be placed on the under‐identification of ASD in girls as well as current screening measures’ ability to tap into the topography of ASD symptoms across genders. Autism Res 2020, 13: 157–166. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Read full publication here.
In-Home Speech and Language Screening for Young Children: A Proof-of-Concept Study Using Interactive Mobile Storytime
Du Y1,2, Abbas H2, Taraman S1,2,3, Segar S2, Bischoff N2. Author information 1University of California, Irvine, CA, USA.2Cognoa Inc., Palo Alto, CA, USA.3Children’s Hospital of Orange County, Orange, CA, USA. Abstract Early identification and intervention of speech and language delays in children contribute to better communication and literacy skills for school readiness and are protective against behavioral and mental health problems. Through collaboration between the data science and clinical teams at Cognoa, we designed Storytime, an interactive storytelling experience on a mobile device using a virtual avatar to mediate speech and language screening for children ages 4 to 6 years old. Our proof-of-concept study collects Storytime session footage from 71 pairs of parents and children including 57 typically developing children and 14 children with a current or prior history of communication impairments. Initial findings suggest that participating children verbally engaged with the video avatar without significant differences in performance across age, gender, and experimental location, leading to promising implications for using Storytime as a future tracking tool with automated feature analyses to detect speech and language delays. See full publication here.
When Are We Sure? Predictors of Clinician Certainty in the Diagnosis of Autism Spectrum Disorder.
Journal of Autism and Developmental Disorders. Abstract Differential diagnosis of autism spectrum disorder (ASD) is challenging, and uncertainty regarding a child’s diagnosis may result in under-identification or prolonged diagnostic pathways. The current study examined diagnostic certainty, or how sure clinicians were that their diagnosis was accurate, among 478 toddler and preschool-aged children referred for possible ASD to academic medical specialty clinics. Overall, 60 percent of diagnoses were made with complete certainty. Clinicians were more certain when positively identifying ASD than ruling it out. Children presenting with a moderate (vs high or low) level of observable ASD symptoms were less likely to have a certain diagnosis. Further, clinicians rated less diagnostic certainty for older children, those with public insurance, and those with higher IQ and adaptive behavior abilities. Access full publication here.
Exploratory study examining the at-home feasibility of a wearable tool for social-affective learning in children with autism.
npj Digital Medicine. Published: 02 August 2018. Abstract Although standard behavioral interventions for autism spectrum disorder (ASD) are effective therapies for social deficits, they face criticism for being time-intensive and overdependent on specialists. Earlier starting age of therapy is a strong predictor of later success, but waitlists for therapies can be 18 months long. To address these complications, we developed Superpower Glass, a machine-learning-assisted software system that runs on Google Glass and an Android smartphone, designed for use during social interactions. This pilot exploratory study examines our prototype tool’s potential for social-affective learning for children with autism. We sent our tool home with 14 families and assessed changes from intake to conclusion through the Social Responsiveness Scale (SRS-2), a facial affect recognition task (EGG), and qualitative parent reports. A repeated-measures one-way ANOVA demonstrated a decrease in SRS-2 total scores by an average 7.14 points (F(1,13) = 33.20, p = <.001, higher scores indicate higher ASD severity). EGG scores also increased by an average 9.55 correct responses (F(1,10) = 11.89, p = <.01). Parents reported increased eye contact and greater social acuity. This feasibility study supports using mobile technologies for potential therapeutic purposes. Npj Digital Medicine. Access publication here.
Screening in toddlers and preschoolers at risk for autism spectrum disorder: Evaluating a novel mobile‐health screening tool.
Wiley Online Library. Abstract There are many available tools with varying levels of accuracy designed to screen for Autism Spectrum Disorder (ASD) in young children, both in the general population and specifically among those referred for developmental concerns. With burgeoning waitlists for comprehensive diagnostic ASD assessments, finding accurate methods and tools for advancing diagnostic triage becomes increasingly important. The current study compares the efficacy of four oft used paper and pencil measures, the Modified Checklist for Autism in Toddlers Revised with Follow‐up, the Social Responsiveness Scale, Second Edition, and the Social Communication Questionnaire, and the Child Behavior Checklist to a novel mobile‐health screening tool developed by Cognoa, Inc. (Cognoa) in a group of children 18–72 months of age. The Cognoa tool may have potential benefits as it integrates a series of parent‐report questions with remote clinical ratings of brief video segments uploaded via parent’s smartphones to calculate level of ASD risk. Participants were referred to one of three tertiary care diagnostic centers for ASD‐related concerns (n = 230) and received a best estimate ASD diagnosis. Analysis and comparison of psychometric properties indicated potential advantages for Cognoa within this clinical sample across age ranges not often covered by another single measure/tool. Autism Res 2018, 11: 1038–1049. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. Access full publication here.
Clinical Evaluation of a Novel and Mobile Autism Risk Assessment.
Journal of Autism and Developmental Disorders. Abstract The Mobile Autism Risk Assessment (MARA) is a new, electronically administered, 7-question autism spectrum disorder (ASD) screen to triage those at highest risk for ASD. Children 16 months–17 years (N = 222) were screened during their first visit in a developmental-behavioral pediatric clinic. MARA scores were compared to diagnosis from the clinical encounter. Participant median age was 5.8 years, 76.1 % were male, and most participants had an intelligence/developmental quotient score >85; 69 of the participants (31 %) received a clinical diagnosis of ASD. The sensitivity of the MARA in detecting ASD was 89.9 % [95 % CI = 82.7–97]; the specificity was 79.7 % [95 % CI = 73.4–86.1]. In a high-risk clinical setting, the MARA shows promise as a screen to distinguish ASD from other developmental/behavioral disorders. Access full publication here.
A Wearable Social Interaction Aid for Children with Autism.
In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. Abstract Over 1 million children under the age of 17 in the US have been identified with Autism Spectrum Disorder (ASD). These children struggle to recognize facial expressions, make eye contact, and engage in social interactions. Gaining these skills requires intensive behavioral interventions that are often expensive, difficult to access, and inconsistently administered.nWe have developed a system to automate facial expression recognition that runs on wearable glasses and delivers real time social cues, with the goal of creating a behavioral aid for children with ASD that maximizes behavioral feedback while minimizing the distractions to the child. This paper describes the design of our system and interface decisions resulting from initial observations gathered during multiple preliminary trials. Read full publication here.
Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning.
Translational Psychiatry. Abstract Although the prevalence of autism spectrum disorder (ASD) has risen sharply in the last few years reaching 1 in 68, the average age of diagnosis in the United States remains close to 4—well past the developmental window when early intervention has the largest gains. This emphasizes the importance of developing accurate methods to detect risk faster than the current standards of care. In the present study, we used machine learning to evaluate one of the best and most widely used instruments for clinical assessment of ASD, the Autism Diagnostic Observation Schedule (ADOS) to test whether only a subset of behaviors can differentiate between children on and off the autism spectrum. ADOS relies on behavioral observation in a clinical setting and consists of four modules, with module 2 reserved for individuals with some vocabulary and module 3 for higher levels of cognitive functioning. We ran eight machine learning algorithms using stepwise backward feature selection on score sheets from modules 2 and 3 from 4540 individuals. We found that 9 of the 28 behaviors captured by items from module 2, and 12 of the 28 behaviors captured by module 3 are sufficient to detect ASD risk with 98.27% and 97.66% accuracy, respectively. A greater than 55% reduction in the number of behaviorals with negligible loss of accuracy across both modules suggests a role for computational and statistical methods to streamline ASD risk detection and screening. These results may help enable development of mobile and parent-directed methods for preliminary risk evaluation and/or clinical triage that reach a larger percentage of the population and help to lower the average age of detection and diagnosis. Read full publication here.