@Article{info:doi/10.2196/58337, author="Kato, Daigo and Okuno, Akiko and Ishikawa, Tetsuo and Itakura, Shoji and Oguchi, Shinji and Kasahara, Yoshiyuki and Kanenishi, Kenji and Kitadai, Yuzo and Kimura, Yoshitaka and Shimojo, Naoki and Nakahara, Kazushige and Hanai, Akiko and Hamada, Hiromichi and Mogami, Haruta and Morokuma, Seiichi and Sakurada, Kazuhiro and Konishi, Yukuo and Kawakami, Eiryo", title="Multilevel Factors and Indicators of Atypical Neurodevelopment During Early Infancy in Japan: Prospective, Longitudinal, Observational Study", journal="JMIR Pediatr Parent", year="2025", month="Apr", day="4", volume="8", pages="e58337", keywords="early developmental signs", keywords="neurodevelopmental screening", keywords="risk factors", keywords="prediction", keywords="early intervention", keywords="longitudinal study", abstract="Background: The early identification of developmental concerns requires understanding individual differences that may represent early signs of neurodevelopmental conditions. However, few studies have longitudinally examined how child and maternal factors interact to shape these early developmental characteristics. Objective: We aim to identify factors from the perinatal to infant periods associated with early developmental characteristics that may precede formal diagnoses and propose a method for evaluating individual differences in neurodevelopmental trajectories. Methods: A prospective longitudinal observational study of 147 mother-child pairs was conducted from gestation to 12 months post partum. Assessments included prenatal questionnaires and blood collection, cord blood at delivery, and postpartum questionnaires at 1, 6, and 12 months. The Modified Checklist for Autism in Toddlers (M-CHAT) was used to evaluate developmental characteristics that might indicate early signs of atypical neurodevelopment. Polychoric or polyserial correlation coefficients assessed relationships between M-CHAT scores and longitudinal variables. L2-regularized logistic regression and Shapley Additive Explanations predicted M-CHAT scores and determined feature contributions. Results: Twenty-one factors (4 prenatal, 3 at birth, and 14 postnatal) showed significant associations with M-CHAT scores (adjusted P values<.05). The predictive accuracy for M-CHAT scores demonstrated reasonable predictive accuracy (area under the receiver operating characteristic curve=0.79). Key predictors included infant sleep status after 6 months (nighttime sleep duration, bedtime, and difficulties falling asleep), maternal Kessler Psychological Distress Scale scores, and Mother-to-Infant Bonding Scale scores after late gestation. Conclusion: Maternal psychological distress, mother-infant bonding, and infant sleep patterns were identified as significant predictors of early developmental characteristics that may indicate emerging developmental concerns. This study advances our understanding of early developmental assessment by providing a novel approach to identifying and evaluating early indicators of atypical neurodevelopment. ", doi="10.2196/58337", url="https://pediatrics.jmir.org/2025/1/e58337" } @Article{info:doi/10.2196/72354, author="Eckardt, Peter Jens", title="Is the Pinball Machine a Blind Spot in Serious Games Research?", journal="JMIR Serious Games", year="2025", month="Apr", day="2", volume="13", pages="e72354", keywords="serious games", keywords="research", keywords="interventions", keywords="arcade technology", keywords="digital game paradigm", keywords="pinball gaming", keywords="arcade gaming", keywords="executive functions", keywords="neurodiversity", keywords="cognitive training", keywords="therapeutic interventions", doi="10.2196/72354", url="https://games.jmir.org/2025/1/e72354" } @Article{info:doi/10.2196/73034, author="Rodr{\'i}guez Timan{\'a}, Carlos Luis and Castillo Garc{\'i}a, Ferney Javier and Bastos Filho, Teodiano and Ocampo Gonz{\'a}lez, Alexander Alvaro and Hincapi{\'e} Monsalve, Rocio Nazly and Valencia Jimenez, Jacobo Nicolas", title="Authors' Reply: Is the Pinball Machine a Blind Spot in Serious Games Research?", journal="JMIR Serious Games", year="2025", month="Apr", day="2", volume="13", pages="e73034", keywords="serious games", keywords="research", keywords="interventions", keywords="arcade technology", keywords="digital game paradigm", keywords="pinball gaming", keywords="arcade gaming", keywords="executive functions", keywords="neurodiversity", keywords="cognitive training", keywords="therapeutic interventions", doi="10.2196/73034", url="https://games.jmir.org/2025/1/e73034" } @Article{info:doi/10.2196/65767, author="Touali, Rachid and Zerouaoui, Jamal and Chakir, Mahjoub El and Bui, Tien Hung and Leone, Mario and Allisse, Maxime", title="Impact of a Sensorimotor Integration and Hyperstimulation Program on Global Motor Skills in Moroccan Children With Autism Spectrum Disorder: Exploratory Clinical Quasi-Experimental Study", journal="JMIR Form Res", year="2025", month="Mar", day="26", volume="9", pages="e65767", keywords="classical physical education", keywords="children with a neurotypical profile", keywords="children with ASD", keywords="UQAC-UQAM test battery", keywords="University of Qu{\'e}bec in Chicoutimi-University of Qu{\'e}bec in Montr{\'e}al", keywords="sensorimotor integration", keywords="hyperstimulation", keywords="Morocco", keywords="sensorimotor", keywords="integration", keywords="motor skill", keywords="Moroccan children", keywords="Moroccan", keywords="children", keywords="autism spectrum disorder", keywords="ASD", keywords="exploratory study", keywords="autism", keywords="mental health", keywords="young", keywords="youth", keywords="feasibility", abstract="Background: Children with autism spectrum disorders (ASDs) often struggle with processing information, which can impact their coordination, balance, and other motor skills. Studies have demonstrated that intervention programs based on sensory integration can enhance motor performance in these children. Objective: The objective of this study is to evaluate the applicability of a standardized battery of gross motor skill tests for Moroccan children aged 6 to 12 years with ASD. The objective is to assess the potential efficacy of an innovative pedagogical approach focused on sensorimotor integration and hyperstimulation. This approach will be compared to traditional physical education (PE) sessions to determine its feasibility and potential to bridge the developmental gaps in motor skills between children with ASD and those with a neurotypical profile. Methods: A convenience sample of 14 Moroccan children with ASD aged 6 to 12 years participated in this exploratory study. Children with ASD were divided into an experimental group (n=7) and a control group (n=7) based on age, sex, motor performance, and socioeconomic status. The control group followed the standard PE program, while the experimental group underwent a specialized program combining sensorimotor integration and hyperstimulation for a period of 15 weeks. All participants were classified as level 2 (moderate) on the Autism Severity Rating Scale based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR) criteria. Gross motor skills were measured at baseline and after 15 weeks of intervention using the UQAC-UQAM (University of Qu{\'e}bec in Chicoutimi-University of Qu{\'e}bec in Montr{\'e}al) test battery protocol, which includes 10 items. Results: At baseline (T1), no significant difference was observed between the control and experimental groups of children with ASD. Following the 15-week intervention, the group participating in traditional PE showed an overall improvement in motor skills of approximately 14.5\%. Conversely, the results of the ASD experimental group suggest a more substantial improvement of 44.5\%. Additionally, the experimental group exhibited significant better performance across all motor skill variables compared to the control group (minimum P values of <.02) with large effect sizes (>0.80). In this regard, a 2-way repeated measures ANOVA confirms the efficiency of the program implemented within the experimental group, demonstrating significant effects associated with both group and time factors as well as a clinically highly significant group{\texttimes}time interaction across all measured variables ($\eta$2p>0.14). Conclusions: The results of this study suggest that the approach that emphasizes sensorimotor integration and management of hyperstimulation was more effective in improving motor skills in this population. However, other more exhaustive studies will need to be carried out in order to be able to more precisely measure the full potential of this approach. ", doi="10.2196/65767", url="https://formative.jmir.org/2025/1/e65767", url="http://www.ncbi.nlm.nih.gov/pubmed/40137439" } @Article{info:doi/10.2196/59913, author="Lenker, Puzino Kristina and Felix, L. Laura and Cichy, Sarah and Lehman, Erik and Logan, M. Jeanne and Murray, Michael and Kraschnewski, L. Jennifer", title="Using the Community Resilience Model and Project ECHO to Build Resiliency in Direct Support Professionals: Protocol for a Longitudinal Survey", journal="JMIR Res Protoc", year="2025", month="Mar", day="6", volume="14", pages="e59913", keywords="neurodiversity", keywords="community resilience model", keywords="Project ECHO", keywords="direct support professionals", keywords="autism", keywords="telementoring", keywords="methods and feasibility", keywords="resiliency", keywords="intellectual disabilities", keywords="ASD", keywords="autism spectrum disorder", keywords="DSP", keywords="supportive care", keywords="community resilience", keywords="burnout", keywords="resilience", keywords="neurodivergent client", keywords="neurodevelopmental disorders", keywords="evidence-based knowledge", abstract="Background: Individuals with intellectual disabilities or autism spectrum disorder (ID/A) sometimes require supportive services from direct support professionals (DSPs). The supportive care provided to individuals with ID/A by DSPs can vary from assistance with daily living activities to navigating society. The COVID-19 pandemic not only exacerbated poor outcomes for individuals with ID/A but also for DSPs, who report experiencing burnout in the aftermath of the pandemic. DSPs are critical to providing much-needed support to individuals with ID/A. Objective: The goal of this study is to evaluate the impact of the community resilience model on DSP burnout and neurodivergent client outcomes using the Project ECHO (Extension for Community Healthcare Outcomes) telementoring platform as a dissemination tool. Methods: This protocol leverages community resilience theory and telementoring through the Project ECHO model to foster resilience in DSPs and their neurodiverse client population. ECHO participants' resilience behaviors will be evaluated via surveys including the Connor Davison Resilience Scale and the WHO-5 Well-Being Index. These surveys will be administered preprogram, at the end of the 8-week ECHO program, and 90 days after the ECHO program's completion. Pre-post relationships will be assessed using generalized estimating equations. The main outcomes will be self-reported changes in knowledge, self-efficacy, and resilience. Results: All ECHO program cohorts and follow-up data collection have concluded, with 131 survey participants. The project team is currently analyzing and interpreting the data. We anticipate having all data analyzed and interpreted by February 2025. Conclusions: DSPs provide critical services to individuals with ID/A. By providing skills in resiliency via the ECHO model, participants will be able to apply resiliency to their own professional lives while fostering resilience within their neurodiverse client base, leading to increased positive outcomes for both groups. International Registered Report Identifier (IRRID): DERR1-10.2196/59913 ", doi="10.2196/59913", url="https://www.researchprotocols.org/2025/1/e59913", url="http://www.ncbi.nlm.nih.gov/pubmed/40053792" } @Article{info:doi/10.2196/65562, author="Miranda, Macedo Juliana and Browne, Vieira Rodrigo Alberto and da Silva, Alves Weslley Quirino and Rodrigues dos Santos, Paulo Jo{\~a}o and Campbell, Grubert Carmen Silvia and Ramos, Almeida Isabela", title="Effects of a Session of Exergames and Traditional Games on Inhibitory Control in Children With Autism Spectrum Disorder: Randomized Controlled Crossover Trial", journal="JMIR Serious Games", year="2025", month="Mar", day="5", volume="13", pages="e65562", keywords="children", keywords="pediatric", keywords="autism", keywords="ASD, autistic", keywords="behavior", keywords="exergame", keywords="physical education", keywords="exercise", keywords="physical activity", keywords="cognition", keywords="anthropometric", keywords="Flanker test", keywords="inhibitory control", keywords="randomized control trial", keywords="crossover", abstract="Background: Autism spectrum disorder (ASD) is characterized by deficits in executive functions, such as inhibitory control, which affect behavior and social adaptation. Although physical activity--based interventions, such as exergames, have shown potential to improve these functions, their comparative effects with active traditional games remain underexplored, particularly regarding inhibitory control in children with ASD. Objective: We aim to analyze the effects of a session of exergames and active traditional games on inhibitory control in children with ASD. Methods: This randomized controlled crossover trial included 9 male children with ASD (mean age 8.6, SD 1.4 y). Participants completed three 20-minute experimental sessions in random order, with a minimum interval of 48 hours: (1) active traditional games, (2) exergames using Just Dance 2022, and (3) a control session with manual painting activities. Inhibitory control was assessed 5 minutes postsession using a modified flanker task in the E-Prime (version 3.0; Psychological Software Tools Inc) program, recording reaction time (RT) and accuracy in congruent and incongruent phases. Repeated measures ANOVA was used to compare RT and accuracy between experimental and control conditions. Data are presented as means and 95\% CIs. Results: There was a statistically significant effect of condition on RT in the incongruent phase (P=.02). RT in the exergame session (849 ms, 95\% CI 642 to 1057) was lower compared to the traditional games (938 ms, 95\% CI 684 to 1191; P=.02) and control (969 ms, 95\% CI 742, 1196 to P=.01) sessions. No significant differences were observed in RT during the congruent phase or in accuracy across either phase. Conclusions: A 20-minute session of exergame improved inhibitory control performance in children with ASD compared to active traditional games and painting activities. Trial Registration: Brazilian Registry of Clinical Trials (ReBEC) RBR-5r9xzbq, Universal Trial Number U1111-1302-3490; https://ensaiosclinicos.gov.br/rg/RBR-5r9xzbq ", doi="10.2196/65562", url="https://games.jmir.org/2025/1/e65562" } @Article{info:doi/10.2196/63235, author="Mills, Jodie and Duffy, Orla", title="Speech and Language Therapists' Perspectives of Virtual Reality as a Clinical Tool for Autism: Cross-Sectional Survey", journal="JMIR Rehabil Assist Technol", year="2025", month="Feb", day="27", volume="12", pages="e63235", keywords="virtual reality", keywords="VR", keywords="autistic", keywords="ASD", keywords="speech", keywords="language", keywords="autism", keywords="speech and language therapy", keywords="speech-language pathology", keywords="SLT", keywords="immersive", keywords="voice", keywords="vocal", keywords="cross sectional", keywords="surveys", keywords="questionnaires", keywords="experiences", keywords="attitudes", keywords="opinions", keywords="perceptions", keywords="perspectives", keywords="autism spectrum disorder", abstract="Background: Persistent difficulties with social skills form part of the diagnostic criteria for autism and in the past have required speech and language therapy (SLT) management. However, many speech and language therapists are moving toward neuro-affirmative practices, meaning that social skills approaches are now becoming redundant. Research demonstrates that virtual reality (VR) interventions have shown promise in overcoming challenges and promoting skill generalization for autistic children; however, the majority of these focus on social skills interventions. While VR is emerging as an SLT intervention, its application for autism remains unexamined in clinical practice. Objective: This research aimed to examine speech and language therapists' knowledge and attitudes toward immersive VR as a clinical tool for autistic children and explore the reasons for its limited integration into clinical practice. Methods: A web-based cross-sectional survey was available from April 3, 2023 to June 30, 2023. The survey, consisting of 23 questions, focused on VR knowledge, attitudes, and the support required by speech and language therapists to incorporate VR into clinical practice. Dissemination occurred through the Royal College of Speech and Language Therapists Clinical Excellence Networks to recruit speech therapists specializing in autism. Results: Analysis included a total of 53 responses from the cross-sectional survey. Approximately 92\% (n=49) of speech and language therapists were aware of VR but had not used it, and 1.82\% (n=1) had used VR with autistic children. Three key themes that emerged were (1) mixed general knowledge of VR, which was poor in relation to applications for autism; (2) positive and negative attitudes toward VR, with uncertainty about autism specific considerations for VR; and (3) barriers to adoption were noted and speech and language therapists required an improved neuro-affirming evidence base, guidelines, and training to adopt VR into clinical practice. Conclusions: While some speech and language therapists perceive VR as a promising intervention tool for autistic children, various barriers must be addressed before its full integration into the clinical toolkit. This study establishes a foundation for future co-design, development, and implementation of VR applications as clinical tools for autistic children. This study is the first to explore clinical implementation factors for the use of VR in SLT field, specifically with autistic children. Poor autism-specific VR knowledge, and mixed attitudes toward VR, highlight that specific barriers must be addressed before the technology can successfully integrate into the SLT clinical toolkit. Speech and language therapists require support from employers, funding, a robust neuro-affirming evidence base, and education and training to adopt VR into practice. Recommendations for a SLT VR education and training program for use with autistic children, are provided. ", doi="10.2196/63235", url="https://rehab.jmir.org/2025/1/e63235" } @Article{info:doi/10.2196/55741, author="Mirzaei, Venus and Wolstencroft, Jeanne and Lockwood Estrin, Georgia and Buckley, Eleanor and Sayani, Shermina and Katakis, Panos and Anand, Reena and Squire, Tessa and Short, Eleanor and Frankson, Paige and Skuse, David and Heys, Michelle", title="Novel Procedures for Evaluating Autism Online in a Culturally Diverse Population of Children: Protocol for a Mixed Methods Pathway Development Study", journal="JMIR Res Protoc", year="2025", month="Feb", day="11", volume="14", pages="e55741", keywords="autism", keywords="child", keywords="telehealth", keywords="co-development", keywords="feasibility", keywords="acceptability", keywords="assessment", keywords="diagnosis", keywords="online", keywords="evaluation", keywords="diagnostic", keywords="intervention", keywords="pilot implementation evaluation study", abstract="Background: Current autism assessment procedures are costly and resource-intensive. The COVID-19 pandemic accelerated the adoption of telemedicine, highlighting the benefits of innovative diagnostic tools. Telemedicine-based pathways could enhance accessibility and equity in autism diagnostics. Objective: The Children with Autism Technology Enabled Assessment (CHATA) project aims to develop and pilot an open-source autism diagnostic pathway for children up to 5 years old, delivered through telemedicine. The pathway is designed to be culturally and linguistically adaptable, increasing its applicability to diverse populations and integrating with existing National Health Service digital systems. Methods: Initial pathway development was informed by systematic evidence reviews, coproduction, and mixed methods usability. CHATA comprises 2 key elements: online self-completed standardized autism questionnaires and a structured online interview and observation by a trained clinician. Out of 60 families near the top of the local waiting list will be invited to participate in the pilot evaluation, assessed using both the CHATA and usual assessment pathways. Sensitivity and specificity will be calculated by comparing the diagnosis of autism through CHATA with usual care. Quantitative usability assessment will be gathered from all families using the System Usability Scale (where a mean above 68 indicates above-average usability). A subset of CHATA assessments will be reviewed for interrater reliability (measured by the Cohen $\kappa$ for categorical data [diagnosis present or absent], with values indicating the level of agreement; eg, <0 indicating no agreement, 0.61-0.80 indicating substantial agreement). Qualitative data on acceptability, feasibility, and usability will be gathered from semistructured interviews with a subset of families and health care providers. We will recruit 60 families for the main pilot study (including the usability testing) and 10-15 participants for the qualitative substudy. Data will estimate CHATA's diagnostic accuracy, validity, reliability, usability, and acceptability. Patient and public involvement will be integral throughout. The study will take place in a socio-economically deprived, ethnically diverse inner-London Borough within a community-based child health National health service responsible for the Autism assessment of children and young people up to the age of 13 years. Results: Ethics approval was received in June 2023 (Research Ethics Committee reference 22/LO/0751; IRAS project ID 320499). Data collection commenced in April 2023 and completed in October 2024. Project end date is March 2025. As of November 2024, we had enrolled 57 participants to the pilot study and 12 to the qualitative substudy. Conclusions: The CHATA project aims to establish a novel, culturally sensitive, equitable, and accurate online autism assessment pathway. By addressing geographical and linguistic barriers, this pathway seeks to reduce service costs, shorten waiting times, and promote equity in autism diagnosis. The procedures developed are expected to be generalized to other populations nationwide. International Registered Report Identifier (IRRID): DERR1-10.2196/55741 ", doi="10.2196/55741", url="https://www.researchprotocols.org/2025/1/e55741", url="http://www.ncbi.nlm.nih.gov/pubmed/39932780" } @Article{info:doi/10.2196/60845, author="Yang, Xipeng and Wu, Jinlong and Ma, Yudan and Yu, Jingxuan and Cao, Hong and Zeng, Aihua and Fu, Rui and Tang, Yucheng and Ren, Zhanbing", title="Effectiveness of Virtual Reality Technology Interventions in Improving the Social Skills of Children and Adolescents With Autism: Systematic Review", journal="J Med Internet Res", year="2025", month="Feb", day="5", volume="27", pages="e60845", keywords="VR technology", keywords="autism spectrum disorder", keywords="children", keywords="adolescents", keywords="social skills", keywords="virtual reality", keywords="VR", abstract="Background: Virtual reality (VR) technology has shown significant potential in improving the social skills of children and adolescents with autism spectrum disorder (ASD). Objective: This study aimed to systematically review the evidence supporting the effectiveness of VR technology in improving the social skills of children and adolescents with ASD. Methods: The search for eligible studies encompassed 4 databases: PubMed, Web of Science, IEEE, and Scopus. Two (XY and JW) researchers independently assessed the extracted studies according to predefined criteria for inclusion and exclusion. These researchers also independently extracted information regarding gathered data on the sources, samples, measurement methods, primary results, and data related to the main results of the studies that met the inclusion criteria. The quality of the studies was further evaluated using the Physiotherapy Evidence Database scale. Results: This review analyzed 14 studies on using VR technology interventions to improve social skills in children and adolescents with ASD. Our findings indicate that VR interventions have a positive effect on improving social skills in children and adolescents with ASD. Compared with individuals with low-functioning autism (LFA), those with high-functioning autism (HFA) benefited more from the intervention. The duration and frequency of the intervention may also influence its effectiveness. In addition, immersive VR is more suitable for training complex skills in individuals with HFA. At the same time, nonimmersive VR stands out in terms of lower cost and flexibility, making it more appropriate for basic skill interventions for people with LFA. Finally, while VR technology positively enhances social skills, some studies have reported potential adverse side effects. According to the quality assessment using the Physiotherapy Evidence Database scale, of the 14 studies, 6 (43\%) were classified as high quality, 4 (29\%) as moderate quality, and 4 (29\%) as low quality. Conclusions: This systematic review found that VR technology interventions positively impact social skills in children and adolescents with ASD, with particularly significant effects on the enhancement of complex social skills in individuals with HFA. For children and adolescents with LFA, progress was mainly observed in basic skills. Immersive VR interventions are more suitable for the development of complex skills. At the same time, nonimmersive VR, due to its lower cost and greater flexibility, also holds potential for application in specific contexts. However, the use of VR technology may lead to side effects such as dizziness, eye fatigue, and sensory overload, particularly in immersive settings. These potential issues should be carefully addressed in intervention designs to ensure user comfort and safety. Future research should focus on optimizing individualized interventions and further exploring the long-term effects of VR interventions. Trial Registration: International Platform of Registered Systematic Review and Meta-analysis Protocols INPLASY202420079U1; https://inplasy.com/inplasy-2024-2-0079/ ", doi="10.2196/60845", url="https://www.jmir.org/2025/1/e60845" } @Article{info:doi/10.2196/66442, author="Shin, Yoomi and Park, Ju Eun and Lee, Anna", title="Early Intervention for Children With Developmental Disabilities and Their Families via Telehealth: Systematic Review", journal="J Med Internet Res", year="2025", month="Jan", day="17", volume="27", pages="e66442", keywords="developmental disabilities", keywords="developmental delay", keywords="early intervention", keywords="telehealth", keywords="digital intervention", keywords="autistic spectrum disorder", keywords="cerebral palsy", keywords="family-centered care", keywords="multidisciplinary care", keywords="systematic review", abstract="Background: Early intervention during the first 3 years of life is crucial for children with developmental disabilities to optimize developmental outcomes. However, access to such services is often limited by geographical distance and resource constraints. Telehealth can be part of a solution for overcoming these barriers, enabling the delivery of early intervention services. However, a comprehensive understanding of the efficacy and implementation of telehealth in early interventions remains elusive, particularly for children aged 0-3 years. Objective: This systematic review aims to synthesize existing research on the effectiveness and implementation of telehealth interventions in infants and toddlers (aged 0--3 years) who are at risk of or diagnosed with developmental disabilities. The primary objective of the study is to evaluate the ways that telehealth compares to conventional in-person interventions in improving developmental outcomes for children and supporting family well-being. Methods: A systematic search was conducted of 4 electronic databases (PubMed, Embase, CINAHL, and Web of Science), focusing on studies published between 2010 and 2024. The inclusion criteria were studies involving telehealth interventions for children aged 0-3 years who were at high risk or had developmental disabilities, which involved active interactions between the providers and the families. Study quality was assessed using the mixed methods appraisal tool, and a narrative synthesis was used to analyze the data. Results: Eighteen studies met the inclusion criteria: 12 single-case designs, 4 randomized controlled trials, and 2 nonequivalent control group designs. All studies involved caregiver-child dyads, with child ages ranging from 5 to 37 months and having or at risk of autistic spectrum disorder (n=10, 56\%), cerebral palsy (n=4, 22\%), and other conditions (n=4, 22\%). Synchronous videoconferencing was the primary modality for caregiver training and coaching (n=17, 94\%) while 1 intervention used an Internet of Things system. Outcomes were identified in child communication (n=9, 50\%), physical (n=6, 33\%), social or emotional (n=6, 33\%), and adaptive behavior (n=4, 22\%), as well as caregiver implementation (n=12, 66\%). Telehealth demonstrated comparable or superior effectiveness to traditional in-person methods in 2 studies. However, the focus on specific conditions and limited research on cognitive development were notable gaps. Conclusions: Telehealth can be a viable alternative to traditional in-person early interventions for young children who have developmental disabilities and their families. It enhances accessibility and interactions between families and providers at a distance while promoting family-centered care. Challenges exist, including those of technological literacy, and the lack of research on cognitive outcomes must be addressed. Future work should explore more comprehensive interventions, including multidisciplinary approaches and expanded family outcomes, to solidify the role that telehealth plays in early intervention. Trial Registration: PROSPERO CRD42024551286; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=551286 ", doi="10.2196/66442", url="https://www.jmir.org/2025/1/e66442" } @Article{info:doi/10.2196/59261, author="Tanaka, Hiroki and Miyamoto, Kana and Hamet Bagnou, Jennifer and Prigent, Elise and Clavel, C{\'e}line and Martin, Jean-Claude and Nakamura, Satoshi", title="Analysis of Social Performance and Action Units During Social Skills Training: Focus Group Study of Adults With Autism Spectrum Disorder and Schizophrenia", journal="JMIR Form Res", year="2025", month="Jan", day="10", volume="9", pages="e59261", keywords="social performance rating scale", keywords="social skills training", keywords="autism spectrum disorder", keywords="schizophrenia", keywords="facial expressions", keywords="social", keywords="autism", keywords="training", keywords="communication", keywords="trainers", keywords="tool", keywords="neurological", abstract="Background: Social communication is a crucial factor influencing human social life. Quantifying the degree of difficulty faced in social communication is necessary for understanding developmental and neurological disorders and for creating systems used in automatic symptom screening and assistive methods such as social skills training (SST). SST by a human trainer is a well-established method. Previous SST used a modified roleplay test to evaluate human social communication skills. However, there are no widely accepted evaluation criteria or social behavioral markers to quantify social performance during SST. Objective: This paper has 2 objectives. First, we propose applying the Social Performance Rating Scale (SPRS) to SST data to measure social communication skills. We constructed a Japanese version of the SPRS already developed in English and French. Second, we attempt to quantify action units during SST for people with autism spectrum disorder (ASD) or schizophrenia. Methods: We used videos of interactions between trainers, adults with ASD (n=16) or schizophrenia (n=15), and control participants (n=19) during SST sessions. Two raters applied the proposed scale to annotate the collected data. We investigated the differences between roleplay tasks and participant groups (ASD, schizophrenia, and control). Furthermore, the intensity of action units on the OpenFace toolkit was measured in terms of mean and SD during SST roleplaying. Results: We found significantly greater gaze scores in adults with ASD than in adults with schizophrenia. Differences were also found between the ratings of different tasks in the adults with schizophrenia and the control participants. Action units numbered AU06 and AU12 were significantly deactivated in people with schizophrenia compared with the control group. Moreover, AU02 was significantly activated in people with ASD compared with the other groups. Conclusions: The results suggest that the SPRS can be a useful tool for assessing social communication skills in different cultures and different pathologies when used with the modified roleplay test. Furthermore, facial expressions could provide effective social and behavioral markers to characterize psychometric properties. Possible future directions include using the SPRS for assessing social behavior during interaction with a digital agent. ", doi="10.2196/59261", url="https://formative.jmir.org/2025/1/e59261" } @Article{info:doi/10.2196/58693, author="Boulton, Ann Kelsie and Hilton, Makana and Sutton, Emilia and Guastella, John Adam", title="Apps and Digital Resources for Child Neurodevelopment, Mental Health, and Well-Being: Review, Evaluation, and Reflection on Current Resources", journal="J Med Internet Res", year="2025", month="Jan", day="1", volume="27", pages="e58693", keywords="digital tools", keywords="neurodevelopmental conditions", keywords="mental health", keywords="digital health", keywords="implementation", keywords="digital interventions", keywords="child neurodevelopment", keywords="digital technology", keywords="mobile phone", abstract="Background: An increase in the prevalence of neurodevelopmental conditions worldwide, alongside resource constraints within clinical services, has led to increased interest in health information technologies, such as apps and digital resources. Digital tools are often viewed as a solution to bridge this divide and to increase supports for families. There is, however, a paucity of research that has evaluated digital health tools, their potential benefits for child neurodevelopment and associated concerns (eg, mental health, well-being), and their benefit for families. Objective: This study conducted the first review of existing mobile apps and digital resources targeted at supporting the needs of children with developmental concerns or neurodevelopmental conditions. Methods: We identified 3435 separate resources, of which 112 (43 apps and 69 digital resources) met the criteria. These resources were categorized according to their purpose or target and were then reviewed based on their engagement, information quality, and evidence base using the Adapted Mobile App Rating Scale. Results: The most common condition of concern targeted by apps and digital resources was autism (19/112, 17\% resources), with retrieved resources focusing on supporting challenging behaviors, promoting speech, language, and social development, and providing options for alternative and assistive communication. Other common areas of concern targeted by apps and digital resources included language and communication (16/112, 14.3\%) and attention-deficit/hyperactivity disorder (11/112, 9.8\%). Results showed that reviewed resources were engaging, with high levels of accessibility and functionality. Resources had various functions, including developmental or behavioral tasks targeted at children, assistive communication support, scheduling support, journaling, and advice, activities, and strategies for parents. The information quality of resources, such as credibility of source and evidence base was, however, mostly low. Apps and digital resources with good credibility and an existing evidence base were largely developed in partnership with research, health, or government institutions, and were rated significantly higher on overall quality compared with apps and digital resources not developed in partnership with such institutions (apps; t41=--4.35, P<.001; digital resources; t67=--4.95, P<.001). Conclusions: The lack of evidence base across resources means that it is extremely difficult to provide recommendations to families with respect to apps or digital resources that may support their needs. Frameworks for the development of new tools are discussed, highlighting the novel approaches required to demonstrate the efficacy of tools for improving outcomes for children and families. Such a framework requires collaboration with multiple stakeholders (software developers, researchers, regulatory bodies, clinicians, children, and families) and engagement across multiple levels of expertise (app development, implementation, and dissemination within services, policy, and clinical regulations), to harness the potential of digital health for improving outcomes and promoting support in child neurodevelopment, which at this juncture remains largely underdeveloped. ", doi="10.2196/58693", url="https://www.jmir.org/2025/1/e58693" } @Article{info:doi/10.2196/59053, author="Rodr{\'i}guez Timan{\'a}, Carlos Luis and Castillo Garc{\'i}a, Ferney Javier and Bastos Filho, Teodiano and Ocampo Gonz{\'a}lez, Alexander Alvaro and Hincapi{\'e} Monsalve, Rocio Nazly and Valencia Jimenez, Jacobo Nicolas", title="Use of Serious Games in Interventions of Executive Functions in Neurodiverse Children: Systematic Review", journal="JMIR Serious Games", year="2024", month="Dec", day="18", volume="12", pages="e59053", keywords="executive functions", keywords="neurodiversity", keywords="serious games", keywords="cognitive training", keywords="therapeutic interventions", abstract="Background: Serious games (SG) have emerged as promising tools for cognitive training and therapeutic interventions, especially for enhancing executive functions. These games have demonstrated the potential to support individuals with diverse health conditions, including neurodevelopmental and cognitive disorders, through engaging and interactive experiences. However, a comprehensive understanding of the effectiveness of SG in enhancing executive functions is needed. Objective: This systematic review aims to assess the impact of serious games on executive functions (EF), focusing on attention, working memory, cognitive flexibility, and inhibitory control. In addition, it explores the integration of SG into educational and therapeutic settings for individuals with cognitive and neurodevelopmental conditions. Only open access articles published from 2019 to the search date were included to capture the most recent advancements in the field. Methods: A comprehensive search was conducted on June 20, 2024, across Scopus, Web of Science, and PubMed databases. Due to limited direct results linking SG and neurodiversity, separate searches were performed to analyze the relationship between SG and EF, as well as SG and neurodiverse populations. Two independent reviewers assessed the quality and risk of bias of the included studies using the Risk of Bias 2 tool for randomized studies and the Risk of Bias in Non-Randomized Studies of Interventions tool for nonrandomized studies. Results: The review identified 16 studies that met the inclusion criteria. Of these, 15 addressed the use of SG for improving EF in neurodiverse populations, such as children with attention-deficit/hyperactivity disorder, autism spectrum disorder, and down syndrome. These studies demonstrated significant improvements in various EF domains, including attention, working memory, and cognitive flexibility. However, there was notable heterogeneity in sample sizes, participant ages, and game types. Three studies specifically focused on individuals with down syndrome, showing promising results in improving cognitive functions. Conclusions: SG hold considerable potential as therapeutic tools for enhancing EF across neurodiverse populations. They have shown positive effects in improving cognitive skills and promoting inclusion in both educational and therapeutic settings. However, further research is required to optimize game design, assess long-term outcomes, and address the variability in study quality. The exclusive inclusion of open access studies may have limited the scope of the review, and future research should incorporate a broader range of studies to provide a more comprehensive understanding of SG's impact on neurodiversity. Trial Registration: PROSPERO CRD42024563231; https://tinyurl.com/ycxdymyb ", doi="10.2196/59053", url="https://games.jmir.org/2024/1/e59053", url="http://www.ncbi.nlm.nih.gov/pubmed/39693133" } @Article{info:doi/10.2196/59696, author="Zhao, Yanan and Fan, Huiyun and Luo, Yanan and Zhang, Rong and Zheng, Xiaoying", title="Gender Inequalities in Employment of Parents Caring for Children With Autism Spectrum Disorder in China: Cross-Sectional Study", journal="JMIR Pediatr Parent", year="2024", month="Dec", day="17", volume="7", pages="e59696", keywords="autism spectrum disorder", keywords="family", keywords="employment status", keywords="influencing factors", keywords="autism", keywords="child care", keywords="children", keywords="China", keywords="parent", keywords="online survey", keywords="mother", keywords="father", keywords="adolescent", keywords="youth", keywords="ASD", keywords="children with autism", abstract="Background: The increasing need for child care is placing a burden on parents, including those with children with autism. Objective: The aim of this study was to examine the employment status of Chinese mothers and fathers with children with autism spectrum disorder (ASD), as well as to investigate the factors that affected their employment decisions. Methods: An online national survey was completed by the parents of 5018 children and adolescents with ASD aged 2-17 years (4837 couples, 181 single mothers, and 148 single fathers). The dependent variable was employment status---whether they kept working or quit to take care of their child. The independent variables were those characterizing the needs of the child and the sociodemographic characteristics of the family. Results: The employment rate of mothers with children and adolescents with ASD was 37.3\% (1874/5018), while 96.7\% (4823/4988) of fathers were employed. In addition, 54.3\% (2723/5018) of mothers resigned from employment outside the home to care for their children, while only 2.8\% (139/4988) of fathers resigned due to caring obligations. Mothers' employment was positively associated with their single marital status, lower educational level, and having assistance from grandparents. Having the grandparents' assistance was positively associated with fathers' employment. Conclusions: Gender inequalities in employment exist in China. Mothers caring for children with ASD had lower workforce participation than fathers. More female-friendly policies and a stronger gender equality ideology would be of benefit to Chinese society. ", doi="10.2196/59696", url="https://pediatrics.jmir.org/2024/1/e59696" } @Article{info:doi/10.2196/49305, author="Backman, Anna and Roll-Pettersson, Lise and Mellblom, Are and Norman-Claesson, Elisabet and Sundqvist, Emma and Zander, Eric and Vigerland, Sarah and Hirvikoski, Tatja", title="Internet-Delivered Psychoeducation (SCOPE) for Transition-Aged Autistic Youth: Pragmatic Randomized Controlled Trial", journal="J Med Internet Res", year="2024", month="Nov", day="28", volume="26", pages="e49305", keywords="autism", keywords="internet based", keywords="young adult", keywords="intervention", keywords="digital communication", keywords="life satisfaction", keywords="codeveloped", keywords="ASD", keywords="autism spectrum disorder", keywords="autistic", keywords="RCT", keywords="randomized controlled trial", keywords="randomized", keywords="psychoeducation", keywords="patient education", abstract="Background: Psychoeducation is a recommended first-line intervention for transition-aged autistic youth, but it has not been previously evaluated in an internet-delivered format. SCOPE (Spectrum Computerized Psychoeducation) is an 8-week individual, internet-delivered, therapist-supported psychoeducative intervention. Objective: This study aimed to investigate the effectiveness of SCOPE through a 3-armed randomized controlled trial. The intervention aims to increase participants' understanding of autism and, in doing so, increase their quality of life (QoL). Methods: SCOPE was codeveloped with clinicians and autistic young adults. It contains 8 autism-related modules, each with (1) text describing the module topic, (2) four video vignettes with recurring characters who describe their lives and perspectives on the module topic, (3) a list of neurotypical characteristics related to the module's topic, and (4) self-reflection using 3 or 4 questions about the module topic, answered by multiple-choice bullets and voluntary open-ended written comments. Participants were randomized (2:1:1) to SCOPE, an active control (web-based self-study), or treatment as usual (TAU). The primary outcome was participants' autism knowledge, assessed using the Autism Spectrum Disorder Quiz, and secondary outcomes included acceptance of diagnosis, QoL, and symptoms of mental health problems. All outcomes were assessed at the baseline, postintervention, and 3-month follow-up time points, using mixed-effects models to assess change in outcome measures across time points. Results: Between 2014 and 2020, a total of 141 participants were randomized to 1 of the 3 treatment arms. The SCOPE participants had significantly greater autism knowledge gains at the posttreatment time point compared to TAU participants with a moderate effect size (d=0.47; P=.05); gains were maintained at the 3-month follow-up (d=0.46; P=.05). The self-study participants also had increased knowledge gains compared to TAU participants at the posttreatment time point with a moderate effect size (d=0.60; P=.03) but did not maintain these gains at the 3-month follow-up, and their autism knowledge scores returned to baseline (mean change score: --0.13, 95\% CI --1.20 to 0.94; P=.81). In addition, SCOPE participants reported improved QoL at the postintervention (d=0.37, P=.02) and 3-month follow-up time points (d=0.60; P=.001), compared to the combined controls. The gained autism knowledge was not mirrored by changes in symptoms of anxiety or depression. Conclusions: Effective internet-delivered interventions may facilitate first-line service access to individuals who are unable or unwilling to use traditional health care interventions or who live in geographically remote locations. Additionally, an intervention such as SCOPE could impart and sustain the knowledge gained through psychoeducation in transition-aged autistic youth. For future research, qualitative studies could further our understanding of the lived experiences of intervention participation and outcomes after internet-delivered psychoeducation. Trial Registration: ClinicalTrials.gov NCT03665363; https://clinicaltrials.gov/study/NCT03665363 ", doi="10.2196/49305", url="https://www.jmir.org/2024/1/e49305" } @Article{info:doi/10.2196/55754, author="Gargot, Thomas and Vachaud, Amandine and Gilard, Cl{\'e}mence and Audrain, Alexia and Gomot, Marie and Guidotti, Marco and Briend, Fr{\'e}d{\'e}ric and Malvy, Jo{\"e}lle and Bonnet Brilhault, Fr{\'e}d{\'e}rique", title="A Compressive Armchair (OTO) to Perform Deep Pressure Therapy in Children With Autism Spectrum Disorder: User-Centered Design and Feasibility Study", journal="JMIR Hum Factors", year="2024", month="Nov", day="5", volume="11", pages="e55754", keywords="deep pressure therapy", keywords="proprioception", keywords="compression", keywords="autism spectrum disorder", abstract="Background: Deep pressure therapy (DPT) is widely used to reduce anxiety in children with autism spectrum disorder (ASD), but evidence of its efficacy is limited. Objective: This study aims to design a usable, nonstigmatizing compressive armchair that can be easily controlled, electronically, by the user. Methods: A user-centered approach was used to assess the usability of the device. Testing was carried out in a day hospital for children with ASD in France, with a convenience sample of children with severe forms of ASD and intellectual deficiency (N=39). The Witteman design guideline was used. The System Usability Scale and time of use were reported. Results: The final product is a compressive armchair designed to be user centered, with 4 different cells that can be inflated to induce tailored pressure on the body. The pressure level is recorded electronically. Usability was between good and excellent. The device was used by 39 children, once or twice weekly, over a period of 31 months. Each session lasted between 3 and 20 minutes. The armchair takes up less space than a hug machine. Performing sessions with the chair is feasible. Conclusions: First clinical impressions show a decrease in anxiety, improved emotional regulation, and improved attention. DPT is widely used in occupational therapy and frequently requested by parents, but efficacy studies are too scarce to make evidence-based recommendations for its use. The results presented here support further controlled efficacy studies of DPT in the treatment of anxiety in children with ASD. ", doi="10.2196/55754", url="https://humanfactors.jmir.org/2024/1/e55754" } @Article{info:doi/10.2196/54171, author="Larson, Elizabeth and Mattie, L. Rebecca and Riffkin, A. Sophia", title="Assessment of Acceptability, Usage, and Impact on Caregivers of Children With Autism's Stress and Mindfulness: Multiple-Method Feasibility Study of the 5Minutes4Myself App's Mindfulness Module", journal="JMIR Hum Factors", year="2024", month="Oct", day="31", volume="11", pages="e54171", keywords="autism", keywords="caregiver", keywords="activities", keywords="mindfulness", keywords="mobile application", keywords="stress", keywords="wellness", keywords="app", keywords="application", keywords="usage", keywords="children", keywords="developmental disability", keywords="usability", keywords="acceptability", keywords="meditation", keywords="wellness application", abstract="Background: Caregiver wellness programs need to be easily accessible to address caregivers' constraints to participation. Objective: We aimed to assess the feasibility of 5Minutes4Myself app's mindfulness module (usability, usage, and impact on caregivers' levels of mindfulness and perceived stress). Methods: Before and after participation in the 5Minutes4Myself program, 15 participants were asked to complete the Perceived Stress Scale (PSS) and Five Facet Mindfulness Questionnaire (FFMQ). Data on the usage of app-delivered meditations were collected electronically via the app, and app usability was rated on the Modified System Usability Scale. Analyses assessed participants' frequency of use of app-delivered meditations, app usability, and changes in participants' stress and mindfulness post intervention. Results: Overall, participants completed 10.9 minutes of mindfulness meditations per week and rated the app 76.7, indicating above-average usability. Related samples t tests (2-tailed) found that group PSS (t10=1.20, P=.26) and FFMQ (t10=?1.57, P=.15) pre- or postintervention mean scores were not significantly different. However, a visualization of pre- and post-PSS and mindfulness scores suggested there was a group of responders who had decreased stress with increased mindfulness. This was confirmed via an individual change analysis. The effect size of the FFMQ scores (d=0.47) suggests there may be treatment effects with a larger sample. A hierarchical multiple regression analysis examined the degree mindfulness impacted perceived stress; 20\% of the variance in participants' perceived stress could be attributed to increases in self-rated mindfulness (P=.04) when controlling for preintervention stress levels. Conclusions: Caregivers found the app highly usable and on average used low-dose levels of mindfulness meditations (10 min/wk). For responders, increased mindfulness was related to stress reduction to population-based levels. Trial Registration: ClinicalTrials.gov NCT03771001; https://clinicaltrials.gov/study/NCT03771001 ", doi="10.2196/54171", url="https://humanfactors.jmir.org/2024/1/e54171" } @Article{info:doi/10.2196/62878, author="Garikipati, Anurag and Ciobanu, Madalina and Singh, Preet Navan and Barnes, Gina and Dinenno, A. Frank and Geisel, Jennifer and Mao, Qingqing and Das, Ritankar", title="Parent-Led Applied Behavior Analysis to Impact Clinical Outcomes for Individuals on the Autism Spectrum: Retrospective Chart Review", journal="JMIR Pediatr Parent", year="2024", month="Oct", day="30", volume="7", pages="e62878", keywords="applied behavior analysis", keywords="autism spectrum disorder", keywords="parent training", keywords="patient outcomes", keywords="skill acquisition", keywords="pediatrics", abstract="Background: Autism spectrum disorder (ASD) can have traits that impact multiple domains of functioning and quality of life, which can persevere throughout life. To mitigate the impact of ASD on the long-term trajectory of an individual's life, it is imperative to seek early and adequate treatment via scientifically validated approaches, of which applied behavior analysis (ABA) is the gold standard. ABA treatment must be delivered via a behavior technician with oversight from a board-certified behavior analyst. However, shortages in certified ABA therapists create treatment access barriers for individuals on the autism spectrum. Increased ASD prevalence demands innovations for treatment delivery. Parent-led treatment models for neurodevelopmental conditions are effective yet underutilized and may be used to fill this care gap. Objective: This study reports findings from a retrospective chart review of clinical outcomes for children that received parent-led ABA treatment and intends to examine the sustained impact that modifications to ABA delivery have had on a subset of patients of Montera, Inc. dba Forta (``Forta''), as measured by progress toward skill acquisition within multiple focus areas (FAs). Methods: Parents received ?40 hours of training in ABA prior to initiating treatment, and patients were prescribed focused (<25 hours/week) or comprehensive (>25?40 hours/week) treatment plans. Retrospective data were evaluated over ?90 days for 30 patients. The clinical outcomes of patients were additionally assessed by age (2-5 years, 6-12 years, 13?22 years) and utilization of prescribed treatment. Treatment encompassed skill acquisition goals; to facilitate data collection consistency, successful attempts were logged within a software application built in-house. Results: Improved goal achievement success between weeks 1?20 was observed for older age, all utilization, and both treatment plan type cohorts. Success rates increased over time for most FAs, with the exception of executive functioning in the youngest cohort and comprehensive plan cohort. Goal achievement experienced peaks and declines from week to week, as expected for ABA treatment; however, overall trends indicated increased skill acquisition success rates. Of 40 unique combinations of analysis cohorts and FAs, 20 showed statistically significant positive linear relationships (P<.05). Statistically significant positive linear relationships were observed in the high utilization cohort (communication with P=.04, social skills with P=.02); in the fair and full utilization cohorts (overall success with P=.03 for the fair utilization cohort and P=.001 for the full utilization cohort, and success in emotional regulation with P<.001 for the fair utilization cohort and P<.001 for the full utilization cohort); and in the comprehensive treatment cohort (communication with P=.001, emotional regulation with P=.045). Conclusions: Parent-led ABA can lead to goal achievement and improved clinical outcomes and may be a viable solution to overcome treatment access barriers that delay initiation or continuation of care. ", doi="10.2196/62878", url="https://pediatrics.jmir.org/2024/1/e62878" } @Article{info:doi/10.2196/52295, author="Lee, JooHyun and Lim, JaeHyun and Kang, Soyeon and Kim, Sujin and Jung, Yoon So and Hong, Soon-Beom and Park, Rang Yu", title="Mobile App--Assisted Parent Training Intervention for Behavioral Problems in Children With Autism Spectrum Disorder: Pilot Randomized Controlled Trial", journal="JMIR Hum Factors", year="2024", month="Oct", day="28", volume="11", pages="e52295", keywords="autism spectrum disorder", keywords="parent training program", keywords="parent education", keywords="behavioral problems", keywords="child behavior", keywords="mobile app", keywords="feasibility", keywords="mHealth", keywords="evidence-based parent training", abstract="Background: In children with autism spectrum disorder (ASD), problem behaviors play a dysfunctional role, causing as much difficulty with daily living and adjustment as the core symptoms. If such behaviors are not effectively addressed, they can result in physical, economic, and psychological issues not only for the individual but also for family members. Objective: We aimed to develop and evaluate the feasibility of a mobile app--assisted parent training program for reducing problem behaviors in children with ASD. Methods: This open-label, single-center, randomized controlled trial was conducted among parents of children with ASD aged 36-84 months. Participants were recruited from the Department of Psychiatry at Seoul National University Hospital. Participants were randomly assigned (1:1) by a blinded researcher. Randomization was performed using a stratified block randomization (with a block size of 4). Parents in the intervention group completed the mobile app--assisted parent training program at home over a 12-week period. They continued to receive their usual nondrug treatment in addition to the mobile app--assisted parent training program. The control group continued to receive their usual nonpharmaceutical treatment for 12 weeks without receiving the parent training program intervention. The primary outcome measure was the median change in the Korean Child Behavior Checklist (K-CBCL) scores from before to after the intervention. Lower scores on the K-CBCL indicated a decrease in overall problem behavior. Results: Between November 9, 2022, and December 8, 2022, 64 participants were enrolled. Overall, 42 children (intervention group median age: 49, IQR 41-52.5 months; control group median age: 49, IQR 42-58 months) of the participants joined the program. The intervention group included 20 (48\%) participants and the control group included 22 (52\%) participants. In the intervention group, the K-CBCL total scores showed a decrease after the intervention, with a median difference of --0.5 (95\% CI --4.5 to 3). Pervasive developmental disorder scores also showed a decrease, with a median difference of --2.1 (95\% CI --8.5 to 2.5). However, there was no significant difference in Clinical Global Impression--Severity of Illness scores after the intervention for both the control and intervention groups. Scores on the Korean version of the Social Communication Questionnaire showed a further decrease after the intervention in the intervention group (median difference --2, 95\% CI --4 to 1). Caregivers' stress evaluated using the Korean Parenting Stress Index Fourth Edition--Short Form did not show any significant differences between the control and intervention groups. There were no adverse events related to study participation. Conclusions: The findings demonstrated the feasibility of using mobile devices for evidence-based parent training to reduce problem behaviors in children with ASD. Mobile devices' accessibility and flexibility may provide a viable alternative for offering early intervention for problem behaviors in children with ASD. Trial Registration: CRIS KCT0007841; https://cris.nih.go.kr/cris/search/detailSearch.do?\&seq=23112 ", doi="10.2196/52295", url="https://humanfactors.jmir.org/2024/1/e52295", url="http://www.ncbi.nlm.nih.gov/pubmed/39466295" } @Article{info:doi/10.2196/60399, author="Cho, Yunah and Talboys, L. Sharon", title="Trends in South Korean Medical Device Development for Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder: Narrative Review", journal="JMIR Biomed Eng", year="2024", month="Oct", day="15", volume="9", pages="e60399", keywords="ADHD", keywords="attention-deficit/hyperactivity disorder", keywords="ASD", keywords="autism spectrum disorder", keywords="medical device", keywords="digital therapeutics", abstract="Background: Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are among the most prevalent mental disorders among school-aged youth in South Korea and may play a role in the increasing pressures on teachers and school-based special education programming. A lack of support for special education; tensions between teachers, students, and parents; and limited backup for teacher absences are common complaints among Korean educators. New innovations in technology to screen and treat ADHD and ASD may offer relief to students, parents, and teachers through earlier and efficient diagnosis; access to treatment options; and ultimately, better-managed care and expectations. Objective: This narrative literature review provides an account of medical device use and development in South Korea for the diagnosis and management of ADHD and ASD and highlights research gaps. Methods: A narrative review was conducted across 4 databases (PubMed, Korean National Assembly Library, Scopus, and PsycINFO). Journal articles, dissertations, and government research and development reports were included if they discussed medical devices for ADHD and ASD. Only Korean or English papers were included. Resources were excluded if they did not correspond to the research objective or did not discuss at least 1 topic about medical devices for ADHD and ASD. Journal articles were excluded if they were not peer reviewed. Resources were limited to publications between 2013 and July 22, 2024. Results: A total of 1794 records about trends in Korean medical device development were categorized into 2 major groups: digital therapeutics and traditional therapy. Digital therapeutics resulted in 5 subgroups: virtual reality and artificial intelligence, machine learning and robot, gaming and visual contents, eye-feedback and movement intervention, and electroencephalography and neurofeedback. Traditional therapy resulted in 3 subgroups: cognitive behavioral therapy and working memory; diagnosis and rating scale; and musical, literary therapy, and mindfulness-based stress reduction. Digital therapeutics using artificial intelligence, machine learning, and electroencephalography technologies account for the biggest portions of development in South Korea, rather than traditional therapies. Most resources, 94.15\% (1689/1794), were from the Korean National Assembly Library. Conclusions: Limitations include small sizes of populations to conclude findings in many articles, a lower number of articles discussing medical devices for ASD, and a majority of articles being dissertations. Emerging digital medical devices and those integrated with traditional therapies are important solutions to reducing the prevalence rates of ADHD and ASD in South Korea by promoting early diagnosis and intervention. Furthermore, their application will relieve pressures on teachers and school-based special education programming by providing direct supporting resources to students with ADHD or ASD. Future development of medical devices for ADHD and ASD is predicted to heavily rely on digital technologies, such as those that sense people's behaviors, eye movement, and brainwaves. ", doi="10.2196/60399", url="https://biomedeng.jmir.org/2024/1/e60399" } @Article{info:doi/10.2196/57093, author="Maddalon, Luna and Minissi, Eleonora Maria and Parsons, Thomas and Hervas, Amaia and Alcaniz, Mariano", title="Exploring Adaptive Virtual Reality Systems Used in Interventions for Children With Autism Spectrum Disorder: Systematic Review", journal="J Med Internet Res", year="2024", month="Sep", day="18", volume="26", pages="e57093", keywords="adaptive system", keywords="virtual reality", keywords="autism spectrum disorder", keywords="intervention", keywords="training", keywords="children", keywords="machine learning", keywords="biosignal", abstract="Background: Adaptive systems serve to personalize interventions or training based on the user's needs and performance. The adaptation techniques rely on an underlying engine responsible for processing incoming data and generating tailored responses. Adaptive virtual reality (VR) systems have proven to be efficient in data monitoring and manipulation, as well as in their ability to transfer learning outcomes to the real world. In recent years, there has been significant interest in applying these systems to improve deficits associated with autism spectrum disorder (ASD). This is driven by the heterogeneity of symptoms among the population affected, highlighting the need for early customized interventions that target each individual's specific symptom configuration. Objective: Recognizing these technology-driven therapeutic tools as efficient solutions, this systematic review aims to explore the application of adaptive VR systems in interventions for young individuals with ASD. Methods: An extensive search was conducted across 3 different databases---PubMed Central, Scopus, and Web of Science---to identify relevant studies from approximately the past decade. Each author independently screened the included studies to assess the risk of bias. Studies satisfying the following inclusion criteria were selected: (1) the experimental tasks were delivered via a VR system, (2) system adaptation was automated, (3) the VR system was designed for intervention or training of ASD symptoms, (4) participants' ages ranged from 6 to 19 years, (5) the sample included at least 1 group with ASD, and (6) the adaptation strategy was thoroughly explained. Relevant information extracted from the studies included the sample size and mean age, the study's objectives, the skill trained, the implemented device, the adaptive strategy used, the engine techniques, and the signal used to adapt the systems. Results: Overall, a total of 10 articles were included, involving 129 participants, 76\% of whom had ASD. The studies included level switching (7/10, 70\%), adaptive feedback strategies (9/10, 90\%), and weighing the choice between a machine learning (ML) adaptive engine (3/10, 30\%) and a non-ML adaptive engine (8/10, 80\%). Adaptation signals ranged from explicit behavioral indicators (6/10, 60\%), such as task performance, to implicit biosignals, such as motor movements, eye gaze, speech, and peripheral physiological responses (7/10, 70\%). Conclusions: The findings reveal promising trends in the field, suggesting that automated VR systems leveraging real-time progression level switching and verbal feedback driven by non-ML techniques using explicit or, better yet, implicit signal processing have the potential to enhance interventions for young individuals with ASD. The limitations discussed mainly stem from the fact that no technological or automated tools were used to handle data, potentially introducing bias due to human error. ", doi="10.2196/57093", url="https://www.jmir.org/2024/1/e57093" } @Article{info:doi/10.2196/49029, author="Mahmoudi, Ebrahim and Yejong Yoo, Paul and Chandra, Ananya and Cardoso, Roberta and Denner Dos Santos, Carlos and Majnemer, Annette and Shikako, Keiko", title="Gamification in Mobile Apps for Children With Disabilities: Scoping Review", journal="JMIR Serious Games", year="2024", month="Sep", day="6", volume="12", pages="e49029", keywords="mobile health", keywords="mHealth", keywords="gamification", keywords="children with disabilities", keywords="mobile phone", abstract="Background: Children with disabilities face numerous challenges in accessing health services. Mobile health is an emerging field that could significantly reduce health inequities by providing more accessible services. Many mobile apps incorporate gamification elements such as feedback, points, and stories to increase engagement and motivation; however, little is known about how gamification has been incorporated in mobile apps for children with disabilities. Objective: This scoping review aims to identify and synthesize the existing research evidence on the use of gamification in mobile apps for children with disabilities. Specifically, the objectives were to (1) identify the categories of these mobile apps (eg, treatment and educational) (2), describe the health-related outcomes they target, (3) assess the types and levels of gamification elements used within these apps, and (4) determine the reasons for incorporating gamification elements into mobile apps. Methods: We searched MEDLINE, PsycINFO, CINAHL, Embase, the ACM Digital Library, and IEEE Xplore databases to identify papers published between 2008 and 2023. Original empirical research studies reporting on gamified mobile apps for children with disabilities that implemented at least 1 gamification strategy or tactic were included. Studies investigating serious games or full-fledged games were excluded. Results: A total of 38 studies reporting on 32 unique gamified mobile apps were included. Findings showed that gamified apps focus on communication skills and oral health in children with autism spectrum disorder while also addressing self-management and academic skills for other disability groups. Gamified mobile apps have demonstrated potential benefits across different populations and conditions; however, there were mixed results regarding their impact.?The gamification strategies included fun and playfulness (23/32, 72\%), feedback on performance (17/32, 53\%), and reinforcement (17/32, 53\%) in more than half of apps, whereas social connectivity was used as a gamification strategy in only 4 (12\%) mobile apps. There were 2 main reasons for integrating gamification elements into mobile apps described in 16 (42\%) studies: increasing user engagement and motivation and enhancing intervention effects. Conclusions: This scoping review offers researchers a comprehensive review of the gamification elements currently used in mobile apps for the purposes of treatment, education, symptom management, and assessment for children with disabilities. In addition, it indicates that studies on certain disability groups and examinations of health-related outcomes have been neglected, highlighting the need for further investigations in these areas. Furthermore, research is needed to investigate the effectiveness of mobile-based gamification elements on health and health behavior outcomes, as well as the healthy development of children with disabilities. ", doi="10.2196/49029", url="https://games.jmir.org/2024/1/e49029", url="http://www.ncbi.nlm.nih.gov/pubmed/39240675" } @Article{info:doi/10.2196/51054, author="Shea, Lindsay and Cooper, Dylan and Ventimiglia, Jonas and Frisbie, Shelby and Carlton, Conner and Song, Wei and Salzer, Mark and Lee, Brian and Hotez, Emily and Vanness, J. David", title="Self-Reported COVID-19 Vaccine and Booster Acceptance and Hesitancy Among Autistic Adults in Pennsylvania: Cross-Sectional Analysis of Survey Data", journal="JMIR Public Health Surveill", year="2024", month="Aug", day="28", volume="10", pages="e51054", keywords="autism", keywords="COVID-19", keywords="vaccination", keywords="public health", keywords="autism spectrum disorder", keywords="autistic", keywords="vaccine", keywords="vaccines", keywords="acceptance", keywords="hesitancy", keywords="immunize", keywords="immunization", keywords="immunizations", keywords="attitude", keywords="attitudes", keywords="opinion", keywords="perception", keywords="perceptions", keywords="perspective", keywords="perspectives", keywords="neurodevelopmental", keywords="infectious", keywords="respiratory", keywords="survey", keywords="surveys", abstract="Background: The autistic population is rapidly increasing; meanwhile, autistic adults face disproportionate risks for adverse COVID-19 outcomes. Limited research indicates that autistic individuals have been accepting of initial vaccination, but research has yet to document this population's perceptions and acceptance of COVID-19 boosters. Objective: This study aims to identify person-level and community characteristics associated with COVID-19 vaccination and booster acceptance among autistic adults, along with self-reported reasons for their stated preferences. Understanding this information is crucial in supporting this vulnerable population given evolving booster guidelines and the ending of the public health emergency for the COVID-19 pandemic. Methods: Data are from a survey conducted in Pennsylvania from April 11 to September 12, 2022. Demographic characteristics, COVID-19 experiences, and COVID-19 vaccine decisions were compared across vaccination status groups. Chi-square analyses and 1-way ANOVA were conducted to test for significant differences. Vaccination reasons were ranked by frequency; co-occurrence was identified using phi coefficient correlation plots. Results: Most autistic adults (193/266, 72.6\%) intended to receive or received the vaccine and booster, 15\% (40/266) did not receive or intend to receive any vaccine, and 12.4\% (33/266) received or intended to receive the initial dose but were hesitant to accept booster doses. Reasons for vaccine acceptance or hesitancy varied by demographic factors and COVID-19 experiences. The most significant were previously contracting COVID-19, desire to access information about COVID-19, and discomfort with others not wearing a mask (all P=.001). County-level factors, including population density (P=.02) and percentage of the county that voted for President Biden (P=.001) were also significantly associated with differing vaccination acceptance levels. Reasons for accepting the initial COVID-19 vaccine differed among those who were or were not hesitant to accept a booster. Those who accepted a booster were more likely to endorse protecting others and trusting the vaccine as the basis for their acceptance, whereas those who were hesitant about the booster indicated that their initial vaccine acceptance came from encouragement from someone they trusted. Among the minority of those hesitant to any vaccination, believing that the vaccine was unsafe and would make them feel unwell were the most often reported reasons. Conclusions: Intention to receive or receiving the COVID-19 vaccination and booster was higher among autistic adults than the population that received vaccines in Pennsylvania. Autistic individuals who accepted vaccines prioritized protecting others, while autistic individuals who were vaccine hesitant had safety concerns about vaccines. These findings inform public health opportunities and strategies to further increase vaccination and booster rates among generally accepting autistic adults, to better support the already strained autism services and support system landscape. Vaccination uptake could be improved by leveraging passive information diffusion to combat vaccination misinformation among those not actively seeking COVID-19 information to better alleviate safety concerns. ", doi="10.2196/51054", url="https://publichealth.jmir.org/2024/1/e51054", url="http://www.ncbi.nlm.nih.gov/pubmed/39196609" } @Article{info:doi/10.2196/56043, author="Burke, Meghan and Li, Chak and Cheung, Catherine Waifong and Terol, Kaori Adriana and Johnston, Amanda and Schueller, M. Stephen", title="Leveraging Feedback From Families of Children With Autism to Create Digital Support for Service Navigation: Descriptive Study", journal="JMIR Form Res", year="2024", month="Aug", day="14", volume="8", pages="e56043", keywords="human-centered design", keywords="autism", keywords="service access", keywords="families", keywords="digital support", keywords="autistic children", keywords="autistic", keywords="children", keywords="child", keywords="app", keywords="apps", keywords="application", keywords="applications", keywords="digital tool", keywords="tool", keywords="tools", abstract="Background: It is difficult for families to navigate and access services for their children with autism. Barriers to service access are compounded among families from low-resourced backgrounds. Objective: The purpose of our study was to explore the development of an app to facilitate access to services among families of children with autism from low-resourced backgrounds. Our specific aims were to explore feedback from an advisory board about the app and to explore feedback from navigators about the app. Methods: Via a multistage codevelopment process, we elicited feedback from 5 key parties: the research team, a community organization, the app development team, the advisory board, and family navigators. Collectively, 36 individuals provided feedback about the development of the app via individual interviews, focus groups, observations, and surveys. The key features of the app included a dashboard showing the service needs of the family and related resources, a messaging feature between the family, the navigator, and the supervisor, and a fidelity checklist and evaluation feature. Results: The advisory board provided feedback about the app to increase its user-friendliness, include the ability to develop an action plan, improve the identification of needed services, and add information about service providers. Navigators suggested that the app should connect navigators to one another, have a clearer purpose for the notes section, and reflect an easier log-in process. Navigators also wanted training to role-play using the app. After participating in a role play using the app, navigators reported significantly more satisfaction with the app and greater usefulness (P<.001). Conclusions: Our work sheds light on the importance of eliciting feedback from end users, especially users who are often overlooked by the research community and app developers. Further, it is important to elicit feedback in multiple ways to improve the app. ", doi="10.2196/56043", url="https://formative.jmir.org/2024/1/e56043", url="http://www.ncbi.nlm.nih.gov/pubmed/39141412" } @Article{info:doi/10.2196/55339, author="Tepencelik, Necip Onur and Wei, Wenchuan and Luo, Mirabel and Cosman, Pamela and Dey, Sujit", title="Behavioral Intervention for Adults With Autism on Distribution of Attention in Triadic Conversations: A/B-Tested Pre-Post Study", journal="JMIR Form Res", year="2024", month="Aug", day="12", volume="8", pages="e55339", keywords="autism spectrum condition", keywords="social attention", keywords="social orienting", keywords="behavioral intervention", keywords="attention distribution", keywords="triadic conversation", abstract="Background: Cross-neurotype differences in social communication patterns contribute to high unemployment rates among adults with autism. Adults with autism can be unsuccessful in job searches or terminated from employment due to mismatches between their social attention behaviors and society's expectations on workplace communication. Objective: We propose a behavioral intervention concerning distribution of attention in triadic (three-way) conversations. Specifically, the objective is to determine whether providing personalized feedback to each individual with autism based on an analysis of their attention distribution behavior during an initial conversation session would cause them to modify their orientation behavior in a subsequent conversation session. Methods: Our system uses an unobtrusive head orientation estimation model to track the focus of attention of each individual. Head orientation sequences from a conversation session are analyzed based on five statistical domains (eg, maximum exclusion duration and average contact duration) representing different types of attention distribution behavior. An intervention is provided to a participant if they exceeded the nonautistic average for that behavior by at least 2 SDs. The intervention uses data analysis and video modeling along with a constructive discussion about the targeted behaviors. Twenty-four individuals with autism with no intellectual disabilities participated in the study. The participants were divided into test and control groups of 12 participants each. Results: Based on their attention distribution behavior in the initial conversation session, 11 of the 12 participants in the test group received an intervention in at least one domain. Of the 11 participants who received the intervention, 10 showed improvement in at least one domain on which they received feedback. Independent t tests for larger test groups (df>15) confirmed that the group improvements are statistically significant compared with the corresponding controls (P<.05). Crawford-Howell t tests confirmed that 78\% of the interventions resulted in significant improvements when compared individually against corresponding controls (P<.05). Additional t tests comparing the first conversation sessions of the test and control groups and comparing the first and second conversation sessions of the control group resulted in nonsignificant differences, pointing to the intervention being the main effect behind the behavioral changes displayed by the test group, as opposed to confounding effects or group differences. Conclusions: Our proposed behavioral intervention offers a useful framework for practicing social attention behavior in multiparty conversations that are common in social and professional settings. ", doi="10.2196/55339", url="https://formative.jmir.org/2024/1/e55339" } @Article{info:doi/10.2196/54577, author="Grazioli, Silvia and Crippa, Alessandro and Buo, Noemi and Busti Ceccarelli, Silvia and Molteni, Massimo and Nobile, Maria and Salandi, Antonio and Trabattoni, Sara and Caselli, Gabriele and Colombo, Paola", title="Use of Machine Learning Models to Differentiate Neurodevelopment Conditions Through Digitally Collected Data: Cross-Sectional Questionnaire Study", journal="JMIR Form Res", year="2024", month="Jul", day="29", volume="8", pages="e54577", keywords="digital-aided clinical assessment", keywords="machine learning", keywords="random forest", keywords="logistic regression", keywords="computational psychometrics", keywords="telemedicine", keywords="neurodevelopmental conditions", keywords="parent-report questionnaires", keywords="attention-deficit/hyperactivity disorder", keywords="autism spectrum disorder", keywords="ASD", keywords="autism", keywords="autistic", keywords="attention deficit", keywords="hyperactivity", keywords="classification", abstract="Background: Diagnosis of child and adolescent psychopathologies involves a multifaceted approach, integrating clinical observations, behavioral assessments, medical history, cognitive testing, and familial context information. Digital technologies, especially internet-based platforms for administering caregiver-rated questionnaires, are increasingly used in this field, particularly during the screening phase. The ascent of digital platforms for data collection has propelled advanced psychopathology classification methods such as supervised machine learning (ML) into the forefront of both research and clinical environments. This shift, recently called psycho-informatics, has been facilitated by gradually incorporating computational devices into clinical workflows. However, an actual integration between telemedicine and the ML approach has yet to be fulfilled. Objective: Under these premises, exploring the potential of ML applications for analyzing digitally collected data may have significant implications for supporting the clinical practice of diagnosing early psychopathology. The purpose of this study was, therefore, to exploit ML models for the classification of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) using internet-based parent-reported socio-anamnestic data, aiming at obtaining accurate predictive models for new help-seeking families. Methods: In this retrospective, single-center observational study, socio-anamnestic data were collected from 1688 children and adolescents referred for suspected neurodevelopmental conditions. The data included sociodemographic, clinical, environmental, and developmental factors, collected remotely through the first Italian internet-based screening tool for neurodevelopmental disorders, the Medea Information and Clinical Assessment On-Line (MedicalBIT). Random forest (RF), decision tree, and logistic regression models were developed and evaluated using classification accuracy, sensitivity, specificity, and importance of independent variables. Results: The RF model demonstrated robust accuracy, achieving 84\% (95\% CI 82-85; P<.001) for ADHD and 86\% (95\% CI 84-87; P<.001) for ASD classifications. Sensitivities were also high, with 93\% for ADHD and 95\% for ASD. In contrast, the DT and LR models exhibited lower accuracy (DT 74\%, 95\% CI 71-77; P<.001 for ADHD; DT 79\%, 95\% CI 77-82; P<.001 for ASD; LR 61\%, 95\% CI 57-64; P<.001 for ADHD; LR 63\%, 95\% CI 60-67; P<.001 for ASD) and sensitivities (DT: 82\% for ADHD and 88\% for ASD; LR: 62\% for ADHD and 68\% for ASD). The independent variables considered for classification differed in importance between the 2 models, reflecting the distinct characteristics of the 3 ML approaches. Conclusions: This study highlights the potential of ML models, particularly RF, in enhancing the diagnostic process of child and adolescent psychopathology. Altogether, the current findings underscore the significance of leveraging digital platforms and computational techniques in the diagnostic process. While interpretability remains crucial, the developed approach might provide valuable screening tools for clinicians, highlighting the significance of embedding computational techniques in the diagnostic process. ", doi="10.2196/54577", url="https://formative.jmir.org/2024/1/e54577", url="http://www.ncbi.nlm.nih.gov/pubmed/39073858" } @Article{info:doi/10.2196/59794, author="Jaiswal, Aditi and Shah, Aekta and Harjadi, Christopher and Windgassen, Erik and Washington, Peter", title="Ethics of the Use of Social Media as Training Data for AI Models Used for Digital Phenotyping", journal="JMIR Form Res", year="2024", month="Jul", day="17", volume="8", pages="e59794", keywords="social media analytics", keywords="machine learning", keywords="ethics", keywords="research ethics", keywords="consent", keywords="scientific integrity", doi="10.2196/59794", url="https://formative.jmir.org/2024/1/e59794" } @Article{info:doi/10.2196/59349, author="Jaiswal, Aditi and Shah, Aekta and Harjadi, Christopher and Windgassen, Erik and Washington, Peter", title="Addendum: Using \#ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study", journal="JMIR Form Res", year="2024", month="Jul", day="17", volume="8", pages="e59349", doi="10.2196/59349", url="https://formative.jmir.org/2024/1/e59349" } @Article{info:doi/10.2196/58565, author="Denis, Fabrice and Le Goff, Florian and Desbois, Madhu and Gepner, Agnes and Feliciano, Guillaume and Silber, Denise and Zeitoun, Jean-David and Assuied, Peretz Guedalia", title="Early Detection of 5 Neurodevelopmental Disorders of Children and Prevention of Postnatal Depression With a Mobile Health App: Observational Cross-Sectional Study", journal="JMIR Public Health Surveill", year="2024", month="Jun", day="18", volume="10", pages="e58565", keywords="mobile phone", keywords="pediatric", keywords="infant", keywords="baby", keywords="neonate", keywords="newborn", keywords="toddler", keywords="child", keywords="early detection", keywords="app", keywords="application", keywords="screening", keywords="algorithm", keywords="NDD", keywords="neurodevelopmental disorder", keywords="autism", keywords="ASD", keywords="autism spectrum disorder", keywords="attention deficit/hyperactivity disorder", keywords="ADHD", keywords="attention deficit", keywords="PND", keywords="postnatal depression", keywords="mHealth", keywords="mobile health", keywords="real-world study", keywords="smartphone", keywords="dyspraxia", keywords="delayed", keywords="language", keywords="dyslexia", keywords="incidence", keywords="prevalence", abstract="Background: Delay in the diagnosis of neurodevelopmental disorders (NDDs) in toddlers and postnatal depression (PND) is a major public health issue. In both cases, early intervention is crucial but too rarely implemented in practice. Objective: Our goal was to determine if a dedicated mobile app can improve screening of 5 NDDs (autism spectrum disorder [ASD], language delay, dyspraxia, dyslexia, and attention-deficit/hyperactivity disorder [ADHD]) and reduce PND incidence. Methods: We performed an observational, cross-sectional, data-based study in a population of young parents in France with at least 1 child aged <10 years at the time of inclusion and regularly using Malo, an ``all-in-one'' multidomain digital health record electronic patient-reported outcome (PRO) app for smartphones. We included the first 50,000 users matching the criteria and agreeing to participate between May 1, 2022, and February 8, 2024. Parents received periodic questionnaires assessing skills in neurodevelopment domains via the app. Mothers accessed a support program to prevent PND and were requested to answer regular PND questionnaires. When any PROs matched predefined criteria, an in-app recommendation was sent to book an appointment with a family physician or pediatrician. The main outcomes were the median age of the infant at the time of notification for possible NDD and the incidence of PND detection after childbirth. One secondary outcome was the relevance of the NDD notification by consultation as assessed by health professionals. Results: Among 55,618 children median age 4 months (IQR 9), 439 (0.8\%) had at least 1 disorder for which consultation was critically necessary. The median ages of notification for probable ASD, language delay, dyspraxia, dyslexia, and ADHD were 32.5 (IQR 12.8), 16 (IQR 13), 36 (IQR 22.5), 80 (IQR 5), and 61 (IQR 15.5) months, respectively. The rate of probable ADHD, ASD, dyslexia, language delay, and dyspraxia in the population of children of the age included between the detection limits of each alert was 1.48\%, 0.21\%, 1.52\%, 0.91\%, and 0.37\%, respectively. Sensitivity of alert notifications for suspected NDDs as assessed by the physicians was 78.6\% and specificity was 98.2\%. Among 8243 mothers who completed a PND questionnaire, highly probable PND was detected in 938 (11.4\%), corresponding to a reduction of --31\% versus our previous study without a support program. Suspected PND was detected a median 96 days (IQR 86) after childbirth. Among 130 users who filled in the satisfaction survey, 99.2\% (129/130) found the app easy to use and 70\% (91/130) reported that the app improved follow-up of their child. The app was rated 4.8/5 on Apple's App Store. Conclusions: Algorithm-based early alerts suggesting NDDs were highly specific with good sensitivity as assessed by real-life practitioners. Early detection of 5 NDDs and PNDs was efficient and led to a possible 31\% reduction in PND incidence. Trial Registration: ClinicalTrials.gov NCT06301087; https://www.clinicaltrials.gov/study/NCT06301087 ", doi="10.2196/58565", url="https://publichealth.jmir.org/2024/1/e58565", url="http://www.ncbi.nlm.nih.gov/pubmed/38888952" } @Article{info:doi/10.2196/56812, author="Shi, M. Jiaxiao and Chiu, Y. Vicki and Avila, C. Chantal and Lewis, Sierra and Park, Daniella and Peltier, R. Morgan and Getahun, Darios", title="Coding of Childhood Psychiatric and Neurodevelopmental Disorders in Electronic Health Records of a Large Integrated Health Care System: Validation Study", journal="JMIR Ment Health", year="2024", month="May", day="14", volume="11", pages="e56812", keywords="autism", keywords="autism spectrum disorder", keywords="ASD", keywords="attention deficit hyperactivity disorder", keywords="ADHD", keywords="disruptive behavioral disorders", keywords="DBD", keywords="anxiety disorders", keywords="AD", keywords="major depression disorder", keywords="MDD", keywords="autistic", keywords="coding", keywords="neurodevelopmental", keywords="psychiatric", keywords="electronic health record", keywords="electronic health records", keywords="validation", keywords="accuracy", keywords="mental health", keywords="emotional", keywords="behavior", keywords="behaviors", keywords="behavioral", keywords="disorder", keywords="disorders", keywords="pediatric", keywords="pediatrics", keywords="paediatric", keywords="infant", keywords="paediatrics", keywords="infants", keywords="infancy", keywords="baby", keywords="babies", keywords="neonate", keywords="neotnates", keywords="neonatal", keywords="toddler", keywords="toddlers", keywords="child", keywords="children", keywords="hospital", keywords="hospitals", abstract="Background: Mental, emotional, and behavioral disorders are chronic pediatric conditions, and their prevalence has been on the rise over recent decades. Affected children have long-term health sequelae and a decline in health-related quality of life. Due to the lack of a validated database for pharmacoepidemiological research on selected mental, emotional, and behavioral disorders, there is uncertainty in their reported prevalence in the literature. Objectives: We aimed to evaluate the accuracy of coding related to pediatric mental, emotional, and behavioral disorders in a large integrated health care system's electronic health records (EHRs) and compare the coding quality before and after the implementation of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding as well as before and after the COVID-19 pandemic. Methods: Medical records of 1200 member children aged 2-17 years with at least 1 clinical visit before the COVID-19 pandemic (January 1, 2012, to December 31, 2014, the ICD-9-CM coding period; and January 1, 2017, to December 31, 2019, the ICD-10-CM coding period) and after the COVID-19 pandemic (January 1, 2021, to December 31, 2022) were selected with stratified random sampling from EHRs for chart review. Two trained research associates reviewed the EHRs for all potential cases of autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), major depression disorder (MDD), anxiety disorder (AD), and disruptive behavior disorders (DBD) in children during the study period. Children were considered cases only if there was a mention of any one of the conditions (yes for diagnosis) in the electronic chart during the corresponding time period. The validity of diagnosis codes was evaluated by directly comparing them with the gold standard of chart abstraction using sensitivity, specificity, positive predictive value, negative predictive value, the summary statistics of the F-score, and Youden J statistic. $\kappa$ statistic for interrater reliability among the 2 abstractors was calculated. Results: The overall agreement between the identification of mental, behavioral, and emotional conditions using diagnosis codes compared to medical record abstraction was strong and similar across the ICD-9-CM and ICD-10-CM coding periods as well as during the prepandemic and pandemic time periods. The performance of AD coding, while strong, was relatively lower compared to the other conditions. The weighted sensitivity, specificity, positive predictive value, and negative predictive value for each of the 5 conditions were as follows: 100\%, 100\%, 99.2\%, and 100\%, respectively, for ASD; 100\%, 99.9\%, 99.2\%, and 100\%, respectively, for ADHD; 100\%, 100\%, 100\%, and 100\%, respectively for DBD; 87.7\%, 100\%, 100\%, and 99.2\%, respectively, for AD; and 100\%, 100\%, 99.2\%, and 100\%, respectively, for MDD. The F-score and Youden J statistic ranged between 87.7\% and 100\%. The overall agreement between abstractors was almost perfect ($\kappa$=95\%). Conclusions: Diagnostic codes are quite reliable for identifying selected childhood mental, behavioral, and emotional conditions. The findings remained similar during the pandemic and after the implementation of the ICD-10-CM coding in the EHR system. ", doi="10.2196/56812", url="https://mental.jmir.org/2024/1/e56812" } @Article{info:doi/10.2196/54706, author="He, Wenjie and Zhang, Wenyan and Jin, Ya and Zhou, Qiang and Zhang, Huadan and Xia, Qing", title="Physician Versus Large Language Model Chatbot Responses to Web-Based Questions From Autistic Patients in Chinese: Cross-Sectional Comparative Analysis", journal="J Med Internet Res", year="2024", month="Apr", day="30", volume="26", pages="e54706", keywords="artificial intelligence", keywords="chatbot", keywords="ChatGPT", keywords="ERNIE Bot", keywords="autism", abstract="Background: There is a dearth of feasibility assessments regarding using large language models (LLMs) for responding to inquiries from autistic patients within a Chinese-language context. Despite Chinese being one of the most widely spoken languages globally, the predominant research focus on applying these models in the medical field has been on English-speaking populations. Objective: This study aims to assess the effectiveness of LLM chatbots, specifically ChatGPT-4 (OpenAI) and ERNIE Bot (version 2.2.3; Baidu, Inc), one of the most advanced LLMs in China, in addressing inquiries from autistic individuals in a Chinese setting. Methods: For this study, we gathered data from DXY---a widely acknowledged, web-based, medical consultation platform in China with a user base of over 100 million individuals. A total of 100 patient consultation samples were rigorously selected from January 2018 to August 2023, amounting to 239 questions extracted from publicly available autism-related documents on the platform. To maintain objectivity, both the original questions and responses were anonymized and randomized. An evaluation team of 3 chief physicians assessed the responses across 4 dimensions: relevance, accuracy, usefulness, and empathy. The team completed 717 evaluations. The team initially identified the best response and then used a Likert scale with 5 response categories to gauge the responses, each representing a distinct level of quality. Finally, we compared the responses collected from different sources. Results: Among the 717 evaluations conducted, 46.86\% (95\% CI 43.21\%-50.51\%) of assessors displayed varying preferences for responses from physicians, with 34.87\% (95\% CI 31.38\%-38.36\%) of assessors favoring ChatGPT and 18.27\% (95\% CI 15.44\%-21.10\%) of assessors favoring ERNIE Bot. The average relevance scores for physicians, ChatGPT, and ERNIE Bot were 3.75 (95\% CI 3.69-3.82), 3.69 (95\% CI 3.63-3.74), and 3.41 (95\% CI 3.35-3.46), respectively. Physicians (3.66, 95\% CI 3.60-3.73) and ChatGPT (3.73, 95\% CI 3.69-3.77) demonstrated higher accuracy ratings compared to ERNIE Bot (3.52, 95\% CI 3.47-3.57). In terms of usefulness scores, physicians (3.54, 95\% CI 3.47-3.62) received higher ratings than ChatGPT (3.40, 95\% CI 3.34-3.47) and ERNIE Bot (3.05, 95\% CI 2.99-3.12). Finally, concerning the empathy dimension, ChatGPT (3.64, 95\% CI 3.57-3.71) outperformed physicians (3.13, 95\% CI 3.04-3.21) and ERNIE Bot (3.11, 95\% CI 3.04-3.18). Conclusions: In this cross-sectional study, physicians' responses exhibited superiority in the present Chinese-language context. Nonetheless, LLMs can provide valuable medical guidance to autistic patients and may even surpass physicians in demonstrating empathy. However, it is crucial to acknowledge that further optimization and research are imperative prerequisites before the effective integration of LLMs in clinical settings across diverse linguistic environments can be realized. Trial Registration: Chinese Clinical Trial Registry ChiCTR2300074655; https://www.chictr.org.cn/bin/project/edit?pid=199432 ", doi="10.2196/54706", url="https://www.jmir.org/2024/1/e54706", url="http://www.ncbi.nlm.nih.gov/pubmed/38687566" } @Article{info:doi/10.2196/47396, author="Carter, Sarah and Lin, C. Jane and Chow, Ting and Martinez, P. Mayra and Qiu, Chunyuan and Feldman, Klara R. and McConnell, Rob and Xiang, H. Anny", title="Preeclampsia Onset, Days to Delivery, and Autism Spectrum Disorders in Offspring: Clinical Birth Cohort Study", journal="JMIR Public Health Surveill", year="2024", month="Apr", day="17", volume="10", pages="e47396", keywords="autism spectrum disorders", keywords="autism", keywords="clinical management", keywords="diagnosis", keywords="expectant management", keywords="fetal exposure", keywords="fetal", keywords="management", keywords="preeclampsia", keywords="pregnancy", keywords="pregnant women", keywords="risk", abstract="Background: Maternal preeclampsia is associated with a risk of autism spectrum disorders (ASD) in offspring. However, it is unknown whether the increased ASD risk associated with preeclampsia is due to preeclampsia onset or clinical management of preeclampsia after onset, as clinical expectant management of preeclampsia allows pregnant women with this complication to remain pregnant for potentially weeks depending on the onset and severity. Identifying the risk associated with preeclampsia onset and exposure provides evidence to support the care of high-risk pregnancies and reduce adverse effects on offspring. Objective: This study aimed to fill the knowledge gap by assessing the ASD risk in children associated with the gestational age of preeclampsia onset and the number of days from preeclampsia onset to delivery. Methods: This retrospective population-based clinical cohort study included 364,588 mother-child pairs of singleton births between 2001 and 2014 in a large integrated health care system in Southern California. Maternal social demographic and pregnancy health data, as well as ASD diagnosis in children by the age of 5 years, were extracted from electronic medical records. Cox regression models were used to assess hazard ratios (HRs) of ASD risk in children associated with gestational age of the first occurrence of preeclampsia and the number of days from first occurrence to delivery. Results: Preeclampsia occurred in 16,205 (4.4\%) out of 364,588 pregnancies; among the 16,205 pregnancies, 2727 (16.8\%) first occurred at <34 weeks gestation, 4466 (27.6\%) first occurred between 34 and 37 weeks, and 9012 (55.6\%) first occurred at ?37 weeks. Median days from preeclampsia onset to delivery were 4 (IQR 2,16) days, 1 (IQR 1,3) day, and 1 (IQR 0,1) day for those first occurring at <34, 34-37, and ?37 weeks, respectively. Early preeclampsia onset was associated with greater ASD risk (P=.003); HRs were 1.62 (95\% CI 1.33-1.98), 1.43 (95\% CI 1.20-1.69), and 1.23 (95\% CI 1.08-1.41), respectively, for onset at <34, 34-37, and ?37 weeks, relative to the unexposed group. Within the preeclampsia group, the number of days from preeclampsia onset to delivery was not associated with ASD risk in children; the HR was 0.995 (95\% CI 0.986-1.004) after adjusting for gestational age of preeclampsia onset. Conclusions: Preeclampsia during pregnancy was associated with ASD risk in children, and the risk was greater with earlier onset. However, the number of days from first preeclampsia onset to delivery was not associated with ASD risk in children. Our study suggests that ASD risk in children associated with preeclampsia is not increased by expectant management of preeclampsia in standard clinical practice. Our results emphasize the need to identify effective approaches to preventing the onset of preeclampsia, especially during early pregnancy. Further research is needed to confirm if this finding applies across different populations and clinical settings. ", doi="10.2196/47396", url="https://publichealth.jmir.org/2024/1/e47396", url="http://www.ncbi.nlm.nih.gov/pubmed/38630528" } @Article{info:doi/10.2196/50652, author="Klein, Z. Ari and Guti{\'e}rrez G{\'o}mez, Agust{\'i}n Jos{\'e} and Levine, D. Lisa and Gonzalez-Hernandez, Graciela", title="Using Longitudinal Twitter Data for Digital Epidemiology of Childhood Health Outcomes: An Annotated Data Set and Deep Neural Network Classifiers", journal="J Med Internet Res", year="2024", month="Mar", day="25", volume="26", pages="e50652", keywords="natural language processing", keywords="machine learning", keywords="data mining", keywords="social media", keywords="Twitter", keywords="pregnancy", keywords="epidemiology", keywords="developmental disabilities", keywords="asthma", doi="10.2196/50652", url="https://www.jmir.org/2024/1/e50652", url="http://www.ncbi.nlm.nih.gov/pubmed/38526542" } @Article{info:doi/10.2196/52658, author="McMaughan, Jones Darcy and Lewis, Casey and McGehee, Amy and Noreen, Dani and Parker, Elliot and Criss, M. Michael", title="Meaningful Social Inclusion and Mental Well-Being Among Autistic Adolescents and Emerging Adults: Protocol for a Community-Based Mixed Methods Study", journal="JMIR Res Protoc", year="2024", month="Mar", day="14", volume="13", pages="e52658", keywords="autism", keywords="community-based", keywords="mixed methods", keywords="social inclusion", keywords="well-being", abstract="Background: In the United States, autistic people face high rates of co-occurring mental illnesses and premature death due to self-harm, which are indicators of threats to mental well-being. Social inclusion may enhance mental well-being and resilience among autistic people. According to Simplican and colleague's (2015) model of social inclusion for people with intellectual and developmental disabilities, social inclusion is an interaction between community participation and interpersonal relationships. There is limited research on social inclusion that includes the integration of interpersonal relationships and community participation among autistic people or the impact of social inclusion on the well-being of autistic people. Additionally, little evidence exists regarding how autistic people prefer to be included in the community or form interpersonal relationships. Objective: The long-term objective of this project is to improve social inclusion factors to support the mental well-being of autistic people. This protocol describes a community-based, mixed methods pilot study to develop a definition of meaningful social inclusion for autistic people and to understand the relationship between meaningful social inclusion and mental well-being among autistic adolescents and emerging adults. Methods: The project uses a community-based, sequential mixed methods design with a formative phase (Phase 1) that informs a survey phase (Phase 2) and concludes with a process evaluation of the community engagement process (Phase 3). During Phase 1, we will recruit 10 community partners (autistic adults and stakeholders) and conduct sharing sessions to cocreate a definition of meaningful social inclusion and a survey of meaningful social inclusion and well-being. During Phase 2, we will recruit 200 participants (100 autistic adolescents and emerging adults and 100 caregivers) to complete the survey. We will examine whether meaningful social inclusion predicts well-being given sociodemographic factors using ordered logistic regression, with well-being categorized as low, medium, and high. During Phase 3, the community partners from Phase 1 will complete a survey on their experiences with the project. Results: Ethics approval was obtained for this project in March 2023. We have recruited community partners and started the Phase 1 focus groups as of September 2023. Phase 2 and Phase 3 have not yet started. We expect to complete this study by March 2025. Conclusions: Using a community-based, mixed methods approach, we intended to develop a definition of meaningful social inclusion for autistic people and understand the role meaningful social inclusion plays in the well-being of autistic people. International Registered Report Identifier (IRRID): PRR1-10.2196/52658 ", doi="10.2196/52658", url="https://www.researchprotocols.org/2024/1/e52658", url="http://www.ncbi.nlm.nih.gov/pubmed/38483470" } @Article{info:doi/10.2196/51749, author="Kurokawa, Shunya and Nomura, Kensuke and Hosogane, Nana and Nagasawa, Takashi and Kawade, Yuko and Matsumoto, Yu and Morinaga, Shuichi and Kaise, Yuriko and Higuchi, Ayana and Goto, Akiko and Inada, Naoko and Kodaira, Masaki and Kishimoto, Taishiro", title="Reliability of Telepsychiatry Assessments Using the Attention-Deficit/Hyperactivity Disorder Rating Scale-IV for Children With Neurodevelopmental Disorders and Their Caregivers: Randomized Feasibility Study", journal="J Med Internet Res", year="2024", month="Feb", day="19", volume="26", pages="e51749", keywords="acceptability", keywords="ADHD", keywords="application", keywords="attention-deficit/hyperactivity disorder", keywords="autism spectrum disorders", keywords="autism", keywords="child", keywords="children", keywords="diagnosis", keywords="management", keywords="neurodevelopmental disorder", keywords="neurodevelopmental", keywords="psychiatrists", keywords="reliability", keywords="telepsychitatry", abstract="Background: Given the global shortage of child psychiatrists and barriers to specialized care, remote assessment is a promising alternative for diagnosing and managing attention-deficit/hyperactivity disorder (ADHD). However, only a few studies have validated the accuracy and acceptability of these remote methods. Objective: This study aimed to test the agreement between remote and face-to-face assessments. Methods: Patients aged between 6 and 17 years with confirmed Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition diagnoses of ADHD or autism spectrum disorder (ASD) were recruited from multiple institutions. In a randomized order, participants underwent 2 evaluations, face-to-face and remotely, with distinct evaluators administering the ADHD Rating Scale-IV (ADHD-RS-IV). Intraclass correlation coefficient (ICC) was used to assess the reliability of face-to-face and remote assessments. Results: The participants included 74 Japanese children aged between 6 and 16 years who were primarily diagnosed with ADHD (43/74, 58\%) or ASD (31/74, 42\%). A total of 22 (30\%) children were diagnosed with both conditions. The ADHD-RS-IV ICCs between face-to-face and remote assessments showed ``substantial'' agreement in the total ADHD-RS-IV score (ICC=0.769, 95\% CI 0.654-0.849; P<.001) according to the Landis and Koch criteria. The ICC in patients with ADHD showed ``almost perfect'' agreement (ICC=0.816, 95\% CI 0.683-0.897; P<.001), whereas in patients with ASD, it showed ``substantial'' agreement (ICC=0.674, 95\% CI 0.420-0.831; P<.001), indicating the high reliability of both methods across both conditions. Conclusions: Our study validated the feasibility and reliability of remote ADHD testing, which has potential benefits such as reduced hospital visits and time-saving effects. Our results highlight the potential of telemedicine in resource-limited areas, clinical trials, and treatment evaluations, necessitating further studies to explore its broader application. Trial Registration: UMIN Clinical Trials Registry UMIN000039860; http://tinyurl.com/yp34x6kh ", doi="10.2196/51749", url="https://www.jmir.org/2024/1/e51749", url="http://www.ncbi.nlm.nih.gov/pubmed/38373022" } @Article{info:doi/10.2196/49906, author="Toma, Marian-Vladut and Turcu, Elena Cristina and Turcu, Octavian Corneliu and Vlad, Sorin and Tiliute, Eugen Doru and Pascu, Paul", title="Extended Reality--Based Mobile App Solutions for the Therapy of Children With Autism Spectrum Disorders: Systematic Literature Review", journal="JMIR Serious Games", year="2024", month="Feb", day="19", volume="12", pages="e49906", keywords="autism", keywords="autistic", keywords="autism spectrum disorder", keywords="ASD", keywords="virtual reality", keywords="augmented reality", keywords="extended reality", keywords="mixed reality", keywords="mobile app", keywords="children", keywords="preschool", keywords="mobile phone", abstract="Background: The increasing prevalence of autism spectrum disorder (ASD) has driven research interest on the therapy of individuals with autism, especially children, as early diagnosis and appropriate treatment can lead to improvement in the condition. With the widespread availability of virtual reality, augmented reality (AR), and mixed reality technologies to the public and the increasing popularity of mobile devices, the interest in the use of applications and technologies to provide support for the therapy of children with autism is growing. Objective: This study aims to describe the literature on the potential of virtual reality, AR, and mixed reality technologies in the context of therapy for children with ASD. We propose to investigate and analyze the temporal distribution of relevant papers, identify the target audience for studies related to extended reality apps in ASD therapy, examine the technologies used in the development of these apps, assess the skills targeted for improvement in primary studies, explore the purposes of the proposed solutions, and summarize the results obtained from their application. Methods: For the systematic literature review, 6 research questions were defined in the first phase, after which 5 international databases (Web of Science, Scopus, ScienceDirect, IEEE Xplore Digital Library, and ACM Digital Library) were searched using specific search strings. Results were centralized, filtered, and processed applying eligibility criteria and using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The results were refined using a technical and IT-oriented approach. The quality criteria assessed whether the research addressed ASDs, focused on children's therapy, involved targeted technologies, deployed solutions on mobile devices, and produced results relevant to our study. Results: In the first step, 179 publications were identified in Zotero reference manager software (Corporation for Digital Scholarship). After excluding articles that did not meet the eligibility or quality assessment criteria, 28 publications were finalized. The analysis revealed an increase in publications related to apps for children with autism starting in 2015 and peaking in 2019. Most studies (22/28, 79\%) focused on mobile AR solutions for Android devices, which were developed using the Unity 3D platform and the Vuforia engine. Although 68\% (19/28) of these apps were tested with children, 32\% (9/28) were tested exclusively by developers. More than half (15/28, 54\%) of the studies used interviews as an evaluation method, yielding mostly favorable although preliminary results, indicating the need for more extensive testing. Conclusions: The findings reported in the studies highlight the fact that these technologies are appropriate for the therapy of children with ASD. Several studies showed a distinct trend toward the use of AR technology as an educational tool for people with ASD. This trend entails multidisciplinary cooperation and an integrated research approach, with an emphasis on comprehensive empirical evaluations and technology ethics. ", doi="10.2196/49906", url="https://games.jmir.org/2024/1/e49906", url="http://www.ncbi.nlm.nih.gov/pubmed/38373032" } @Article{info:doi/10.2196/52660, author="Jaiswal, Aditi and Washington, Peter", title="Using \#ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study", journal="JMIR Form Res", year="2024", month="Feb", day="14", volume="8", pages="e52660", keywords="autism", keywords="autism spectrum disorder", keywords="machine learning", keywords="natural language processing", keywords="public health", keywords="sentiment analysis", keywords="social media analysis", keywords="Twitter", abstract="Background: The increasing use of social media platforms has given rise to an unprecedented surge in user-generated content, with millions of individuals publicly sharing their thoughts, experiences, and health-related information. Social media can serve as a useful means to study and understand public health. Twitter (subsequently rebranded as ``X'') is one such social media platform that has proven to be a valuable source of rich information for both the general public and health officials. We conducted the first study applying Twitter data mining to autism screening. Objective: We aimed to study the feasibility of autism screening from Twitter data and discuss the ethical implications of such models. Methods: We developed a machine learning model to attempt to distinguish individuals with autism from their neurotypical peers based on the textual patterns from their public communications on Twitter. We collected 6,515,470 tweets from users' self-identification with autism using ``\#ActuallyAutistic'' and a separate control group. To construct the data set, we targeted English-language tweets using the search query ``\#ActuallyAutistic'' posted from January 1, 2014 to December 31, 2022. We encrypted all user IDs and stripped the tweets of identifiable information such as the associated email address prior to analysis. From these tweets, we identified unique users who used keywords such as ``autism'' OR ``autistic'' OR ``neurodiverse'' in their profile description and collected all the tweets from their timelines. To build the control group data set, we formulated a search query excluding the hashtag ``\#ActuallyAutistic'' and collected 1000 tweets per day during the same time period. We trained a word2vec model and an attention-based, bidirectional long short-term memory model to validate the performance of per-tweet and per-profile classification models. We deleted the data set and the models after our analysis. Results: Our tweet classifier reached a 73\% accuracy, a 0.728 area under the receiver operating characteristic curve score, and an 0.71 F1-score using word2vec representations fed into a logistic regression model, while the user profile classifier achieved an 0.78 area under the receiver operating characteristic curve score and an F1-score of 0.805 using an attention-based, bidirectional long short-term memory model. Conclusions: We have shown that it is feasible to train machine learning models using social media data to predict use of the \#ActuallyAutistic hashtag, an imperfect proxy for self-reported autism. While analyzing textual differences in naturalistic text has the potential to help clinicians screen for autism, there remain ethical questions that must be addressed for such research to move forward and to translate into the real world. While machine learning has the potential to improve behavioral research, there are still a plethora of ethical issues in digital phenotyping studies using social media with respect to user consent of marginalized populations. Achieving this requires a more inclusive approach during the model development process that involves the autistic community directly in the ideation and consent processes. ", doi="10.2196/52660", url="https://formative.jmir.org/2024/1/e52660", url="http://www.ncbi.nlm.nih.gov/pubmed/38354045" } @Article{info:doi/10.2196/52205, author="Jaiswal, Aditi and Kruiper, Ruben and Rasool, Abdur and Nandkeolyar, Aayush and Wall, P. Dennis and Washington, Peter", title="Digitally Diagnosing Multiple Developmental Delays Using Crowdsourcing Fused With Machine Learning: Protocol for a Human-in-the-Loop Machine Learning Study", journal="JMIR Res Protoc", year="2024", month="Feb", day="8", volume="13", pages="e52205", keywords="machine learning", keywords="crowdsourcing", keywords="autism spectrum disorder", keywords="ASD", keywords="attention-deficit/hyperactivity disorder", keywords="ADHD", keywords="precision health", abstract="Background: A considerable number of minors in the United States are diagnosed with developmental or psychiatric conditions, potentially influenced by underdiagnosis factors such as cost, distance, and clinician availability. Despite the potential of digital phenotyping tools with machine learning (ML) approaches to expedite diagnoses and enhance diagnostic services for pediatric psychiatric conditions, existing methods face limitations because they use a limited set of social features for prediction tasks and focus on a single binary prediction, resulting in uncertain accuracies. Objective: This study aims to propose the development of a gamified web system for data collection, followed by a fusion of novel crowdsourcing algorithms with ML behavioral feature extraction approaches to simultaneously predict diagnoses of autism spectrum disorder and attention-deficit/hyperactivity disorder in a precise and specific manner. Methods: The proposed pipeline will consist of (1) gamified web applications to curate videos of social interactions adaptively based on the needs of the diagnostic system, (2) behavioral feature extraction techniques consisting of automated ML methods and novel crowdsourcing algorithms, and (3) the development of ML models that classify several conditions simultaneously and that adaptively request additional information based on uncertainties about the data. Results: A preliminary version of the web interface has been implemented, and a prior feature selection method has highlighted a core set of behavioral features that can be targeted through the proposed gamified approach. Conclusions: The prospect for high reward stems from the possibility of creating the first artificial intelligence--powered tool that can identify complex social behaviors well enough to distinguish conditions with nuanced differentiators such as autism spectrum disorder and attention-deficit/hyperactivity disorder. International Registered Report Identifier (IRRID): PRR1-10.2196/52205 ", doi="10.2196/52205", url="https://www.researchprotocols.org/2024/1/e52205", url="http://www.ncbi.nlm.nih.gov/pubmed/38329783" } @Article{info:doi/10.2196/52157, author="Kim, Sung-In and Jang, So-youn and Kim, Taewan and Kim, Bogoan and Jeong, Dayoung and Noh, Taehyung and Jeong, Mingon and Hall, Kaely and Kim, Meelim and Yoo, Jeong Hee and Han, Kyungsik and Hong, Hwajung and Kim, G. Jennifer", title="Promoting Self-Efficacy of Individuals With Autism in Practicing Social Skills in the Workplace Using Virtual Reality and Physiological Sensors: Mixed Methods Study", journal="JMIR Form Res", year="2024", month="Jan", day="11", volume="8", pages="e52157", keywords="autism", keywords="virtual reality", keywords="workplace", keywords="self-efficacy", keywords="social skills", keywords="data reflection", abstract="Background: Individuals with autism often experience heightened anxiety in workplace environments because of challenges in communication and sensory overload. As these experiences can result in negative self-image, promoting their self-efficacy in the workplace is crucial. Virtual reality (VR) systems have emerged as promising tools for enhancing the self-efficacy of individuals with autism in navigating social scenarios, aiding in the identification of anxiety-inducing situations, and preparing for real-world interactions. However, there is limited research exploring the potential of VR to enhance self-efficacy by facilitating an understanding of emotional and physiological states during social skills practice. Objective: This study aims to develop and evaluate a VR system that enabled users to experience simulated work-related social scenarios and reflect on their behavioral and physiological data through data visualizations. We intended to investigate how these data, combined with the simulations, can support individuals with autism in building their self-efficacy in social skills. Methods: We developed WorkplaceVR, a comprehensive VR system designed for engagement in simulated work-related social scenarios, supplemented with data-driven reflections of users' behavioral and physiological responses. A within-subject deployment study was subsequently conducted with 14 young adults with autism to examine WorkplaceVR's feasibility. A mixed methods approach was used, compassing pre- and postsystem use assessments of participants' self-efficacy perceptions. Results: The study results revealed WorkplaceVR's effectiveness in enhancing social skills and self-efficacy among individuals with autism. First, participants exhibited a statistically significant increase in perceived self-efficacy following their engagement with the VR system (P=.02). Second, thematic analysis of the interview data confirmed that the VR system and reflections on the data fostered increased self-awareness among participants about social situations that trigger their anxiety, as well as the behaviors they exhibit during anxious moments. This increased self-awareness prompted the participants to recollect their related experiences in the real world and articulate anxiety management strategies. Furthermore, the insights uncovered motivated participants to engage in self-advocacy, as they wanted to share the insights with others. Conclusions: This study highlights the potential of VR simulations enriched with physiological and behavioral sensing as a valuable tool for augmenting self-efficacy in workplace social interactions for individuals with autism. Data reflection facilitated by physiological sensors helped participants with autism become more self-aware of their emotions and behaviors, advocate for their characteristics, and develop positive self-beliefs. ", doi="10.2196/52157", url="https://formative.jmir.org/2024/1/e52157", url="http://www.ncbi.nlm.nih.gov/pubmed/38206652" } @Article{info:doi/10.2196/51719, author="Gabrielli, Silvia and Cristofolini, Melanie and Dianti, Marco and Alvari, Gianpaolo and Vallefuoco, Ersilia and Bentenuto, Arianna and Venuti, Paola and Mayora Ibarra, Oscar and Salvadori, Elio", title="Co-Design of a Virtual Reality Multiplayer Adventure Game for Adolescents With Autism Spectrum Disorder: Mixed Methods Study", journal="JMIR Serious Games", year="2023", month="Dec", day="8", volume="11", pages="e51719", keywords="co-design", keywords="virtual reality environments", keywords="autism", keywords="social skills interventions", keywords="multiplayer game design", keywords="serious games", abstract="Background: Virtual reality (VR) adventure games can offer ideal technological solutions for training social skills in adolescents with autism spectrum disorder (ASD), leveraging their support for multisensory and multiplayer interactions over distance, which may lower barriers to training access and increase user motivation. However, the design of VR-based game environments for social skills training is still understudied and deserves the deployment of an inclusive design approach to ensure its acceptability by target users. Objective: We aimed to present the inclusive design process that we had followed to develop the Zentastic VR adventure game to foster social skills training in adolescents with ASD and to investigate its feasibility as a training environment for adolescents. Methods: The VR game supports multiplayer training sessions involving small groups of adolescents and their therapists, who act as facilitators. Adolescents with ASD and their therapists were involved in the design and in an explorative acceptability study of an initial prototype of the gaming environment, as well as in a later feasibility multisession evaluation of the VR game final release. Results: The feasibility study demonstrated good acceptability of the VR game by adolescents and an enhancement of their social skills from baseline to posttraining. Conclusions: The findings provide preliminary evidence of the benefits that VR-based games can bring to the training of adolescents with ASD and, potentially, other neurodevelopmental disorders. ", doi="10.2196/51719", url="https://games.jmir.org/2023/1/e51719", url="http://www.ncbi.nlm.nih.gov/pubmed/38064258" } @Article{info:doi/10.2196/52377, author="Ponzo, Sonia and May, Merle and Tamayo-Elizalde, Miren and Bailey, Kerri and Shand, J. Alanna and Bamford, Ryan and Multmeier, Jan and Griessel, Ivan and Szulyovszky, Benedek and Blakey, William and Valentine, Sophie and Plans, David", title="App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review", journal="JMIR Mhealth Uhealth", year="2023", month="Nov", day="17", volume="11", pages="e52377", keywords="autism", keywords="diagnostics", keywords="digital biomarkers", keywords="digital health", keywords="mobile apps", keywords="neurodevelopmental conditions", abstract="Background: Diagnostic delays in autism are common, with the time to diagnosis being up to 3 years from the onset of symptoms. Such delays have a proven detrimental effect on individuals and families going through the process. Digital health products, such as mobile apps, can help close this gap due to their scalability and ease of access. Further, mobile apps offer the opportunity to make the diagnostic process faster and more accurate by providing additional and timely information to clinicians undergoing autism assessments. Objective: The aim of this scoping review was to synthesize the available evidence about digital biomarker tools to aid clinicians, researchers in the autism field, and end users in making decisions as to their adoption within clinical and research settings. Methods: We conducted a structured literature search on databases and search engines to identify peer-reviewed studies and regulatory submissions that describe app characteristics, validation study details, and accuracy and validity metrics of commercial and research digital biomarker apps aimed at aiding the diagnosis of autism. Results: We identified 4 studies evaluating 4 products: 1 commercial and 3 research apps. The accuracy of the identified apps varied between 28\% and 80.6\%. Sensitivity and specificity also varied, ranging from 51.6\% to 81.6\% and 18.5\% to 80.5\%, respectively. Positive predictive value ranged from 20.3\% to 76.6\%, and negative predictive value fluctuated between 48.7\% and 97.4\%. Further, we found a lack of details around participants' demographics and, where these were reported, important imbalances in sex and ethnicity in the studies evaluating such products. Finally, evaluation methods as well as accuracy and validity metrics of available tools were not clearly reported in some cases and varied greatly across studies. Different comparators were also used, with some studies validating their tools against the Diagnostic and Statistical Manual of Mental Disorders criteria and others through self-reported measures. Further, while in most cases, 2 classes were used for algorithm validation purposes, 1 of the studies reported a third category (indeterminate). These discrepancies substantially impact the comparability and generalizability of the results, thus highlighting the need for standardized validation processes and the reporting of findings. Conclusions: Despite their popularity, systematic evaluations and syntheses of the current state of the art of digital health products are lacking. Standardized and transparent evaluations of digital health tools in diverse populations are needed to assess their real-world usability and validity, as well as help researchers, clinicians, and end users safely adopt novel tools within clinical and research practices. ", doi="10.2196/52377", url="https://mhealth.jmir.org/2023/1/e52377", url="http://www.ncbi.nlm.nih.gov/pubmed/37976084" } @Article{info:doi/10.2196/45836, author="Chu, Liting and Shen, Li and Ma, Chenhuan and Chen, Jinjin and Tian, Yuan and Zhang, Chuncao and Gong, Zilan and Li, Mengfan and Wang, Chengjie and Pan, Lizhu and Zhu, Peiying and Wu, Danmai and Wang, Yu and Yu, Guangjun", title="Effects of a Nonwearable Digital Therapeutic Intervention on Preschoolers With Autism Spectrum Disorder in China: Open-Label Randomized Controlled Trial", journal="J Med Internet Res", year="2023", month="Aug", day="24", volume="25", pages="e45836", keywords="autism spectrum disorder", keywords="digital therapy", keywords="nonwearable", keywords="preschoolers", keywords="randomized controlled trial", keywords="autism", keywords="neurodevelopmental disorder", keywords="difficulty with communication", keywords="social interaction", keywords="ADHD", keywords="attention-deficit/hyperactivity disorder", keywords="digital intervention", abstract="Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that can cause difficulty with communication and social interactions as well as complicated family dynamics. Digital health interventions can reduce treatment costs and promote healthy lifestyle changes. These therapies can be adjunctive or replace traditional treatments. However, issues with cooperation and compliance prevent preschool patients with ASD from applying these tools. In this open-label, randomized controlled trial, we developed a nonwearable digital therapy called virtual reality--incorporated cognitive behavioral therapy (VR-CBT). Objective: The aim of this study was to assess the adjunctive function of VR-CBT by comparing the effects of VR-CBT plus learning style profile (LSP) intervention with those of LSP-only intervention in preschool children with ASD. Methods: This trial was performed in China on 78 preschool children (age 3-6 years, IQ>70) diagnosed with ASD who were randomized to receive a 20-week VR-CBT plus LSP intervention (intervention group, 39/78, 50\%) or LSP intervention only (control group, 39/78, 50\%). The primary outcome was the change of scores from baseline to week 20, assessed by using the parent-rated Autism Behavior Checklist (ABC). Secondary outcomes included the Childhood Autism Rating Scale (CARS), Attention-Deficit/Hyperactivity Disorder Rating Scale-IV (ADHD-RS-IV), and behavioral performance data (accuracy and reaction time) in go/no-go tasks. All primary and secondary outcomes were analyzed in the intention-to-treat population. Results: After the intervention, there was an intervention effect on total ABC ($\beta$=--5.528; P<.001) and CARS scores ($\beta$=--1.365; P=.02). A similar trend was observed in the ABC subscales: sensory ($\beta$=--1.133; P=.047), relating ($\beta$=--1.512; P=.03), body and object use ($\beta$=--1.211; P=.03), and social and self-help ($\beta$=--1.593; P=.03). The intervention also showed statistically significant effects in improving behavioral performance (go/no-go task, accuracy, $\beta$=2.923; P=.04). Moreover, a significant improvement of ADHD hyperactivity-impulsivity symptoms was observed in 53 children with comorbid ADHD based on ADHD-RS-IV ($\beta$=--1.269; P=.02). No statistically significant intervention effect was detected in the language subscale of ABC ($\beta$=--.080; P=.83). Intervention group girls had larger improvements in ABC subscales, that is, sensory and body and object use and in the CARS score and accuracy of go/no-go task (all P<.05) than the control group girls. Statistically significant intervention effects could be observed in hyperactivity-impulsivity symptoms in the intervention group boys with comorbid ADHD compared with those in the control group boys ($\beta$=--1.333; P=.03). Conclusions: We found potentially positive effects of nonwearable digital therapy plus LSP on core symptoms associated with ASD, leading to a modest improvement in the function of sensory, motor, and response inhibition, while reducing impulsivity and hyperactivity in preschoolers with both ASD and ADHD. VR-CBT was found to be an effective and feasible adjunctive digital tool. Trial Registration: Chinese Clinical Trial Registry ChiCTR2100053165; http://www.chictr.org.cn/showproj.aspx?proj=137016 ", doi="10.2196/45836", url="https://www.jmir.org/2023/1/e45836", url="http://www.ncbi.nlm.nih.gov/pubmed/37616029" } @Article{info:doi/10.2196/40383, author="Li, Longxi and Wang, Anni and Fang, Qun and Moosbrugger, E. Michelle", title="Physical Activity Interventions for Improving Cognitive Functions in Children With Autism Spectrum Disorder: Protocol for a Network Meta-Analysis of Randomized Controlled Trials", journal="JMIR Res Protoc", year="2023", month="Jun", day="28", volume="12", pages="e40383", keywords="children", keywords="autism spectrum disorder", keywords="physical activity", keywords="cognitive outcomes", keywords="network meta-analysis", abstract="Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects millions of children worldwide, with a current prevalence of approximately 1 in 54 children in the United States. Although the precise mechanisms underlying ASD remain unclear, research has shown that early intervention can have a significant impact on cognitive development and outcomes in children with ASD. Physical activity interventions have emerged as a promising intervention for children with ASD, but the efficacy of different types of interventions remains unclear. Objective: This study protocol aims to update the knowledge on extant literature and explore the efficacy of physical activity intervention strategies on cognitive functions in children with ASD. Methods: A systematic review and network meta-analysis (NMA) will be conducted following the PRISMA-NMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols for Network Meta-Analyses) statement. A total of 9 bibliographic databases (APA PsycInfo, CENTRAL, Dimensions, ERIC, MEDLINE Complete, PubMed, Scopus, SPORTDiscus, and Web of Science) will be systematically searched to screen eligible articles based on a series of inclusion and exclusion criteria. A study will be considered for inclusion if it is not classified as a systematic review with or without meta-analysis, was published from inception to present, includes children aged 0 to 12 years with ASD, quantitively measures cognitive outcomes, and examines treatment comprising at least 1 physical activity intervention strategy. The internal validity and quality of evidence will be evaluated using the Grading of Recommendations Assessment, Development, and Evaluation framework. Statistical analyses will be performed in the RStudio software (version 3.6; RStudio Inc) with the BUGSnet package and the Comprehensive Meta-Analysis software (version 3.3; Biostat Inc). The results of our NMA will be illustrated through network diagrams accompanied by geometry and league tables. Further, to rank the interventions based on their efficacy, we will use the surface under the cumulative ranking curve. Results: Our preliminary search identified 3778 potentially relevant studies. The screening of the studies based on the inclusion and exclusion criteria is ongoing, and we anticipate that the final number of eligible studies will be in the range of 30 to 50. Conclusions: This study will provide a comprehensive review of the literature on physical activity interventions for children with ASD and will use NMA to compare the efficacy of different types of interventions on cognitive outcomes. Our findings will have important implications for clinical practice and future research in this area and will contribute to the growing body of evidence supporting the use of physical activity interventions as a key component of early intervention for children with ASD. Trial Registration: PROSPERO CRD42021279054; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=279054 International Registered Report Identifier (IRRID): DERR1-10.2196/40383 ", doi="10.2196/40383", url="https://www.researchprotocols.org/2023/1/e40383", url="http://www.ncbi.nlm.nih.gov/pubmed/37379078" } @Article{info:doi/10.2196/45852, author="Palermo, H. Emma and Young, V. Amanda and Deswert, Sky and Brown, Alyssa and Goldberg, Miranda and Sultanik, Evan and Tan, Jessica and Mazefsky, A. Carla and Brookman-Frazee, Lauren and McPartland, C. James and Goodwin, S. Matthew and Pennington, Jeffrey and Marcus, C. Steven and Beidas, S. Rinad and Mandell, S. David and Nuske, J. Heather", title="A Digital Mental Health App Incorporating Wearable Biosensing for Teachers of Children on the Autism Spectrum to Support Emotion Regulation: Protocol for a Pilot Randomized Controlled Trial", journal="JMIR Res Protoc", year="2023", month="Jun", day="26", volume="12", pages="e45852", keywords="digital mental health", keywords="just-in-time adaptive intervention augmentation", keywords="JITAI", keywords="autism", keywords="heart rate tracking", keywords="emotion dysregulation", keywords="challenging behavior", keywords="evidence-based strategies", keywords="student progress monitoring", keywords="mobile phone", abstract="Background: As much as 80\% of children on the autism spectrum exhibit challenging behaviors (ie, behaviors dangerous to the self or others, behaviors that interfere with learning and development, and behaviors that interfere with socialization) that can have a devastating impact on personal and family well-being, contribute to teacher burnout, and even require hospitalization. Evidence-based practices to reduce these behaviors emphasize identifying triggers (events or antecedents that lead to challenging behaviors); however, parents and teachers often report that challenging behaviors surface with little warning. Exciting recent advances in biometric sensing and mobile computing technology allow the measurement of momentary emotion dysregulation using physiological indexes. Objective: We present the framework and protocol for a pilot trial that will test a mobile digital mental health app, the KeepCalm app. School-based approaches to managing challenging behaviors in children on the autism spectrum are limited by 3 key factors: children on the autism spectrum often have difficulties in communicating their emotions; it is challenging to implement evidence-based, personalized strategies for individual children in group settings; and it is difficult for teachers to track which strategies are successful for each child. KeepCalm aims to address those barriers by communicating children's stress to their teachers using physiological signaling (emotion dysregulation detection), supporting the implementation of emotion regulation strategies via smartphone pop-up notifications of top strategies for each child according to their behavior (emotion regulation strategy implementation), and easing the task of tracking outcomes by providing the child's educational team with a tool to track the most effective emotion regulation strategies for that child based on physiological stress reduction data (emotion regulation strategy evaluation). Methods: We will test KeepCalm with 20 educational teams of students on the autism spectrum with challenging behaviors (no exclusion based on IQ or speaking ability) in a pilot randomized waitlist-controlled field trial over a 3-month period. We will examine the usability, acceptability, feasibility, and appropriateness of KeepCalm as primary outcomes. Secondary preliminary efficacy outcomes include clinical decision support success, false positives or false negatives of stress alerts, and the reduction of challenging behaviors and emotion dysregulation. We will also examine technical outcomes, including the number of artifacts and the proportion of time children are engaged in high physical movement based on accelerometry data; test the feasibility of our recruitment strategies; and test the response rate and sensitivity to change of our measures, in preparation for a future fully powered large-scale randomized controlled trial. Results: The pilot trial will begin by September 2023. Conclusions: Results will provide key data about important aspects of implementing KeepCalm in preschools and elementary schools and will provide preliminary data about its efficacy to reduce challenging behaviors and support emotion regulation in children on the autism spectrum. Trial Registration: ClinicalTrials.gov NCT05277194; https://www.clinicaltrials.gov/ct2/show/NCT05277194 International Registered Report Identifier (IRRID): PRR1-10.2196/45852 ", doi="10.2196/45852", url="https://www.researchprotocols.org/2023/1/e45852", url="http://www.ncbi.nlm.nih.gov/pubmed/37358908" } @Article{info:doi/10.2196/39720, author="Bui, An Truong and Rosenfelt, Scott Cory and Whitlock, Hope Kerri and Leclercq, Mickael and Weber, Savannah and Droit, Arnaud and Wiebe, A. Sandra and Pei, Jacqueline and Bolduc, V. Francois", title="Long-term Memory Testing in Children With Typical Development and Neurodevelopmental Disorders: Remote Web-based Image Task Feasibility Study", journal="JMIR Pediatr Parent", year="2023", month="May", day="8", volume="6", pages="e39720", keywords="memory", keywords="neurodevelopmental disorder", keywords="autism spectrum disorder", keywords="intellectual disability", keywords="developmental delay", keywords="hippocampus", keywords="recognition", keywords="paired association learning", keywords="remote testing", keywords="autism", keywords="disorder", keywords="genetics", keywords="developmental", keywords="developmental disorder", keywords="game", keywords="remote", keywords="testing", keywords="diagnose", keywords="diagnosis", abstract="Background: Neurodevelopmental disorders (NDD) cause individuals to have difficulty in learning facts, procedures, or social skills. NDD has been linked to several genes, and several animal models have been used to identify potential therapeutic candidates based on specific learning paradigms for long-term and associative memory. In individuals with NDD, however, such testing has not been used so far, resulting in a gap in translating preclinical results to clinical practice. Objective: We aim to assess if individuals with NDD could be tested for paired association learning and long-term memory deficit, as shown in previous animal models. Methods: We developed an image-based paired association task, which can be performed at different time points using remote web-based testing, and evaluated its feasibility in children with typical development (TD), as well as NDD. We included 2 tasks: object recognition as a simpler task and paired association. Learning was tested immediately after training and also the next day for long-term memory. Results: We found that children aged 5-14 years with TD (n=128) and with NDD of different types (n=57) could complete testing using the Memory Game. Children with NDD showed deficits in both recognition and paired association tasks on the first day of learning, in both 5-9--year old (P<.001 and P=.01, respectively) and 10-14--year old groups (P=.001 and P<.001, respectively). The reaction times to stimuli showed no significant difference between individuals with TD or NDD. Children with NDD exhibited a faster 24-hour memory decay for the recognition task than those with TD in the 5-9--year old group. This trend is reversed for the paired association task. Interestingly, we found that children with NDD had their retention for recognition improved and matched with typically developing individuals by 10-14 years of age. The NDD group also showed improved retention deficits in the paired association task at 10-14 years of age compared to the TD group. Conclusions: We showed that web-based learning testing using simple picture association is feasible for children with TD, as well as with NDD. We showed how web-based testing allows us to train children to learn the association between pictures, as shown in immediate test results and those completed 1 day after. This is important as many models for learning deficits in NDD target both short- and long-term memory for therapeutic intervention. We also demonstrated that despite potential confounding factors, such as self-reported diagnosis bias, technical issues, and varied participation, the Memory Game shows significant differences between typically developing children and those with NDD. Future experiments will leverage this potential of web-based testing for larger cohorts and cross-validation with other clinical or preclinical cognitive tasks. ", doi="10.2196/39720", url="https://pediatrics.jmir.org/2023/1/e39720", url="http://www.ncbi.nlm.nih.gov/pubmed/37155237" } @Article{info:doi/10.2196/44354, author="Tomas, Vanessa and Hsu, Shaelynn and Kingsnorth, Shauna and Anagnostou, Evdokia and Kirsh, Bonnie and Lindsay, Sally", title="Development and Usability Testing of a Web-Based Workplace Disability Disclosure Decision Aid Tool for Autistic Youth and Young Adults: Qualitative Co-design Study", journal="JMIR Form Res", year="2023", month="Apr", day="27", volume="7", pages="e44354", keywords="autism", keywords="decision aids", keywords="co-design", keywords="disability disclosure", keywords="employment", keywords="knowledge translation", keywords="patient-oriented research", keywords="qualitative", keywords="usability testing", keywords="youth and young adults", abstract="Background: Deciding whether and how to disclose one's autism at work is complex, especially for autistic youth and young adults who are newly entering the labor market and still learning important decision-making and self-determination skills. Autistic youth and young adults may benefit from tools to support disclosure processes at work; however, to our knowledge, no evidence-based, theoretically grounded tool exists specifically for this population. There is also limited guidance on how to pursue the development of such a tool in collaboration with knowledge users. Objective: This study aimed to co-design a prototype of a disclosure decision aid tool with and for Canadian autistic youth and young adults, explore the perceived usability of the prototype (usefulness, satisfaction, and ease of use) and make necessary revisions, and outline the process used to achieve the aforementioned objectives. Methods: Taking a patient-oriented research approach, we engaged 4 autistic youths and young adults as collaborators on this project. Prototype development was guided by co-design principles and strategies, and tool content was informed by a previous needs assessment led by our team, the autistic collaborators' lived experiences, considering intersectionality, research on knowledge translation (KT) tool development, and recommendations from the International Patient Decision Aid Standards. We co-designed a web-based PDF prototype. To assess perceived usability and experiences with the prototype, we conducted 4 participatory design and focus group Zoom (Zoom Video Communications) sessions with 19 Canadian autistic youths and young adults aged 16 to 29 (mean 22.8, SD 4.1) years. We analyzed the data using a combined conventional (inductive) and modified framework method (deductive) analysis to map the data onto usability indicators (usefulness, satisfaction, and ease of use). Grounded in participants' feedback, considering factors of feasibility and availability of resources, and ensuring tool fidelity, we revised the prototype. Results: We developed 4 categories pertaining to the perceived usability of and participant experiences with the prototype: past disclosure experiences, prototype information and activities, prototype design and structure, and overall usability. Participant feedback was favorable and indicative of the tool's potential impact and usability. The usability indicator requiring the most attention was ease of use, which was prioritized when revising the prototype. Our findings highlight the importance of engaging knowledge users throughout the entire prototype co-design and testing processes; incorporating co-design strategies and principles; and having content informed by relevant theories, evidence, and knowledge users' experiences. Conclusions: We outline an innovative co-design process that other researchers, clinicians, and KT practitioners may consider when developing KT tools. We also developed a novel, evidence-based, and theoretically informed web-based disclosure decision aid tool that may help autistic youth and young adults navigate disclosure processes and improve their transitional outcomes as they enter the workforce. ", doi="10.2196/44354", url="https://formative.jmir.org/2023/1/e44354", url="http://www.ncbi.nlm.nih.gov/pubmed/37104002" } @Article{info:doi/10.2196/40722, author="Simmons, A. Christina and Moretti, E. Abigail and Lobo, F. Andrea and Tremoulet, D. Patrice", title="Direct Support Professionals' Perspectives on Using Technology to Help Support Adults With Autism Spectrum Disorder: Mixed Methods Study", journal="JMIR Form Res", year="2023", month="Apr", day="25", volume="7", pages="e40722", keywords="technology", keywords="data collection", keywords="documentation", keywords="direct support professionals", keywords="autism", keywords="mobile phone", abstract="Background: Documentation is a critical responsibility for direct support professionals (DSPs) who work with adults with autism spectrum disorder (ASD); however, it contributes significantly to their workload. Targeted efforts must be made to mitigate the burden of necessary data collection and documentation, which contributes to high DSP turnover rates and poor job satisfaction. Objective: This mixed methods study aimed to explore how technology could assist DSPs who work with adults with ASD and prioritize aspects of technology that would be most useful for future development efforts. Methods: In the first study, 15 DSPs who worked with adults with ASD participated in 1 of the 3 online focus groups. The topics included daily tasks, factors that would influence the adoption of technology, and how DSPs would like to interact with technologies to provide information about their clients. Responses were thematically analyzed across focus groups and ranked by salience. In the second study, 153 DSPs across the United States rated the usefulness of technology features and data entry methods and provided qualitative responses on their concerns regarding the use of technology for data collection and documentation. Quantitative responses were ranked based on their usefulness across participants, and rank-order correlations were calculated between different work settings and age groups. The qualitative responses were thematically analyzed. Results: In study 1, participants described difficulties with paper-and-pencil data collection, noted benefits and concerns about using technology instead, identified benefits and concerns about particular technology features, and specified work-environment factors that impact data collection. In study 2, participants rated multiple features of technology as useful, with the highest usefulness percentages endorsed for task views (ie, by shift, client, and DSP), logging completed tasks, and setting reminders for specific tasks. Participants also rated most data entry methods (eg, typing on a phone or tablet, typing on a keyboard, and choosing from options on a touch screen) as useful. Rank-order correlations indicated that the usefulness of technology features and data entry methods differed across work settings and age groups. Across both studies, DSPs cited some concerns with technology, such as confidentiality, reliability and accuracy, complexity and efficiency, and data loss from technology failure. Conclusions: Understanding the challenges faced by DSPs who work with adults with ASD, and their thoughts about using technology to meet those challenges, represents an essential first step toward developing technology solutions that can increase DSPs' effectiveness and job satisfaction. The survey results indicate that technology innovations should incorporate multiple features to account for different needs across DSPs, settings, and age groups. Future research should explore barriers to adopting data collection and documentation tools and elicit input from agency directors, families, and others interested in reviewing data about adults with ASD. ", doi="10.2196/40722", url="https://formative.jmir.org/2023/1/e40722", url="http://www.ncbi.nlm.nih.gov/pubmed/37097738" } @Article{info:doi/10.2196/39917, author="Banerjee, Agnik and Mutlu, Cezmi Onur and Kline, Aaron and Surabhi, Saimourya and Washington, Peter and Wall, Paul Dennis", title="Training and Profiling a Pediatric Facial Expression Classifier for Children on Mobile Devices: Machine Learning Study", journal="JMIR Form Res", year="2023", month="Mar", day="21", volume="7", pages="e39917", keywords="edge computing", keywords="affective computing", keywords="autism spectrum disorder", keywords="autism", keywords="ASD", keywords="classifier", keywords="classification", keywords="model", keywords="algorithm", keywords="mobile health", keywords="computer vision", keywords="deep learning", keywords="machine learning for health", keywords="pediatrics", keywords="emotion recognition", keywords="mHealth", keywords="diagnostic tool", keywords="digital therapy", keywords="child", keywords="developmental disorder", keywords="smartphone", keywords="image analysis", keywords="machine learning", keywords="Image classification", keywords="neural network", abstract="Background: Implementing automated facial expression recognition on mobile devices could provide an accessible diagnostic and therapeutic tool for those who struggle to recognize facial expressions, including children with developmental behavioral conditions such as autism. Despite recent advances in facial expression classifiers for children, existing models are too computationally expensive for smartphone use. Objective: We explored several state-of-the-art facial expression classifiers designed for mobile devices, used posttraining optimization techniques for both classification performance and efficiency on a Motorola Moto G6 phone, evaluated the importance of training our classifiers on children versus adults, and evaluated the models' performance against different ethnic groups. Methods: We collected images from 12 public data sets and used video frames crowdsourced from the GuessWhat app to train our classifiers. All images were annotated for 7 expressions: neutral, fear, happiness, sadness, surprise, anger, and disgust. We tested 3 copies for each of 5 different convolutional neural network architectures: MobileNetV3-Small 1.0x, MobileNetV2 1.0x, EfficientNetB0, MobileNetV3-Large 1.0x, and NASNetMobile. We trained the first copy on images of children, second copy on images of adults, and third copy on all data sets. We evaluated each model against the entire Child Affective Facial Expression (CAFE) set and by ethnicity. We performed weight pruning, weight clustering, and quantize-aware training when possible and profiled each model's performance on the Moto G6. Results: Our best model, a MobileNetV3-Large network pretrained on ImageNet, achieved 65.78\% accuracy and 65.31\% F1-score on the CAFE and a 90-millisecond inference latency on a Moto G6 phone when trained on all data. This accuracy is only 1.12\% lower than the current state of the art for CAFE, a model with 13.91x more parameters that was unable to run on the Moto G6 due to its size, even when fully optimized. When trained solely on children, this model achieved 60.57\% accuracy and 60.29\% F1-score. When trained only on adults, the model received 53.36\% accuracy and 53.10\% F1-score. Although the MobileNetV3-Large trained on all data sets achieved nearly a 60\% F1-score across all ethnicities, the data sets for South Asian and African American children achieved lower accuracy (as much as 11.56\%) and F1-score (as much as 11.25\%) than other groups. Conclusions: With specialized design and optimization techniques, facial expression classifiers can become lightweight enough to run on mobile devices and achieve state-of-the-art performance. There is potentially a ``data shift'' phenomenon between facial expressions of children compared with adults; our classifiers performed much better when trained on children. Certain underrepresented ethnic groups (e.g., South Asian and African American) also perform significantly worse than groups such as European Caucasian despite similar data quality. Our models can be integrated into mobile health therapies to help diagnose autism spectrum disorder and provide targeted therapeutic treatment to children. ", doi="10.2196/39917", url="https://formative.jmir.org/2023/1/e39917", url="http://www.ncbi.nlm.nih.gov/pubmed/35962462" } @Article{info:doi/10.2196/41839, author="Ruan, Hui and Eungpinichpong, Wichai and Wu, Hua and Aonsri, Chanada", title="Physiological and Psychological Effects of Parent-Delivered Traditional Thai Massage in Children With Autism: Protocol for a Randomized Controlled Trial", journal="JMIR Res Protoc", year="2023", month="Feb", day="8", volume="12", pages="e41839", keywords="autism", keywords="massage", keywords="randomized controlled trial", keywords="protocol", abstract="Background: Although many autistic children receive massage as a complementary therapy, it is not included in evidence-based practice for autism because evidence of its efficacy is lacking. Further, prior studies have failed to identify objective indicators of core symptoms or elucidate their mechanisms. Objective: We developed a parent-delivered traditional Thai massage (TTM) intervention for children with autism, aiming to experimentally determine whether children with autism truly experience positive effects from parent-delivered TTM and determine possible mechanisms of the observed effects. Methods: A 2-armed, parallel randomized controlled trial was conducted between February 2022 and June 2022. Forty-eight children with autism (aged 7-12 years) were recruited from the Hainan Special Education School and randomly assigned to either a parental TTM or control group at a ratio of 1:1 based on random numbers generated with Online Research Randomizer. The generated sequences were concealed in an opaque envelope. Individuals in the parental TTM group received 16 parent-delivered TTM sessions over 8 weeks at the school's health room after school, and the control group maintained a normal daily routine. Outcomes were assessed on admission, after 8 weeks, and at a 2-month follow-up and included the effect of massage treatment on autism symptoms, measured with the Autism Treatment Evaluation Checklist score (evaluated by parents and a blinded teacher), physiological parameters (ie, heart rate variability and gait), and the Parenting Stress Index, Fourth Edition--Short Form. Results: We finished all data collection on June 20, 2022. Data analysis will be started, and we expect to publish results in 2023. Conclusions: This study will provide further evidence for massage treatment of autism and provide support for family-based care. Trial Registration: Chinese Clinical Trial Registry ChiCTR2100051355; https://tinyurl.com/3dwjxsw5 International Registered Report Identifier (IRRID): DERR1-10.2196/41839 ", doi="10.2196/41839", url="https://www.researchprotocols.org/2023/1/e41839", url="http://www.ncbi.nlm.nih.gov/pubmed/36753320" } @Article{info:doi/10.2196/32520, author="Drapalik, N. Krista and Grodberg, David and Ventola, Pamela", title="Feasibility and Acceptability of Delivering Pivotal Response Treatment for Autism Spectrum Disorder via Telehealth: Pilot Pre-Post Study", journal="JMIR Pediatr Parent", year="2022", month="Sep", day="6", volume="5", number="3", pages="e32520", keywords="autism spectrum disorder", keywords="ASD", keywords="pivotal response treatment", keywords="PRT", keywords="telehealth", keywords="parent-implemented intervention", keywords="parent training", keywords="pediatrics", keywords="autism", keywords="children", keywords="digital health", keywords="online modules", keywords="online health", keywords="online treatment", keywords="pilot study", keywords="communication", abstract="Background: Pivotal response treatment (PRT), an evidence-based and parent-delivered intervention, is designed to improve social communication in autistic individuals. Objective: The aim of this study was to assess the feasibility, acceptability, and clinical effects of an online model of PRT delivered via MindNest Health, a telehealth platform that aims to provide self-directed and engaging online modules, real-time coaching and feedback, and accessible stepped-care to large populations of parents seeking resources for their autistic children. Methods: Male and female autistic children, aged 2-7 years with single-word to phrase-level speech, and their parents were eligible to participate in the study. Families were randomized to the online parent training condition or control condition. The online component of the intervention consisted of eight 20-minute online courses of content describing parent training principles in PRT. Four 1-hour videoconferences were held after course 1, course 3, course 5, and course 8. Parents were given 1-2 weeks to complete each course. Parents completed the Client Credibility Questionnaire (CCQ) at week 2 and at the study endpoint, as well as the Behavioral Intervention Rating Scale (BIRS) at the study endpoint to assess parental expectancies, and treatment acceptability and effectiveness. Results: Nine of 14 participants completed the study curriculum in the online parent training condition, and 6 of 12 participants completed the control condition. Thus, a total of 58\% (15/26) participants across both groups completed the study curriculum by study closure. Within the online parent training condition, there was a significant increase in mean CCQ total scores, from 25.38 (SD 3.25) at baseline to 27.5 (SD 3.74) at study endpoint (P=.04); mean CCQ confidence scores, from 6.0 (SD 1.07) at baseline to 6.75 (SD 0.89) at study endpoint (P=.02); and mean CCQ other improvement scores, from 5.25 (SD 0.89) at baseline to 6.25 (SD 1.28) at study endpoint (P=.009). Within the control condition, a modest increase in mean CCQ scores was noted (Confidence, difference=+0.25; Recommend, difference=+0.25; Total Score, difference=+0.50), but the differences were not statistically significant (Confidence P=.38, Recommend P=.36, Total Score P=.43). Among the 11 parents who completed the BIRS at the study endpoint, 82\% (n=9) endorsed that they slightly agree or agree with over 93\% of the Acceptability factor items on the BIRS. Conclusions: The feasibility of this online treatment is endorsed by the high rate of online module completion and attendance to videoconferences within the online parent training group. Acceptability of treatment is supported by strong ratings on the CCQ and significant improvements in scores, as well as strong ratings on the BIRS. This study's small sample size limits the conclusions that can be drawn; however, the PRT MindNest Health platform holds promise to support parents of autistic children who are unable to access traditional, in-person parent-mediated interventions for their child. ", doi="10.2196/32520", url="https://pediatrics.jmir.org/2022/3/e32520", url="http://www.ncbi.nlm.nih.gov/pubmed/36066927" } @Article{info:doi/10.2196/37994, author="Coulter, Helen and Donnelly, Mark and Mallett, John and Kernohan, George W.", title="Heart Rate Variability Biofeedback to Treat Anxiety in Young People With Autism Spectrum Disorder: Findings From a Home-Based Pilot Study", journal="JMIR Form Res", year="2022", month="Aug", day="26", volume="6", number="8", pages="e37994", keywords="autism", keywords="anxiety", keywords="biofeedback", keywords="remote intervention", keywords="mobile phone", abstract="Background: People with autism spectrum disorder (ASD) frequently experience high levels of anxiety. Despite this, many clinical settings do not provide specialist ASD mental health services, and demand for professional support frequently outstrips supply. Across many sectors of health, investigators have explored digital health solutions to mitigate demand and extend the reach of professional practice beyond traditional clinical settings. Objective: This critical appraisal and pilot feasibility study examines heart rate variability (HRV) biofeedback as an approach to help young people with ASD to manage anxiety symptoms outside of formal settings. The aim is to explore the use of portable biofeedback devices to manage anxiety, while also highlighting the risks and benefits of this approach with this population. Methods: We assessed the feasibility of using home-based HRV biofeedback for self-management of anxiety in young people with ASD. We adopted coproduction, involving people with ASD, to facilitate development of the study design. Next, a separate pilot with 20 participants with ASD (n=16, 80\% male participants and n=4, 20\% female participants, aged 13-24 years; IQ>70) assessed adoption and acceptability of HRV biofeedback devices for home use over a 12-week period. Data were collected from both carers and participants through questionnaires and interviews; participants also provided single-lead electrocardiogram recordings as well as daily reports through smartphone on adoption and use of their device. Results: Pre-post participant questionnaires indicated a significant reduction in anxiety in children (t6=2.55; P=.04; Cohen d=0.99) as well as adults (t7=3.95; P=.006; Cohen d=0.54). Participant age was significantly negatively correlated with all HRV variables at baseline, namely high-frequency heart rate variability (HF-HRV: P=.02), the root mean square of successive differences in normal heartbeat contractions (RMSSD: P=.02) and the variability of normal-to-normal interbeat intervals (SDNN: P=.04). At follow-up, only SDNN was significantly negatively correlated with age (P=.05). Levels of ASD symptoms were positively correlated with heart rate both before (P=.04) and after the intervention (P=.01). The majority (311/474, 65.6\%) of reports from participants indicated that the devices helped when used. Difficulties with the use of some devices and problems with home testing of HRV were noted. These initial findings are discussed within the context of the strengths and challenges of remotely delivering a biofeedback intervention for people with ASD. Conclusions: HRV biofeedback devices have shown promise in this pilot study. There is now a need for larger evaluation of biofeedback to determine which delivery methods achieve the greatest effect for people with ASD. Trial Registration: ClinicalTrials.gov NCT04955093; https://clinicaltrials.gov/ct2/show/NCT04955093 ", doi="10.2196/37994", url="https://formative.jmir.org/2022/8/e37994", url="http://www.ncbi.nlm.nih.gov/pubmed/36018712" } @Article{info:doi/10.2196/37901, author="Spain, Debbie and Stewart, R. Gavin and Mason, David and Milner, Victoria and Fairhurst, Bryony and Robinson, Janine and Gillan, Nicola and Ensum, Ian and Stark, Eloise and Happe, Francesca", title="Telehealth Autism Diagnostic Assessments With Children, Young People, and Adults: Qualitative Interview Study With England-Wide Multidisciplinary Health Professionals", journal="JMIR Ment Health", year="2022", month="Jul", day="20", volume="9", number="7", pages="e37901", keywords="autism", keywords="COVID-19 pandemic", keywords="autism diagnostic assessment", keywords="telehealth", keywords="health professionals", keywords="clinical supervision", keywords="training", keywords="COVID-19", abstract="Background: Autism spectrum disorder (hereafter, autism) is a common neurodevelopmental condition. Core traits can range from subtle to severe and fluctuate depending on context. Individuals can present for diagnostic assessments during childhood or adulthood. However, waiting times for assessment are typically lengthy, and many individuals wait months or even years to be seen. Traditionally, there has been a lack of standardization between services regarding how many and which multidisciplinary health professionals are involved in the assessment and the methods (diagnostic tools) that are used. The COVID-19 pandemic has affected routine service provision because of stay-at-home mandates and social distancing guidelines. Autism diagnostic services have had to adapt, such as by switching from conducting assessments in person to doing these fully via telehealth (defined as the use of remote technologies for the provision of health care) or using blended in-person or telehealth methods. Objective: This study explored health professionals' experiences of and perspectives about conducting telehealth autism diagnostic assessments, including barriers and facilitators to this, during the COVID-19 pandemic; potential telehealth training and supervision needs of health professionals; how the quality and effectiveness of telehealth autism diagnostic services can be enhanced; and experiences of delivering postdiagnostic support remotely. Methods: A total of 45 health professionals, working in varied settings across England, participated in one-off, in-depth semistructured qualitative interviews. These were conducted via videoconferencing or telephone. Altogether, participants represented 7 professional disciplines (psychiatry, medicine, psychology, speech and language therapy, occupational therapy, nursing, and social work). The data were then analyzed thematically. Results: Thematic analysis indicated the following 7 themes: practicalities of telehealth, telehealth autism diagnostic assessments, diagnostic conclusions, clinical considerations, postdiagnostic support, future ways of working, and health professionals' experiences and needs. Overall, telehealth autism diagnostic assessments were deemed by many participants to be convenient, flexible, and efficient for some patients, families, and health professionals. However, not all patients could be assessed in this way, for example, because of digital poverty, complex clinical presentation, or concerns about risk and safeguarding. Working remotely encouraged innovation, including the development of novel assessment measures. However, some participants expressed significant concerns about the validity and reliability of remotely assessing social communication conditions. Conclusions: A shift to telehealth meant that autism diagnostic services remained operational during the COVID-19 pandemic. However, this method of working has potentially affected the parity of service, with people presenting with clinical complexity having to potentially wait longer to be seen or given a diagnostic opinion. There is also a lack of standardization in the provision of services. Further research should identify evidence-based ways of enhancing the timeliness, accessibility, and robustness of the autism diagnostic pathway, as well as the validity and reliability of telehealth methods. ", doi="10.2196/37901", url="https://mental.jmir.org/2022/7/e37901", url="http://www.ncbi.nlm.nih.gov/pubmed/35857358" } @Article{info:doi/10.2196/37576, author="Sohl, Kristin and Kilian, Rachel and Brewer Curran, Alicia and Mahurin, Melissa and Nanclares-Nogu{\'e}s, Valeria and Liu-Mayo, Stuart and Salomon, Carmela and Shannon, Jennifer and Taraman, Sharief", title="Feasibility and Impact of Integrating an Artificial Intelligence--Based Diagnosis Aid for Autism Into the Extension for Community Health Outcomes Autism Primary Care Model: Protocol for a Prospective Observational Study", journal="JMIR Res Protoc", year="2022", month="Jul", day="19", volume="11", number="7", pages="e37576", keywords="autism spectrum disorder", keywords="diagnosis", keywords="artificial intelligence", keywords="primary care", keywords="machine learning", keywords="Software as a Medical Device", keywords="mobile phone", abstract="Background: The Extension for Community Health Outcomes (ECHO) Autism Program trains clinicians to screen, diagnose, and care for children with autism spectrum disorder (ASD) in primary care settings. This study will assess the feasibility and impact of integrating an artificial intelligence (AI)--based ASD diagnosis aid (the device) into the existing ECHO Autism Screening Tool for Autism in Toddlers and Young Children (STAT) diagnosis model. The prescription-only Software as a Medical Device, designed for use in children aged 18 to 72 months at risk for developmental delay, produces ASD diagnostic recommendations after analyzing behavioral features from 3 distinct inputs: a caregiver questionnaire, 2 short home videos analyzed by trained video analysts, and a health care provider questionnaire. The device is not a stand-alone diagnostic and should be used in conjunction with clinical judgment. Objective: This study aims to assess the feasibility and impact of integrating an AI-based ASD diagnosis aid into the ECHO Autism STAT diagnosis model. The time from initial ECHO Autism clinician concern to ASD diagnosis is the primary end point. Secondary end points include the time from initial caregiver concern to ASD diagnosis, time from diagnosis to treatment initiation, and clinician and caregiver experience of device use as part of the ASD diagnostic journey. Methods: Research participants for this prospective observational study will be patients suspected of having ASD (aged 18-72 months) and their caregivers and up to 15 trained ECHO Autism clinicians recruited by the ECHO Autism Communities research team from across rural and suburban areas of the United States. Clinicians will provide routine clinical care and conduct best practice ECHO Autism diagnostic evaluations in addition to prescribing the device. Outcome data will be collected via a combination of electronic questionnaires, reviews of standard clinical care records, and analysis of device outputs. The expected study duration is no more than 12 months. The study was approved by the institutional review board of the University of Missouri-Columbia (institutional review board--assigned project number 2075722). Results: Participant recruitment began in April 2022. As of June 2022, a total of 41 participants have been enrolled. Conclusions: This prospective observational study will be the first to evaluate the use of a novel AI-based ASD diagnosis aid as part of a real-world primary care diagnostic pathway. If device integration into primary care proves feasible and efficacious, prolonged delays between the first ASD concern and eventual diagnosis may be reduced. Streamlining primary care ASD diagnosis could potentially reduce the strain on specialty services and allow a greater proportion of children to commence early intervention during a critical neurodevelopmental window. Trial Registration: ClinicalTrials.gov NCT05223374; https://clinicaltrials.gov/ct2/show/NCT05223374 International Registered Report Identifier (IRRID): PRR1-10.2196/37576 ", doi="10.2196/37576", url="https://www.researchprotocols.org/2022/7/e37576", url="http://www.ncbi.nlm.nih.gov/pubmed/35852831" } @Article{info:doi/10.2196/32912, author="Gauld, Christophe and Maquet, Julien and Micoulaud-Franchi, Jean-Arthur and Dumas, Guillaume", title="Popular and Scientific Discourse on Autism: Representational Cross-Cultural Analysis of Epistemic Communities to Inform Policy and Practice", journal="J Med Internet Res", year="2022", month="Jun", day="15", volume="24", number="6", pages="e32912", keywords="autism spectrum disorder", keywords="Twitter", keywords="natural language processing", keywords="network analysis", keywords="popular understanding of illness", keywords="knowledge translation", keywords="autism", keywords="tweets", keywords="psychiatry", keywords="text mining", abstract="Background: Social media provide a window onto the circulation of ideas in everyday folk psychiatry, revealing the themes and issues discussed both by the public and by various scientific communities. Objective: This study explores the trends in health information about autism spectrum disorder within popular and scientific communities through the systematic semantic exploration of big data gathered from Twitter and PubMed. Methods: First, we performed a natural language processing by text-mining analysis and with unsupervised (machine learning) topic modeling on a sample of the last 10,000 tweets in English posted with the term \#autism (January 2021). We built a network of words to visualize the main dimensions representing these data. Second, we performed precisely the same analysis with all the articles using the term ``autism'' in PubMed without time restriction. Lastly, we compared the results of the 2 databases. Results: We retrieved 121,556 terms related to autism in 10,000 tweets and 5.7x109 terms in 57,121 biomedical scientific articles. The 4 main dimensions extracted from Twitter were as follows: integration and social support, understanding and mental health, child welfare, and daily challenges and difficulties. The 4 main dimensions extracted from PubMed were as follows: diagnostic and skills, research challenges, clinical and therapeutical challenges, and neuropsychology and behavior. Conclusions: This study provides the first systematic and rigorous comparison between 2 corpora of interests, in terms of lay representations and scientific research, regarding the significant increase in information available on autism spectrum disorder and of the difficulty to connect fragments of knowledge from the general population. The results suggest a clear distinction between the focus of topics used in the social media and that of scientific communities. This distinction highlights the importance of knowledge mobilization and exchange to better align research priorities with personal concerns and to address dimensions of well-being, adaptation, and resilience. Health care professionals and researchers can use these dimensions as a framework in their consultations to engage in discussions on issues that matter to beneficiaries and develop clinical approaches and research policies in line with these interests. Finally, our study can inform policy makers on the health and social needs and concerns of individuals with autism and their caregivers, especially to define health indicators based on important issues for beneficiaries. ", doi="10.2196/32912", url="https://www.jmir.org/2022/6/e32912", url="http://www.ncbi.nlm.nih.gov/pubmed/35704359" } @Article{info:doi/10.2196/35960, author="Lee, JooHyun and Lee, Seon Tae and Lee, SeungWoo and Jang, JiHye and Yoo, SuYoung and Choi, YeJin and Park, Rang Yu", title="Development and Application of a Metaverse-Based Social Skills Training Program for Children With Autism Spectrum Disorder to Improve Social Interaction: Protocol for a Randomized Controlled Trial", journal="JMIR Res Protoc", year="2022", month="Jun", day="8", volume="11", number="6", pages="e35960", keywords="metaverse", keywords="social skills", keywords="Autism", keywords="ASD", keywords="digital therapy", keywords="Roblox", keywords="RCT", keywords="social skill", keywords="social interaction", keywords="human interaction", keywords="child", keywords="youth", keywords="development", keywords="wearable", keywords="biometric", keywords="communication", keywords="digital technology", keywords="eHealth", keywords="mhealth", keywords="stress", keywords="emotional change", keywords="online platform", abstract="Background: Autism spectrum disorder (ASD) is characterized by abnormalities in social communication and limited and repetitive behavioral patterns. Children with ASD who lack social communication skills will eventually not interact with others and will lack peer relationships when compared to ordinary people. Thus, it is necessary to develop a program to improve social communication abilities using digital technology in people with ASD. Objective: We intend to develop and apply a metaverse-based child social skills training program aimed at improving the social interaction abilities of children with ASD aged 7-12 years. We plan to compare and analyze the biometric information collected through wearable devices when applying the metaverse-based social skills training program to evaluate emotional changes in children with ASD in stressful situations. Methods: This parallel randomized controlled study will be conducted on children aged 7-12 years diagnosed with ASD. A metaverse-based social skills training program using digital technology will be administered to children who voluntarily wish to participate in the research with consent from their legal guardians. The treatment group will participate in the metaverse-based social skills training program developed by this research team once a week for 60 minutes per session for 4 weeks. The control group will not intervene during the experiment. The treatment group will use wearable devices during the experiment to collect real-time biometric information. Results: The study is expected to recruit and enroll participants in March 2022. After registering the participants, the study will be conducted from March 2022 to May 2022. This research will be jointly conducted by Yonsei University and Dobrain Co Ltd. Children participating in the program will use the internet-based platform. Conclusions: The metaverse-based Program for the Education and Enrichment of Relational Skills (PEERS) will be effective in improving the social skills of children with ASD, similar to the offline PEERS program. The metaverse-based PEERS program offers excellent accessibility and is inexpensive because it can be administered at home; thus, it is expected to be effective in many children with ASD. If a method can be applied to detect children's emotional changes early using biometric information collected through wearable devices, then emotional changes such as anxiety and anger can be alleviated in advance, thus reducing issues in children with ASD. Trial Registration: Clinical Research Information Service KCT0006859; https://tinyurl.com/4r3k7cmj International Registered Report Identifier (IRRID): PRR1-10.2196/35960 ", doi="10.2196/35960", url="https://www.researchprotocols.org/2022/6/e35960", url="http://www.ncbi.nlm.nih.gov/pubmed/35675112" } @Article{info:doi/10.2196/33771, author="Lakkapragada, Anish and Kline, Aaron and Mutlu, Cezmi Onur and Paskov, Kelley and Chrisman, Brianna and Stockham, Nathaniel and Washington, Peter and Wall, Paul Dennis", title="The Classification of Abnormal Hand Movement to Aid in Autism Detection: Machine Learning Study", journal="JMIR Biomed Eng", year="2022", month="Jun", day="6", volume="7", number="1", pages="e33771", keywords="deep learning", keywords="machine learning", keywords="activity recognition", keywords="applied machine learning", keywords="landmark detection", keywords="autism", keywords="diagnosis", keywords="health informatics", keywords="detection", keywords="feasibility", keywords="video", keywords="model", keywords="neural network", abstract="Background: A formal autism diagnosis can be an inefficient and lengthy process. Families may wait several months or longer before receiving a diagnosis for their child despite evidence that earlier intervention leads to better treatment outcomes. Digital technologies that detect the presence of behaviors related to autism can scale access to pediatric diagnoses. A strong indicator of the presence of autism is self-stimulatory behaviors such as hand flapping. Objective: This study aims to demonstrate the feasibility of deep learning technologies for the detection of hand flapping from unstructured home videos as a first step toward validation of whether statistical models coupled with digital technologies can be leveraged to aid in the automatic behavioral analysis of autism. To support the widespread sharing of such home videos, we explored privacy-preserving modifications to the input space via conversion of each video to hand landmark coordinates and measured the performance of corresponding time series classifiers. Methods: We used the Self-Stimulatory Behavior Dataset (SSBD) that contains 75 videos of hand flapping, head banging, and spinning exhibited by children. From this data set, we extracted 100 hand flapping videos and 100 control videos, each between 2 to 5 seconds in duration. We evaluated five separate feature representations: four privacy-preserved subsets of hand landmarks detected by MediaPipe and one feature representation obtained from the output of the penultimate layer of a MobileNetV2 model fine-tuned on the SSBD. We fed these feature vectors into a long short-term memory network that predicted the presence of hand flapping in each video clip. Results: The highest-performing model used MobileNetV2 to extract features and achieved a test F1 score of 84 (SD 3.7; precision 89.6, SD 4.3 and recall 80.4, SD 6) using 5-fold cross-validation for 100 random seeds on the SSBD data (500 total distinct folds). Of the models we trained on privacy-preserved data, the model trained with all hand landmarks reached an F1 score of 66.6 (SD 3.35). Another such model trained with a select 6 landmarks reached an F1 score of 68.3 (SD 3.6). A privacy-preserved model trained using a single landmark at the base of the hands and a model trained with the average of the locations of all the hand landmarks reached an F1 score of 64.9 (SD 6.5) and 64.2 (SD 6.8), respectively. Conclusions: We created five lightweight neural networks that can detect hand flapping from unstructured videos. Training a long short-term memory network with convolutional feature vectors outperformed training with feature vectors of hand coordinates and used almost 900,000 fewer model parameters. This study provides the first step toward developing precise deep learning methods for activity detection of autism-related behaviors. ", doi="10.2196/33771", url="https://biomedeng.jmir.org/2022/1/e33771", url="http://www.ncbi.nlm.nih.gov/pubmed/27666281" } @Article{info:doi/10.2196/36094, author="Guemghar, Imane and Pires de Oliveira Padilha, Paula and Abdel-Baki, Amal and Jutras-Aswad, Didier and Paquette, Jesseca and Pomey, Marie-Pascale", title="Social Robot Interventions in Mental Health Care and Their Outcomes, Barriers, and Facilitators: Scoping Review", journal="JMIR Ment Health", year="2022", month="Apr", day="19", volume="9", number="4", pages="e36094", keywords="social robots", keywords="socially assistive robots", keywords="SARs", keywords="mental health", keywords="mental health services", keywords="dementia", keywords="autism spectrum disorder", keywords="schizophrenia", keywords="depression", keywords="scoping review", abstract="Background: The use of social robots as innovative therapeutic tools has been increasingly explored in recent years in an effort to address the growing need for alternative intervention modalities in mental health care. Objective: The aim of this scoping review was to identify and describe social robot interventions in mental health facilities and to highlight their outcomes as well as the barriers and facilitators to their implementation. Methods: A scoping review of the literature published since 2015 was conducted using the Arksey and O'Malley's framework. The MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and PsycINFO databases were searched, and 2239 papers were retrieved. The papers included were primary empirical studies published in peer-reviewed literature. Eligible studies were set in mental health facilities and they included participants with a known mental health disorder. The methodological quality of the included papers was also assessed using the Mixed Methods Appraisal Tool. Results: A total of 30 papers met the eligibility criteria for this review. Studies involved participants with dementia, cognitive impairment, schizophrenia, depression, autism spectrum disorder, attention-deficit hyperactivity disorder, and an intellectual disability. The outcomes studied included engagement, social interaction, emotional state, agitation, behavior, and quality of life. Conclusions: The methodological weaknesses of the studies conducted this far and the lack of diversity in the conditions studied limit the generalizability of the results. However, despite the presence of certain barriers to their implementation (eg, technical problems, unsuitable environment, staff resistance), social robot interventions generally show positive effects on patients with mental health disorders. Studies of stronger methodological quality are needed to further understand the benefits and the place of social robots in mental health care. ", doi="10.2196/36094", url="https://mental.jmir.org/2022/4/e36094", url="http://www.ncbi.nlm.nih.gov/pubmed/35438639" } @Article{info:doi/10.2196/28276, author="Ntalindwa, Theoneste and Nduwingoma, Mathias and Uworwabayeho, Alphonse and Nyirahabimana, Pascasie and Karangwa, Evariste and Rashid Soron, Tanjir and Westin, Thomas and Karunaratne, Thashmee and Hansson, Henrik", title="Adapting the Use of Digital Content to Improve the Learning of Numeracy Among Children With Autism Spectrum Disorder in Rwanda: Thematic Content Analysis Study", journal="JMIR Serious Games", year="2022", month="Apr", day="19", volume="10", number="2", pages="e28276", keywords="autism", keywords="learning", keywords="ICT", keywords="e-learning", keywords="education", keywords="children", keywords="ASD", keywords="teaching", keywords="teachers", keywords="communication", keywords="communication technology", keywords="online content", keywords="Rwanda", keywords="gamification", keywords="school", keywords="school-age children", keywords="behavior", abstract="Background: Many teachers consider it challenging to teach children with autism spectrum disorder (ASD) in an inclusive classroom due to their unique needs and challenges. The integration of information communication technology (ICT) in the education system allows children with ASD to improve their learning. However, these ICT tools should meet their needs to lead a productive life. Objective: This study aimed to examine the possibilities of re-creating and adapting digital content to improve the learning of numeracy among children with ASD in inclusive school settings. Methods: We conducted 7 focus group discussions (FGDs) with 56 teachers from 7 schools and 14 parents from April to November 2019. Each of the FGDs took around 1 hour. Two clustered sets of questions were used: (1) general knowledge about teaching children with ASD and (2) analysis of selected online educational video content of early math (specifically, counting numbers). The researchers used video to understand current methodologies used in teaching children with ASD, possibilities of adaptation of the content in the current teaching environment, future challenges when the content is adapted, and possible solutions to overcome those challenges. All data, including audio recordings, field notes, and participants' comments, were transcribed, recorded, and analyzed following the steps recommended in qualitative data analysis. Results: The researchers identified ten themes from the analysis of the data: (1) awareness of the existence of ASD among children in schools and the community, (2) acceptance of children with ASD in an inclusive classroom and the community, (3) methods and models used when teaching children with ASD, (4)realia used to improve the learning of children with ASD, (5) the design of educational digital content, (6) the accessibility of online educational content, (7) quality of the content of the educational multimedia, (8) the opportunity of using the translated and re-created content inside and outside the classroom, (9) the relevance of the digital content in the Rwandan educational system, and (10) enhancement of the accessibility and quality of the digital content. We found that participants assumed that the content translation, gamification, and re-creation would help teach children with ASD. Moreover, they recommended contextualizing the content, increasing access to digital devices, and further research in the education of different subjects. Conclusions: Although many studies have identified the possibilities of using ICT to support children with ASD, few studies have documented the possibilities of integrating the existing technologies tested in the international community. This study is charting new territory to investigate online content to suit the context of schools. This study recommends further exploration of possible methodologies, such as applied behavior analysis or verbal behavior therapy, and the development of contextualized technologies that respond to the educational needs of children with ASD. ", doi="10.2196/28276", url="https://games.jmir.org/2022/2/e28276", url="http://www.ncbi.nlm.nih.gov/pubmed/35438638" } @Article{info:doi/10.2196/35406, author="Chi, A. Nathan and Washington, Peter and Kline, Aaron and Husic, Arman and Hou, Cathy and He, Chloe and Dunlap, Kaitlyn and Wall, P. Dennis", title="Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study", journal="JMIR Pediatr Parent", year="2022", month="Apr", day="14", volume="5", number="2", pages="e35406", keywords="autism", keywords="mHealth", keywords="machine learning", keywords="artificial intelligence", keywords="speech", keywords="audio", keywords="child", keywords="digital data", keywords="mobile app", keywords="diagnosis", abstract="Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that results in altered behavior, social development, and communication patterns. In recent years, autism prevalence has tripled, with 1 in 44 children now affected. Given that traditional diagnosis is a lengthy, labor-intensive process that requires the work of trained physicians, significant attention has been given to developing systems that automatically detect autism. We work toward this goal by analyzing audio data, as prosody abnormalities are a signal of autism, with affected children displaying speech idiosyncrasies such as echolalia, monotonous intonation, atypical pitch, and irregular linguistic stress patterns. Objective: We aimed to test the ability for machine learning approaches to aid in detection of autism in self-recorded speech audio captured from children with ASD and neurotypical (NT) children in their home environments. Methods: We considered three methods to detect autism in child speech: (1) random forests trained on extracted audio features (including Mel-frequency cepstral coefficients); (2) convolutional neural networks trained on spectrograms; and (3) fine-tuned wav2vec 2.0---a state-of-the-art transformer-based speech recognition model. We trained our classifiers on our novel data set of cellphone-recorded child speech audio curated from the Guess What? mobile game, an app designed to crowdsource videos of children with ASD and NT children in a natural home environment. Results: The random forest classifier achieved 70\% accuracy, the fine-tuned wav2vec 2.0 model achieved 77\% accuracy, and the convolutional neural network achieved 79\% accuracy when classifying children's audio as either ASD or NT. We used 5-fold cross-validation to evaluate model performance. Conclusions: Our models were able to predict autism status when trained on a varied selection of home audio clips with inconsistent recording qualities, which may be more representative of real-world conditions. The results demonstrate that machine learning methods offer promise in detecting autism automatically from speech without specialized equipment. ", doi="10.2196/35406", url="https://pediatrics.jmir.org/2022/2/e35406", url="http://www.ncbi.nlm.nih.gov/pubmed/35436234" } @Article{info:doi/10.2196/31269, author="van Eijndhoven, Philip and Collard, Rose and Vrijsen, Janna and Geurts, M. Dirk E. and Vasquez, Arias Alejandro and Schellekens, Arnt and van den Munckhof, Eva and Brolsma, Sophie and Duyser, Fleur and Bergman, Annemiek and van Oort, Jasper and Tendolkar, Indira and Schene, Aart", title="Measuring Integrated Novel Dimensions in Neurodevelopmental and Stress-Related Mental Disorders (MIND-SET): Protocol for a Cross-sectional Comorbidity Study From a Research Domain Criteria Perspective", journal="JMIRx Med", year="2022", month="Mar", day="29", volume="3", number="1", pages="e31269", keywords="psychiatry", keywords="mental health", keywords="psychiatric disorders", keywords="neuropsychology", keywords="stress", keywords="comorbidity", abstract="Background: It is widely acknowledged that comorbidity between psychiatric disorders is common. Shared and diverse underpinnings of psychiatric disorders cannot be systematically understood based on symptom-based categories of mental disorders, which map poorly onto pathophysiological mechanisms. In the Measuring Integrated Novel Dimensions in Neurodevelopmental and Stress-Related Mental Disorders (MIND-SET) study, we make use of current concepts of comorbidity that transcend the current diagnostic categories. We test this approach to psychiatric problems in patients with frequently occurring psychiatric disorders and their comorbidities (excluding psychosis). Objective: The main aim of the MIND-SET project is to determine the shared and specific mechanisms of neurodevelopmental and stress-related psychiatric disorders at different observational levels. Methods: This is an observational cross-sectional study. Data from different observational levels as defined in the Research Domain Criteria (genetics, physiology, neuropsychology, system-level neuroimaging, behavior, self-report, and experimental neurocognitive paradigms) are collected over four time points. Included are adult (aged ?18 years), nonpsychotic, psychiatric patients with a clinical diagnosis of a stress-related disorder (mood disorder, anxiety disorder, or substance use disorder) or a neurodevelopmental disorder (autism spectrum disorder or attention-deficit/hyperactivity disorder). Individuals with no current or past psychiatric diagnosis are included as neurotypical controls. Data collection started in June 2016 with the aim to include a total of 650 patients and 150 neurotypical controls by 2021. The data collection procedure includes online questionnaires and three subsequent sessions with (1) standardized clinical examination, physical examination, and blood sampling; (2) psychological constructs, neuropsychological tests, and biological marker sampling; and (3) neuroimaging measures. Results: We aim to include a total of 650 patients and 150 neurotypical control participants in the time period between 2016 and 2022. In October 2021, we are at 95\% of our target. Conclusions: The MIND-SET study enables us to investigate the mechanistic underpinnings of nonpsychotic psychiatric disorders transdiagnostically. We will identify both shared and disorder-specific markers at different observational levels that can be used as targets for future diagnostic and treatment approaches. ", doi="10.2196/31269", url="https://med.jmirx.org/2022/1/e31269", url="http://www.ncbi.nlm.nih.gov/pubmed/37725542" } @Article{info:doi/10.2196/33560, author="Welch, Victoria and Wy, Joshua Tom and Ligezka, Anna and Hassett, C. Leslie and Croarkin, E. Paul and Athreya, P. Arjun and Romanowicz, Magdalena", title="Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry: Scoping Review", journal="J Med Internet Res", year="2022", month="Mar", day="14", volume="24", number="3", pages="e33560", keywords="mobile computing", keywords="artificial intelligence", keywords="wearable technologies", keywords="child psychiatry", abstract="Background: Mental health disorders are a leading cause of medical disabilities across an individual's lifespan. This burden is particularly substantial in children and adolescents because of challenges in diagnosis and the lack of precision medicine approaches. However, the widespread adoption of wearable devices (eg, smart watches) that are conducive for artificial intelligence applications to remotely diagnose and manage psychiatric disorders in children and adolescents is promising. Objective: This study aims to conduct a scoping review to study, characterize, and identify areas of innovations with wearable devices that can augment current in-person physician assessments to individualize diagnosis and management of psychiatric disorders in child and adolescent psychiatry. Methods: This scoping review used information from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive search of several databases from 2011 to June 25, 2021, limited to the English language and excluding animal studies, was conducted. The databases included Ovid MEDLINE and Epub ahead of print, in-process and other nonindexed citations, and daily; Ovid Embase; Ovid Cochrane Central Register of Controlled Trials; Ovid Cochrane Database of Systematic Reviews; Web of Science; and Scopus. Results: The initial search yielded 344 articles, from which 19 (5.5\%) articles were left on the final source list for this scoping review. Articles were divided into three main groups as follows: studies with the main focus on autism spectrum disorder, attention-deficit/hyperactivity disorder, and internalizing disorders such as anxiety disorders. Most of the studies used either cardio-fitness chest straps with electrocardiogram sensors or wrist-worn biosensors, such as watches by Fitbit. Both allowed passive data collection of the physiological signals. Conclusions: Our scoping review found a large heterogeneity of methods and findings in artificial intelligence studies in child psychiatry. Overall, the largest gap identified in this scoping review is the lack of randomized controlled trials, as most studies available were pilot studies and feasibility trials. ", doi="10.2196/33560", url="https://www.jmir.org/2022/3/e33560", url="http://www.ncbi.nlm.nih.gov/pubmed/35285812" } @Article{info:doi/10.2196/32752, author="Gabarron, Elia and Dechsling, Anders and Skafle, Ingjerd and Nordahl-Hansen, Anders", title="Discussions of Asperger Syndrome on Social Media: Content and Sentiment Analysis on Twitter", journal="JMIR Form Res", year="2022", month="Mar", day="7", volume="6", number="3", pages="e32752", keywords="social media", keywords="autism spectrum disorder", keywords="health literacy", keywords="famous persons", keywords="Asperger", keywords="Elon Musk", keywords="twitter", keywords="tweets", keywords="mental health", keywords="autism", keywords="sentiment analysis", abstract="Background: On May 8, 2021, Elon Musk, a well-recognized entrepreneur and business magnate, revealed on a popular television show that he has Asperger syndrome. Research has shown that people's perceptions of a condition are modified when influential individuals in society publicly disclose their diagnoses. It was anticipated that Musk's disclosure would contribute to discussions on the internet about the syndrome, and also to a potential change in the perception of this condition. Objective: The objective of this study was to compare the types of information contained in popular tweets about Asperger syndrome as well as their engagement and sentiment before and after Musk's disclosure. Methods: We extracted tweets that were published 1 week before and after Musk's disclosure that had received >30 likes and included the terms ``Aspergers'' or ``Aspie.'' The content of each post was classified by 2 independent coders as to whether the information provided was valid, contained misinformation, or was neutral. Furthermore, we analyzed the engagement on these posts and the expressed sentiment by using the AFINN sentiment analysis tool. Results: We extracted a total of 227 popular tweets (34 posted the week before Musk's announcement and 193 posted the week after). We classified 210 (92.5\%) of the tweets as neutral, 13 (5.7\%) tweets as informative, and 4 (1.8\%) as containing misinformation. Both informative and misinformative tweets were posted after Musk's disclosure. Popular tweets posted before Musk's disclosure were significantly more engaging (received more comments, retweets, and likes) than the tweets posted the week after. We did not find a significant difference in the sentiment expressed in the tweets posted before and after the announcement. Conclusions: The use of social media platforms by health authorities, autism associations, and other stakeholders has the potential to increase the awareness and acceptance of knowledge about autism and Asperger syndrome. When prominent figures disclose their diagnoses, the number of posts about their particular condition tends to increase and thus promote a potential opportunity for greater outreach to the general public about that condition. ", doi="10.2196/32752", url="https://formative.jmir.org/2022/3/e32752", url="http://www.ncbi.nlm.nih.gov/pubmed/35254265" } @Article{info:doi/10.2196/31830, author="Varma, Maya and Washington, Peter and Chrisman, Brianna and Kline, Aaron and Leblanc, Emilie and Paskov, Kelley and Stockham, Nate and Jung, Jae-Yoon and Sun, Woo Min and Wall, P. Dennis", title="Identification of Social Engagement Indicators Associated With Autism Spectrum Disorder Using a Game-Based Mobile App: Comparative Study of Gaze Fixation and Visual Scanning Methods", journal="J Med Internet Res", year="2022", month="Feb", day="15", volume="24", number="2", pages="e31830", keywords="mobile health", keywords="autism spectrum disorder", keywords="social phenotyping", keywords="computer vision", keywords="gaze", keywords="mobile diagnostics", keywords="pattern recognition", keywords="autism", keywords="diagnostic", keywords="pattern", keywords="engagement", keywords="gaming", keywords="app", keywords="insight", keywords="vision", keywords="video", abstract="Background: Autism spectrum disorder (ASD) is a widespread neurodevelopmental condition with a range of potential causes and symptoms. Standard diagnostic mechanisms for ASD, which involve lengthy parent questionnaires and clinical observation, often result in long waiting times for results. Recent advances in computer vision and mobile technology hold potential for speeding up the diagnostic process by enabling computational analysis of behavioral and social impairments from home videos. Such techniques can improve objectivity and contribute quantitatively to the diagnostic process. Objective: In this work, we evaluate whether home videos collected from a game-based mobile app can be used to provide diagnostic insights into ASD. To the best of our knowledge, this is the first study attempting to identify potential social indicators of ASD from mobile phone videos without the use of eye-tracking hardware, manual annotations, and structured scenarios or clinical environments. Methods: Here, we used a mobile health app to collect over 11 hours of video footage depicting 95 children engaged in gameplay in a natural home environment. We used automated data set annotations to analyze two social indicators that have previously been shown to differ between children with ASD and their neurotypical (NT) peers: (1) gaze fixation patterns, which represent regions of an individual's visual focus and (2) visual scanning methods, which refer to the ways in which individuals scan their surrounding environment. We compared the gaze fixation and visual scanning methods used by children during a 90-second gameplay video to identify statistically significant differences between the 2 cohorts; we then trained a long short-term memory (LSTM) neural network to determine if gaze indicators could be predictive of ASD. Results: Our results show that gaze fixation patterns differ between the 2 cohorts; specifically, we could identify 1 statistically significant region of fixation (P<.001). In addition, we also demonstrate that there are unique visual scanning patterns that exist for individuals with ASD when compared to NT children (P<.001). A deep learning model trained on coarse gaze fixation annotations demonstrates mild predictive power in identifying ASD. Conclusions: Ultimately, our study demonstrates that heterogeneous video data sets collected from mobile devices hold potential for quantifying visual patterns and providing insights into ASD. We show the importance of automated labeling techniques in generating large-scale data sets while simultaneously preserving the privacy of participants, and we demonstrate that specific social engagement indicators associated with ASD can be identified and characterized using such data. ", doi="10.2196/31830", url="https://www.jmir.org/2022/2/e31830", url="http://www.ncbi.nlm.nih.gov/pubmed/35166683" } @Article{info:doi/10.2196/33123, author="Hijab, Fadi Mohamad Hassan and Al-Thani, Dena and Banire, Bilikis", title="A Multimodal Messaging App (MAAN) for Adults With Autism Spectrum Disorder: Mixed Methods Evaluation Study", journal="JMIR Form Res", year="2021", month="Dec", day="7", volume="5", number="12", pages="e33123", keywords="autism", keywords="assistive technology", keywords="mobile app", keywords="social and communication skills", abstract="Background: Individuals with autism spectrum disorder (ASD) often exhibit difficulties in social and communication skills. For more than 30 years, specialists, parents, and caregivers have used techniques, such as applied behavioral analysis, augmentative and alternative communication, and the picture exchange communication system to support the social and communication skills of people with ASD. Even though there are many techniques devised to enhance communication, these techniques are not considered in existing social media apps for people with ASD. Objective: This study aimed to investigate the effect of adding accessibility features, such as text-to-speech (TTS), speech-to-text (STT), and communication symbols (CS), to a messaging app (MAAN). We hypothesized that these accessibility features can enhance the social and communication skills of adults with ASD. We also hypothesized that usage of this app can reduce social loneliness in adults with ASD. Methods: Semistructured interviews were conducted with 5 experts working in fields related to ASD to help design the app. Seven adults with ASD participated in the study for a period of 10 to 16 weeks. Data logs of participants' interactions with the app were collected. Additionally, 6 participants' parents and 1 caregiver were asked to complete a short version of the Social and Emotional Loneliness Scale for Adults (SELSA-S) questionnaire to compare pre-post study results. The Mobile Application Rating Scale: user version questionnaire was also used to evaluate the app's usability. Following the study, interviews were conducted with participants to discuss their experiences with the app. Results: The SELSA-S questionnaire results showed no change in the family subscale; however, the social loneliness subscale showed a difference between prestudy and poststudy. The Wilcoxon signed-rank test indicated that poststudy SELSA-S results were statistically significantly higher than prestudy results (z=?2.047; P=.04). Point-biserial correlation indicated that the SELSA-S rate of change was strongly related to usage of the TTS feature (r=0.708; P=.04) and CS feature (r=?0.917; P=.002), and moderately related to usage of the STT feature (r=0.428; P=.17). Lastly, we adopted grounded theory to analyze the interview data, and the following 5 categories emerged: app support, feature relevance, user interface design, overall feedback, and recommendations. Conclusions: This study discusses the potential for improving the communication skills of adults with ASD through special features in mobile messaging apps. The developed app aims to support the inclusion and independent life of adults with ASD. The study results showed the importance of using TTS, STT, and CS features to enhance social and communication skills, as well as reduce social loneliness in adults with ASD. ", doi="10.2196/33123", url="https://formative.jmir.org/2021/12/e33123", url="http://www.ncbi.nlm.nih.gov/pubmed/34878998" } @Article{info:doi/10.2196/28196, author="Aylward, Marion Shannon and Farrell, Alison and Walsh, Anna and Godwin, Marshall and Chafe, Roger and Asghari, Shabnam", title="Quality of Primary Care for the Adult Population With Autism Spectrum Disorder: Protocol for a Scoping Review", journal="JMIR Res Protoc", year="2021", month="Nov", day="19", volume="10", number="11", pages="e28196", keywords="autism spectrum disorder", keywords="primary care", keywords="family physician", keywords="quality", keywords="scoping review", keywords="protocol", abstract="Background: A strong primary care system is vital to overall health. Research on the primary care of people with autism spectrum disorder (ASD) has mostly focused on children. A synthesis of the existing literature related to the quality of primary care for the adult population with ASD would elucidate what is known about the topic as well as inform future research and clinical practice. Objective: The purpose of our scoping review is to describe what is known about the quality of primary care for adults with ASD and identify knowledge gaps. Methods: Prior to beginning the literature search, we reviewed literature related to defining both primary care and primary care quality to establish the context and concept of the research question. The search strategy was designed and executed by a research librarian. The MEDLINE, CINAHL, EMBASE, PsycINFO, and ProQuest Dissertations and Theses databases were searched for relevant literature. Grey literature will include relevant reports from government websites and associations with a focus on ASD. Two members of the research team will independently screen the academic and grey literature. Quantitative, qualitative, or mixed methods study designs involving the quality of primary care services or patient-centered care for adults with ASD are eligible for inclusion in our scoping review. Studies that make it past the full-text review will undergo data extraction and quality appraisal by 2 independent reviewers. The data extraction results will be presented in a tabular format to clearly present what is known about the quality of primary care for adults with ASD; this table will be accompanied by a narrative synthesis. Literature selected for extraction will be coded for themes, which will form the basis of a thematic synthesis. The scoping review will follow the guidance proposed by the Joanna Briggs Institute. Results: The search of electronic databases was conducted in October 2020, and it returned 2820 results. This research is still in progress. The results from our scoping review are expected to be available by fall 2021. Conclusions: The results from our scoping review will be useful for guiding future research on the quality of primary care for adults with ASD. International Registered Report Identifier (IRRID): PRR1-10.2196/28196 ", doi="10.2196/28196", url="https://www.researchprotocols.org/2021/11/e28196", url="http://www.ncbi.nlm.nih.gov/pubmed/34806989" } @Article{info:doi/10.2196/27706, author="Cilia, Federica and Carette, Romuald and Elbattah, Mahmoud and Dequen, Gilles and Gu{\'e}rin, Jean-Luc and Bosche, J{\'e}r{\^o}me and Vandromme, Luc and Le Driant, Barbara", title="Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning", journal="JMIR Hum Factors", year="2021", month="Oct", day="25", volume="8", number="4", pages="e27706", keywords="autism spectrum disorder", keywords="screening", keywords="eye tracking", keywords="data visualization", keywords="machine learning", keywords="deep learning", keywords="AI", keywords="ASS", keywords="artificial intelligence", keywords="ML", keywords="adolescent", keywords="diagnosis", abstract="Background: The early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which can make the identification of ASD even more difficult. Although diagnosis tests are largely developed by experts, they are still subject to human bias. In this respect, computer-assisted technologies can play a key role in supporting the screening process. Objective: This paper follows on the path of using eye tracking as an integrated part of screening assessment in ASD based on the characteristic elements of the eye gaze. This study adds to the mounting efforts in using eye tracking technology to support the process of ASD screening Methods: The proposed approach basically aims to integrate eye tracking with visualization and machine learning. A group of 59 school-aged participants took part in the study. The participants were invited to watch a set of age-appropriate photographs and videos related to social cognition. Initially, eye-tracking scanpaths were transformed into a visual representation as a set of images. Subsequently, a convolutional neural network was trained to perform the image classification task. Results: The experimental results demonstrated that the visual representation could simplify the diagnostic task and also attained high accuracy. Specifically, the convolutional neural network model could achieve a promising classification accuracy. This largely suggests that visualizations could successfully encode the information of gaze motion and its underlying dynamics. Further, we explored possible correlations between the autism severity and the dynamics of eye movement based on the maximal information coefficient. The findings primarily show that the combination of eye tracking, visualization, and machine learning have strong potential in developing an objective tool to assist in the screening of ASD. Conclusions: Broadly speaking, the approach we propose could be transferable to screening for other disorders, particularly neurodevelopmental disorders. ", doi="10.2196/27706", url="https://humanfactors.jmir.org/2021/4/e27706", url="http://www.ncbi.nlm.nih.gov/pubmed/34694238" } @Article{info:doi/10.2196/20892, author="Leung, Shun Phil Wai and Li, Xin Shirley and Tsang, Oi Carmen Sze and Chow, Ching Bellavista Long and Wong, Wai William Chi", title="Effectiveness of Using Mobile Technology to Improve Cognitive and Social Skills Among Individuals With Autism Spectrum Disorder: Systematic Literature Review", journal="JMIR Ment Health", year="2021", month="Sep", day="28", volume="8", number="9", pages="e20892", keywords="autism spectrum disorder", keywords="mobile devices", keywords="systematic review", keywords="randomized controlled trial", keywords="social skills", keywords="cognitive skills", abstract="Background: Mobile technology has become a necessity in the lives of people in many countries. Its characteristics and advantages also make it a potential medium of intervention for people with autism spectrum disorder (ASD). Objective: The objective of this review was to evaluate previous evidence, obtained in randomized controlled trials (RCTs), on the effectiveness of using mobile devices as the medium of intervention targeting social and cognitive skills among individuals with ASD. Methods: Literature search was conducted on electronic databases including Medline, PsycInfo, PsycArticles, Education Resources Information Centre, and Social Science Citation Index. Only RCTs published in English and after year 2000 were included for this review. Data extraction was carried out by 2 independent reviewers using constant comparative methods. Results: Totally 10 RCTs were identified. Most of the findings indicated that mobile devices could be an effective medium of intervention for people with ASD, among which 6 indicated significant intervention effects and 2 showed mixed findings. Effective intervention was more likely to be achieved in the studies that recruited older participants (aged over 9 years), targeting practical skills that could be readily applied in real life, or using pictures or materials that were highly relevant in daily life in the apps or mobile devices. Furthermore, the use of mobile devices was also reported to promote participation in the intervention among individuals with ASD. Conclusions: The results suggested that mobile devices could be a promising means for the delivery of interventions targeting people with ASD. Although including a small number of studies was a limitation of this review, the results provided useful implications for designing effective mobile technology--assisted interventions for the ASD population in future studies. ", doi="10.2196/20892", url="https://mental.jmir.org/2021/9/e20892", url="http://www.ncbi.nlm.nih.gov/pubmed/34581681" } @Article{info:doi/10.2196/29845, author="Burbach, R. Frank and Stiles, M. Katie", title="Digital Mental Health and Neurodevelopmental Services: Case-Based Realist Evaluation", journal="JMIR Form Res", year="2021", month="Sep", day="17", volume="5", number="9", pages="e29845", keywords="telehealth", keywords="young people", keywords="adolescents", keywords="online psychological therapy", keywords="online neurodevelopmental assessments", keywords="digital services", keywords="realist evaluation", keywords="multiple case study", keywords="CBT", keywords="autism", abstract="Background: The rapid movement of mental health services on the internet following the onset of the COVID-19 pandemic has demonstrated the potential advantages of digital delivery and has highlighted the need to learn from prepandemic digital services. Objective: The aim of this study is to explore the different elements of interconnected digital mental health and neurodevelopmental services of a well-established provider to the UK National Health Service and how web-based delivery enables young people and their families to access high-quality assessments and interventions in a more timely, flexible, and person-centered manner than in-person delivery. Methods: A realist evaluation multiple case--study design was used, with 9 pediatric cases (aged 8-15 years) identified as representative of the services provided by Healios. Presenting concerns included autism and ADHD, anxiety and panic attacks, low self-esteem, anger and self-harm. The research literature was used to define the program theory and six context-mechanism-outcome (CMO) statements. The CMOs formed the basis for the initial data extraction, with novel elements added via an iterative process. Results: We identified 10 key elements of web-based services: flexible delivery and timely response, personalized care to the individual, comprehensive care enabled by multiple interconnected services, effective client engagement and productive therapeutic alliances, use of multiple communication tools, client satisfaction with the service, good clinical outcomes, ease of family involvement throughout sessions or from different locations, facilitation of multi-agency working and integration with National Health Services, and management of risk and safeguarding. These elements supported the six CMOs; there was clear evidence that young people and their families valued the responsiveness and flexibility of the web-based mental health service and, in particular, how quickly they were seen. There was also clear evidence of individual needs being met, good therapeutic alliances, and client satisfaction. Multiple communication tools appeared to maximize engagement and working digitally facilitated multi-agency communication and delivery of safe care. The abovementioned factors may be related to the finding of good clinical outcomes, but the methodology of this study does not allow any conclusions to be drawn regarding causality. Conclusions: This study demonstrates the effectiveness of interconnected digital mental health and neurodevelopmental services as well as how web-based delivery enables young people and their families to access assessments and interventions in a more timely, flexible, and person-centered manner than in-person delivery. The 10 key elements of web-based service delivery identified through the 9 case studies suggest the potential advantages of web-based work. These elements can inform future research and aid in the delivery of high-quality digital services. ", doi="10.2196/29845", url="https://formative.jmir.org/2021/9/e29845", url="http://www.ncbi.nlm.nih.gov/pubmed/34369382" } @Article{info:doi/10.2196/27803, author="Bonnot, Olivier and Adrien, Vladimir and Venelle, Veronique and Bonneau, Dominique and Gollier-Briant, Fanny and Mouchabac, Stephane", title="Mobile App for Parental Empowerment for Caregivers of Children With Autism Spectrum Disorders: Prospective Open Trial", journal="JMIR Ment Health", year="2021", month="Sep", day="15", volume="8", number="9", pages="e27803", keywords="autism spectrum disorders", keywords="empowerment, smartphone application", keywords="autism", keywords="smartphone", keywords="app", keywords="children", keywords="caregivers", abstract="Background: Conflicting data emerge from literature regarding the actual use of smartphone apps in medicine; some considered the introduction of smartphone apps in medicine to be a breakthrough, while others suggested that, in real-life, the use of smartphone apps in medicine is disappointingly low. Yet, digital tools become more present in medicine daily. To empower parents of a child with autism spectrum disorder, we developed the Smartautism smartphone app, which asks questions and provides feedback, using a screen with simple curves. Objective: The purpose of this study was to evaluate usage of the app by caregivers of individuals with autism spectrum disorders. Methods: We conducted a prospective longitudinal exploratory open study with families that have a child with autism spectrum disorder. Data were recorded over a period of 6 months, and the outcome criteria were (1) overall response rates for a feedback screen and qualitative questionnaires, and (2) response rates by degree of completion and by user interest, based on attrition. Results: Participants (n=65) had a very high intent to use the app during the 6-month period (3698/3900 instances, 94.8\%); however, secondary analysis showed that only 46\% of participants (30/65) had constant response rates over 50\%. Interestingly, these users were characterized by higher use and satisfaction with the feedback screen when compared to low (P<.001) and moderate (P=.007) users. Conclusions: We found that real or perceived utility is an important incentive for parents who use empowerment smartphone apps. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2016-012135 ", doi="10.2196/27803", url="https://mental.jmir.org/2021/9/e27803", url="http://www.ncbi.nlm.nih.gov/pubmed/34524101" } @Article{info:doi/10.2196/21471, author="Ntalindwa, Theoneste and Nduwingoma, Mathias and Karangwa, Evariste and Rashid Soron, Tanjir and Uworwabayeho, Alphonse and Uwineza, Annette", title="Development of a Mobile App to Improve Numeracy Skills of Children With Autism Spectrum Disorder: Participatory Design and Usability Study", journal="JMIR Pediatr Parent", year="2021", month="Aug", day="31", volume="4", number="3", pages="e21471", keywords="autism spectrum disorder", keywords="mobile app", keywords="learning", keywords="information and communication technologies", keywords="education", keywords="numeracy", keywords="mathematics", abstract="Background: The use of information and communication technologies is transforming the lives of millions of people including children with autism spectrum disorder (ASD). However, the process of developing a user-friendly and effective mobile app needs to follow a complex standard protocol and culture-sensitive customization, and involves multiple sectors. This complex work becomes even more challenging when considering children with ASD in low- and middle-income countries as the users. Objective: This study aimed to design and develop a more intuitive mobile app to improve numeracy skills of children with ASD in Rwanda and evaluate the usability of the app. Methods: A participatory design approach was utilized in this study in which 40 children with ASD, 5 teachers, and 10 parents of children with ASD participated in focus group discussions (FGDs) and usability testing. A narrative literature review was performed to explore existing mobile apps and compare previous studies to design the questions for FGD and facilitate a framework for designing the app. The agile methodology was used to develop the mobile app, and the heuristics evaluation method was used to test and evaluate the usability of the initial version of the app to improve its functionalities. The interviews were recorded, transcribed, and analyzed following the guidelines of the qualitative narrative analysis (QNA) method. Results: During the FGDs the respondents shared their need for a mobile app in teaching and learning numeracy for children with ASD and pointed to possibilities of integrating the mobile app into existing curriculum. Ten themes emerged from the FGDs and exercise of developing the mobile app. The themes were related to (1) teaching and learning numeracy for children with ASD, (2) planning and development of a mobile app for a person with ASD, (3) testing a mobile app, (4) strength of the developed app against the existing ones, (5) behavioral maintenance and relapse prevention, (6) possibilities to integrate the mobile app into the existing curriculum, (7) data protection for users, (8) social implications, (9) challenges in Rwanda, and (10) focus on future. Conclusions: The community plays an important role in the planning, development, and evaluation of a mobile app for children with ASD. In this study, inputs from teachers and parents resulted in an optimally designed mobile app that can improve numeracy skills in children diagnosed with ASD to support the implementation of competency-based curriculum in Rwanda. ", doi="10.2196/21471", url="https://pediatrics.jmir.org/2021/3/e21471", url="http://www.ncbi.nlm.nih.gov/pubmed/34463629" } @Article{info:doi/10.2196/29328, author="Zhao, Zhong and Tang, Haiming and Zhang, Xiaobin and Qu, Xingda and Hu, Xinyao and Lu, Jianping", title="Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation", journal="J Med Internet Res", year="2021", month="Aug", day="26", volume="23", number="8", pages="e29328", keywords="autism spectrum disorder", keywords="eye tracking", keywords="face-to-face interaction", keywords="machine learning", keywords="visual fixation", abstract="Background: Previous studies have shown promising results in identifying individuals with autism spectrum disorder (ASD) by applying machine learning (ML) to eye-tracking data collected while participants viewed varying images (ie, pictures, videos, and web pages). Although gaze behavior is known to differ between face-to-face interaction and image-viewing tasks, no study has investigated whether eye-tracking data from face-to-face conversations can also accurately identify individuals with ASD. Objective: The objective of this study was to examine whether eye-tracking data from face-to-face conversations could classify children with ASD and typical development (TD). We further investigated whether combining features on visual fixation and length of conversation would achieve better classification performance. Methods: Eye tracking was performed on children with ASD and TD while they were engaged in face-to-face conversations (including 4 conversational sessions) with an interviewer. By implementing forward feature selection, four ML classifiers were used to determine the maximum classification accuracy and the corresponding features: support vector machine (SVM), linear discriminant analysis, decision tree, and random forest. Results: A maximum classification accuracy of 92.31\% was achieved with the SVM classifier by combining features on both visual fixation and session length. The classification accuracy of combined features was higher than that obtained using visual fixation features (maximum classification accuracy 84.62\%) or session length (maximum classification accuracy 84.62\%) alone. Conclusions: Eye-tracking data from face-to-face conversations could accurately classify children with ASD and TD, suggesting that ASD might be objectively screened in everyday social interactions. However, these results will need to be validated with a larger sample of individuals with ASD (varying in severity and balanced sex ratio) using data collected from different modalities (eg, eye tracking, kinematic, electroencephalogram, and neuroimaging). In addition, individuals with other clinical conditions (eg, developmental delay and attention deficit hyperactivity disorder) should be included in similar ML studies for detecting ASD. ", doi="10.2196/29328", url="https://www.jmir.org/2021/8/e29328", url="http://www.ncbi.nlm.nih.gov/pubmed/34435957" } @Article{info:doi/10.2196/23829, author="Yechiam, Eldad and Yom-Tov, Elad", title="Unique Internet Search Strategies of Individuals With Self-Stated Autism: Quantitative Analysis of Search Engine Users' Investigative Behaviors", journal="J Med Internet Res", year="2021", month="Jul", day="6", volume="23", number="7", pages="e23829", keywords="autism", keywords="decision making", keywords="exploration", keywords="search", keywords="internet", abstract="Background: Although autism is often characterized in literature by the presence of repetitive behavior, in structured decision tasks, individuals with autism spectrum disorder (ASD) have been found to examine more options in a given time period than controls. Objective: We aimed to examine whether this investigative tendency emerges in information searches conducted via the internet. Methods: In total, 1746 search engine users stated that they had ASD in 2019. This group's naturally occurring responses following 1491 unique general queries and 78 image queries were compared to those of all other users of the search engine. The main dependent measure was scrolled distance, which denoted the extent to which additional results were scanned beyond the initial results presented on-screen. Additionally, we examined the number of clicks on search results as an indicator of the degree of search outcome exploitation and assessed whether there was a trade-off between increased search range and the time invested in viewing initial search results. Results: After issuing general queries, individuals with self-stated ASD scanned more results than controls. The scrolled distance in the results page of general queries was 45\% larger for the group of individuals with ASD (P<.001; d=0.45). The group of individuals with ASD also made the first scroll faster than the controls (P<.001; d=0.51). The differences in scrolled distance were larger for popular queries. No group differences in scrolled distance emerged for image queries, suggesting that visual load impeded the investigative behavior of individuals with ASD. No differences emerged in the number of clicks on search results. Conclusions: Individuals who self-stated that they had ASD scrutinized more general search results and fewer image search results than the controls. Thus, our results at least partially support the notion that individuals with ASD exhibit investigative behaviors and suggest that textual searches are an important context for expressing such tendencies. ", doi="10.2196/23829", url="https://www.jmir.org/2021/7/e23829", url="http://www.ncbi.nlm.nih.gov/pubmed/34255644" } @Article{info:doi/10.2196/24543, author="Caine, A. Joshua and Klein, Britt and Edwards, L. Stephen", title="The Impact of a Novel Mimicry Task for Increasing Emotion Recognition in Adults with Autism Spectrum Disorder and Alexithymia: Protocol for a Randomized Controlled Trial", journal="JMIR Res Protoc", year="2021", month="Jun", day="17", volume="10", number="6", pages="e24543", keywords="alexithymia hypothesis", keywords="training facial expression emotion recognition", keywords="mimicry task", keywords="autism spectrum disorder", keywords="interoception", keywords="facial expression", keywords="emotion", keywords="emotion recognition", keywords="autism", keywords="spectrum disorder", keywords="mimicry", keywords="therapy", keywords="protocol", keywords="expression", keywords="disability", abstract="Background: Impaired facial emotion expression recognition (FEER) has typically been considered a correlate of autism spectrum disorder (ASD). Now, the alexithymia hypothesis is suggesting that this emotion processing problem is instead related to alexithymia, which frequently co-occurs with ASD. By combining predictive coding theories of ASD and simulation theories of emotion recognition, it is suggested that facial mimicry may improve the training of FEER in ASD and alexithymia. Objective: This study aims to evaluate a novel mimicry task to improve FEER in adults with and without ASD and alexithymia. Additionally, this study will aim to determine the contributions of alexithymia and ASD to FEER ability and assess which of these 2 populations benefit from this training task. Methods: Recruitment will primarily take place through an ASD community group with emphasis put on snowball recruiting. Included will be 64 consenting adults equally divided between participants without an ASD and participants with an ASD. Participants will be screened online using the Kessler Psychological Distress Scale (K-10; cut-off score of 22), Autism Spectrum Quotient (AQ-10), and Toronto Alexithymia Scale (TAS-20) followed by a clinical interview with a provisional psychologist at the Federation University psychology clinic. The clinical interview will include assessment of ability, anxiety, and depression as well as discussion of past ASD diagnosis and confirmatory administration of the Autism Mental Status Exam (AMSE). Following the clinical interview, the participant will complete the Bermond-Vorst Alexithymia Questionnaire (BVAQ) and then undertake a baseline assessment of FEER. Consenting participants will then be assigned using a permuted blocked randomization method into either the control task condition or the mimicry task condition. A brief measure of satisfaction of the task and a debriefing session will conclude the study. Results: The study has Federation University Human Research Ethics Committee approval and is registered with the Australian New Zealand Clinical Trials. Participant recruitment is predicted to begin in the third quarter of 2021. Conclusions: This study will be the first to evaluate the use of a novel facial mimicry task condition to increase FEER in adults with ASD and alexithymia. If efficacious, this task could prove useful as a cost-effective adjunct intervention that could be used at home and thus remove barriers to entry. This study will also explore the unique effectiveness of this task in people without an ASD, with an ASD, and with alexithymia. Trial Registration: Australian New Zealand Clinical Trial Registry ACTRN12619000705189p; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377455 International Registered Report Identifier (IRRID): PRR1-10.2196/24543 ", doi="10.2196/24543", url="https://www.researchprotocols.org/2021/6/e24543/", url="http://www.ncbi.nlm.nih.gov/pubmed/34170257" } @Article{info:doi/10.2196/29242, author="Haque, M. Munirul and Rabbani, Masud and Dipal, Das Dipranjan and Zarif, Islam Md Ishrak and Iqbal, Anik and Schwichtenberg, Amy and Bansal, Naveen and Soron, Rashid Tanjir and Ahmed, Ishtiaque Syed and Ahamed, Iqbal Sheikh", title="Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach", journal="JMIR Med Inform", year="2021", month="Jun", day="8", volume="9", number="6", pages="e29242", keywords="autism spectrum disorders", keywords="machine learning", keywords="digital health", keywords="mobile health", keywords="mhealth", keywords="predictive modeling", keywords="milestone parameters", keywords="Autism and Developmental Disabilities Monitoring (ADDM)", keywords="early intervention", abstract="Background: Care for children with autism spectrum disorder (ASD) can be challenging for families and medical care systems. This is especially true in low- and- middle-income countries such as Bangladesh. To improve family--practitioner communication and developmental monitoring of children with ASD, mCARE (Mobile-Based Care for Children with Autism Spectrum Disorder Using Remote Experience Sampling Method) was developed. Within this study, mCARE was used to track child milestone achievement and family sociodemographic assets to inform mCARE feasibility/scalability and family asset--informed practitioner recommendations. Objective: The objectives of this paper are threefold. First, it documents how mCARE can be used to monitor child milestone achievement. Second, it demonstrates how advanced machine learning models can inform our understanding of milestone achievement in children with ASD. Third, it describes family/child sociodemographic factors that are associated with earlier milestone achievement in children with ASD (across 5 machine learning models). Methods: Using mCARE-collected data, this study assessed milestone achievement in 300 children with ASD from Bangladesh. In this study, we used 4 supervised machine learning algorithms (decision tree, logistic regression, K-nearest neighbor [KNN], and artificial neural network [ANN]) and 1 unsupervised machine learning algorithm (K-means clustering) to build models of milestone achievement based on family/child sociodemographic details. For analyses, the sample was randomly divided in half to train the machine learning models and then their accuracy was estimated based on the other half of the sample. Each model was specified for the following milestones: Brushes teeth, Asks to use the toilet, Urinates in the toilet or potty, and Buttons large buttons. Results: This study aimed to find a suitable machine learning algorithm for milestone prediction/achievement for children with ASD using family/child sociodemographic characteristics. For Brushes teeth, the 3 supervised machine learning models met or exceeded an accuracy of 95\% with logistic regression, KNN, and ANN as the most robust sociodemographic predictors. For Asks to use toilet, 84.00\% accuracy was achieved with the KNN and ANN models. For these models, the family sociodemographic predictors of ``family expenditure'' and ``parents' age'' accounted for most of the model variability. The last 2 parameters, Urinates in toilet or potty and Buttons large buttons, had an accuracy of 91.00\% and 76.00\%, respectively, in ANN. Overall, the ANN had a higher accuracy (above {\textasciitilde}80\% on average) among the other algorithms for all the parameters. Across the models and milestones, ``family expenditure,'' ``family size/type,'' ``living places,'' and ``parent's age and occupation'' were the most influential family/child sociodemographic factors. Conclusions: mCARE was successfully deployed in a low- and middle-income country (ie, Bangladesh), providing parents and care practitioners a mechanism to share detailed information on child milestones achievement. Using advanced modeling techniques this study demonstrates how family/child sociodemographic elements can inform child milestone achievement. Specifically, families with fewer sociodemographic resources reported later milestone attainment. Developmental science theories highlight how family/systems can directly influence child development and this study provides a clear link between family resources and child developmental progress. Clinical implications for this work could include supporting the larger family system to improve child milestone achievement. ", doi="10.2196/29242", url="https://medinform.jmir.org/2021/6/e29242", url="http://www.ncbi.nlm.nih.gov/pubmed/33984830" } @Article{info:doi/10.2196/27793, author="Gardner-Hoag, Julie and Novack, Marlena and Parlett-Pelleriti, Chelsea and Stevens, Elizabeth and Dixon, Dennis and Linstead, Erik", title="Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study", journal="JMIR Med Inform", year="2021", month="Jun", day="2", volume="9", number="6", pages="e27793", keywords="autism spectrum disorder", keywords="challenging behaviors", keywords="unsupervised machine learning", keywords="subtypes", keywords="treatment response", keywords="autism", keywords="treatment", keywords="behavior", keywords="machine learning", keywords="impact", keywords="efficacy", keywords="disorder", keywords="engagement", keywords="retrospective", abstract="Background: Challenging behaviors are prevalent among individuals with autism spectrum disorder; however, research exploring the impact of challenging behaviors on treatment response is lacking. Objective: The purpose of this study was to identify types of autism spectrum disorder based on engagement in different challenging behaviors and evaluate differences in treatment response between groups. Methods: Retrospective data on challenging behaviors and treatment progress for 854 children with autism spectrum disorder were analyzed. Participants were clustered based on 8 observed challenging behaviors using k means, and multiple linear regression was performed to test interactions between skill mastery and treatment hours, cluster assignment, and gender. Results: Seven clusters were identified, which demonstrated a single dominant challenging behavior. For some clusters, significant differences in treatment response were found. Specifically, a cluster characterized by low levels of stereotypy was found to have significantly higher levels of skill mastery than clusters characterized by self-injurious behavior and aggression (P<.003). Conclusions: These findings have implications on the treatment of individuals with autism spectrum disorder. Self-injurious behavior and aggression were prevalent among participants with the worst treatment response, thus interventions targeting these challenging behaviors may be worth prioritizing. Furthermore, the use of unsupervised machine learning models to identify types of autism spectrum disorder shows promise. ", doi="10.2196/27793", url="https://medinform.jmir.org/2021/6/e27793", url="http://www.ncbi.nlm.nih.gov/pubmed/34076577" } @Article{info:doi/10.2196/24754, author="Wang, Haishuai and Avillach, Paul", title="Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning", journal="JMIR Med Inform", year="2021", month="Apr", day="7", volume="9", number="4", pages="e24754", keywords="deep learning", keywords="autism spectrum disorder", keywords="common genetic variants, diagnostic classification", abstract="Background: In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25\% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. Objective: Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. Methods: After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network--based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning--based classifiers and randomly selected common variants. Results: The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic individuals from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88\% for identifying autistic individuals from nonautistic individuals. Our classifier demonstrated a considerable improvement of {\textasciitilde}13\% in terms of classification accuracy compared to standard autism screening tools. Conclusions: Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism. ", doi="10.2196/24754", url="https://medinform.jmir.org/2021/4/e24754", url="http://www.ncbi.nlm.nih.gov/pubmed/33714937" } @Article{info:doi/10.2196/19765, author="Terlouw, Gijs and Kuipers, Derek and van 't Veer, Job and Prins, T. Jelle and Pierie, N. Jean Pierre E.", title="The Development of an Escape Room--Based Serious Game to Trigger Social Interaction and Communication Between High-Functioning Children With Autism and Their Peers: Iterative Design Approach", journal="JMIR Serious Games", year="2021", month="Mar", day="23", volume="9", number="1", pages="e19765", keywords="serious game", keywords="autism", keywords="design research", keywords="boundary object", abstract="Background: Children with autism spectrum disorder (ASD) have social deficits that affect social interactions, communication, and relationships with peers. Many existing interventions focus mainly on improving social skills in clinical settings. In addition to the direct instruction--based programs, activity-based programs could be of added value, especially to bridge the relational gap between children with ASD and their peers. Objective: The aim of this study is to describe an iterative design process for the development of an escape room--based serious game as a boundary object. The purpose of the serious game is to facilitate direct communication between high-functioning children with ASD and their peers, for the development of social skills on the one hand and strengthening relationships with peers through a fun and engaging activity on the other hand. Methods: This study is structured around the Design Research Framework to develop an escape room through an iterative-incremental process. With a pool of 37 children, including 23 children diagnosed with ASD (5 girls) and 14 children (7 girls) attending special primary education for other additional needs, 4 testing sessions around different prototypes were conducted. The beta prototype was subsequently reviewed by experts (n=12). During the design research process, we examined in small steps whether the developed prototypes are feasible and whether they have the potential to achieve the formulated goals of different stakeholders. Results: By testing various prototypes, several insights were found and used to improve the design. Insights were gained in finding a fitting and appealing theme for the children, composing the content, and addressing different constraints in applying the goals from the children's and therapeutic perspectives. Eventually, a multiplayer virtual escape room, AScapeD, was developed. Three children can play the serious game in the same room on tablets. The first test shows that the game enacts equal cooperation and communication among the children. Conclusions: This paper presents an iterative design process for AScapeD. AScapeD enacts equal cooperation and communication in a playful way between children with ASD and their peers. The conceptual structure of an escape room contributes to the natural emergence of communication and cooperation. The iterative design process has been beneficial for finding a constructive game structure to address all formulated goals, and it contributed to the design of a serious game as a boundary object that mediates the various objectives of different stakeholders. We present 5 lessons learned from the design process. The developed prototype is feasible and has the potential to achieve the goals of the serious game. ", doi="10.2196/19765", url="https://games.jmir.org/2021/1/e19765", url="http://www.ncbi.nlm.nih.gov/pubmed/33755023" } @Article{info:doi/10.2196/23917, author="Liu, Guihua and Wang, Shuo and Liao, Jinhua and Ou, Ping and Huang, Longsheng and Xie, Namei and He, Yingshuang and Lin, Jinling and He, Hong-Gu and Hu, Rongfang", title="The Efficacy of WeChat-Based Parenting Training on the Psychological Well-being of Mothers With Children With Autism During the COVID-19 Pandemic: Quasi-Experimental Study", journal="JMIR Ment Health", year="2021", month="Feb", day="10", volume="8", number="2", pages="e23917", keywords="coronavirus disease 2019", keywords="autism spectrum disorder", keywords="parenting training", keywords="psychological well-being", keywords="social media", keywords="WeChat", keywords="COVID-19", keywords="autism", keywords="parenting", keywords="mental health", keywords="well-being", keywords="anxiety", keywords="depression", keywords="stress", abstract="Background: During the COVID-19 pandemic, special education schools for children in most areas of China were closed between the end of January and the beginning of June in 2020. The sudden interruption in schooling and the pandemic itself caused parents to be anxious and even to panic. Mobile-based parenting skills education has been demonstrated to be an effective method for improving the psychological well-being of mothers with children with autism. However, whether it can improve the psychological states of mothers in the context of the COVID-19 pandemic is a subject that should be urgently investigated. Objective: The aim of this study is to evaluate the efficacy of WeChat-based parenting training on anxiety, depression, parenting stress, and hope in mothers with children with autism, as well as the feasibility of the program during the COVID-19 pandemic. Methods: This was a quasi-experimental trial. A total of 125 mothers with preschool children with autism were recruited in January 2020. The participants were assigned to the control group (n=60), in which they received routine care, or the intervention group (n=65), in which they received the 12-week WeChat-based parenting training plus routine care, according to their preferences. Anxiety, depression, parenting stress, hope, satisfaction, and adherence to the intervention were measured at three timepoints: baseline (T0), postintervention (T1), and a 20-week follow-up (T2). Results: In total, 109 mothers completed the T1 assessment and 104 mothers completed the T2 assessment. The results of the linear mixed model analysis showed statistically significant group {\texttimes} time interaction effects for the intervention on anxiety (F=14.219, P<.001), depression (F=26.563, P<.001), parenting stress (F=68.572, P<.001), and hope (F=197.608, P<.001). Of all mothers in the intervention group, 90.4\% (48.8/54) reported that they were extremely satisfied with the WeChat-based parenting training. In total, 40.0\% (26/65) logged their progress in home training each week and 61.5\% (40/65) logged their progress more than 80\% of the time for all 20 weeks. Conclusions: The WeChat-based parenting training is acceptable and appears to be an effective approach for reducing anxiety, depression, and parenting stress, as well as increasing hope in mothers with children with autism during the global COVID-19 pandemic. Future studies with rigorous designs and longer follow-up periods are needed to further detect the effectiveness of the WeChat-based parenting training. Trial Registration: Chinese Clinical Trial Registry ChiCTR2000031772; http://www.chictr.org.cn/showproj.aspx?proj=52165 ", doi="10.2196/23917", url="https://mental.jmir.org/2021/2/e23917", url="http://www.ncbi.nlm.nih.gov/pubmed/33481751" } @Article{info:doi/10.2196/20172, author="Tanaka, Masanori and Saito, Manabu and Takahashi, Michio and Adachi, Masaki and Nakamura, Kazuhiko", title="Interformat Reliability of Web-Based Parent-Rated Questionnaires for Assessing Neurodevelopmental Disorders Among Preschoolers: Cross-sectional Community Study", journal="JMIR Pediatr Parent", year="2021", month="Feb", day="4", volume="4", number="1", pages="e20172", keywords="neurodevelopmental disorders", keywords="web-based questionnaire", keywords="preschoolers", keywords="parents", keywords="interformat reliability", abstract="Background: Early detection and intervention for neurodevelopmental disorders are effective. Several types of paper questionnaires have been developed to assess these conditions in early childhood; however, the psychometric equivalence between the web-based and the paper versions of these questionnaires is unknown. Objective: This study examined the interformat reliability of the web-based parent-rated version of the Autism Spectrum Screening Questionnaire (ASSQ), Attention-Deficit/Hyperactivity Disorder Rating Scale (ADHD-RS), Developmental Coordination Disorder Questionnaire 2007 (DCDQ), and Strengths and Difficulties Questionnaire (SDQ) among Japanese preschoolers in a community developmental health check-up setting. Methods: A set of paper-based questionnaires were distributed for voluntary completion to parents of children aged 5 years. The package of the paper format questionnaires included the ASSQ, ADHD-RS, DCDQ, parent-reported SDQ (P-SDQ), and several additional demographic questions. Responses were received from 508 parents of children who agreed to participate in the study. After 3 months, 300 parents, who were among the initial responders, were randomly selected and asked to complete the web-based versions of these questionnaires. A total of 140 parents replied to the web-based format and were included as a final sample in this study. Results: We obtained the McDonald $\omega$ coefficients for both the web-based and paper formats of the ASSQ (web-based: $\omega$=.90; paper: $\omega$=.86), ADHD-RS total and subscales (web-based: $\omega$=.88-.94; paper: $\omega$=.87-.93), DCDQ total and subscales (web-based: $\omega$=.82-.94; paper: $\omega$=.74-.92), and P-SDQ total and subscales (web-based: $\omega$=.55-.81; paper: $\omega$=.52-.80). The intraclass correlation coefficients between the web-based and paper formats were all significant at the 99.9\% confidence level: ASSQ (r=0.66, P<.001); ADHD-RS total and subscales (r=0.66-0.74, P<.001); DCDQ total and subscales (r=0.66-0.71, P<.001); P-SDQ Total Difficulties and subscales (r=0.55-0.73, P<.001). There were no significant differences between the web-based and paper formats for total mean score of the ASSQ (P=.76), total (P=.12) and subscale (P=.11-.47) mean scores of DCDQ, and the P-SDQ Total Difficulties mean score (P=.20) and mean subscale scores (P=.28-.79). Although significant differences were found between the web-based and paper formats for mean ADHD-RS scores (total: t132=2.83, P=.005; Inattention subscale: t133=2.15, P=.03; Hyperactivity/Impulsivity subscale: t133=3.21, P=.002), the effect sizes were small (Cohen d=0.18-0.22). Conclusions: These results suggest that the web-based versions of the ASSQ, ADHD-RS, DCDQ, and P-SDQ were equivalent, with the same level of internal consistency and intrarater reliability as the paper versions, indicating the applicability of the web-based versions of these questionnaires for assessing neurodevelopmental disorders. ", doi="10.2196/20172", url="https://pediatrics.jmir.org/2021/1/e20172", url="http://www.ncbi.nlm.nih.gov/pubmed/33455899" } @Article{info:doi/10.2196/20011, author="Bernie, Charmaine and Williams, Katrina and Graham, Fiona and May, Tamara", title="Coaching While Waiting for Autism Spectrum Disorder Assessment: Protocol of a Pilot Feasibility Study for a Randomized Controlled Trial on Occupational Performance Coaching and Service Navigation Support", journal="JMIR Res Protoc", year="2021", month="Jan", day="7", volume="10", number="1", pages="e20011", keywords="coaching", keywords="Occupational Performance Coaching", keywords="feasibility", keywords="parents", keywords="caregivers", keywords="ASD", keywords="autism", keywords="waiting list", keywords="referral", keywords="service navigation", abstract="Background: In Australia, the average time between a first concern of autism spectrum disorder (ASD) and diagnosis is over 2 years. After referral for assessment, families often wait 6-12 months before their appointment. This can be a time of uncertainty and stress for families. For some families, other forms of assistance are not accessible and thus timely intervention opportunities are missed. There is little evidence about how to provide the best support for children or caregivers while on assessment waiting lists. Objective: The aim of this study is to determine whether use of a coaching intervention called Occupational Performance Coaching (OPC) combined with service navigation support is feasible for families waiting for ASD assessment, as a crucial first step in planning a randomized controlled trial. Methods: A pilot and feasibility study will be conducted using recommended constructs and associated measures, which will be reported using CONSORT (Consolidated Standards or Reporting Trials) guidance. Participants will be child and caregiver dyads or triads, recruited within 4 months of their child (aged 1-7 years) being referred to one of two services for an ASD assessment in Victoria, Australia. A blinded randomization procedure will be used to allocate participants to one of three trial arms: (1) coaching and support intervention delivered face to face, (2) coaching and support intervention via videoconference, and (3) usual care. Descriptive statistics will be used to describe the sample characteristics of parents and children, inclusive of service access at baseline and follow up. Recruitment rates will be reported, and retention rates will be evaluated against a predicted rate of 70\%-80\% in each intervention arm. Goal attainment, using the Canadian Occupational Performance Measure, will indicate preliminary evidence for efficacy within the intervention arms, with an increase of 2 or more points on a 10-point performance and satisfaction scale considered clinically significant. Results: The study was approved by The Royal Children's Hospital Research Ethics and Governance Department in September 2018. As of October 2020, 16 families have been recruited to the study. Data analysis is ongoing and results are expected to be published in 2021. Conclusions: Study findings will support planning for a future randomized controlled trial to assess the efficacy of OPC and service navigation support for caregivers of children awaiting ASD assessment. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12620000164998; www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378793\&isReview=true International Registered Report Identifier (IRRID): DERR1-10.2196/20011 ", doi="10.2196/20011", url="https://www.researchprotocols.org/2021/1/e20011", url="http://www.ncbi.nlm.nih.gov/pubmed/33410761" } @Article{info:doi/10.2196/19658, author="Sehlin, Helena and Hedman Ahlstr{\"o}m, Britt and Bertilsson, Ingrid and Andersson, Gerhard and Wentz, Elisabet", title="Internet-Based Support and Coaching With Complementary Clinic Visits for Young People With Attention-Deficit/Hyperactivity Disorder and Autism: Controlled Feasibility Study", journal="J Med Internet Res", year="2020", month="Dec", day="31", volume="22", number="12", pages="e19658", keywords="attention-deficit/hyperactivity disorder", keywords="autism", keywords="coaching", keywords="internet-based intervention", keywords="social support", abstract="Background: Individuals with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) can experience obstacles in traditional health care situations due to difficulties associated with their impairment. Objective: This controlled study aims to investigate the feasibility of an internet-based support and coaching intervention (IBSC), including 2 weekly chat sessions and 2 complementary clinic visits with coaches over the course of 8 weeks, for adolescents and young adults with ADHD and/or ASD in 2 naturalistic routine care settings. Methods: Individuals with ADHD and/or ASD aged 15-32 years were recruited in 2 clinical settings, where they received either IBSC (n=24) or treatment as usual (TAU; n=20). Outcome measures included self-report questionnaires assessing quality of life (Manchester Short Assessment for Quality of Life), sense of coherence (Sense Of Coherence 29), self-esteem (Rosenberg Self-Esteem Scale), and anxiety and depressive symptoms (Hospital Anxiety and Depression Scale [HADS] and Montgomery-{\AA}sberg Depression Rating Scale-Self-reported, respectively). Results: Significant between-group effects were observed in measures of anxiety (HADS) at postintervention (P=.02) as well as at the 6-month follow-up (P=.004). Significant between-group effects were also noted for depressive symptoms (HADS) postintervention (P=.04). The between-group effects were partially explained by a deterioration in the TAU group. A significant increase in self-esteem (P=.04) as well as a decrease in anxiety (P=.003) at the 6-month follow-up was observed in the intervention group following IBSC. Findings from a qualitative study of the intervention are consistent with the results. Conclusions: The findings from this study suggest that IBSC holds promise as a feasible complement or alternative to traditional face-to-face health care meetings. ", doi="10.2196/19658", url="http://www.jmir.org/2020/12/e19658/", url="http://www.ncbi.nlm.nih.gov/pubmed/33382381" } @Article{info:doi/10.2196/18004, author="Dodds, Lynn Robin", title="Helping Optimize Language Acquisition (HOLA) Online Parent Training Modules for Latinx Parents of Toddlers at Risk for ASD: Protocol for a Pilot Funded by the Organization for Autism Research", journal="JMIR Res Protoc", year="2020", month="Dec", day="10", volume="9", number="12", pages="e18004", keywords="autism spectrum disorders", keywords="cultural diversity", keywords="parent training", keywords="pivotal response treatment", keywords="health disparities", keywords="online training", keywords="autism", keywords="intervention delay", keywords="online learning", keywords="pediatrics", abstract="Background: Culturally competent parent training in evidence-based intervention for autism spectrum disorder (ASD) can provide young Latinx children from underserved communities with early interventional support while they wait for professional services, thus reducing the impact of intervention delays. Providing parents with brief bilingual training in Pivotal Response Treatment (PRT) is a strategy that can overcome these barriers and is inexpensive to disseminate. Brief PRT training has been shown to significantly improve joint attention, expressive language, responsivity, and adaptive skills in young children with ASD. However, it is unknown whether an interactive, culturally competent online parent training in PRT is effective in a Latinx population. Objective: To this end, we will recruit 24 children (16-36 months old) at risk for ASD and their parent(s) from East and South Los Angeles and provide them with a series of 6 online learning modules in their choice of Spanish or English. Methods: This pilot study will utilize a single-group, pilot, pre-post design with follow-up assessments 6 weeks later. Linear mixed-effects model analysis will be used to explore most parent-reported and coded outcomes. Results: Brief online parent training in evidence-based treatments has the capacity to increase access to culturally competent early communication interventions for young children at risk for ASD. Conclusions: The results of this trial may have particular salience in additional underresourced communities where children have limited access to interventions prior to entering school. International Registered Report Identifier (IRRID): PRR1-10.2196/18004 ", doi="10.2196/18004", url="http://www.researchprotocols.org/2020/12/e18004/", url="http://www.ncbi.nlm.nih.gov/pubmed/33300494" } @Article{info:doi/10.2196/20913, author="Saposnik, E. Florencia and Huber, F. Joelene", title="Trends in Web Searches About the Causes and Treatments of Autism Over the Past 15 Years: Exploratory Infodemiology Study", journal="JMIR Pediatr Parent", year="2020", month="Dec", day="7", volume="3", number="2", pages="e20913", keywords="autism", keywords="infodemiology", keywords="infoveillance", keywords="informatics", keywords="Google Trends", abstract="Background: Ninety percent of adults in the United States use the internet, and the majority of internet users report looking on the web for health information using search engines. The rising prevalence of autism spectrum disorder (ASD), uncertainty surrounding its etiology, and variety of intervention approaches contribute to questions about its causes and treatments. It is not known which terms people search most frequently about ASD and whether web search queries have changed over time. Infodemiology is an area of health informatics research using big data analytics to understand web search behavior. Objective: The objectives were to (1) use infodemiological data to analyze trends in web-based searches about the causes and treatments of ASD over time and (2) inform clinicians and ASD organizations about web queries regarding ASD. Methods: Google Trends was used to analyze web searches about the causes and treatments of ASD in the United States from 2004 to 2019. The search terms analyzed for queries about causes of ASD included vaccines, genetics, environmental factors, and microbiome and those for therapies included applied behavior analysis (ABA), gluten-free diet, chelation therapy, marijuana, probiotics, and stem cell therapy. Results: Google Trends results are normalized on a scale ranging from 0 to 100 to represent the frequency and relative interest of search topics. For searches about ASD causes, vaccines had the greatest frequency compared to other terms, with an initial search peak observed in 2008 (scaled score of 81), reaching the highest frequency in 2015 (scaled score of 100), and a current upward trend. In comparison, searches about genetics, environmental factors, and microbiome occurred less frequently. For web searches about ASD therapies, ABA consistently had a high frequency of search interest since 2004, reaching a maximum scaled score of 100 in 2019. The analyses of chelation therapy and gluten-free diet showed trending interest in 2005 (scaled score of 68) and 2007 (scaled score of 100), respectively, followed by a steady decline since (scaled scores of only 10 and 16, respectively, in 2019). Searches related to ASD and marijuana showed a rise in 2009 (scaled score of 35), and they continue to trend upward. Searches about probiotics and stem cell therapy have been relatively low (scaled scores of 22 and 18, respectively), but are gradually gaining interest. Web search volumes for stem cell therapy in 2019 surpassed both gluten-free diet and chelation therapy as web-searched interventions for ASD. Conclusions: Google Trends is an effective infodemiology tool to analyze large-scale web search trends about ASD. The results showed informative variation in search trends over 15 years. These data are useful to inform clinicians and organizations about web queries on topics related to ASD, identify knowledge gaps, and target web-based education and knowledge translation strategies. ", doi="10.2196/20913", url="http://pediatrics.jmir.org/2020/2/e20913/", url="http://www.ncbi.nlm.nih.gov/pubmed/33284128" } @Article{info:doi/10.2196/15786, author="Rabba, Stacey Aspasia and Dissanayake, Cheryl and Barbaro, Josephine", title="Development of a Web-Based Resource for Parents of Young Children Newly Diagnosed With Autism: Participatory Research Design", journal="JMIR Pediatr Parent", year="2020", month="Sep", day="30", volume="3", number="2", pages="e15786", keywords="autism", keywords="diagnosis", keywords="parents", keywords="support", keywords="co-design", keywords="eHealth", abstract="Background: The internet provides an ideal avenue to share information, advice, and support regarding autism. However, many websites lack quality control and rarely provide a one-stop resource for families to access necessary, evidence-based information. Objective: This study aims to use participatory action research (PAR) with end users (ie, parents) and clinicians to develop a web-based resource (Pathways Beyond Diagnosis) to improve timely access to quality, evidence-based information, and support for families after their child is diagnosed with autism. Methods: The PAR approach involves 4 phases: (1) cooperative researcher-stakeholder planning, (2) cooperative researcher-stakeholder--based action, (3) stakeholder observation, and (4) cooperative researcher-stakeholder reflection. A total of 15 participants (parents, n=3; clinicians, n=9; and researchers, n=3) attended individual or group participatory design workshops. This was followed by the translation of knowledge and ideas generated during the workshops to produce mockups of webpages and content, rapid prototyping, and one-on-one consultations with end users to assess the usability of the website developed. Results: A total of 3 participatory design workshops were held with the participants, each followed by a knowledge translation session. At the end of the PAR cycle, an alpha prototype of the website was built and a series of one-on-one end user consultation sessions were conducted. The PAR cycle revealed the importance of 6 key topic areas (understanding autism, accessing services, support, gaining funding, putting it all together, and looking into the future) associated with the time of diagnosis, which were incorporated into the beta version of the website. Conclusions: The development of the Pathways Beyond Diagnosis website using PAR ensures that families have ready access to practical and evidence-based information following a young child's diagnosis. The website guides families to access relevant, reputable, and evidence-based information in addition to summarizing key challenges encountered after diagnosis (ie, grief, sharing the diagnosis) and the importance of self-care. ", doi="10.2196/15786", url="http://pediatrics.jmir.org/2020/2/e15786/", url="http://www.ncbi.nlm.nih.gov/pubmed/32996890" } @Article{info:doi/10.2196/16752, author="Ahmed, L. Kelli and Simon, R. Andrea and Dempsey, R. Jack and Samaco, C. Rodney and Goin-Kochel, P. Robin", title="Evaluating Two Common Strategies for Research Participant Recruitment Into Autism Studies: Observational Study", journal="J Med Internet Res", year="2020", month="Sep", day="24", volume="22", number="9", pages="e16752", keywords="autism spectrum disorder", keywords="participant recruitment", keywords="social media", keywords="Facebook", keywords="radio", keywords="genetic studies", abstract="Background: Ongoing research is necessary to better understand the causes of autism spectrum disorder (ASD), the developmental outcomes for individuals diagnosed with ASD, and the efficacy of the interventions. However, it is often difficult to recruit sufficient numbers of participants for studies, and despite the prevalence of ASD (currently estimated to affect 1 in 54 children), little research has focused on how to efficiently recruit participants with ASD. Objective: The aim of this study was to determine the efficacy of two different paid advertisements---social media and radio advertising---in recruiting participants for a study enrolling people with ASD and their family members by examining the number of participants enrolled, the cost per participant, and the geographic reach of each type of advertising. Methods: We examined participant enrollment in a study following nonoverlapping paid advertisements on a popular FM radio station (aired in three cities across two states) and Facebook (six advertisements that ran in five cities across two states). The total paid investment in the radio campaign was \$12,030 and that in the Facebook campaign was \$2950. Following the advertising campaigns, 1391 participants in the study who were affiliated with the Houston, Texas, site received email invitations to participate in a brief survey about the ways in which they learned about the study (eg, social media, medical provider, website) and which of these were most influential in their decisions to participate; 374 (26.8\%) of the participants completed this survey. Results: Social media advertising outperformed radio in all three parameters examined by enrolling more participants (338 vs 149), with a lower average cost per participant (\$8.73 vs \$80.74) and a wider geographic reach, based on a comparison of the number of zip codes within and outside of Texas for questionnaire respondents who rated social media as the most influential method of contact (n=367, $\chi$21=5.85, P=.02). Of the 374 survey participants, 139 (37.2\%) reported that they had seen the study on social media prior to enrollment, while only 9 (2.4\%) said they heard about it via radio. Conclusions: Our findings suggest that advertising on social media can efficiently reach a large pool of potential participants with ASD, increasing the likelihood of meeting study enrollment goals. Researchers should consider allocating at least some portion of recruitment dollars to social media platforms as a means of quickly and inexpensively reaching out to their target populations, including for studies with in-person procedures. ", doi="10.2196/16752", url="http://www.jmir.org/2020/9/e16752/", url="http://www.ncbi.nlm.nih.gov/pubmed/32969826" } @Article{info:doi/10.2196/17260, author="Terlouw, Gijs and van 't Veer, TB Job and Prins, T. Jelle and Kuipers, A. Derek and Pierie, N. Jean-Pierre E.", title="Design of a Digital Comic Creator (It's Me) to Facilitate Social Skills Training for Children With Autism Spectrum Disorder: Design Research Approach", journal="JMIR Ment Health", year="2020", month="Jul", day="10", volume="7", number="7", pages="e17260", keywords="autism", keywords="serious media", keywords="boundary object", keywords="comic", keywords="design research", abstract="Background: Children with autism spectrum disorder (ASD) often face difficulties in social situations and are often lagging in terms of social skills. Many interventions designed for children with ASD emphasize improving social skills. Although many interventions demonstrate that targeted social skills can be improved in clinical settings, developed social skills are not necessarily applied in children's daily lives at school, sometimes because classmates continue to show negative bias toward children with ASD. Children with ASD do not blame the difficult social situations they encounter on their lack of social skills; their main goal is to be accepted by peers. Objective: This study aims to design a comic creator---It's me---that would create comics to serve as transformational boundary objects to facilitate and enact a horizontal interaction structure between high-functioning children with ASD and their peers, aiming to increase mutual understanding between children at school. Methods: This research project and this study are structured around the Design Research Framework in order to develop the comic through an iterative-incremental process. Three test sessions, which included 13, 6, and 47 children, respectively, were initiated where the focus shifted in time from usability during the first two tests to the initial assessment of acceptance and feasibility in the third session. A stakeholder review, which included six experts, took place after the second test session. Results: A digital comic creator, It's me, was produced within this study. Children can create their own personal comic by filling in a digital questionnaire. Based on concepts of peer support, psychoeducation, and horizontal interaction, It's me has a rigorous base of underlying concepts that have been translated into design. Based on the first test sessions, the comic has shown its potential to initiate personal conversations between children. Teachers are convinced that It's me can be of added value in their classrooms. Conclusions: It's me aims to initiate more in-depth conversations between peers, which should lead to more mutual understanding and better relationships between children with ASD and their peers. The first test sessions showed that It's me has the potential to enact horizontal interaction and greater understanding among peers. It's me was designed as a boundary object, aiming to connect the objectives of different stakeholders, and to trigger reflection and transformation learning mechanisms. The applied design research approach might be of added value in the acceptance and adoption of the intervention because children, professionals, and teachers see added value in the tool, each from their own perspectives. ", doi="10.2196/17260", url="http://mental.jmir.org/2020/7/e17260/", url="http://www.ncbi.nlm.nih.gov/pubmed/32673273" } @Article{info:doi/10.2196/18279, author="O'Donovan, Rebecca and Sezgin, Emre and Bambach, Sven and Butter, Eric and Lin, Simon", title="Detecting Screams From Home Audio Recordings to Identify Tantrums: Exploratory Study Using Transfer Machine Learning", journal="JMIR Form Res", year="2020", month="Jun", day="16", volume="4", number="6", pages="e18279", keywords="machine learning", keywords="scream detection", keywords="audio event detection", keywords="tantrum identification", keywords="autism", keywords="behavioral disorder", keywords="data-driven approach", abstract="Background: Qualitative self- or parent-reports used in assessing children's behavioral disorders are often inconvenient to collect and can be misleading due to missing information, rater biases, and limited validity. A data-driven approach to quantify behavioral disorders could alleviate these concerns. This study proposes a machine learning approach to identify screams in voice recordings that avoids the need to gather large amounts of clinical data for model training. Objective: The goal of this study is to evaluate if a machine learning model trained only on publicly available audio data sets could be used to detect screaming sounds in audio streams captured in an at-home setting. Methods: Two sets of audio samples were prepared to evaluate the model: a subset of the publicly available AudioSet data set and a set of audio data extracted from the TV show Supernanny, which was chosen for its similarity to clinical data. Scream events were manually annotated for the Supernanny data, and existing annotations were refined for the AudioSet data. Audio feature extraction was performed with a convolutional neural network pretrained on AudioSet. A gradient-boosted tree model was trained and cross-validated for scream classification on the AudioSet data and then validated independently on the Supernanny audio. Results: On the held-out AudioSet clips, the model achieved a receiver operating characteristic (ROC)--area under the curve (AUC) of 0.86. The same model applied to three full episodes of Supernanny audio achieved an ROC-AUC of 0.95 and an average precision (positive predictive value) of 42\% despite screams only making up 1.3\% (n=92/7166 seconds) of the total run time. Conclusions: These results suggest that a scream-detection model trained with publicly available data could be valuable for monitoring clinical recordings and identifying tantrums as opposed to depending on collecting costly privacy-protected clinical data for model training. ", doi="10.2196/18279", url="http://formative.jmir.org/2020/6/e18279/", url="http://www.ncbi.nlm.nih.gov/pubmed/32459656" } @Article{info:doi/10.2196/18105, author="Mitchell, Jane Marijke and Newall, Helen Fiona and Sokol, Jennifer and Williams, Jane Katrina", title="Simulation-Based Education for Staff Managing Aggression and Externalizing Behaviors in Children With Autism Spectrum Disorder in the Hospital Setting: Pilot and Feasibility Study Protocol for a Cluster Randomized Controlled Trial", journal="JMIR Res Protoc", year="2020", month="Jun", day="4", volume="9", number="6", pages="e18105", keywords="feasibility studies", keywords="autism spectrum disorder", keywords="intellectual disability", keywords="high-fidelity simulation training", keywords="pediatric nursing", keywords="child", keywords="adolescent", keywords="aggression", abstract="Background: Children with autism spectrum disorder (ASD) frequently demonstrate aggression and externalizing behaviors in the acute care hospital environment. Pediatric acute care nursing staff are often not trained in managing aggression and, in particular, lack confidence in preventing and managing externalizing behaviors in children with ASD. High-fidelity simulation exercises will be used in this study to provide deliberate practice for acute care pediatric nursing staff in the management of aggressive and externalizing behaviors. Objective: The purpose of this study is to conduct a pilot and feasibility cluster randomized controlled trial (RCT) to evaluate the effectiveness of simulation-based education for staff in managing aggression and externalizing behaviors of children with ASD in the hospital setting. Methods: This study has a mixed design, with between-group and within-participant comparisons to explore the acceptability and feasibility of delivering a large-scale cluster RCT. The trial process, including recruitment, completion rates, contamination, and completion of outcome measures, will be assessed and reported as percentages. This study will assess the acceptability of the simulation-based training format for two scenarios involving an adolescent with autism, with or without intellectual disability, who displays aggressive and externalizing behaviors and the resulting change in confidence in managing clinical aggression. Two pediatric wards of similar size and patient complexity will be selected to participate in the study; they will be randomized to receive either simulation-based education plus web-based educational materials or the web-based educational materials only. Change in confidence will be assessed using pre- and posttraining surveys for bedside nursing staff exposed to the training and the control group who will receive the web-based training materials. Knowledge retention 3 months posttraining, as well as continued confidence and exposure to clinical aggression, will be assessed via surveys. Changes in confidence and competence will be compared statistically with the chi-square test using before-and-after data to compare the proportion of those who have high confidence between the two arms at baseline and at follow-up. The simulation-based education will be recorded with trained assessors reviewing participants' abilities to de-escalate aggressive behaviors using a validated tool. This data will be analyzed using mean values and SDs to understand the variation in performance of individuals who undertake the training. Data from each participating ward will be collected during each shift for the duration of the study to assess the number of aggressive incidents and successful de-escalation for patients with ASD. Total change in Code Grey activations will also be assessed, with both datasets analyzed using descriptive statistics. Results: This study gained ethical approval from The Royal Children's Hospital Melbourne Human Research Ethics Committee (HREC) on November 1, 2019 (HREC reference number: 56684). Data collection was completed in February 2020. Data analysis is due to commence with results anticipated by August 2020. Conclusions: We hypothesize that this study is feasible to be conducted as a cluster RCT and that simulation-based training will be acceptable for acute care pediatric nurses. We anticipate that the intervention ward will have increased confidence in managing clinical aggression in children with ASD immediately and up to 3 months posttraining. Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620000139976; http://www.ANZCTR.org.au/ACTRN12620000139976.aspx International Registered Report Identifier (IRRID): DERR1-10.2196/18105 ", doi="10.2196/18105", url="http://www.researchprotocols.org/2020/6/e18105/", url="http://www.ncbi.nlm.nih.gov/pubmed/32495742" } @Article{info:doi/10.2196/17184, author="Oser, K. Tamara and Oser, M. Sean and Parascando, A. Jessica and Grisolano, Ann Lee and Krishna, Bangalore Kanthi and Hale, E. Daniel and Litchman, Michelle and Majidi, Shideh and Haidet, Paul", title="Challenges and Successes in Raising a Child With Type 1 Diabetes and Autism Spectrum Disorder: Mixed Methods Study", journal="J Med Internet Res", year="2020", month="Jun", day="3", volume="22", number="6", pages="e17184", keywords="type 1 diabetes", keywords="autism spectrum disorder", keywords="child", keywords="blogs", keywords="social media", keywords="qualitative research", abstract="Background: Self-management of type 1 diabetes (T1D) requires numerous decisions and actions by people with T1D and their caregivers and poses many daily challenges. For those with T1D and a developmental disorder such as autism spectrum disorder (ASD), more complex challenges arise, though these remain largely unstudied. Objective: This study aimed to better understand the barriers and facilitators of raising a child with T1D and ASD. Secondary analysis of web-based content (phase 1) and telephone interviews (phase 2) were conducted to further expand the existing knowledge on the challenges and successes faced by these families. Methods: Phase 1 involved a qualitative analysis of publicly available online forums and blog posts by caregivers of children with both T1D and ASD. Themes from phase 1 were used to create an interview guide for further in-depth exploration via interviews. In phase 2, caregivers of children with both T1D and ASD were recruited from Penn State Health endocrinology clinics and through the web from social media posts to T1D-focused groups and sites. Interested respondents were directed to a secure web-based eligibility assessment. Information related to T1D and ASD diagnosis, contact information, and demographics were collected. On the basis of survey responses, participants were selected for a follow-up telephone interview and were asked to complete the adaptive behavior assessment system, third edition parent form to assess autism severity and upload a copy of their child's most recent hemoglobin A1c (HbA1c) result. Interviews were transcribed, imported into NVivo qualitative data management software, and analyzed to determine common themes related to barriers and facilitators of raising a child with both ASD and T1D. Results: For phase 1, 398 forum posts and blog posts between 2009 and 2016 were analyzed. Common themes related to a lack of understanding by the separate ASD and T1D caregiver communities, advice on coping techniques, rules and routines, and descriptions of the health care experience. For phase 2, 12 eligible respondents were interviewed. For interviewees, the average age of the child at diagnosis with T1D and ASD was 7.92 years and 5.55 years, respectively. Average self-reported and documented HbA1c levels for children with T1D and ASD were 8.6\% (70 mmol/mol) and 8.7\% (72 mmol/mol), respectively. Common themes from the interviews related to increased emotional burden, frustration surrounding the amount of information they are expected to learn, and challenges in the school setting. Conclusions: Caregivers of children with both T1D and ASD face unique challenges, distinct from those faced by caregivers of individuals who have either disorder alone. Understanding these challenges may help health care providers in caring for this unique population. Referral to the diabetes online community may be a potential resource to supplement the care received by the medical community. ", doi="10.2196/17184", url="https://www.jmir.org/2020/6/e17184", url="http://www.ncbi.nlm.nih.gov/pubmed/32217508" } @Article{info:doi/10.2196/14369, author="Cadieux, Lee and Keenan, Mickey", title="Can Social Communication Skills for Children Diagnosed With Autism Spectrum Disorder Rehearsed Inside the Video Game Environment of Minecraft Generalize to the Real World?", journal="JMIR Serious Games", year="2020", month="May", day="12", volume="8", number="2", pages="e14369", keywords="autism", keywords="behavior analysis", keywords="serious games", keywords="social skills", keywords="gamification", keywords="Lego", keywords="neurodiversity", keywords="Minecraft", keywords="virtual worlds", keywords="virtual reality", doi="10.2196/14369", url="http://games.jmir.org/2020/2/e14369/", url="http://www.ncbi.nlm.nih.gov/pubmed/32396129" } @Article{info:doi/10.2196/15767, author="Chen, Tao and Chen, Ye and Yuan, Mengxue and Gerstein, Mark and Li, Tingyu and Liang, Huiying and Froehlich, Tanya and Lu, Long", title="The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study", journal="JMIR Med Inform", year="2020", month="May", day="8", volume="8", number="5", pages="e15767", keywords="autism spectrum disorder", keywords="magnetic resonance imaging", keywords="neuroimaging", keywords="brain", keywords="histogram of oriented gradients", keywords="cluster analysis", keywords="classification", keywords="machine learning", abstract="Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with an unknown etiology. Early diagnosis and intervention are key to improving outcomes for patients with ASD. Structural magnetic resonance imaging (sMRI) has been widely used in clinics to facilitate the diagnosis of brain diseases such as brain tumors. However, sMRI is less frequently used to investigate neurological and psychiatric disorders, such as ASD, owing to the subtle, if any, anatomical changes of the brain. Objective: This study aimed to investigate the possibility of identifying structural patterns in the brain of patients with ASD as potential biomarkers in the diagnosis and evaluation of ASD in clinics. Methods: We developed a novel 2-level histogram-based morphometry (HBM) classification framework in which an algorithm based on a 3D version of the histogram of oriented gradients (HOG) was used to extract features from sMRI data. We applied this framework to distinguish patients with ASD from healthy controls using 4 datasets from the second edition of the Autism Brain Imaging Data Exchange, including the ETH Z{\"u}rich (ETH), NYU Langone Medical Center: Sample 1, Oregon Health and Science University, and Stanford University (SU) sites. We used a stratified 10-fold cross-validation method to evaluate the model performance, and we applied the Naive Bayes approach to identify the predictive ASD-related brain regions based on classification contributions of each HOG feature. Results: On the basis of the 3D HOG feature extraction method, our proposed HBM framework achieved an area under the curve (AUC) of >0.75 in each dataset, with the highest AUC of 0.849 in the ETH site. We compared the 3D HOG algorithm with the original 2D HOG algorithm, which showed an accuracy improvement of >4\% in each dataset, with the highest improvement of 14\% (6/42) in the SU site. A comparison of the 3D HOG algorithm with the scale-invariant feature transform algorithm showed an AUC improvement of >18\% in each dataset. Furthermore, we identified ASD-related brain regions based on the sMRI images. Some of these regions (eg, frontal gyrus, temporal gyrus, cingulate gyrus, postcentral gyrus, precuneus, caudate, and hippocampus) are known to be implicated in ASD in prior neuroimaging literature. We also identified less well-known regions that may play unrecognized roles in ASD and be worth further investigation. Conclusions: Our research suggested that it is possible to identify neuroimaging biomarkers that can distinguish patients with ASD from healthy controls based on the more cost-effective sMRI images of the brain. We also demonstrated the potential of applying data-driven artificial intelligence technology in the clinical setting of neurological and psychiatric disorders, which usually harbor subtle anatomical changes in the brain that are often invisible to the human eye. ", doi="10.2196/15767", url="https://medinform.jmir.org/2020/5/e15767", url="http://www.ncbi.nlm.nih.gov/pubmed/32041690" } @Article{info:doi/10.2196/16085, author="Baker, Jess and Kohlhoff, Jane and Onobrakpor, Se-Inyenede and Woolfenden, Sue and Smith, Rebecca and Knebel, Constanze and Eapen, Valsamma", title="The Acceptability and Effectiveness of Web-Based Developmental Surveillance Programs: Rapid Review", journal="JMIR Mhealth Uhealth", year="2020", month="Apr", day="23", volume="8", number="4", pages="e16085", keywords="public health surveillance", keywords="mass screening", keywords="developmental disabilities", keywords="neurodevelopmental disorders", keywords="review literature as topic", keywords="health care disparities", abstract="Background: Web-based developmental surveillance programs may be an innovative solution to improving the early detection of childhood developmental difficulties, especially within disadvantaged populations. Objective: This review aimed to identify the acceptability and effectiveness of web-based developmental surveillance programs for children aged 0 to 6 years. Methods: A total of 6 databases and gray literature were searched using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses--informed protocol. Data extraction included variables related to health equity. Results: In total, 20 studies were identified. Most papers implemented web-based versions of the Modified Checklist for Autism in Toddlers, Revised with Follow-Up screener for autism spectrum disorder or Parent Evaluation of Developmental Status screeners for broad developmental delay. Caregivers and practitioners indicated a preference for web-based screeners, primarily for user-friendliness, improved follow-up accuracy, time, and training efficiencies. Conclusions: Although evidence is limited as to the necessity of web- versus face-to-face--based developmental screening, there are clear efficiencies in its use. Trial Registration: PROSPERO CRD42019127894; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=127894 ", doi="10.2196/16085", url="http://mhealth.jmir.org/2020/4/e16085/", url="http://www.ncbi.nlm.nih.gov/pubmed/32324149" } @Article{info:doi/10.2196/13810, author="Nag, Anish and Haber, Nick and Voss, Catalin and Tamura, Serena and Daniels, Jena and Ma, Jeffrey and Chiang, Bryan and Ramachandran, Shasta and Schwartz, Jessey and Winograd, Terry and Feinstein, Carl and Wall, P. Dennis", title="Toward Continuous Social Phenotyping: Analyzing Gaze Patterns in an Emotion Recognition Task for Children With Autism Through Wearable Smart Glasses", journal="J Med Internet Res", year="2020", month="Apr", day="22", volume="22", number="4", pages="e13810", keywords="autism spectrum disorder", keywords="translational medicine", keywords="eye tracking", keywords="wearable technologies", keywords="artificial intelligence", keywords="machine learning", keywords="precision health", keywords="digital therapy", abstract="Background: Several studies have shown that facial attention differs in children with autism. Measuring eye gaze and emotion recognition in children with autism is challenging, as standard clinical assessments must be delivered in clinical settings by a trained clinician. Wearable technologies may be able to bring eye gaze and emotion recognition into natural social interactions and settings. Objective: This study aimed to test: (1) the feasibility of tracking gaze using wearable smart glasses during a facial expression recognition task and (2) the ability of these gaze-tracking data, together with facial expression recognition responses, to distinguish children with autism from neurotypical controls (NCs). Methods: We compared the eye gaze and emotion recognition patterns of 16 children with autism spectrum disorder (ASD) and 17 children without ASD via wearable smart glasses fitted with a custom eye tracker. Children identified static facial expressions of images presented on a computer screen along with nonsocial distractors while wearing Google Glass and the eye tracker. Faces were presented in three trials, during one of which children received feedback in the form of the correct classification. We employed hybrid human-labeling and computer vision--enabled methods for pupil tracking and world--gaze translation calibration. We analyzed the impact of gaze and emotion recognition features in a prediction task aiming to distinguish children with ASD from NC participants. Results: Gaze and emotion recognition patterns enabled the training of a classifier that distinguished ASD and NC groups. However, it was unable to significantly outperform other classifiers that used only age and gender features, suggesting that further work is necessary to disentangle these effects. Conclusions: Although wearable smart glasses show promise in identifying subtle differences in gaze tracking and emotion recognition patterns in children with and without ASD, the present form factor and data do not allow for these differences to be reliably exploited by machine learning systems. Resolving these challenges will be an important step toward continuous tracking of the ASD phenotype. ", doi="10.2196/13810", url="http://www.jmir.org/2020/4/e13810/", url="http://www.ncbi.nlm.nih.gov/pubmed/32319961" } @Article{info:doi/10.2196/13174, author="Kalantarian, Haik and Jedoui, Khaled and Dunlap, Kaitlyn and Schwartz, Jessey and Washington, Peter and Husic, Arman and Tariq, Qandeel and Ning, Michael and Kline, Aaron and Wall, Paul Dennis", title="The Performance of Emotion Classifiers for Children With Parent-Reported Autism: Quantitative Feasibility Study", journal="JMIR Ment Health", year="2020", month="Apr", day="1", volume="7", number="4", pages="e13174", keywords="mobile phone", keywords="emotion", keywords="autism", keywords="digital data", keywords="mobile app", keywords="mHealth", keywords="affect", keywords="machine learning", keywords="artificial intelligence", keywords="digital health", abstract="Background: Autism spectrum disorder (ASD) is a developmental disorder characterized by deficits in social communication and interaction, and restricted and repetitive behaviors and interests. The incidence of ASD has increased in recent years; it is now estimated that approximately 1 in 40 children in the United States are affected. Due in part to increasing prevalence, access to treatment has become constrained. Hope lies in mobile solutions that provide therapy through artificial intelligence (AI) approaches, including facial and emotion detection AI models developed by mainstream cloud providers, available directly to consumers. However, these solutions may not be sufficiently trained for use in pediatric populations. Objective: Emotion classifiers available off-the-shelf to the general public through Microsoft, Amazon, Google, and Sighthound are well-suited to the pediatric population, and could be used for developing mobile therapies targeting aspects of social communication and interaction, perhaps accelerating innovation in this space. This study aimed to test these classifiers directly with image data from children with parent-reported ASD recruited through crowdsourcing. Methods: We used a mobile game called Guess What? that challenges a child to act out a series of prompts displayed on the screen of the smartphone held on the forehead of his or her care provider. The game is intended to be a fun and engaging way for the child and parent to interact socially, for example, the parent attempting to guess what emotion the child is acting out (eg, surprised, scared, or disgusted). During a 90-second game session, as many as 50 prompts are shown while the child acts, and the video records the actions and expressions of the child. Due in part to the fun nature of the game, it is a viable way to remotely engage pediatric populations, including the autism population through crowdsourcing. We recruited 21 children with ASD to play the game and gathered 2602 emotive frames following their game sessions. These data were used to evaluate the accuracy and performance of four state-of-the-art facial emotion classifiers to develop an understanding of the feasibility of these platforms for pediatric research. Results: All classifiers performed poorly for every evaluated emotion except happy. None of the classifiers correctly labeled over 60.18\% (1566/2602) of the evaluated frames. Moreover, none of the classifiers correctly identified more than 11\% (6/51) of the angry frames and 14\% (10/69) of the disgust frames. Conclusions: The findings suggest that commercial emotion classifiers may be insufficiently trained for use in digital approaches to autism treatment and treatment tracking. Secure, privacy-preserving methods to increase labeled training data are needed to boost the models' performance before they can be used in AI-enabled approaches to social therapy of the kind that is common in autism treatments. ", doi="10.2196/13174", url="https://mental.jmir.org/2020/4/e13174", url="http://www.ncbi.nlm.nih.gov/pubmed/32234701" } @Article{info:doi/10.2196/16066, author="Bossenbroek, Rineke and Wols, Aniek and Weerdmeester, Joanneke and Lichtwarck-Aschoff, Anna and Granic, Isabela and van Rooij, W. Marieke M. J.", title="Efficacy of a Virtual Reality Biofeedback Game (DEEP) to Reduce Anxiety and Disruptive Classroom Behavior: Single-Case Study", journal="JMIR Ment Health", year="2020", month="Mar", day="24", volume="7", number="3", pages="e16066", keywords="anxiety", keywords="disruptive behavior", keywords="single-case study", keywords="applied game", keywords="serious games", keywords="special education", keywords="attention-deficit/hyperactivity disorder (ADHD)", keywords="autism spectrum disorder (ASD)", keywords="adolescents", abstract="Background: Many adolescents in special education are affected by anxiety in addition to their behavioral problems. Anxiety leads to substantial long-term problems and may underlie disruptive behaviors in the classroom as a result of the individual's inability to tolerate anxiety-provoking situations. Thus, interventions in special needs schools that help adolescents cope with anxiety and, in turn, diminish disruptive classroom behaviors are needed. Objective: This study aimed to evaluate the effect of a virtual reality biofeedback game, DEEP, on daily levels of state-anxiety and disruptive classroom behavior in a clinical sample. In addition, the study also aimed to examine the duration of the calm or relaxed state after playing DEEP. Methods: A total of 8 adolescents attending a special secondary school for students with behavioral and psychiatric problems participated in a single-case experimental ABAB study. Over a 4-week period, participants completed 6 DEEP sessions. In addition, momentary assessments (ie, 3 times a day) of self-reported state-anxiety and teacher-reported classroom behavior were collected throughout all A and B phases. Results: From analyzing the individual profiles, it was found that 6 participants showed reductions in anxiety, and 5 participants showed reductions in disruptive classroom behaviors after the introduction of DEEP. On a group level, results showed a small but significant reduction of anxiety (d=--0.29) and a small, nonsignificant reduction of disruptive classroom behavior (d=?0.16) on days when participants played DEEP. Moreover, it was found that the calm or relaxed state of participants after playing DEEP lasted for about 2 hours on average. Conclusions: This study demonstrates the potential of the game, DEEP, as an intervention for anxiety and disruptive classroom behavior in a special school setting. Future research is needed to fully optimize and personalize DEEP as an intervention for the heterogeneous special school population. ", doi="10.2196/16066", url="http://mental.jmir.org/2020/3/e16066/", url="http://www.ncbi.nlm.nih.gov/pubmed/32207697" } @Article{info:doi/10.2196/14108, author="Moon, Jae Sun and Hwang, Jinseub and Kana, Rajesh and Torous, John and Kim, Won Jung", title="Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies", journal="JMIR Ment Health", year="2019", month="Dec", day="20", volume="6", number="12", pages="e14108", keywords="autism spectrum disorder", keywords="machine learning", keywords="sensitivity and specificity", keywords="systematic review", keywords="meta-analysis", abstract="Background: In the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, their application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder (ASD). However, given their complexity and potential clinical implications, there is an ongoing need for further research on their accuracy. Objective: This study aimed to perform a systematic review and meta-analysis to summarize the available evidence for the accuracy of machine learning algorithms in diagnosing ASD. Methods: The following databases were searched on November 28, 2018: MEDLINE, EMBASE, CINAHL Complete (with Open Dissertations), PsycINFO, and Institute of Electrical and Electronics Engineers Xplore Digital Library. Studies that used a machine learning algorithm partially or fully for distinguishing individuals with ASD from control subjects and provided accuracy measures were included in our analysis. The bivariate random effects model was applied to the pooled data in a meta-analysis. A subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false-negative, and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw Summary Receiver Operating Characteristics curves, and obtain the area under the curve (AUC) and partial AUC (pAUC). Results: A total of 43 studies were included for the final analysis, of which a meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural magnetic resonance imaging (sMRI) subgroup meta-analysis (12 samples with 1776 participants) showed a sensitivity of 0.83 (95\% CI 0.76-0.89), a specificity of 0.84 (95\% CI 0.74-0.91), and AUC/pAUC of 0.90/0.83. A functional magnetic resonance imaging/deep neural network subgroup meta-analysis (5 samples with 1345 participants) showed a sensitivity of 0.69 (95\% CI 0.62-0.75), specificity of 0.66 (95\% CI 0.61-0.70), and AUC/pAUC of 0.71/0.67. Conclusions: The accuracy of machine learning algorithms for diagnosis of ASD was considered acceptable by few accuracy measures only in cases of sMRI use; however, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of machine learning algorithms to clinical settings. Trial Registration: PROSPERO CRD42018117779; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=117779 ", doi="10.2196/14108", url="https://mental.jmir.org/2019/12/e14108", url="http://www.ncbi.nlm.nih.gov/pubmed/31562756" } @Article{info:doi/10.2196/13478, author="Khan, Kareem and Hall, L. Charlotte and Davies, Bethan E. and Hollis, Chris and Glazebrook, Cris", title="The Effectiveness of Web-Based Interventions Delivered to Children and Young People With Neurodevelopmental Disorders: Systematic Review and Meta-Analysis", journal="J Med Internet Res", year="2019", month="Nov", day="1", volume="21", number="11", pages="e13478", keywords="online intervention", keywords="effectiveness", keywords="neurodevelopmental disorders", keywords="children and young people", keywords="methodology", keywords="systematic review", abstract="Background: The prevalence of certain neurodevelopmental disorders, specifically autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), has been increasing over the last four decades. Nonpharmacological interventions are available that can improve outcomes and reduce associated symptoms such as anxiety, but these are often difficult to access. Children and young people are using the internet and digital technology at higher rates than any other demographic, but although Web-based interventions have the potential to improve health outcomes in those with long-term conditions, no previous reviews have investigated the effectiveness of Web-based interventions delivered to children and young people with neurodevelopmental disorders. Objective: This study aimed to review the effectiveness of randomized controlled trials (RCTs) of Web-based interventions delivered to children and young people with neurodevelopmental disorders. Methods: Six databases and one trial register were searched in August and September 2018. RCTs were included if they were published in a peer-reviewed journal. Interventions were included if they (1) aimed to improve the diagnostic symptomology of the targeted neurodevelopmental disorder or associated psychological symptoms as measured by a valid and reliable outcome measure; (2) were delivered on the Web; (3) targeted a youth population (aged ?18 years or reported a mean age of ?18 years) with a diagnosis or suspected diagnosis of a neurodevelopmental disorder. Methodological quality was rated using the Joanna Briggs Institute Critical Appraisal Checklist for RCTs. Results: Of 5140 studies retrieved, 10 fulfilled the inclusion criteria. Half of the interventions were delivered to children and young people with ASDs with the other five targeting ADHD, tic disorder, dyscalculia, and specific learning disorder. In total, 6 of the 10 trials found that a Web-based intervention was effective in improving condition-specific outcomes or reducing comorbid psychological symptoms in children and young people. The 4 trials that failed to find an effect were all delivered by apps. The meta-analysis was conducted on five of the trials and did not show a significant effect, with a high level of heterogeneity detected (n=182 [33.4\%, 182/545], 5 RCTs; pooled standardized mean difference=--0.39; 95\% CI --0.98 to 0.20; Z=--1.29; P=.19 [I2=72\%; P=.006]). Conclusions: Web-based interventions can be effective in reducing symptoms in children and young people with neurodevelopmental disorders; however, caution should be taken when interpreting these findings owing to methodological limitations, the minimal number of papers retrieved, and small samples of included studies. Overall, the number of studies was small and mainly limited to ASD, thus restricting the generalizability of the findings. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews: CRD42018108824; http://www.crd.york.ac.uk/PROSPERO/display\_record.php?ID=CRD42018108824 ", doi="10.2196/13478", url="https://www.jmir.org/2019/11/e13478", url="http://www.ncbi.nlm.nih.gov/pubmed/31682573" } @Article{info:doi/10.2196/14429, author="Ravindran, Vijay and Osgood, Monica and Sazawal, Vibha and Solorzano, Rita and Turnacioglu, Sinan", title="Virtual Reality Support for Joint Attention Using the Floreo Joint Attention Module: Usability and Feasibility Pilot Study", journal="JMIR Pediatr Parent", year="2019", month="Sep", day="30", volume="2", number="2", pages="e14429", keywords="autism spectrum disorder", keywords="interpersonal skills", keywords="virtual reality, instructional", abstract="Background: Advances in virtual reality (VR) technology offer new opportunities to design supports for the core behaviors associated with autism spectrum disorder (ASD) that promote progress toward optimal outcomes. Floreo has developed a novel mobile VR platform that pairs a user receiving instruction on target skills with an adult monitor. Objective: The primary objective of this pilot study was to explore the feasibility of using Floreo's Joint Attention Module in school-aged children with autism in a special education setting. A secondary objective was to explore a novel joint attention measure designed for use with school-aged children and to observe whether there was a suggestion of change in joint attention skills from preintervention to postintervention. Methods: A total of 12 participants (age range: 9 to 16 years) received training with the Joint Attention Module for 14 sessions over 5 weeks. Results: No serious side effects were reported, and no participants dropped out of the study because of undesirable side effects. On the basis of monitor data, 95.4\% (126/132) of the time participants tolerated the headset, 95.4\% (126/132) of the time participants seemed to enjoy using Floreo's platform, and 95.5\% (128/134) of the time the VR experience was reported as valuable. In addition, scoring of the joint attention measure suggested a positive change in participant skills related to the total number of interactions, use of eye contact, and initiation of interactions. Conclusions: The study results suggest that Floreo's Joint Attention Module is safe and well tolerated by students with ASD, and preliminary data also suggest that its use is related to improvements in fundamental joint attention skills. ", doi="10.2196/14429", url="http://pediatrics.jmir.org/2019/2/e14429/", url="http://www.ncbi.nlm.nih.gov/pubmed/31573921" } @Article{info:doi/10.2196/12176, author="Ntalindwa, Theoneste and Soron, Rashid Tanjir and Nduwingoma, Mathias and Karangwa, Evariste and White, Rebecca", title="The Use of Information Communication Technologies Among Children With Autism Spectrum Disorders: Descriptive Qualitative Study", journal="JMIR Pediatr Parent", year="2019", month="Sep", day="27", volume="2", number="2", pages="e12176", keywords="autism spectrum disorders", keywords="information communication technologies", keywords="inclusive education", abstract="Background: The prevalence of Autism Spectrum Disorder (ASD) appears to be increasing globally due to the complex interaction of multiple biopsychosocial and environmental factors. Mobile phones, tablets, and other electronic gadgets have transformed our means of communication, and have also changed both healthcare and how we learn. These technological enhancements may have a positive impact on the lives of children, but there is currently a global scarcity of information on how information technology influences the education of children with ASD. Objective: This study was conducted in Rwandan schools and communities, and aimed to understand the perceptions of students with ASD, their parents, and their teachers, on the use of Information and Communication Technology (ICT) in the education of those with ASD. Methods: This qualitative descriptive study was conducted from December 2017 to July 2018. Researchers conducted four focus group discussions (FGDs) with 54 participants from different backgrounds: teachers, parents, and students with ASD. Each of the FGDs took approximately two and a half hours. A predefined set of open-ended questions were selected to discover people's perceptions regarding assistive technologies used in ASD, their effectiveness, the scope of using them in their context, and upcoming challenges during implementation. The interviews were recorded, transcribed, and analyzed. Results: The findings of the study revealed seven key themes: (1) the use of ICT for the education of children with ASD; (2) existing augmentative facilities for learning; (3) current patterns of use of ICT in education; (4) preferred areas of learning for ASD students; (5) integration of ICT into educational programs; (6) areas of interest outside the classroom; and (7) future opportunities and challenges in Rwanda. We found most of the study participants assumed that appropriate technology and related innovations might solve the challenges faced by learners with ASD in classrooms. Moreover, they thought that children with ASD more so enjoyed watching television, playing digital games, and drawing objects using gadgets than interacting with people or playing with other children. Conclusions: The use of various low-cost technical devices can aid with teaching and the education of children with autism in Rwanda. However, this area requires further research to discover the impact ICT can have on the education of children with ASD, so this study may become a starting point for further research in the area. ", doi="10.2196/12176", url="http://pediatrics.jmir.org/2019/2/e12176/", url="http://www.ncbi.nlm.nih.gov/pubmed/31573940" } @Article{info:doi/10.2196/13094, author="Ning, Michael and Daniels, Jena and Schwartz, Jessey and Dunlap, Kaitlyn and Washington, Peter and Kalantarian, Haik and Du, Michael and Wall, P. Dennis", title="Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study", journal="J Med Internet Res", year="2019", month="Jul", day="10", volume="21", number="7", pages="e13094", keywords="autism", keywords="autism spectrum disorder", keywords="crowdsourcing", keywords="prevalence", keywords="resources", keywords="infodemiology", keywords="epidemiology", abstract="Background: Autism affects 1 in every 59 children in the United States, according to estimates from the Centers for Disease Control and Prevention's Autism and Developmental Disabilities Monitoring Network in 2018. Although similar rates of autism are reported in rural and urban areas, rural families report greater difficulty in accessing resources. An overwhelming number of families experience long waitlists for diagnostic and therapeutic services. Objective: The objective of this study was to accurately identify gaps in access to autism care using GapMap, a mobile platform that connects families with local resources while continuously collecting up-to-date autism resource epidemiological information. Methods: After being extracted from various databases, resources were deduplicated, validated, and allocated into 7 categories based on the keywords identified on the resource website. The average distance between the individuals from a simulated autism population and the nearest autism resource in our database was calculated for each US county. Resource load, an approximation of demand over supply for diagnostic resources, was calculated for each US county. Results: There are approximately 28,000 US resources validated on the GapMap database, each allocated into 1 or more of the 7 categories. States with the greatest distances to autism resources included Alaska, Nevada, Wyoming, Montana, and Arizona. Of the 7 resource categories, diagnostic resources were the most underrepresented, comprising only 8.83\% (2472/28,003) of all resources. Alarmingly, 83.86\% (2635/3142) of all US counties lacked any diagnostic resources. States with the highest diagnostic resource load included West Virginia, Kentucky, Maine, Mississippi, and New Mexico. Conclusions: Results from this study demonstrate the sparsity and uneven distribution of diagnostic resources in the United States, which may contribute to the lengthy waitlists and travel distances---barriers to be overcome to be able to receive diagnosis in specific regions. More data are needed on autism diagnosis demand to better quantify resource needs across the United States. ", doi="10.2196/13094", url="http://www.jmir.org/2019/7/e13094/", url="http://www.ncbi.nlm.nih.gov/pubmed/31293243" } @Article{info:doi/10.2196/13668, author="Washington, Peter and Kalantarian, Haik and Tariq, Qandeel and Schwartz, Jessey and Dunlap, Kaitlyn and Chrisman, Brianna and Varma, Maya and Ning, Michael and Kline, Aaron and Stockham, Nathaniel and Paskov, Kelley and Voss, Catalin and Haber, Nick and Wall, Paul Dennis", title="Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks", journal="J Med Internet Res", year="2019", month="May", day="23", volume="21", number="5", pages="e13668", keywords="crowdsourcing", keywords="autism", keywords="mechanical turk", keywords="pediatrics", keywords="diagnostics", keywords="diagnosis", keywords="neuropsychiatric conditions", keywords="human-computer interaction", keywords="citizen healthcare", keywords="biomedical data science", keywords="mobile health", keywords="digital health", abstract="Background: Obtaining a diagnosis of neuropsychiatric disorders such as autism requires long waiting times that can exceed a year and can be prohibitively expensive. Crowdsourcing approaches may provide a scalable alternative that can accelerate general access to care and permit underserved populations to obtain an accurate diagnosis. Objective: We aimed to perform a series of studies to explore whether paid crowd workers on Amazon Mechanical Turk (AMT) and citizen crowd workers on a public website shared on social media can provide accurate online detection of autism, conducted via crowdsourced ratings of short home video clips. Methods: Three online studies were performed: (1) a paid crowdsourcing task on AMT (N=54) where crowd workers were asked to classify 10 short video clips of children as ``Autism'' or ``Not autism,'' (2) a more complex paid crowdsourcing task (N=27) with only those raters who correctly rated ?8 of the 10 videos during the first study, and (3) a public unpaid study (N=115) identical to the first study. Results: For Study 1, the mean score of the participants who completed all questions was 7.50/10 (SD 1.46). When only analyzing the workers who scored ?8/10 (n=27/54), there was a weak negative correlation between the time spent rating the videos and the sensitivity ($\rho$=--0.44, P=.02). For Study 2, the mean score of the participants rating new videos was 6.76/10 (SD 0.59). The average deviation between the crowdsourced answers and gold standard ratings provided by two expert clinical research coordinators was 0.56, with an SD of 0.51 (maximum possible SD is 3). All paid crowd workers who scored 8/10 in Study 1 either expressed enjoyment in performing the task in Study 2 or provided no negative comments. For Study 3, the mean score of the participants who completed all questions was 6.67/10 (SD 1.61). There were weak correlations between age and score (r=0.22, P=.014), age and sensitivity (r=--0.19, P=.04), number of family members with autism and sensitivity (r=--0.195, P=.04), and number of family members with autism and precision (r=--0.203, P=.03). A two-tailed t test between the scores of the paid workers in Study 1 and the unpaid workers in Study 3 showed a significant difference (P<.001). Conclusions: Many paid crowd workers on AMT enjoyed answering screening questions from videos, suggesting higher intrinsic motivation to make quality assessments. Paid crowdsourcing provides promising screening assessments of pediatric autism with an average deviation <20\% from professional gold standard raters, which is potentially a clinically informative estimate for parents. Parents of children with autism likely overfit their intuition to their own affected child. This work provides preliminary demographic data on raters who may have higher ability to recognize and measure features of autism across its wide range of phenotypic manifestations. ", doi="10.2196/13668", url="http://www.jmir.org/2019/5/e13668/", url="http://www.ncbi.nlm.nih.gov/pubmed/31124463" } @Article{info:doi/10.2196/13822, author="Tariq, Qandeel and Fleming, Lanyon Scott and Schwartz, Nicole Jessey and Dunlap, Kaitlyn and Corbin, Conor and Washington, Peter and Kalantarian, Haik and Khan, Z. Naila and Darmstadt, L. Gary and Wall, Paul Dennis", title="Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study", journal="J Med Internet Res", year="2019", month="Apr", day="24", volume="21", number="4", pages="e13822", keywords="autism", keywords="autism spectrum disorder", keywords="machine learning", keywords="developmental delays", keywords="clinical resources", keywords="Bangladesh", keywords="Biomedical Data Science", abstract="Background: Autism spectrum disorder (ASD) is currently diagnosed using qualitative methods that measure between 20-100 behaviors, can span multiple appointments with trained clinicians, and take several hours to complete. In our previous work, we demonstrated the efficacy of machine learning classifiers to accelerate the process by collecting home videos of US-based children, identifying a reduced subset of behavioral features that are scored by untrained raters using a machine learning classifier to determine children's ``risk scores'' for autism. We achieved an accuracy of 92\% (95\% CI 88\%-97\%) on US videos using a classifier built on five features. Objective: Using videos of Bangladeshi children collected from Dhaka Shishu Children's Hospital, we aim to scale our pipeline to another culture and other developmental delays, including speech and language conditions. Methods: Although our previously published and validated pipeline and set of classifiers perform reasonably well on Bangladeshi videos (75\% accuracy, 95\% CI 71\%-78\%), this work improves on that accuracy through the development and application of a powerful new technique for adaptive aggregation of crowdsourced labels. We enhance both the utility and performance of our model by building two classification layers: The first layer distinguishes between typical and atypical behavior, and the second layer distinguishes between ASD and non-ASD. In each of the layers, we use a unique rater weighting scheme to aggregate classification scores from different raters based on their expertise. We also determine Shapley values for the most important features in the classifier to understand how the classifiers' process aligns with clinical intuition. Results: Using these techniques, we achieved an accuracy (area under the curve [AUC]) of 76\% (SD 3\%) and sensitivity of 76\% (SD 4\%) for identifying atypical children from among developmentally delayed children, and an accuracy (AUC) of 85\% (SD 5\%) and sensitivity of 76\% (SD 6\%) for identifying children with ASD from those predicted to have other developmental delays. Conclusions: These results show promise for using a mobile video-based and machine learning--directed approach for early and remote detection of autism in Bangladeshi children. This strategy could provide important resources for developmental health in developing countries with few clinical resources for diagnosis, helping children get access to care at an early age. Future research aimed at extending the application of this approach to identify a range of other conditions and determine the population-level burden of developmental disabilities and impairments will be of high value. ", doi="10.2196/13822", url="http://www.jmir.org/2019/4/e13822/", url="http://www.ncbi.nlm.nih.gov/pubmed/31017583" } @Article{info:doi/10.2196/12974, author="Soreni, Noam and Cameron, H. Duncan and Streiner, L. David and Rowa, Karen and McCabe, E. Randi", title="Seasonality Patterns of Internet Searches on Mental Health: Exploratory Infodemiology Study", journal="JMIR Ment Health", year="2019", month="Apr", day="24", volume="6", number="4", pages="e12974", keywords="anxiety", keywords="depression", keywords="OCD", keywords="schizophrenia", keywords="autism", keywords="suicide", keywords="seasonality", keywords="Google", keywords="internet", keywords="infodemiology", keywords="infoveillance", keywords="mental health", abstract="Background: The study of seasonal patterns of public interest in psychiatric disorders has important theoretical and practical implications for service planning and delivery. The recent explosion of internet searches suggests that mining search databases yields unique information on public interest in mental health disorders, which is a significantly more affordable approach than population health studies. Objective: This study aimed to investigate seasonal patterns of internet mental health queries in Ontario, Canada. Methods: Weekly data on health queries in Ontario from Google Trends were downloaded for a 5-year period (2012-2017) for the terms ``schizophrenia,'' ``autism,'' ``bipolar,'' ``depression,'' ``anxiety,'' ``OCD'' (obsessive-compulsive disorder), and ``suicide.'' Control terms were overall search results for the terms ``health'' and ``how.'' Time-series analyses using a continuous wavelet transform were performed to isolate seasonal components in the search volume for each term. Results: All mental health queries showed significant seasonal patterns with peak periodicity occurring over the winter months and troughs occurring during summer, except for ``suicide.'' The comparison term ``health'' also exhibited seasonal periodicity, while the term ``how'' did not, indicating that general information seeking may not follow a seasonal trend in the way that mental health information seeking does. Conclusions: Seasonal patterns of internet search volume in a wide range of mental health terms were observed, with the exception of ``suicide.'' Our study demonstrates that monitoring internet search trends is an affordable, instantaneous, and naturalistic method to sample public interest in large populations and inform health policy planners. ", doi="10.2196/12974", url="https://mental.jmir.org/2019/4/e12974/", url="http://www.ncbi.nlm.nih.gov/pubmed/31017582" } @Article{info:doi/10.2196/11365, author="Bangerter, Abigail and Manyakov, V. Nikolay and Lewin, David and Boice, Matthew and Skalkin, Andrew and Jagannatha, Shyla and Chatterjee, Meenakshi and Dawson, Geraldine and Goodwin, S. Matthew and Hendren, Robert and Leventhal, Bennett and Shic, Frederick and Ness, Seth and Pandina, Gahan", title="Caregiver Daily Reporting of Symptoms in Autism Spectrum Disorder: Observational Study Using Web and Mobile Apps", journal="JMIR Ment Health", year="2019", month="Mar", day="26", volume="6", number="3", pages="e11365", keywords="autism spectrum disorder", keywords="ecological momentary assessment", keywords="symptom assessment", keywords="mobile app", keywords="mHealth", keywords="affect", keywords="patient reported outcome measures", abstract="Background: Currently, no medications are approved to treat core symptoms of autism spectrum disorder (ASD). One barrier to ASD medication development is the lack of validated outcome measures able to detect symptom change. Current ASD interventions are often evaluated using retrospective caregiver reports that describe general clinical presentation but often require recall of specific behaviors weeks after they occur, potentially reducing accuracy of the ratings. My JAKE, a mobile and Web-based mobile health (mHealth) app that is part of the Janssen Autism Knowledge Engine---a dynamically updated clinical research system---was designed to help caregivers of individuals with ASD to continuously log symptoms, record treatments, and track progress, to mitigate difficulties associated with retrospective reporting. Objective: My JAKE was deployed in an exploratory, noninterventional clinical trial to evaluate its utility and acceptability to monitor clinical outcomes in ASD. Hypotheses regarding relationships among daily tracking of symptoms, behavior, and retrospective caregiver reports were tested. Methods: Caregivers of individuals with ASD aged 6 years to adults (N=144) used the My JAKE app to make daily reports on their child's sleep quality, affect, and other self-selected specific behaviors across the 8- to 10-week observational study. The results were compared with commonly used paper-and-pencil scales acquired over a concurrent period at regular 4-week intervals. Results: Caregiver reporting of behaviors in real time was successfully captured by My JAKE. On average, caregivers made reports 2-3 days per week across the study period. Caregivers were positive about their use of the system, with over 50\% indicating that they would like to use My JAKE to track behavior outside of a clinical trial. More positive average daily reporting of overall type of day was correlated with 4 weekly reports of lower caregiver burden made at 4-week intervals (r=--0.27, P=.006, n=88) and with ASD symptoms (r=--0.42, P<.001, n=112). Conclusions: My JAKE reporting aligned with retrospective Web-based or paper-and-pencil scales. Use of mHealth apps, such as My JAKE, has the potential to increase the validity and accuracy of caregiver-reported outcomes and could be a useful way of identifying early changes in response to intervention. Such systems may also assist caregivers in tracking symptoms and behavior outside of a clinical trial, help with personalized goal setting, and monitoring of progress, which could collectively improve understanding of and quality of life for individuals with ASD and their families. Trial Registration: ClinicalTrials.gov NCT02668991;?https://clinicaltrials.gov/ct2/show/NCT02668991? ", doi="10.2196/11365", url="http://mental.jmir.org/2019/3/e11365/", url="http://www.ncbi.nlm.nih.gov/pubmed/30912762" } @Article{info:doi/10.2196/12264, author="Hswen, Yulin and Gopaluni, Anuraag and Brownstein, S. John and Hawkins, B. Jared", title="Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study", journal="JMIR Mhealth Uhealth", year="2019", month="Feb", day="12", volume="7", number="2", pages="e12264", keywords="autism", keywords="digital data", keywords="emotion", keywords="mobile phone", keywords="obsessive-compulsive disorder", keywords="social media", keywords="textual analysis", keywords="tweets", keywords="Twitter", keywords="infodemiology", abstract="Background: More than 3.5 million Americans live with autism spectrum disorder (ASD). Major challenges persist in diagnosing ASD as no medical test exists to diagnose this disorder. Digital phenotyping holds promise to guide in the clinical diagnoses and screening of ASD. Objective: This study aims to explore the feasibility of using the Web-based social media platform Twitter to detect psychological and behavioral characteristics of self-identified persons with ASD. Methods: Data from Twitter were retrieved from 152 self-identified users with ASD and 182 randomly selected control users from March 22, 2012 to July 20, 2017. We conducted a between-group comparative textual analysis of tweets about repetitive and obsessive-compulsive behavioral characteristics typically associated with ASD. In addition, common emotional characteristics of persons with ASD, such as fear, paranoia, and anxiety, were examined between groups through textual analysis. Furthermore, we compared the timing of tweets between users with ASD and control users to identify patterns in communication. Results: Users with ASD posted a significantly higher frequency of tweets related to the specific repetitive behavior of counting compared with control users (P<.001). The textual analysis of obsessive-compulsive behavioral characteristics, such as fixate, excessive, and concern, were significantly higher among users with ASD compared with the control group (P<.001). In addition, emotional terms related to fear, paranoia, and anxiety were tweeted at a significantly higher rate among users with ASD compared with control users (P<.001). Users with ASD posted a smaller proportion of tweets during time intervals of 00:00-05:59 (P<.001), 06:00-11:59 (P<.001), and 18:00-23.59 (P<.001), as well as a greater proportion of tweets from 12:00 to 17:59 (P<.001) compared with control users. Conclusions: Social media may be a valuable resource for observing unique psychological characteristics of self-identified persons with ASD. Collecting and analyzing data from these digital platforms may afford opportunities to identify the characteristics of ASD and assist in the diagnosis or verification of ASD. This study highlights the feasibility of leveraging digital data for gaining new insights into various health conditions. ", doi="10.2196/12264", url="http://mhealth.jmir.org/2019/2/e12264/", url="http://www.ncbi.nlm.nih.gov/pubmed/30747718" } @Article{info:doi/10.2196/11402, author="Alkhalifah, Shahad and Aldhalaan, Hesham", title="Telehealth Services for Children With Autism Spectrum Disorders in Rural Areas of the Kingdom of Saudi Arabia: Overview and Recommendations", journal="JMIR Pediatr Parent", year="2018", month="Nov", day="15", volume="1", number="2", pages="e11402", keywords="autism spectrum disorders", keywords="intervention", keywords="Saudi Arabia", keywords="telehealth", doi="10.2196/11402", url="http://pediatrics.jmir.org/2018/2/e11402/", url="http://www.ncbi.nlm.nih.gov/pubmed/31518306" } @Article{info:doi/10.2196/10497, author="Leroy, Gondy and Gu, Yang and Pettygrove, Sydney and Galindo, K. Maureen and Arora, Ananyaa and Kurzius-Spencer, Margaret", title="Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application", journal="J Med Internet Res", year="2018", month="Nov", day="07", volume="20", number="11", pages="e10497", keywords="parser", keywords="natural language processing", keywords="complex entity extraction", keywords="Autism Spectrum Disorder", keywords="DSM", keywords="electronic health records", keywords="decision tree", keywords="machine learning", abstract="Background: Electronic health records (EHRs) bring many opportunities for information utilization. One such use is the surveillance conducted by the Centers for Disease Control and Prevention to track cases of autism spectrum disorder (ASD). This process currently comprises manual collection and review of EHRs of 4- and 8-year old children in 11 US states for the presence of ASD criteria. The work is time-consuming and expensive. Objective: Our objective was to automatically extract from EHRs the description of behaviors noted by the clinicians in evidence of the diagnostic criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM). Previously, we reported on the classification of entire EHRs as ASD or not. In this work, we focus on the extraction of individual expressions of the different ASD criteria in the text. We intend to facilitate large-scale surveillance efforts for ASD and support analysis of changes over time as well as enable integration with other relevant data. Methods: We developed a natural language processing (NLP) parser to extract expressions of 12 DSM criteria using 104 patterns and 92 lexicons (1787 terms). The parser is rule-based to enable precise extraction of the entities from the text. The entities themselves are encompassed in the EHRs as very diverse expressions of the diagnostic criteria written by different people at different times (clinicians, speech pathologists, among others). Due to the sparsity of the data, a rule-based approach is best suited until larger datasets can be generated for machine learning algorithms. Results: We evaluated our rule-based parser and compared it with a machine learning baseline (decision tree). Using a test set of 6636 sentences (50 EHRs), we found that our parser achieved 76\% precision, 43\% recall (ie, sensitivity), and >99\% specificity for criterion extraction. The performance was better for the rule-based approach than for the machine learning baseline (60\% precision and 30\% recall). For some individual criteria, precision was as high as 97\% and recall 57\%. Since precision was very high, we were assured that criteria were rarely assigned incorrectly, and our numbers presented a lower bound of their presence in EHRs. We then conducted a case study and parsed 4480 new EHRs covering 10 years of surveillance records from the Arizona Developmental Disabilities Surveillance Program. The social criteria (A1 criteria) showed the biggest change over the years. The communication criteria (A2 criteria) did not distinguish the ASD from the non-ASD records. Among behaviors and interests criteria (A3 criteria), 1 (A3b) was present with much greater frequency in the ASD than in the non-ASD EHRs. Conclusions: Our results demonstrate that NLP can support large-scale analysis useful for ASD surveillance and research. In the future, we intend to facilitate detailed analysis and integration of national datasets. ", doi="10.2196/10497", url="https://www.jmir.org/2018/11/e10497/", url="http://www.ncbi.nlm.nih.gov/pubmed/30404767" } @Article{info:doi/10.2196/mental.9564, author="Benyakorn, Songpoom and Calub, A. Catrina and Riley, J. Steven and Schneider, Andrea and Iosif, Ana-Maria and Solomon, Marjorie and Hessl, David and Schweitzer, B. Julie", title="Computerized Cognitive Training in Children With Autism and Intellectual Disabilities: Feasibility and Satisfaction Study", journal="JMIR Ment Health", year="2018", month="May", day="25", volume="5", number="2", pages="e40", keywords="autism", keywords="training", keywords="working memory", keywords="intellectual disability", keywords="treatment adherence", keywords="satisfaction", abstract="Background: Researchers are increasingly interested in testing and developing computerized cognitive training interventions for individuals with autism spectrum disorder due to the limited accessibility of treatments for this disorder. Understanding the feasibility of testing cognitive interventions for this population is critical, especially for individuals with ASD who have low to moderate intellectual ability. Objective: The aim of the study was to evaluate the feasibility of computerized cognitive training as measured by attrition rate and a parent satisfaction survey. Methods: A total of 26 participants aged 8-17 years with an autism spectrum disorder diagnosis and significant intellectual impairment were enrolled (mean age 11.1 years). They were instructed to complete 25 sessions of Cogmed Working Memory Training in 5 to 6 weeks with coach assistance. Attrition rate and parent satisfaction surveys were measured after the completion of training. Results: Most participants (96\%, 25/26) completed the training and indicated high satisfaction (>88\%). However, among the participants who completed the training, 5 participants (19\%) were unable to finish in 6 weeks, the recommended training period by Cogmed. Parents noted various positive (eg, voice-overs) and negative (eg, particular graphic and sounds associated with a stimulus) features of the game that they thought affected their child's response. Conclusions: Children with autism spectrum disorder and intellectual impairments can successfully participate in computerized cognitive training interventions but may require additional weeks to complete the training beyond the time needed for children without intellectual impairments. The overall completion rate, with extended time to complete the training, was high. Developers of cognitive training programs for this population should take into account potential issues regarding the noise level of stimuli and characteristics of the visual graphics. ", doi="10.2196/mental.9564", url="http://mental.jmir.org/2018/2/e40/", url="http://www.ncbi.nlm.nih.gov/pubmed/29802090" } @Article{info:doi/10.2196/jmir.9496, author="Ben-Sasson, Ayelet and Robins, L. Diana and Yom-Tov, Elad", title="Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach", journal="J Med Internet Res", year="2018", month="Apr", day="24", volume="20", number="4", pages="e134", keywords="autistic disorder", keywords="early diagnosis", keywords="screening", keywords="parents", keywords="child", keywords="expression of concern", keywords="technology", keywords="machine learning", abstract="Background: Parents are likely to seek Web-based communities to verify their suspicions of autism spectrum disorder markers in their child. Automated tools support human decisions in many domains and could therefore potentially support concerned parents. Objective: The objective of this study was to test the feasibility of assessing autism spectrum disorder risk in parental concerns from Web-based sources, using automated text analysis tools and minimal standard questioning. Methods: Participants were 115 parents with concerns regarding their child's social-communication development. Children were 16- to 30-months old, and 57.4\% (66/115) had a family history of autism spectrum disorder. Parents reported their concerns online, and completed an autism spectrum disorder-specific screener, the Modified Checklist for Autism in Toddlers-Revised, with Follow-up (M-CHAT-R/F), and a broad developmental screener, the Ages and Stages Questionnaire (ASQ). An algorithm predicted autism spectrum disorder risk using a combination of the parent's text and a single screening question, selected by the algorithm to enhance prediction accuracy. Results: Screening measures identified 58\% (67/115) to 88\% (101/115) of children at risk for autism spectrum disorder. Children with a family history of autism spectrum disorder were 3 times more likely to show autism spectrum disorder risk on screening measures. The prediction of a child's risk on the ASQ or M-CHAT-R was significantly more accurate when predicted from text combined with an M-CHAT-R question selected (automatically) than from the text alone. The frequently automatically selected M-CHAT-R questions that predicted risk were: following a point, make-believe play, and concern about deafness. Conclusions: The internet can be harnessed to prescreen for autism spectrum disorder using parental concerns by administering a few standardized screening questions to augment this process. ", doi="10.2196/jmir.9496", url="http://www.jmir.org/2018/4/e134/", url="http://www.ncbi.nlm.nih.gov/pubmed/29691210" } @Article{info:doi/10.2196/mental.9631, author="Vahabzadeh, Arshya and Keshav, U. Neha and Salisbury, P. Joseph and Sahin, T. Ned", title="Improvement of Attention-Deficit/Hyperactivity Disorder Symptoms in School-Aged Children, Adolescents, and Young Adults With Autism via a Digital Smartglasses-Based Socioemotional Coaching Aid: Short-Term, Uncontrolled Pilot Study", journal="JMIR Ment Health", year="2018", month="Apr", day="02", volume="5", number="2", pages="e25", keywords="autism spectrum disorder", keywords="Asperger syndrome", keywords="augmented reality", keywords="virtual reality", keywords="artificial intelligence", keywords="affective computing", keywords="patient education as a topic", keywords="ADHD", keywords="attention deficit disorder with hyperactivity", keywords="attention", keywords="smartglasses", abstract="Background: People with autism spectrum disorder (ASD) commonly experience symptoms related to attention-deficit/hyperactivity disorder (ADHD), including hyperactivity, inattention, and impulsivity. One-third of ASD cases may be complicated by the presence of ADHD. Individuals with dual diagnoses face greater barriers to accessing treatment for ADHD and respond less positively to primary pharmacologic interventions. Nonpharmacologic technology-aided tools for hyperactivity and inattention in people with ASD are being developed, although research into their efficacy and safety remains limited. Objective: The objective of this preliminary study was to describe the changes in ADHD-related symptoms in children, adolescents, and young adults with ASD immediately after use of the Empowered Brain system, a behavioral and social communication aid for ASD running on augmented reality smartglasses. Methods: We recruited 8 children, adolescents, and young adults with ASD (male to female ratio of 7:1, mean age 15 years, range 11.7-20.5 years) through a Web-based research signup form. The baseline score on the hyperactivity subscale of the Aberrant Behavioral Checklist (ABC-H), a measure of hyperactivity, inattention, and impulsivity, determined their classification into a high ADHD-related symptom group (n=4, ABC-H?13) and a low ADHD-related symptom group (n=4, ABC-H<13). All participants received an intervention with Empowered Brain, where they used smartglasses-based social communication and behavioral modules while interacting with their caregiver. We then calculated caregiver-reported ABC-H scores at 24 and 48 hours after the session. Results: All 8 participants were able to complete the intervention session. Postintervention ABC-H scores were lower for most participants at 24 hours (n=6, 75\%) and for all participants at 48 hours (n=8, 100\%). At 24 hours after the session, average participant ABC-H scores decreased by 54.9\% in the high ADHD symptom group and by 20\% in the low ADHD symptom group. At 48 hours after the session, ABC-H scores compared with baseline decreased by 56.4\% in the high ADHD symptom group and by 66.3\% in the low ADHD symptom group. Conclusions: This study provides initial evidence for the possible potential of the Empowered Brain system to reduce ADHD-related symptoms, such as hyperactivity, inattention, and impulsivity, in school-aged children, adolescents, and young adults with ASD. This digital smartglasses intervention can potentially be targeted at a broader array of mental health conditions that exhibit transdiagnostic attentional and social communication deficits, including schizophrenia and bipolar disorder. Further research is required to understand the clinical importance of these observed changes and to conduct longitudinal studies on this intervention with control groups and larger sample sizes. ", doi="10.2196/mental.9631", url="http://mental.jmir.org/2018/2/e25/", url="http://www.ncbi.nlm.nih.gov/pubmed/29610109" } @Article{info:doi/10.2196/games.8428, author="Sim{\~o}es, Marco and Bernardes, Miguel and Barros, Fernando and Castelo-Branco, Miguel", title="Virtual Travel Training for Autism Spectrum Disorder: Proof-of-Concept Interventional Study", journal="JMIR Serious Games", year="2018", month="Mar", day="20", volume="6", number="1", pages="e5", keywords="Autism Spectrum Disorder", keywords="serious games", keywords="virtual reality", keywords="virtual reality therapy", keywords="travel train", keywords="bus.", abstract="Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and repetitive patterns of behavior, which can lead to deficits in adaptive behavior. In this study, a serious game was developed to train individuals with ASD for an important type of outdoor activity, which is the use of buses as a means of transportation. Objective: The aim of this study was to develop a serious game that defines a ``safe environment'' where the players became familiar with the process of taking a bus and to validate if it could be used effectively to teach bus-taking routines and adaptive procedures to individuals with ASD. Methods: In the game, players were placed in a three-dimensional city and were submitted to a set of tasks that involved taking buses to reach specific destinations. Participants with ASD (n=10) underwent between 1 to 3 training sessions. Participants with typical development (n=10) were also included in this study for comparison purposes and received 1 control session. Results: We found a statistically significant increase in the measures of knowledge of the process of riding a bus, a reduction in the electrodermal activity (a metric of anxiety) measured inside the bus environments, and a high success rate of their application within the game (93.8\%). Conclusions: The developed game proved to be potentially useful in the context of emerging immersive virtual reality technologies, of which use in therapies and serious games is still in its infancy. Our findings suggest that serious games, using these technologies, can be used effectively in helping people with ASD become more independent in outdoor activities, specifically regarding the use of buses for transportation. ", doi="10.2196/games.8428", url="http://games.jmir.org/2018/1/e5/", url="http://www.ncbi.nlm.nih.gov/pubmed/29559425" } @Article{info:doi/10.2196/humanfactors.8785, author="Sahin, T. Ned and Keshav, U. Neha and Salisbury, P. Joseph and Vahabzadeh, Arshya", title="Second Version of Google Glass as a Wearable Socio-Affective Aid: Positive School Desirability, High Usability, and Theoretical Framework in a Sample of Children with Autism", journal="JMIR Hum Factors", year="2018", month="Jan", day="04", volume="5", number="1", pages="e1", keywords="autism", keywords="technology", keywords="digital health", keywords="augmented reality", keywords="virtual reality", keywords="smartglasses", keywords="usability", keywords="schools", keywords="education", keywords="classroom", keywords="IDEA", keywords="IEP", keywords="special education", abstract="Background: Computerized smartglasses are being developed as an assistive technology for daily activities in children and adults with autism spectrum disorder (ASD). While smartglasses may be able to help with educational and behavioral needs, their usability and acceptability in children with ASD is largely unknown. There have been reports of negative social perceptions surrounding smartglasses use in mainstream populations, a concern given that assistive technologies may already carry their own stigma. Children with ASD may also have a range of additional behavioral, developmental, and social challenges when asked to use this emerging technology in school and home settings. Objective: The usability and acceptability of Glass Enterprise Edition (Glass), the successor to Google Glass smartglasses, were explored in children with ASD and their caregivers. Methods: Eight children with ASD and their caregivers were recruited to attend a demonstration session with Glass smartglasses the week they were publicly released. The children had a wide range of ability, including limited speech to speaking, and represented a full range of school ages (6 to 17 years). Children and caregivers were interviewed about their experience of using the smartglasses and whether they would use them at school and home. Results: All 8 children succeeded in using Glass and did not feel stressed (8/8, 100\%) or experience any overwhelming sensory or emotional issues during the session (8/8, 100\%). All 8 children (8/8, 100\%) endorsed that they would be willing to wear and use the device in both home and school settings. Caregivers felt the experience was fun for the children (8/8, 100\%), and most caregivers felt the experience was better than they had expected (6/8, 75\%). Conclusions: A wide age and ability range of children with ASD used Glass immediately after it was released and found it to be usable and acceptable. Despite concerns about potential stigma or social acceptability, all of the children were prepared to use the technology in both home and school settings. Encouragingly, most caregivers noted a very positive response. There were no behavioral, developmental, or social- or stigma-related concerns during or after the session. Smartglasses may be a useful future technology for children with ASD and are readily accepted for use by children with ASD and their caregivers. ", doi="10.2196/humanfactors.8785", url="http://humanfactors.jmir.org/2018/1/e1/", url="http://www.ncbi.nlm.nih.gov/pubmed/29301738" } @Article{info:doi/10.2196/resprot.8260, author="Naheed, Aliya and Koly, Nahar Kamrun and Uddin Ahmed, Helal and Akhter, Shaheen and Uddin, Jalal M. M. and Smith Fawzi, C. Mary and Chandir, Subhash and Mannan, Muzharul and Hossain, Saima and Nelson, Charles and Munir, Kerim", title="Implementing a Mental Health Care Program and Home-Based Training for Mothers of Children With Autism Spectrum Disorder in an Urban Population in Bangladesh: Protocol for a Feasibility Assessment Study", journal="JMIR Res Protoc", year="2017", month="Dec", day="14", volume="6", number="12", pages="e251", keywords="depression", keywords="psychosocial", keywords="counseling", keywords="autism spectrum disorder", keywords="mothers", keywords="training", abstract="Background: Mothers of children with autism spectrum disorder (ASD) have reported a higher level of depression than mothers of children with other neurodevelopmental disorders in both developed and developing countries. Mothers are the lifetime caregivers of children with ASD, and a high burden of depression can negatively impact their ability to provide care. However, access to mental health services in primary care is limited, given the scarcity of qualified providers in Bangladesh. Objective: We aim to pilot the feasibility of integrating mental health services for the mothers of children with ASD attending schools offering ASD care and improve skills of mothers for child care through a home-based training program. Methods: The study will be conducted in two selected schools in Dhaka in Bangladesh that have been offering services for ASD for more than 10 years. A female psychologist will be deployed at the schools to offer nonpharmacological services for all mothers having a depressive episode. Referral for pharmacological treatment will be made at the discretion of supervising psychiatrists. An ASD special educator will provide training to the mothers for enhancing their child care skills at home on a monthly basis. The proposed intervention package will be implemented over a period of 4-6 months, and the feasibility of the intervention will be assessed through a pre- and postintervention evaluation by obtaining the perspectives of various stakeholders involved in the implementation of mental health services and maternal training. The primary outcome will include assessment of acceptability, adaptability, demand, practicality, implementation, and integration of the package intervention in the school settings. The secondary outcomes will include assessment of: 1) the prevalence of maternal depression; 2) children's behavioral, social, and communication skills; and 3) the intervention participation costs incurred by institutions and families. Results: Between February and March 2017, 188 mothers of children with ASD were screened for depression following a written informed consent. Based on the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV), the Structured Clinical Interview for the DSM-IV (SCID-1) was administered to 66 mothers. In-depth interviews were conducted with 10 mothers and 8 various stakeholders. Between January-June 2017, the team finalized a draft psychosocial counseling module and a maternal training module. Between April-May 2017, mental health services were provided by psychologists to 41 mothers who attended the counseling centers at each school. Three special educators have been trained in June 2017 to initiate training of the participating mothers. Conclusions: This is the first study of a mental health intervention for mothers of children with ASD to reduce their burden of depression and improve the outcomes of their children. The findings will inform the provision of services for children with ASD and their mothers in Bangladesh and similar settings. ", doi="10.2196/resprot.8260", url="http://www.researchprotocols.org/2017/12/e251/", url="http://www.ncbi.nlm.nih.gov/pubmed/29242177" } @Article{info:doi/10.2196/mhealth.8534, author="Keshav, U. Neha and Salisbury, P. Joseph and Vahabzadeh, Arshya and Sahin, T. Ned", title="Social Communication Coaching Smartglasses: Well Tolerated in a Diverse Sample of Children and Adults With Autism", journal="JMIR Mhealth Uhealth", year="2017", month="Sep", day="21", volume="5", number="9", pages="e140", keywords="autism", keywords="tech", keywords="digital health", keywords="smartglasses", keywords="augmented reality", keywords="autism spectrum disorder", keywords="technology", keywords="medtech", keywords="education", abstract="Background: Augmented reality (AR) smartglasses are an emerging technology that is under investigation as a social communication aid for children and adults with autism spectrum disorder (ASD) and as a research tool to aid with digital phenotyping. Tolerability of this wearable technology in people with ASD is an important area for research, especially as these individuals may experience sensory, cognitive, and attentional challenges. Objective: The aim of this study was to assess the tolerability and usability of a novel smartglasses system that has been designed as a social communication aid for children and adults with autism (the Brain Power Autism System [BPAS]). BPAS runs on Google Glass Explorer Edition and other smartglasses, uses both AR and affective artificial intelligence, and helps users learn key social and emotional skills. Methods: A total of 21 children and adults with ASD across a spectrum of severity used BPAS for a coaching session. The user's tolerability to the smartglasses, user being able to wear the smartglasses for 1 minute (initial tolerability threshold), and user being able to wear the smartglasses for the entire duration of the coaching session (whole session tolerability threshold) were determined through caregiver report. Results: Of 21 users, 19 (91\%) demonstrated tolerability on all 3 measures. Caregivers reported 21 out of 21 users (100\%) as tolerating the experience, while study staff found only 19 out of 21 users managed to demonstrate initial tolerability (91\%). Of the 19 users who demonstrated initial tolerability, all 19 (100\%) were able to use the smartglasses for the entire session (whole session tolerability threshold). Caregivers reported that 19 out of 21 users (91\%) successfully used BPAS, and users surpassed caregiver expectations in 15 of 21 cases (71\%). Users who could communicate reported BPAS as being comfortable (94\%). Conclusions: This preliminary report suggests that BPAS is well tolerated and usable to a diverse age- and severity-range of people with ASD. This is encouraging as these devices are being developed as assistive technologies for people with ASD. Further research should focus on improving smartglasses design and exploring their efficacy in helping with social communication in children and adults with ASD. ", doi="10.2196/mhealth.8534", url="http://mhealth.jmir.org/2017/9/e140/", url="http://www.ncbi.nlm.nih.gov/pubmed/28935618" } @Article{info:doi/10.2196/jmir.6651, author="Parsons, Dave and Cordier, Reinie and Vaz, Sharmila and Lee, C. Hoe", title="Parent-Mediated Intervention Training Delivered Remotely for Children With Autism Spectrum Disorder Living Outside of Urban Areas: Systematic Review", journal="J Med Internet Res", year="2017", month="Aug", day="14", volume="19", number="8", pages="e198", keywords="Autistic disorder", keywords="Internet", keywords="parents", keywords="rural health services", keywords="telemedicine", abstract="Background: Parent training programs for families living outside of urban areas can be used to improve the social behavior and communication skills in children with autism spectrum disorder (ASD). However, no review has been conducted to investigate these programs. Objective: The aim of this study was to (1) systematically review the existing evidence presented by studies on parent-mediated intervention training, delivered remotely for parents having children with ASD and living outside of urban areas; (2) provide an overview of current parent training interventions used with this population; (3) and provide an overview of the method of delivery of the parent training interventions used with this population. Methods: Guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, we conducted a comprehensive review across 5 electronic databases (CINAHL, Embase, ERIC, PsycINFO, and Pubmed) on July 4, 2016, searching for studies investigating parent-mediated intervention training for families living outside of urban centers who have a child diagnosed with ASD. Two independent researchers reviewed the articles for inclusion, and assessment of methodological quality was based on the Kmet appraisal checklist. Results: Seven studies met the eligibility criteria, including 2 prepost cohort studies, 3 multiple baseline studies, and 2 randomized controlled trials (RCTs). Interventions included mostly self-guided websites: with and without therapist assistance (n=6), with training videos, written training manuals, and videoconferencing. Post intervention, studies reported significant improvements (P<.05) in parent knowledge (n=4), parent intervention fidelity (n=6), and improvements in children's social behavior and communication skills (n=3). A high risk of bias existed within all of the studies because of a range of factors including small sample sizes, limited use of standardized outcome measures, and a lack of control groups to negate confounding factors. Conclusions: There is preliminary evidence that parent-mediated intervention training delivered remotely may improve parent knowledge, increase parent intervention fidelity, and improve the social behavior and communication skills for children with ASD. A low number of RCTs, difficulty in defining the locality of the population, and a paucity of standardized measures limit the generalization of the findings to the target population. Future studies should investigate the appropriateness and feasibility of the interventions, include RCTs to control for bias, and utilize standard outcome measures. ", doi="10.2196/jmir.6651", url="http://www.jmir.org/2017/8/e198/", url="http://www.ncbi.nlm.nih.gov/pubmed/28807892" } @Article{info:doi/10.2196/jmir.7484, author="Ingersoll, Brooke and Shannon, Katherine and Berger, Natalie and Pickard, Katherine and Holtz, Bree", title="Self-Directed Telehealth Parent-Mediated Intervention for Children With Autism Spectrum Disorder: Examination of the Potential Reach and Utilization in Community Settings", journal="J Med Internet Res", year="2017", month="Jul", day="12", volume="19", number="7", pages="e248", keywords="autism", keywords="parenting education", keywords="telemedicine", abstract="Background: There is a significant need for strategies to increase access to evidence-based interventions for children with autism spectrum disorder (ASD).?One novel approach is to train parents to use evidence-based interventions for their child with ASD via telehealth.?Pilot work examining the efficacy of one such program, ImPACT Online, demonstrated a high rate of parent program engagement, low attrition, and associated gains in parent learning and child social communication. Objective: The objective of this study was to conduct an open trial of ImPACT Online to better understand its dissemination potential. Methods: We examined the reach and representativeness of families who registered (n=36) compared to families who were referred (n=139) to the open trial for one referral site. We then compared the demographics of all families who enrolled in the open trial (n=112) to families who enrolled in one of two controlled trials of the same program (n=50). We also examined metrics of program engagement for the open and controlled trials, the relationship between program engagement and changes in parents' intervention knowledge, and program evaluation for the participants in the open trial. Results: In total, 25.8\% (36/139) of the parents who were given information about the program at their child's diagnostic feedback session registered with the program. The parents who enrolled in the open (OT) and controlled trials (CT), respectively, were similar in gender (OT: 84.8\% (95/112); CT: 88\% (44/50), female), marital status (OT: 80.4\% (90/112) ; CT: 69.6\% (32/46), married), education (OT: 58.0\% (65/112); CT: 54.0\% (27/50), college degree or higher), and employment status (OT: 58.0\% (65/112); CT: 65.3\% (32/49), employed outside the home). The child participants were similar in terms of gender (OT: 83.0\% (93/112); CT: 76.0\% (38/50), male) and race and ethnicity (OT: 38.4\% (43/112); CT: 24.0\% (12/50), minority). However, the mean chronological age of the child participants in the open trial group was significantly higher (Mean=60.0 months) than in the controlled trial group (Mean=43.0 months), with t160=5.22, P<.001. Parents in the open trial engaged with the program at a significantly lower rate than the controlled trial, F3,81=21.14, P<.001. Program engagement was significantly associated with gains in parent intervention knowledge across both the groups, beta=.41, t=2.43, P=.02. Participants in the open access trial evaluated the program highly, but several barriers were noted. Conclusions: These data suggest that additional strategies may need to be developed to support families in using telehealth-based parent-mediated intervention in community settings. ", doi="10.2196/jmir.7484", url="http://www.jmir.org/2017/7/e248/", url="http://www.ncbi.nlm.nih.gov/pubmed/28701294" } @Article{info:doi/10.2196/resprot.7245, author="Chen, Yuzen Robert and Feltes, Robert Jordan and Tzeng, Shun William and Lu, Yunzhu Zoe and Pan, Michael and Zhao, Nan and Talkin, Rebecca and Javaherian, Kavon and Glowinski, Anne and Ross, Will", title="Phone-Based Interventions in Adolescent Psychiatry: A Perspective and Proof of Concept Pilot Study With a Focus on Depression and Autism", journal="JMIR Res Protoc", year="2017", month="Jun", day="16", volume="6", number="6", pages="e114", keywords="telemedicine", keywords="depression", keywords="autistic disorder", keywords="mobile applications", keywords="text messaging", keywords="child", keywords="mental health", abstract="Background: Telemedicine has emerged as an innovative platform to diagnose and treat psychiatric disorders in a cost-effective fashion. Previous studies have laid the functional framework for monitoring and treating child psychiatric disorders electronically using videoconferencing, mobile phones (smartphones), and Web-based apps. However, phone call and text message (short message service, SMS) interventions in adolescent psychiatry are less studied than other electronic platforms. Further investigations on the development of these interventions are needed. Objective: The aim of this paper was to explore the utility of text message interventions in adolescent psychiatry and describe a user feedback-driven iterative design process for text message systems. Methods: We developed automated text message interventions using a platform for both depression (EpxDepression) and autism spectrum disorder (ASD; EpxAutism) and conducted 2 pilot studies for each intervention (N=3 and N=6, respectively). The interventions were prescribed by and accessible to the patients' healthcare providers. EpxDepression and EpxAutism utilized an automated system to triage patients into 1 of 3 risk categories based on their text responses and alerted providers directly via phone and an online interface when patients met provider-specified risk criteria. Rapid text-based feedback from participants and interviews with providers allowed for quick iterative cycles to improve interventions. Results: Patients using EpxDepression had high weekly response rates (100\% over 2 to 4 months), but exhibited message fatigue with daily prompts with mean (SD) overall response rates of 66.3\% (21.6\%) and 64.7\% (8.2\%) for mood and sleep questionnaires, respectively. In contrast, parents using EpxAutism displayed both high weekly and overall response rates (100\% and 85\%, respectively, over 1 to 4 months) that did not decay significantly with time. Monthly participant feedback surveys for EpxDepression (7 surveys) and EpxAutism (18 surveys) preliminarily indicated that for both interventions, daily messages constituted the ``perfect amount'' of contact and that EpxAutism, but not EpxDepression, improved patient communication with providers. Notably, EpxDepression detected thoughts of self-harm in patients before their case managers or caregivers were aware of such ideation. Conclusions: Text-message interventions in adolescent psychiatry can provide a cost-effective and engaging method to track symptoms, behavior, and ideation over time. Following the collection of pilot data and feedback from providers and patients, larger studies are already underway to validate the clinical utility of EpxDepression and EpxAutism. Trial Registration: Clinicaltrials.gov NCT03002311; https://clinicaltrials.gov/ct2/show/NCT03002311 (Archived by WebCite at http://www.webcitation.org/6qQtlCIS0) ", doi="10.2196/resprot.7245", url="http://www.researchprotocols.org/2017/6/e114/", url="http://www.ncbi.nlm.nih.gov/pubmed/28623183" } @Article{info:doi/10.2196/publichealth.7150, author="Albert, Nikhila and Daniels, Jena and Schwartz, Jessey and Du, Michael and Wall, P. Dennis", title="GapMap: Enabling Comprehensive Autism Resource Epidemiology", journal="JMIR Public Health Surveill", year="2017", month="May", day="04", volume="3", number="2", pages="e27", keywords="autism", keywords="autism spectrum disorder", keywords="crowdsourcing", keywords="prevalence", keywords="resources", keywords="epidemiology", abstract="Background: For individuals with autism spectrum disorder (ASD), finding resources can be a lengthy and difficult process. The difficulty in obtaining global, fine-grained autism epidemiological data hinders researchers from quickly and efficiently studying large-scale correlations among ASD, environmental factors, and geographical and cultural factors. Objective: The objective of this study was to define resource load and resource availability for families affected by autism and subsequently create a platform to enable a more accurate representation of prevalence rates and resource epidemiology. Methods: We created a mobile application, GapMap, to collect locational, diagnostic, and resource use information from individuals with autism to compute accurate prevalence rates and better understand autism resource epidemiology. GapMap is hosted on AWS S3, running on a React and Redux front-end framework. The backend framework is comprised of an AWS API Gateway and Lambda Function setup, with secure and scalable end points for retrieving prevalence and resource data, and for submitting participant data. Measures of autism resource scarcity, including resource load, resource availability, and resource gaps were defined and preliminarily computed using simulated or scraped data. Results: The average distance from an individual in the United States to the nearest diagnostic center is approximately 182 km (50 miles), with a standard deviation of 235 km (146 miles). The average distance from an individual with ASD to the nearest diagnostic center, however, is only 32 km (20 miles), suggesting that individuals who live closer to diagnostic services are more likely to be diagnosed. Conclusions: This study confirmed that individuals closer to diagnostic services are more likely to be diagnosed and proposes GapMap, a means to measure and enable the alleviation of increasingly overburdened diagnostic centers and resource-poor areas where parents are unable to diagnose their children as quickly and easily as needed. GapMap will collect information that will provide more accurate data for computing resource loads and availability, uncovering the impact of resource epidemiology on age and likelihood of diagnosis, and gathering localized autism prevalence rates. ", doi="10.2196/publichealth.7150", url="http://publichealth.jmir.org/2017/2/e27/", url="http://www.ncbi.nlm.nih.gov/pubmed/28473303" } @Article{info:doi/10.2196/mental.6605, author="Gwynette, Frampton McLeod and Morriss, Danielle and Warren, Nancy and Truelove, James and Warthen, Jennifer and Ross, Paul Charles and Mood, George and Snook, Anne Charlotte and Borckardt, Jeffrey", title="Social Skills Training for Adolescents With Autism Spectrum Disorder Using Facebook (Project Rex Connect): A Survey Study", journal="JMIR Ment Health", year="2017", month="Jan", day="23", volume="4", number="1", pages="e4", keywords="autism", keywords="social media", keywords="social skills", abstract="Background: Adolescents with autism spectrum disorder (ASD) spend more time using electronic screen media than neurotypical peers; preliminary evidence suggests that computer-assisted or Web-based interventions may be beneficial for social skills acquisition. The current generation of adolescents accesses the Internet through computers or phones almost daily, and Facebook is the most frequently used social media platform among teenagers. This is the first research study to explore the use of Facebook as a therapeutic tool for adolescents with ASD. Objective: To study the feasibility and clinical impact of using a Web-based social platform in combination with social skills training for adolescents with ASD. Methods: This pilot study enrolled 6 participants (all males; mean age 14.1 years) in an online social skills training group using Facebook. Data was collected on the participants' social and behavioral functioning at the start and conclusion of the intervention. Outcome measures included the Social Responsiveness Scale-2, the Social Skills Improvement System Rating Scale, and the Project Rex Parent Survey. Participants were surveyed at the conclusion of the intervention regarding their experience. Results: No statistically significant differences in measurable outcomes were observed. However, the online addition of Facebook was well received by participants and their parents. The Facebook intervention was able to be executed with a careful privacy protocol in place and at minimal safety risk to participants. Conclusions: The utilization of Facebook to facilitate delivery of social skills training for adolescents with ASD appears to be feasible, although the clinical impact of such an addition is still unclear. It is important to note that social difficulties of participants persisted with the addition of the online platform and participants still required assistance to engage with peers in an online environment. A Web-based intervention such as the one utilized in this study has the potential to reach a mass number of patients with ASD and could address disparities in access to in-person treatment services. However, the complexity and evolving nature of Facebook's website and privacy settings leads to a number of unique online safety concerns that may limit its clinical utility. Issues encountered in our study support the development of an alternative and closed Web-based social platform designed specifically for the target audience with ASD; this platform could be a safer and more easily moderated setting for aiding in social skills development. Despite a small sample size with no statistically significant improvements of target symptoms, the use of electronic screen media as a therapeutic tool for adolescents with ASD is still a promising area of research warranting further investigation. Our study helps inform future obstacles regarding feasibility and safety. ", doi="10.2196/mental.6605", url="http://mental.jmir.org/2017/1/e4/", url="http://www.ncbi.nlm.nih.gov/pubmed/28115297" } @Article{info:doi/10.2196/jmir.6722, author="DeHoff, A. Beth and Staten, K. Lisa and Rodgers, Christine Rylin and Denne, C. Scott", title="The Role of Online Social Support in Supporting and Educating Parents of Young Children With Special Health Care Needs in the United States: A Scoping Review", journal="J Med Internet Res", year="2016", month="Dec", day="22", volume="18", number="12", pages="e333", keywords="health communication", keywords="child", keywords="social media", keywords="health education", keywords="health resources", keywords="early childhood", keywords="disability", keywords="neonatal intensive care unit", keywords="family", keywords="maternal-child health services", abstract="Background: When parents of young children with special health care needs (CSHCN) receive their child's diagnosis, they encounter information they may not understand, emotions they may not know how to cope with, and questions about their child's immediate and long-term future that frequently lack answers. The challenge of health care providers is how to prepare parents for caring for their CSHCN, for coping with any resulting challenges, and for accessing the systems and services that can assist them. Objective: The purpose of this work was to review evidence of the information and support needs of parents of young CSHCN and to determine whether online social support can serve as an avenue for learning and empowerment for these parents. Methods: A scoping review identified the challenges, coping mechanisms, and support needs among parents of CSHCN, and the reach and effectiveness of digital technologies with these families and health care providers. We also conducted interviews with professionals serving parents of CSHCN. Results: The literature review and interviews suggested that parents best learn the information they need, and cope with the emotional challenges of raising a CSHCN, with support from other parents of CSHCN, and that young parents in recent years have most often been finding this parent-to-parent support through digital media, particularly social media, consistent with the theory of online social support. Evidence also shows that social media, particularly Facebook, is used by nearly all women aged 18-29 years across racial and socioeconomic lines in the United States. Conclusions: Parents of young CSHCN experience significant stress but gain understanding, receive support, and develop the ability to care for and be advocates for their child through parent-to-parent emotional and informational social support. Online social support is most effective with young adults of childbearing age, with social media and apps being the most useful within the theoretical framework of social support. This opens new opportunities to effectively educate and support parents of young CSHCN. Providers seeking to inform, educate, and support families of CSHCN should develop strategies to help parents find and use social support through digital resources to facilitate their emotional adjustment and practical abilities to care for and access services for their child. ", doi="10.2196/jmir.6722", url="http://www.jmir.org/2016/12/e333/", url="http://www.ncbi.nlm.nih.gov/pubmed/28007689" } @Article{info:doi/10.2196/iproc.6119, author="Northrup, Matthew C. and Lantz, Johanna and Hamlin, Theresa", title="Wearable Stress Sensors for Children With Autism Spectrum Disorder With In Situ Alerts to Caregivers via a Mobile Phone", journal="iproc", year="2016", month="Dec", day="14", volume="2", number="1", pages="e9", keywords="mHealth", keywords="wearable sensor", keywords="stress", keywords="autism", keywords="app", keywords="design", abstract="Background: Children with autism spectrum disorder (ASD) often exhibit unexpected and difficult to manage self-injurious, aggressive, and/or disruptive and challenging behaviors. These behaviors can lead to restrictive care settings including hospitalizations and lifelong residential care placement. Because children with ASD have significant impairments in social communication skills including lack of facial expression, an inability to clearly articulate feelings, and atypical body language, caregivers could benefit tremendously by knowing when a child is becoming stressed. Objective: To develop a set of customized features in a wearable sensor and mobile app that monitors stress reactivity of children with autism in real time and automatically triggers in situ alerts to a caregiver via a mobile handheld device. Methods: The Center for Discovery (CFD) is a not-for-profit internationally recognized service provider for people with complex developmental disabilities, including a large population of children and adults with autism. Neumitra Inc., is a start-up technology vendor specializing in wearable stress monitoring. Neumitra's wearable sensor called neuma featured an embedded system with automated scoring of electrodermal activity, a well-established method for recording physiological stress responses. The sensor was accompanied by a mobile app for users to self-monitor their own stress levels. The app provided a 10-point color gradient scale as an interpretation of real-time stress and arousal levels. CFD collaborated with Neumitra's development team to develop a set of customized features amenable to the use case presented by caring for children with autism. The research team at CFD trialed the neuma system extensively, developed use case scenarios, and identified the features necessary to successfully implement in situ alerts to caregivers and track stress events to review for patterns of stress. Results: The resulting system is neuma-CFD, a coordinated technological system for in situ monitoring of stress levels to identify correlations in the user's stress increases and contextual events. The system delivers in situ alerts to caregivers via a smartphone or similar handheld devices. A new interface for the mobile app was customized to minimize user burden. The home screen now allows users to create high-frequency calendar events in only two taps. These events include common challenging behaviors and common intervention techniques. Thus, upon review, stress responses can be viewed relevant to both challenging behavioral episodes and intervention techniques. To enhance clinical review, the app now logs the detection of stress events into the calendar. Users can also access an increased granular review of electrodermal activity within a calendar event, such as behavior episodes or classroom routines. Conclusions: In field testing, in situ alerts were reported by caregivers to be beneficial. Furthermore, the integration of color-coding calendar events and routines in an intuitive interface allows multiple users to review the contextual events correlated to stress responses with minimal training. Wear tolerance, a challenging human factor common in ASD, can be addressed through behavioral shaping protocols. The hardware form factor was not amenable to this population due impulsive behaviors including pulling on the device to remove it, causing hardware damage. Exposure to water during handwashing was another challenge in hardware. These concerns are being revised in future versions of hardware. This system can also benefit other healthcare populations, such as patients with anxiety, posttraumatic stress disorder or any other condition for which understanding patterns of stress offers improved health outcomes. ", doi="10.2196/iproc.6119", url="http://www.iproc.org/2016/1/e9/" } @Article{info:doi/10.2196/jmir.5439, author="Ben-Sasson, Ayelet and Yom-Tov, Elad", title="Online Concerns of Parents Suspecting Autism Spectrum Disorder in Their Child: Content Analysis of Signs and Automated Prediction of Risk", journal="J Med Internet Res", year="2016", month="Nov", day="22", volume="18", number="11", pages="e300", keywords="online queries", keywords="autistic disorders", keywords="parents", keywords="machine learning", keywords="early detection", abstract="Background: Online communities are used as platforms by parents to verify developmental and health concerns related to their child. The increasing public awareness of autism spectrum disorders (ASD) leads more parents to suspect ASD in their child. Early identification of ASD is important for early intervention. Objective: To characterize the symptoms mentioned in online queries posed by parents who suspect that their child might have ASD and determine whether they are age-specific. To test the efficacy of machine learning tools in classifying the child's risk of ASD based on the parent's narrative. Methods: To this end, we analyzed online queries posed by parents who were concerned that their child might have ASD and categorized the warning signs they mentioned according to ASD-specific and non-ASD--specific domains. We then used the data to test the efficacy with which a trained machine learning tool classified the degree of ASD risk. Yahoo Answers, a social site for posting queries and finding answers, was mined for queries of parents asking the community whether their child has ASD. A total of 195 queries were sampled for this study (mean child age=38.0 months; 84.7\% [160/189] boys). Content text analysis of the queries aimed to categorize the types of symptoms described and obtain clinical judgment of the child's ASD-risk level. Results: Concerns related to repetitive and restricted behaviors and interests (RRBI) were the most prevalent (75.4\%, 147/195), followed by concerns related to language (61.5\%, 120/195) and emotional markers (50.3\%, 98/195). Of the 195 queries, 18.5\% (36/195) were rated by clinical experts as low-risk, 30.8\% (60/195) as medium-risk, and 50.8\% (99/195) as high-risk. Risk groups differed significantly (P<.001) in the rate of concerns in the language, social, communication, and RRBI domains. When testing whether an automatic classifier (decision tree) could predict if a query was medium- or high-risk based on the text of the query and the coded symptoms, performance reached an area under the receiver operating curve (ROC) curve of 0.67 (CI 95\% 0.50-0.78), whereas predicting from the text and the coded signs resulted in an area under the curve of 0.82 (0.80-0.86). Conclusions: Findings call for health care providers to closely listen to parental ASD-related concerns, as recommended by screening guidelines. They also demonstrate the need for Internet-based screening systems that utilize parents' narratives using a decision tree questioning method. ", doi="10.2196/jmir.5439", url="http://www.jmir.org/2016/11/e300/", url="http://www.ncbi.nlm.nih.gov/pubmed/27876688" } @Article{info:doi/10.2196/jmir.4913, author="Ingersoll, Brooke and Berger, I. Natalie", title="Parent Engagement With a Telehealth-Based Parent-Mediated Intervention Program for Children With Autism Spectrum Disorders: Predictors of Program Use and Parent Outcomes", journal="J Med Internet Res", year="2015", month="Oct", day="06", volume="17", number="10", pages="e227", keywords="autism", keywords="telehealth", keywords="parent training", abstract="Background: There has been growing interest in using telehealth to increase access to parent-mediated interventions for children with ASD. However, little is known about how parents engage with such programs. Objective: This paper presents program engagement data from a pilot study comparing self-directed and therapist-assisted versions of a novel telehealth-based parent-mediated intervention for young children with autism spectrum disorders (ASD). Methods: Parents of young children with ASD were randomly assigned to receive a self-directed or therapist-assisted version of ImPACT Online. Parent engagement and satisfaction with the different components of the program website were examined using the program's automated data collection and a post-treatment evaluation survey. We examined the relationship between program engagement and changes in parent knowledge and implementation and participant characteristics associated with program engagement. Results: Of the 27 parent participants, the majority were female (26/27, 96\%), married (22/27, 81\%), with a college degree or higher (15/27, 56\%), and less than half were not employed outside of the home (10/27, 37\%). The mean chronological age of the child participants was 43.26 months, and the majority were male (19/27, 70\%) and white (21/27, 78\%). Most of the families (19/27, 70\%) resided in a rural or medically underserved area. Parents logged into the website an average of 46.85 times, spent an average of 964.70 minutes on the site, and completed an average of 90.17\% of the lesson learning activities. Participants in the therapist-assisted group were more likely to engage with the website than those in the self-directed group: F2,24=17.65, P<.001. In total, 85\% of participants completed the program, with a significantly greater completion rate in the therapist-assisted group (N=27): $\chi$21=5.06, P=.03. Lesson learning activities were visited significantly more often than the supplemental activities (all Ps<.05). Multiple regression controlling for pretreatment performance indicated that program completion (beta=.51, P=.02) predicted post-treatment intervention knowledge, and program completion (beta=.43, P=.03) and group assignment (beta=-.37, P=.045) predicted post-treatment intervention fidelity. Partial correlations indicated that parent depressive symptoms at pretreatment were negatively associated with program completion (r=-.40, P=.04), but other key parent and child demographic factors were not. Post-treatment measures of website usability (r=.65, P<.001), treatment acceptability (r=.58, P=.002), and overall satisfaction (r=.58, P=.002) were all related to program completion. Conclusions: Parent engagement and satisfaction with ImPACT Online was high for both self-directed and therapist-assisted versions of the program, although therapist assistance increased engagement. Program completion was associated with parent outcomes, providing support for the role of the website in parent learning. This program has the potential to increase access to parent-mediated intervention for families of children with ASD. ", doi="10.2196/jmir.4913", url="http://www.jmir.org/2015/10/e227/", url="http://www.ncbi.nlm.nih.gov/pubmed/26443557" } @Article{info:doi/10.2196/mhealth.4393, author="Nazneen, Nazneen and Rozga, Agata and Smith, J. Christopher and Oberleitner, Ron and Abowd, D. Gregory and Arriaga, I. Rosa", title="A Novel System for Supporting Autism Diagnosis Using Home Videos: Iterative Development and Evaluation of System Design", journal="JMIR mHealth uHealth", year="2015", month="Jun", day="17", volume="3", number="2", pages="e68", keywords="asynchronous telemedicine system", keywords="in-home behavior recording", keywords="naturalistic observation diagnostic assessment", keywords="NODA Connect", keywords="NODA smartCapture", keywords="remote autism diagnosis", abstract="Background: Observing behavior in the natural environment is valuable to obtain an accurate and comprehensive assessment of a child's behavior, but in practice it is limited to in-clinic observation. Research shows significant time lag between when parents first become concerned and when the child is finally diagnosed with autism. This lag can delay early interventions that have been shown to improve developmental outcomes. Objective: To develop and evaluate the design of an asynchronous system that allows parents to easily collect clinically valid in-home videos of their child's behavior and supports diagnosticians in completing diagnostic assessment of autism. Methods: First, interviews were conducted with 11 clinicians and 6 families to solicit feedback from stakeholders about the system concept. Next, the system was iteratively designed, informed by experiences of families using it in a controlled home-like experimental setting and a participatory design process involving domain experts. Finally, in-field evaluation of the system design was conducted with 5 families of children (4 with previous autism diagnosis and 1 child typically developing) and 3 diagnosticians. For each family, 2 diagnosticians, blind to the child's previous diagnostic status, independently completed an autism diagnosis via our system. We compared the outcome of the assessment between the 2 diagnosticians, and between each diagnostician and the child's previous diagnostic status. Results: The system that resulted through the iterative design process includes (1) NODA smartCapture, a mobile phone-based application for parents to record prescribed video evidence at home; and (2) NODA Connect, a Web portal for diagnosticians to direct in-home video collection, access developmental history, and conduct an assessment by linking evidence of behaviors tagged in the videos to the Diagnostic and Statistical Manual of Mental Disorders criteria. Applying clinical judgment, the diagnostician concludes a diagnostic outcome. During field evaluation, without prior training, parents easily (average rating of 4 on a 5-point scale) used the system to record video evidence. Across all in-home video evidence recorded during field evaluation, 96\% (26/27) were judged as clinically useful, for performing an autism diagnosis. For 4 children (3 with autism and 1 typically developing), both diagnosticians independently arrived at the correct diagnostic status (autism versus typical). Overall, in 91\% of assessments (10/11) via NODA Connect, diagnosticians confidently (average rating 4.5 on a 5-point scale) concluded a diagnostic outcome that matched with the child's previous diagnostic status. Conclusions: The in-field evaluation demonstrated that the system's design enabled parents to easily record clinically valid evidence of their child's behavior, and diagnosticians to complete a diagnostic assessment. These results shed light on the potential for appropriately designed telehealth technology to support clinical assessments using in-home video captured by families. This assessment model can be readily generalized to other conditions where direct observation of behavior plays a central role in the assessment process. ", doi="10.2196/mhealth.4393", url="http://mhealth.jmir.org/2015/2/e68/", url="http://www.ncbi.nlm.nih.gov/pubmed/26085230" }