Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study

Understanding autism diagnosis in primary care: Rates of diagnosis from 2004 to 2019 and child age at diagnosis

LAY ABSTRACT

The current demand for autism diagnostic services exceeds the ability of the workforce to assess and diagnose children in a timely manner. One solution may be to equip primary care providers (PCPs) with the tools and expertise needed to diagnose autism within their practice. PCPs are often trusted professionals who have many touchpoints with children during early development, in which they can identify early signs of autism. Recent initiatives have focused on bolstering PCPs' diagnostic capabilities; however, no studies have examined how the rates of autism diagnosis in primary care have changed over time. We aimed to evaluate whether autism diagnosis in primary care has changed over time and how diagnosis in primary care relates to a child's age at the time of diagnosis. We found that the likelihood of a child being diagnosed by a PCP decreased by about 2% with every passing year from 2004 to 2019 when accounting for demographic characteristics. In our sample, PCPs diagnosed children approximately 1 year earlier than non-PCPs (e.g., psychologists and psychiatrists). Further research is needed to understand why the proportion of children diagnosed by PCPs decreases over time. However, this decrease suggests more work is needed to get capacity-building initiatives into community primary care practice. Though we must continue to find effective ways to build community PCPs' ability to diagnose autism, the present findings support the crucial role PCPs can play in early autism diagnosis.

Integration of Family Navigation into ECHO Autism for Pediatric Primary Care in Underserved Communities

Children with autism from underserved communities face complex system-, provider-, and family-level barriers to accessing timely diagnosis and early intervention. The current study evaluated the preliminary effects and feasibility of a new program (ECHO Autism LINKS) that integrated pediatric primary care provider (PCP) training with family navigation (FN) to bridge the gaps between screening, referral, and service access. Three cohorts of PCPs (n = 42) participated in the program, which consisted of 60-minute sessions delivered by Zoom twice per month for 12 months. Each session included didactics, case-based learning, and collaborative discussion with participants and an interdisciplinary team of experts. Family navigators were members of the expert team and provided FN services to families referred by PCP participants. Program attendance and engagement were strong, with 40 cases presented and 258 families referred for FN services, most of whom (83%) needed help accessing and connecting with services, and 13% required ongoing support due to complex needs. PCPs demonstrated significant improvements in self-efficacy in providing best-practice care for children with autism, reported high satisfaction, and observed improved knowledge and practice as a result of the program. The results of this initial pilot provide support for the feasibility, acceptability, and preliminary efficacy of the ECHO Autism LINKS program. The model holds promise in addressing complex barriers to healthcare access by providing both PCPs and families with the knowledge and support they need. Future research is needed to evaluate the efficacy and effectiveness of the program in improving child and family outcomes.

Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study

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

This study used Twitter as the primary source of data to study the behavioral characteristics and real-time emotional projections of individuals identifying with autism spectrum disorder (ASD). We aimed to improve the rigor of ASD analytics research by using the digital footprint of an individual to study the linguistic patterns of individuals with ASD.

METHODS

We developed a machine learning model 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 identify linguistic markers associated with ASD traits. To construct the data set, we targeted English-language tweets using the search query "#ActuallyAutistic" posted from January 1, 2014, to December 31, 2022. 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 timeline. 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 also illustrate the utility of the data set through common natural language processing tasks such as sentiment analysis and topic modeling.

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. This is a promising start, demonstrating the potential for effective digital phenotyping studies and large-scale intervention using text data mined from social media.

CONCLUSIONS

Textual differences in social media communications can help researchers and clinicians conduct symptomatology studies in natural settings.

Utility of the Language Use Inventory in Young Children at Elevated Likelihood of Autism

PURPOSE

The aims of this study were (a) to evaluate the convergent validity of the Language Use Inventory (LUI) with measures of autism spectrum disorder (ASD) symptoms, language, and social skills and (b) to assess discriminant validity of the LUI with measures of nonlanguage skills, including daily living skills and motor development.

METHOD

This study sample included participants from a longitudinal study (n = 239) of infant siblings with elevated familial likelihood of ASD and lower familial likelihood. Assessment measures completed at 36 months included the LUI, the Autism Diagnostic Observation Schedule-Second Edition (ADOS-2), the Mullen Scales of Early Learning, and the Vineland Adaptive Behavior Scales-Second Edition. Bivariate Pearson correlations were estimated between ADOS-2 comparison scores and four language and social skills measures. Additional correlations were estimated between LUI total scores and standard scores from nonlanguage measures. A series of Fisher's Z transformations were applied to evaluate whether bivariate correlations were significantly different.

RESULTS

All four language and social skill measures were moderately to strongly associated with each other and ASD symptom severity scores. The correlation between ADOS-2 comparison scores and LUI total scores was significantly stronger than ADOS-2 correlations with all other measures.

CONCLUSIONS

Our findings provide support for the LUI as a feasible, pragmatic language-targeted instrument for inclusion in early developmental evaluations prompted by language concerns. Administration of the LUI may accelerate earlier referral for a comprehensive assessment of ASD symptoms. Given the high correlation with ADOS-2 scores, an LUI total score in a clinical range of concern may encourage a clinician to refer families for a full diagnostic evaluation of ASD.

A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism

Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. However, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related neurodevelopmental delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide increased access to services for affected families. Several efforts previously conducted by a multitude of research labs have spawned great progress toward improved digital diagnostics and digital therapies for children with autism. We review the literature on digital health methods for autism behavior quantification and beneficial therapies using data science. We describe both case-control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics that integrate machine learning models of autism-related behaviors, including the factors that must be addressed for translational use. Finally, we describe ongoing challenges and potential opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights that are relevant to neurological behavior analysis and digital psychiatry more broadly.

Clinician factors related to the delivery of psychotherapy for autistic youth and youth with attention-deficit hyperactivity disorder

LAY ABSTRACT

Autistic children and youth often experience mental health problems, such as anxiety, depression and behavioural challenges. Although there are therapy programmes that have been found helpful in reducing these issues, such as cognitive behaviour therapy, autistic children often struggle to receive adequate mental health care. Clinicians' knowledge, attitudes, confidence and beliefs about treating mental health problems in autistic people may be related to their choices in providing psychotherapy. Across Ontario, Canada, 611 mental health clinicians, working in publicly funded agencies, completed an online survey about their experiences and opinions on delivering therapy for autistic clients compared to those with attention-deficit hyperactivity disorder. Clinician knowledge was associated with their intention to treat autistic clients or clients with attention-deficit hyperactivity disorder, partly because of their attitudes and the social pressures or values they felt. Clinicians reported feeling less intent on providing therapy to autistic youth compared to youth with attention-deficit hyperactivity disorder because of differences in their attitudes, social pressures and knowledge. This research can inform the training and educational initiatives for mental health practitioners.

Racial and Ethnic Disparities in Geographic Access to Autism Resources Across the US

Importance

While research has identified racial and ethnic disparities in access to autism services, the size, extent, and specific locations of these access gaps have not yet been characterized on a national scale. Mapping comprehensive national listings of autism health care services together with the prevalence of autistic children of various races and ethnicities and evaluating geographic regions defined by localized commuting patterns may help to identify areas within the US where families who belong to minoritized racial and ethnic groups have disproportionally lower access to services.

Objective

To evaluate differences in access to autism health care services among autistic children of various races and ethnicities within precisely defined geographic regions encompassing all serviceable areas within the US.

Design, Setting, and Participants

This population-based cross-sectional study was conducted from October 5, 2021, to June 3, 2022, and involved 530 965 autistic children in kindergarten through grade 12. Core-based statistical areas (CBSAs; defined as areas containing a city and its surrounding commuter region), the Civil Rights Data Collection (CRDC) data set, and 51 071 autism resources (collected from October 1, 2015, to December 18, 2022) geographically distributed into 912 CBSAs were combined and analyzed to understand variation in access to autism health care services among autistic children of different races and ethnicities. Six racial and ethnic categories (American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or other Pacific Islander, and White) assigned by the US Department of Education were included in the analysis.

Main Outcomes and Measures

A regularized least-squares regression analysis was used to measure differences in nationwide resource allocation between racial and ethnic groups. The number of autism resources allocated per autistic child was estimated based on the child's racial and ethnic group. To evaluate how the CBSA population size may have altered the results, the least-squares regression analysis was run on CBSAs divided into metropolitan (>50 000 inhabitants) and micropolitan (10 000-50 000 inhabitants) groups. A Mann-Whitney U test was used to compare the model estimated ratio of autism resources to autistic children among specific racial and ethnic groups comprising the proportions of autistic children in each CBSA.

Results

Among 530 965 autistic children aged 5 to 18 years, 83.9% were male and 16.1% were female; 0.7% of children were American Indian or Alaska Native, 5.9% were Asian, 14.3% were Black or African American, 22.9% were Hispanic or Latino, 0.2% were Native Hawaiian or other Pacific Islander, 51.7% were White, and 4.2% were of 2 or more races and/or ethnicities. At a national scale, American Indian or Alaska Native autistic children (β = 0; 95% CI, 0-0; P = .01) and Hispanic autistic children (β = 0.02; 95% CI, 0-0.06; P = .02) had significant disparities in access to autism resources in comparison with White autistic children. When evaluating the proportion of autistic children in each racial and ethnic group, areas in which Black autistic children (>50% of the population: β = 0.05; <50% of the population: β = 0.07; P = .002) or Hispanic autistic children (>50% of the population: β = 0.04; <50% of the population: β = 0.07; P < .001) comprised greater than 50% of the total population of autistic children had significantly fewer resources than areas in which Black or Hispanic autistic children comprised less than 50% of the total population. Comparing metropolitan vs micropolitan CBSAs revealed that in micropolitan CBSAs, Black autistic children (β = 0; 95% CI, 0-0; P < .001) and Hispanic autistic children (β = 0; 95% CI, 0-0.02; P < .001) had the greatest disparities in access to autism resources compared with White autistic children. In metropolitan CBSAs, American Indian or Alaska Native autistic children (β = 0; 95% CI, 0-0; P = .005) and Hispanic autistic children (β = 0.01; 95% CI, 0-0.06; P = .02) had the greatest disparities compared with White autistic children.

Conclusions and Relevance

In this study, autistic children from several minoritized racial and ethnic groups, including Black and Hispanic autistic children, had access to significantly fewer autism resources than White autistic children in the US. This study pinpointed the specific geographic regions with the greatest disparities, where increases in the number and types of treatment options are warranted. These findings suggest that a prioritized response strategy to address these racial and ethnic disparities is needed.

"Don't Promise Something You can't Deliver:" Caregivers' Advice for Improving Services to Adolescents and Young Adults with Autism

Approximately 50,000 youths with autism spectrum disorders (ASD) exit U.S. high schools yearly to enter adult systems of care, many of whom remain dependent on family for day-to-day care and service system navigation. As part of a larger study, 174 family caregivers for adolescents or young adults with ASD were asked what advice they would give service providers about how to improve services for youth with ASD. Reflexive thematic analysis identified a framework of five directives: (1) provide a roadmap to services; (2) improve service access; (3) fill gaps to address unmet needs; (4) educate themselves, their families, and society about autism; and (5) operate from a relationship-building paradigm with families. Education, health, and social service providers, as well as policymakers, can use these directives to better assist youth with ASD and their families in the transition to adulthood.

Screening for Autism in 2-Year-Old Children: The Application of the Systematic Observation of Red Flags to the Screening Tool for Autism in Toddlers and Young Children

PURPOSE

A multimeasure approach was developed to capitalize on the strengths of two screening measures: the Screening Tool for Autism in Toddlers and Young Children (STAT), an observational measure of social communication, and the Systematic Observation of Red Flags (SORF), a checklist including restricted and repetitive behavior (RRB) items. This approach offers a novel method of identifying autism in toddlers.

METHOD

This was a retrospective study of data collected from a multidisciplinary diagnostic program for 24- to 36-month-olds with developmental delays. Raters with autism expertise but naïve to diagnoses applied the SORF to STAT videos. Psychometrics were derived for the SORF on STAT observations and a multiple-measure approach that used a Least Absolute Shrinkage and Selection Operator modeling framework to construct a STAT-SORF RRB Hybrid, retaining SORF RRB items based on individual predictive abilities.

RESULTS

The SORF alone correctly classified 84% of the sample (84% sensitivity and 86% specificity). The STAT-SORF RRB Hybrid model, which retained four SORF RRB items, correctly classified 90% of a validation sample (95% sensitivity and 75% specificity).

CONCLUSION

These findings highlight the potential utility of using multiple autism identification tools and regression-based scoring to establish presumptive eligibility and facilitate early access to autism interventions.

EXPRESSION PROFILES OF PTEN AND POGZ GENES IN TURKISH PATIENTS WITH AUTISM

Amaç: Otizm spektrum bozukluğu (OSB), karmaşık davranışsal fenotiplerle teşhis edilen, etiyolojik ve klinik olarak heterojen bir grup nörogelişimsel bozukluktur. Uzun yıllar boyunca yapılan kapsamlı çalışmalara rağmen, OSB'nin nedenleri hala bilinmemektedir. PTEN ve POGZ genleri, OSB fenotipinden sorumlu olabilecek aday genler olarak gösterilmiştir. Bu çalışmanın amacı, otistik hastalarda PTEN ve POGZ genlerinin ekspresyon düzeylerini araştırmaktır. Yöntem: DSM-IV ve DSM-V tanı kriterlerine göre OSB tanılı 50 hastada ve yaş-cinsiyet uyumlu 50 sağlıklı kontrolde PTEN, POGZ gen ekspresyonları kantitatif real time PCR (QRT-PCR) ile araştırıldı. Bu çalışma Erciyes Üniversitesi Genom ve Kök Hücre Merkezi'nde (GENKOK) yapılmıştır. Bulgular: POGZ geninin hastalarda kontrollere göre daha fazla eksprese olduğu ve otistik erkeklerde bu genin ekspresyonunun anlamlı olduğu bulundu. PTEN gen ekspresyonu istatistiksel olarak anlamlı değildi ancak hastalarda kontrollere göre daha düşük bulundu (p=0.7884). Bu genlerin ekspresyonu ile bilişsel geriliği olan hastalar arasındaki ilişki anlamlı değildi. Sonuç: Daha büyük hasta grupları ile diğer olası aday genlerin araştırılmasını ve sonuçların farklı klinik belirtilerle karşılaştırılmasını öneriyoruz. Anahtar Kelimeler: Otizm, Otizm Spektrum Bozuklukları, PTEN, POGZ, Ekspresyon

Machine learning models using mobile game play accurately classify children with autism

Digitally-delivered healthcare is well suited to address current inequities in the delivery of care due to barriers of access to healthcare facilities. As the COVID-19 pandemic phases out, we have a unique opportunity to capitalize on the current familiarity with telemedicine approaches and continue to advocate for mainstream adoption of remote care delivery. In this paper, we specifically focus on the ability of GuessWhat? a smartphone-based charades-style gamified therapeutic intervention for autism spectrum disorder (ASD) to generate a signal that distinguishes children with ASD from neurotypical (NT) children. We demonstrate the feasibility of using "in-the-wild", naturalistic gameplay data to distinguish between ASD and NT by children by training a random forest classifier to discern the two classes (AU-ROC = 0.745, recall = 0.769). This performance demonstrates the potential for GuessWhat? to facilitate screening for ASD in historically difficult-to-reach communities. To further examine this potential, future work should expand the size of the training sample and interrogate differences in predictive ability by demographic.

Multivariate Analysis of Metabolomic and Nutritional Profiles among Children with Autism Spectrum Disorder

There have been promising results regarding the capability of statistical and machine-learning techniques to offer insight into unique metabolomic patterns observed in ASD. This work re-examines a comparative study contrasting metabolomic and nutrient measurements of children with ASD (n = 55) against their typically developing (TD) peers (n = 44) through a multivariate statistical lens. Hypothesis testing, receiver characteristic curve assessment, and correlation analysis were consistent with prior work and served to underscore prominent areas where metabolomic and nutritional profiles between the groups diverged. Improved univariate analysis revealed 46 nutritional/metabolic differences that were significantly different between ASD and TD groups, with individual areas under the receiver operator curve (AUROC) scores of 0.6–0.9. Many of the significant measurements had correlations with many others, forming two integrated networks of interrelated metabolic differences in ASD. The TD group had 189 significant correlation pairs between metabolites, vs. only 106 for the ASD group, calling attention to underlying differences in metabolic processes. Furthermore, multivariate techniques identified potential biomarker panels with up to six metabolites that were able to attain a predictive accuracy of up to 98% for discriminating between ASD and TD, following cross-validation. Assessing all optimized multivariate models demonstrated concordance with prior physiological pathways identified in the literature, with some of the most important metabolites for discriminating ASD and TD being sulfate, the transsulfuration pathway, uridine (methylation biomarker), and beta-amino isobutyrate (regulator of carbohydrate and lipid metabolism).

Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study

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.

Perspectives of Canadian Rural Consultant Pediatricians on Diagnosing Autism Spectrum Disorder: A Qualitative Study

BACKGROUND

Consultant pediatricians represent a potential resource for increasing autism spectrum disorder (ASD) diagnostic capacity; however, little is known about how they perceive their roles in ASD diagnosis.

OBJECTIVE

The objective of this study was to examine the perspectives of rural consultant pediatricians regarding their perceived roles, facilitators, and barriers in ASD diagnosis.

METHODS

We performed a qualitative study using thematic analysis. Consultant pediatricians from 3 small-sized and medium-sized Ontario communities were recruited. Semistructured interviews were conducted, transcribed, coded, and analyzed.

RESULTS

Fourteen pediatricians participated in this study. Participants all considered ASD diagnosis to be in their scope of practice. The major theme identified was the process of diagnosing ASD, which occurred in 3 stages: preassessment (gathering information before the first clinic visit), diagnosis, and service access. All these stages are influenced by ecological factors consisting of characteristics of the child, family, individual physician, pediatric group practice, and the broader system of ASD care.

CONCLUSION

Consultant pediatricians practicing in nonurban Ontario communities see ASD diagnosis as part of their scope of practice and collaboratively work within groups to address the needs of their communities. Strategies aimed at increasing diagnostic capacity should target salaried group practices and improve the efficiency of assessments through preclinic information gathering.

Efficacy and safety of balovaptan for socialisation and communication difficulties in autistic adults in North America and Europe: a phase 3, randomised, placebo-controlled trial

BACKGROUND

There are no approved pharmacological therapies to support treatment of the core communication and socialisation difficulties associated with autism spectrum disorder in adults. We aimed to assess the efficacy, safety, and pharmacokinetics of balovaptan, a vasopressin 1a receptor antagonist, versus placebo in autistic adults.

METHODS

V1aduct was a phase 3, randomised, placebo-controlled, double-blind trial, conducted at 46 sites across six countries (the USA, the UK, France, Italy, Spain, and Canada). Eligible participants were aged 18 years or older with an intelligence quotient (IQ) of 70 or higher, and met the criteria for moderate-to-severe autism spectrum disorder (DSM-5 and Autism Diagnostic Observation Schedule). Participants were randomly allocated (1:1), with an independent interactive voice or web-based response system, to receive balovaptan (10 mg) or placebo daily for 24 weeks. Randomisation was stratified by an individual's baseline Vineland-II two-domain composite (2DC) score (<60 or ≥60), sex, region (North America or rest of world), and age (<25 years or ≥25 years). Participants, study site personnel, and the sponsor were masked to treatment assignment. The primary endpoint was change from baseline in Vineland-II 2DC score (the mean composite score across the Vineland-II socialisation and communication domains) at week 24. The primary analysis was done with ANCOVA in the intention-to-treat population. The V1aduct study was terminated for futility after around 50% of participants completed the week 24 visit. This trial is registered with ClinicalTrials.gov (NCT03504917).

FINDINGS

Between Aug 8, 2018, and July 1, 2020, 540 people were screened for eligibility, of whom 322 were allocated to receive balovaptan (164 [51%]) or placebo (158 [49%]). One participant from the balovaptan group was not treated before trial termination and was excluded from the analysis. 60 participants in the balovaptan group and 55 in the placebo group discontinued treatment before week 24. The sample consisted of 64 (20%) women and 257 (80%) men, with 260 (81%) participants from North America and 61 (19%) from Europe. At baseline, mean age was 27·6 years (SD 9·7) and mean IQ score was 104·8 (18·1). Two (1%) participants were American Indian or Alaska Native, eight (2%) were Asian, 15 (5%) were Black or African American, 283 (88%) were White, four (1%) were of multiple races, and nine (3%) were of unknown race. Mean baseline Vineland-II 2DC scores were 67·2 (SD 15·3) in the balovaptan group and 66·2 (17·7) in the placebo group. The interim futility analysis showed no improvement for balovaptan versus placebo in terms of Vineland-II 2DC score at week 24 compared with baseline, with a least-squares mean change of 2·91 (SE 1·52) in the balovaptan group (n=79) and 4·75 (1·60) in the placebo group (n=71; estimated treatment difference -1·84 [95% CI -5·15 to 1·48]). In the final analysis, mean change from baseline in Vineland-II 2DC score at week 24 was 4·56 (SD 10·85) in the balovaptan group (n=111) and 6·83 (12·18) in the placebo group (n=99). Balovaptan was well tolerated, with similar proportions of participants with at least one adverse event in the balovaptan group (98 [60%] of 163) and placebo group (104 [66%] of 158). The most common adverse events were nasopharyngitis (14 [9%] in the balovaptan group and 19 [12%] in the placebo group), diarrhoea (11 [7%] and 14 [9%]), upper respiratory tract infection (ten [6%] and nine [6%]), insomnia (five [3%] and eight [5%]), oropharyngeal pain (five [3%] and eight [5%]), and dizziness (two [1%] and ten [6%]). Serious adverse events were reported for two (1%) participants in the balovaptan group (one each of suicidal ideation and schizoaffective disorder), and five (3%) participants in the placebo group (one each of suicidal ideation, panic disorder, limb abscess, urosepsis, colitis [in the same participant with urosepsis], and death by suicide). No treatment-related deaths occurred.

INTERPRETATION

Balovaptan did not improve social communication in autistic adults. This study provides insights into challenges facing autism spectrum disorder trials, including the considerable placebo response and the selection of appropriate outcome measures.

FUNDING

F Hoffmann-La Roche.

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

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.

Crowd annotations can approximate clinical autism impressions from short home videos with privacy protections

Artificial Intelligence (A.I.) solutions are increasingly considered for telemedicine. For these methods to serve children and their families in home settings, it is crucial to ensure the privacy of the child and parent or caregiver. To address this challenge, we explore the potential for global image transformations to provide privacy while preserving the quality of behavioral annotations. Crowd workers have previously been shown to reliably annotate behavioral features in unstructured home videos, allowing machine learning classifiers to detect autism using the annotations as input. We evaluate this method with videos altered via pixelation, dense optical flow, and Gaussian blurring. On a balanced test set of 30 videos of children with autism and 30 neurotypical controls, we find that the visual privacy alterations do not drastically alter any individual behavioral annotation at the item level. The AUROC on the evaluation set was 90.0% ±7.5% for unaltered videos, 85.0% ±9.0% for pixelation, 85.0% ±9.0% for optical flow, and 83.3% ±9.3% for blurring, demonstrating that an aggregation of small changes across behavioral questions can collectively result in increased misdiagnosis rates. We also compare crowd answers against clinicians who provided the same annotations for the same videos as crowd workers, and we find that clinicians have higher sensitivity in their recognition of autism-related symptoms. We also find that there is a linear correlation (r = 0.75, p < 0.0001) between the mean Clinical Global Impression (CGI) score provided by professional clinicians and the corresponding score emitted by a previously validated autism classifier with crowd inputs, indicating that the classifier’s output probability is a reliable estimate of the clinical impression of autism. A significant correlation is maintained with privacy alterations, indicating that crowd annotations can approximate clinician-provided autism impression from home videos in a privacy-preserved manner.

A Mobile Game Platform for Improving Social Communication in Children with Autism: A Feasibility Study

Background  Many children with autism cannot receive timely in-person diagnosis and therapy, especially in situations where access is limited by geography, socioeconomics, or global health concerns such as the current COVD-19 pandemic. Mobile solutions that work outside of traditional clinical environments can safeguard against gaps in access to quality care.

Objective  The aim of the study is to examine the engagement level and therapeutic feasibility of a mobile game platform for children with autism.

Methods  We designed a mobile application, GuessWhat , which, in its current form, delivers game-based therapy to children aged 3 to 12 in home settings through a smartphone. The phone, held by a caregiver on their forehead, displays one of a range of appropriate and therapeutically relevant prompts (e.g., a surprised face) that the child must recognize and mimic sufficiently to allow the caregiver to guess what is being imitated and proceed to the next prompt. Each game runs for 90 seconds to create a robust social exchange between the child and the caregiver.

Results  We examined the therapeutic feasibility of GuessWhat in 72 children (75% male, average age 8 years 2 months) with autism who were asked to play the game for three 90-second sessions per day, 3 days per week, for a total of 4 weeks. The group showed significant improvements in Social Responsiveness Score-2 (SRS-2) total (3.97, p <0.001) and Vineland Adaptive Behavior Scales-II (VABS-II) socialization standard (5.27, p  = 0.002) scores.

Conclusion  The results support that the GuessWhat mobile game is a viable approach for efficacious treatment of autism and further support the possibility that the game can be used in natural settings to increase access to treatment when barriers to care exist.

Applications of Pose Estimation in Human Health and Performance across the Lifespan

The emergence of pose estimation algorithms represents a potential paradigm shift in the study and assessment of human movement. Human pose estimation algorithms leverage advances in computer vision to track human movement automatically from simple videos recorded using common household devices with relatively low-cost cameras (e.g., smartphones, tablets, laptop computers). In our view, these technologies offer clear and exciting potential to make measurement of human movement substantially more accessible; for example, a clinician could perform a quantitative motor assessment directly in a patient's home, a researcher without access to expensive motion capture equipment could analyze movement kinematics using a smartphone video, and a coach could evaluate player performance with video recordings directly from the field. In this review, we combine expertise and perspectives from physical therapy, speech-language pathology, movement science, and engineering to provide insight into applications of pose estimation in human health and performance. We focus specifically on applications in areas of human development, performance optimization, injury prevention, and motor assessment of persons with neurologic damage or disease. We review relevant literature, share interdisciplinary viewpoints on future applications of these technologies to improve human health and performance, and discuss perceived limitations.

Caregivers of Children with Autism Spectrum Disorder in Rural Areas: A Literature Review of Mental Health and Social Support

PROBLEM

Caregivers of children with Autism Spectrum Disorder (ASD) report high levels of stress, social isolation, and poor mental health. Social and emotional support may buffer negative effects of stress for caregivers of children with ASD, however, those living in rural areas may be disadvantaged due to social isolation and increased distance from resources. This scoping review examined the literature regarding the mental health and impact of support for rural caregivers of children with ASD.

ELIGIBILITY CRITERIA

Articles were limited to those available in the English language and conducted in a high income country. Articles had to include a population of rural caregivers of children with ASD and focus on caregiver mental health and/or the impact of support on caregiver mental health.

SAMPLE

Searches were conducted with Embase, PubMed, CINAHL, ERIC, and PsycINFO and 22 articles were included.

RESULTS

Study findings indicate overall poor mental health for rural caregivers of children with ASD. Formal and informal support appear to be beneficial in decreasing stress for rural caregivers of children with ASD. However, a few studies indicated that formal support may add stress to rural caregivers.

CONCLUSION

There is limited information regarding support needs and the impact of support services on the mental health of rural caregivers of children with ASD.

IMPLICATIONS

There is a need to increase access to support resources in rural areas for caregivers of children with ASD. Healthcare professionals, including nurses, can play a fundamental role in supporting, educating, and connecting caregivers to other support services.

A Local Community-Based Social Network for Mental Health and Well-being (Quokka): Exploratory Feasibility Study

BACKGROUND

Developing healthy habits and maintaining prolonged behavior changes are often difficult tasks. Mental health is one of the largest health concerns globally, including for college students.

OBJECTIVE

Our aim was to conduct an exploratory feasibility study of local community-based interventions by developing Quokka, a web platform promoting well-being activity on university campuses. We evaluated the intervention's potential for promotion of local, social, and unfamiliar activities pertaining to healthy habits.

METHODS

To evaluate this framework's potential for increased participation in healthy habits, we conducted a 6-to-8-week feasibility study via a "challenge" across 4 university campuses with a total of 277 participants. We chose a different well-being theme each week, and we conducted weekly surveys to (1) gauge factors that motivated users to complete or not complete the weekly challenge, (2) identify participation trends, and (3) evaluate the feasibility of the intervention to promote local, social, and novel well-being activities. We tested the hypotheses that Quokka participants would self-report participation in more local activities than remote activities for all challenges (Hypothesis H1), more social activities than individual activities (Hypothesis H2), and new rather than familiar activities (Hypothesis H3).

RESULTS

After Bonferroni correction using a Clopper-Pearson binomial proportion confidence interval for one test, we found that there was a strong preference for local activities for all challenge themes. Similarly, users significantly preferred group activities over individual activities (P<.001 for most challenge themes). For most challenge themes, there were not enough data to significantly distinguish a preference toward familiar or new activities (P<.001 for a subset of challenge themes in some schools).

CONCLUSIONS

We find that local community-based well-being interventions such as Quokka can facilitate positive behaviors. We discuss these findings and their implications for the research and design of location-based digital communities for well-being promotion.

Mental Health Services for Autistic Individuals Across the Lifespan: Recent Advances and Current Gaps

PURPOSE OF REVIEW

This synthesis of recent mental health services research with autistic individuals presents significant advances, current gaps, and recommendations for improving mental healthcare for this population.

RECENT FINDINGS

Recent advances include improved understanding of co-occurring mental health conditions among autistic individuals, a growing evidence base for interventions to address them, the development and implementation of new service models to support mental health for this population, and a substantial increase in mental health services and implementation research focused on autism. Ongoing challenges include a lack of mental health interventions designed for community implementation with autistic individuals, limited workforce capacity, complex and disconnected service systems, and racial, ethnic, and socioeconomic disparities in accessibility and quality of mental health services. Despite the advances in our understanding of mental health needs and mental health services for autistic individuals, several critical gaps remain. We encourage future efforts to develop and test interventions that can be used in community settings, train and incentivize the workforce to provide them, realign policies and funding with best practice, and embrace an equity-focused approach to autism research and care.

Feasibility and acceptability of a telehealth model for autism diagnostic evaluations in children, adolescents, and adults

This study examined the feasibility and acceptability of a telehealth diagnostic model deployed at an autism center in the southwestern United States to safely provide autism spectrum disorder (ASD) diagnostic evaluations to children, adolescents, and adults during the COVID-19 pandemic. Participants included all clients for whom a telehealth diagnostic evaluation was scheduled at the diagnostic clinic (n = 121) over a 6-month period. Of 121 scheduled clients, 102 (84%) completed the telehealth evaluation. A diagnostic determination was made for 91% of clients (93 out of 102) using only telehealth procedures. Nine participants (two females; ages 3 to 11 years) required an in-person evaluation. Responses from psychologist and parent acceptability surveys indicated the model was acceptable for most clients. Psychologist ratings suggested that telehealth modalities used in the current study may be less acceptable for evaluating school-aged children with subtle presentations compared to children in the early developmental period, adolescents, and adults. Parents of females reported higher acceptability than parents of males. Findings contribute to the small but growing literature on feasibility and acceptability of telehealth evaluations for ASD and have implications for improving access to care during and after the COVID-19 pandemic. LAY SUMMARY: This study described telehealth methods for evaluating children, adolescents, and adults for autism spectrum disorder. Telehealth methods were generally acceptable to psychologists conducting the evaluations and parents of diagnostic clients. Psychologists reported the methods to be less acceptable for school-aged children and parents of males found the methods less acceptable than parents of females. The telehealth methods described may help to increase access to diagnostic professionals and reduce wait times for evaluations during and after the COVID-19 pandemic.

Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection

Standard medical diagnosis of mental health conditions requires licensed experts who are increasingly outnumbered by those at risk, limiting reach. We test the hypothesis that a trustworthy crowd of non-experts can efficiently annotate behavioral features needed for accurate machine learning detection of the common childhood developmental disorder Autism Spectrum Disorder (ASD) for children under 8 years old. We implement a novel process for identifying and certifying a trustworthy distributed workforce for video feature extraction, selecting a workforce of 102 workers from a pool of 1,107. Two previously validated ASD logistic regression classifiers, evaluated against parent-reported diagnoses, were used to assess the accuracy of the trusted crowd's ratings of unstructured home videos. A representative balanced sample (N = 50 videos) of videos were evaluated with and without face box and pitch shift privacy alterations, with AUROC and AUPRC scores > 0.98. With both privacy-preserving modifications, sensitivity is preserved (96.0%) while maintaining specificity (80.0%) and accuracy (88.0%) at levels comparable to prior classification methods without alterations. We find that machine learning classification from features extracted by a certified nonexpert crowd achieves high performance for ASD detection from natural home videos of the child at risk and maintains high sensitivity when privacy-preserving mechanisms are applied. These results suggest that privacy-safeguarded crowdsourced analysis of short home videos can help enable rapid and mobile machine-learning detection of developmental delays in children.

Disrupted nocturnal melatonin in autism: Association with tumor necrosis factor and sleep disturbances

Sleep disturbances, abnormal melatonin secretion, and increased inflammation are aspects of autism spectrum disorder (ASD) pathophysiology. The present study evaluated the daily urinary 6-sulfatoxymelatonin (aMT6s) excretion profile and the salivary levels of tumor necrosis factor (TNF) and interleukin-6 (IL-6) in 20 controls and 20 ASD participants, as well as correlating these measures with sleep disturbances. Although 60% of ASD participants showed a significant night-time rise in aMT6s excretion, this rise was significantly attenuated, compared to controls (P < .05). The remaining 40% of ASD individuals showed no significant increase in nocturnal aMT6s. ASD individuals showed higher nocturnal levels of saliva TNF, but not IL-6. Dysfunction in the initiation and maintenance of sleep, as indicated by the Sleep Disturbance Scale for Children, correlated with night-time aMT6s excretion (r = -.28, P < .05). Dysfunction in sleep breathing was inversely correlated with aMT6s (r = -.31, P < .05) and positively associated with TNF level (r = .42, P < .01). Overall such data indicate immune-pineal axis activation, with elevated TNF but not IL-6 levels associated with disrupted pineal melatonin release and sleep dysfunction in ASD. It is proposed that circadian dysregulation in ASD is intimately linked to heightened immune-inflammatory activity. Such two-way interactions of the immune-pineal axis may underpin many aspects of ASD pathophysiology, including sleep disturbances, as well as cognitive and behavioral alterations.

Policy and Practice Barriers to Early Identification of Autism Spectrum Disorder in the California Early Intervention System

Autism spectrum disorder can be reliably diagnosed prior to age 2, and early, intensive intervention has been found to improve long-term outcomes. Nonetheless, most children with ASD do not receive a diagnosis until after age 3, with even later diagnoses for children from non-white ethnic groups. This study conducted telephone surveys with California Part C early intervention managers regarding policies and practices for early identification and intervention for ASD. Findings indicated that 85% of agencies conduct screening for ASD, but only 39% conduct ASD diagnostic assessments prior to age 3. Recommendations for policy changes to align Part C practices with best practice guidelines are provided.

Selection of trustworthy crowd workers for telemedical diagnosis of pediatric autism spectrum disorder

Crowd-powered telemedicine has the potential to revolutionize healthcare, especially during times that require remote access to care. However, sharing private health data with strangers from around the world is not compatible with data privacy standards, requiring a stringent filtration process to recruit reliable and trustworthy workers who can go through the proper training and security steps. The key challenge, then, is to identify capable, trustworthy, and reliable workers through high-fidelity evaluation tasks without exposing any sensitive patient data during the evaluation process. We contribute a set of experimentally validated metrics for assessing the trustworthiness and reliability of crowd workers tasked with providing behavioral feature tags to unstructured videos of children with autism and matched neurotypical controls. The workers are blinded to diagnosis and blinded to the goal of using the features to diagnose autism. These behavioral labels are fed as input to a previously validated binary logistic regression classifier for detecting autism cases using categorical feature vectors. While the metrics do not incorporate any ground truth labels of child diagnosis, linear regression using the 3 correlative metrics as input can predict the mean probability of the correct class of each worker with a mean average error of 7.51% for performance on the same set of videos and 10.93% for performance on a distinct balanced video set with different children. These results indicate that crowd workers can be recruited for performance based largely on behavioral metrics on a crowdsourced task, enabling an affordable way to filter crowd workforces into a trustworthy and reliable diagnostic workforce.

Exploratory spatial analysis of autism rates in New York school districts: role of sociodemographic and language differences

Background

Literature on autism spectrum disorder (ASD) suggests lower ASD prevalence and higher age of diagnosis among children of color, from lower socioeconomic backgrounds, and from families with lower educational levels. These disparities have been attributed to factors such as limited access to diagnostic and treatment services, less opportunity for upward mobility to locales with ample resources, and linguistic barriers. However, few studies describe prevalence and geographic differences of ASD diagnoses by English Language Learner (ELL) status.

Objectives

The primary objectives of this study are to (1) spatially explore the prevalence of ASD among New York State school districts and (2) examine differences of ASD prevalence rates between ELLs and native English-speaking peers.

Methods

Using the 2016–2017 district-level data on public and non-public school age students (3–21 years old) receiving special education services in New York, we analyzed sociodemographic trends among school districts with varying percentages (low, medium, and high ranges) of students with ASD and ELLs. To do this, we conducted exploratory spatial analyses using GIS software, analysis of school district level demographic data, and multivariate linear regression.

Results

In contrast to prior research on ASD prevalence among minority groups, we found disproportionately higher rates of ASD among school districts with higher proportions of Black and Hispanic students. Geographic analysis revealed statistically significant clustering of school districts with high ASD rates in New York City and Albany. Higher proportions of ELLs tended to be concentrated in densely populated, urban, and geographically smaller school districts and had higher proportions of Black, Hispanic, and Asian students.

Conclusions

Schools with higher rates of ASD and ELL students tend to be concentrated in urban regions throughout New York and have higher representation of Black and Hispanic/Latino students, as well as higher rates of learning disabilities in general. Further research is warranted to explore possible reasons for this phenomenon.

Precision Telemedicine through Crowdsourced Machine Learning: Testing Variability of Crowd Workers for Video-Based Autism Feature Recognition

Mobilized telemedicine is becoming a key, and even necessary, facet of both precision health and precision medicine. In this study, we evaluate the capability and potential of a crowd of virtual workers-defined as vetted members of popular crowdsourcing platforms-to aid in the task of diagnosing autism. We evaluate workers when crowdsourcing the task of providing categorical ordinal behavioral ratings to unstructured public YouTube videos of children with autism and neurotypical controls. To evaluate emerging patterns that are consistent across independent crowds, we target workers from distinct geographic loci on two crowdsourcing platforms: an international group of workers on Amazon Mechanical Turk (MTurk) (N = 15) and Microworkers from Bangladesh (N = 56), Kenya (N = 23), and the Philippines (N = 25). We feed worker responses as input to a validated diagnostic machine learning classifier trained on clinician-filled electronic health records. We find that regardless of crowd platform or targeted country, workers vary in the average confidence of the correct diagnosis predicted by the classifier. The best worker responses produce a mean probability of the correct class above 80% and over one standard deviation above 50%, accuracy and variability on par with experts according to prior studies. There is a weak correlation between mean time spent on task and mean performance (r = 0.358, p = 0.005). These results demonstrate that while the crowd can produce accurate diagnoses, there are intrinsic differences in crowdworker ability to rate behavioral features. We propose a novel strategy for recruitment of crowdsourced workers to ensure high quality diagnostic evaluations of autism, and potentially many other pediatric behavioral health conditions. Our approach represents a viable step in the direction of crowd-based approaches for more scalable and affordable precision medicine.

The Performance of Emotion Classifiers for Children With Parent-Reported Autism: Quantitative Feasibility Study

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.

Dysregulation of Circadian Rhythms in Autism Spectrum Disorders

Background:

The alterations in neurological and neuroendocrine functions observed in the autism spectrum disorder (ASD) involves environmentally dependent dysregulation of neurodevelopment, in interaction with multiple coding gene defects. Disturbed sleep-wake patterns, as well as abnormal melatonin and glucocorticoid secretion, show the relevance of an underlying impairment of the circadian timing system to the behavioral phenotype of ASD. Thus, understanding the mechanisms involved in the circadian dysregulation in ASD could help to identify early biomarkers to improve the diagnosis and therapeutics as well as providing a significant impact on the lifelong prognosis.

Objective:

In this review, we discuss the organization of the circadian timing system and explore the connection between neuroanatomic, molecular, and neuroendocrine responses of ASD and its clinical manifestations. Here we propose interconnections between circadian dysregulation, inflammatory baseline and behavioral changes in ASD. Taking into account, the high relevancy of melatonin in orchestrating both circadian timing and the maintenance of physiological immune quiescence, we raise the hypothesis that melatonin or analogs should be considered as a pharmacological approach to suppress inflammation and circadian misalignment in ASD patients.

Strategy:

This review provides a comprehensive update on the state-of-art of studies related to inflammatory states and ASD with a special focus on the relationship with melatonin and clock genes. The hypothesis raised above was analyzed according to the published data.

Conclusion:

Current evidence supports the existence of associations between ASD to circadian dysregulation, behavior problems, increased inflammatory levels of cytokines, sleep disorders, as well as reduced circadian neuroendocrine responses. Indeed, major effects may be related to a low melatonin rhythm. We propose that maintaining the proper rhythm of the circadian timing system may be helpful to improve the health and to cope with several behavioral changes observed in ASD subjects.

Telehealth Approaches to Care Coordination in Autism Spectrum Disorder

This chapter examines the current literature regarding the use of telehealth, and its potential benefits and limitations for diagnosis, treatment, and coordination of care for children diagnosed with Autism Spectrum Disorder (ASD). Barriers to access drive the need to have telehealth as a modality for delivering evidence-based diagnostic and therapeutic processes, which can be impactful in improving developmental trajectories and functional outcomes. The chapter concludes with guidance for clinicians interested in leveraging telehealth, with directions elucidated to further advance the use of telehealth to support families with ASD. More recently, in light of Coronavirus disease 2019 (COVID-19) pandemic and social distancing guidelines and restrictions, this chapter highlights changes in telehealth policy and the use of telehealth for diagnosis and treatment of ASD as well as thoughts about future directions.