• The Journal of Korean Academy of Sensory Integration
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• v.17 no.1
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• pp.30-42
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• 2019

Objective : The purpose of this study is to analyze non-medication interventions for self-injurious behavior of individuals with autism spectrum disorders, using a systematic review, and to provide evidence of appropriate services for individuals with autism spectrum disorders with self-injury behaviors in the clinical practice of occupational therapy. Methods : Using the electronic databases PubMed, Medline (ProQuest), DBpia, RISS, KISS, and NDSL, we searched for articles published in Korean and international journals from December 2004 to November 2018. The main search term were "Autism OR Autism Spectrum Disorder AND Therapy OR Treatment Or Intervention AND Self Injurious Behavior." Qualitative analysis was performed, and the results are presented in the PICO format. Results : A total of 12 articles were selected. The quality of the evidence was highest in level IV and level V. Single studies with an experimental design were the most common. Behavior therapy was the most common type of intervention. The next most common interventions were behavioral therapy, brain stimulation and control, and sensory integration therapy with behavioral therapy. The self-injury behaviors of individuals autism spectrum disorders were decreased, and was statistically significant. Conclusion : This study investigated the use of non-medication interventions for children with autism spectrum disorders who showed self-injury behavior. Future research should use higher-level designs, and investigate differences between various non-medication interventions.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.29 no.4
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• pp.178-184
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• 2018

Objectives: The objective of this study was to investigate the concordance of Diagnostic and Statistical Manual of Mental Disorders (DSM-IV and DSM-5) diagnostic criteria for autism spectrum disorder (ASD). Methods: We retrospectively reviewed the medical records of 170 subjects (age range: 3-23, 140 boys) with developmental delay or social deficit from January 2011 to July 2016 at the Department of Psychiatry of Asan Medical Center. The Autism Diagnostic Interview-Revised (ADI-R), the Autism Diagnostic Observation Schedule (ADOS), and intelligence tests were performed for each subject. Diagnosis was reviewed and confirmed for each subject with DSM-IV Pervasive Developmental Disorder (PDD) and DSM-5 ASD criteria, respectively. Results: Fifty-eight of 145 subjects (34.1%) who were previously diagnosed as having PDD in DSM-IV did not meet DSM-5 ASD criteria. Among them, 28 (48.3%) had Asperger's disorder based on DSM-IV. Most algorithm scores on ADOS and all algorithm scores on ADI-R were highest in subjects who met both DSM-IV PDD criteria and DSM-5 ASD criteria (the Convergent group), followed by subjects with a DSM-IV PDD diagnosis who did not have a DSM-5 ASD diagnosis (the Divergent group), and subjects who did not meet either DSM-IV PDD or DSM-5 ASD criteria (the non-PDD group). Intelligence quotient was lower in the Convergent group than in the Divergent group. Conclusion: The results of our study suggest that ASD prevalence estimates could be lower under DSM-5 than DSM-IV diagnostic criteria. Further prospective study on the impact of new DSM-5 ASD diagnoses in Koreans with ASD is needed.

• The Journal of Korean Academy of Sensory Integration
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• v.20 no.3
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• pp.48-59
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• 2022

Objective : This study was intended to systematically consider the sensory integration intervention methods offered in Korea for children with autism spectrum disorder and to provide an evidence base for the application of sensory integration interventions. Methods : The subjects of this study were published studies in a national journal for the last 10 years from January 2011 to December 2020. The databases used for the search were RISS and DBpia. The search terms were 'autism', 'autism spectrum', 'sensory integration', and 'intervention'. A total of 10 studies were used in the analysis, which were analyzed at the qualitative and methodological quality of the research evidence and the results were presented according to the PICO (Patient, Intervention, Comparison, Outcome). Results : In the research analysis, the quality level of the evidence was highest at level IV, followed by level II. The methodological quality of the evidence was the most common for 'Good' research, followed 'Fair'. The study subjects were children with autism spectrum disorder and their parents, and the experimental design had the highest frequency of single-subject studies. Interventions have the highest number of studies confirming adaptive behavior and sensory modulation, and the evaluation tools used to measure interventions have the highest frequency of sensory profiles and Canadian occupational performance measures (COPM). All 10 studies used in the analysis showed positive improvements and statistically significant effects on various outcome values from sensory integration interventions. Conclusion : In the recent clinical environment, sensory integration interventions have been continuously conducted in children with autism spectrum disorder. In future research, it is necessary to conduct research on various sensory integration intervention methods and the high quality of the evidence for the application of sensory integration interventions.

• Child Health Nursing Research
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• v.24 no.3
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• pp.337-344
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• 2018

Purpose: The purpose of this study is to examine the association between each phase of the Life Transition Process (LTP) and depressive symptoms among parents of children with Autism Spectrum Disorder (ASD). Methods: Using a cross-sectional study design, data pertaining to LTP and depressive symptoms were collected from 285 parents of children with ASD (101 fathers and 184 mothers). Participants were recruited through the Autism Society of Korea and a counseling center for families of children with ASD. Results: Using a cut-off point of 5 or higher on the Beck Depression Inventory-Short Form, more than half of the parents (53.4%) were deemed depressed; these parents experienced moderate (27.4%) to severe (11.6%) levels of depressive symptoms. A hierarchical multiple regression using the socio-demographic characteristics of children and parents and each phase of the LTP as predictors, found that lower income (${\beta}=-.14$, p=.007) and greater scores for the wandering phase (${\beta}=.59$, p<.001) were significant predictors of greater levels of depressive symptoms. Conclusion: These findings confirm the association between LTP and depressive symptoms for parents of children with ASD, who were found to have a greater risk of depressive symptoms during wandering phase.

• Journal of the Korean Society of Food Culture
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• v.36 no.1
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• pp.40-55
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• 2021

The purpose of this study was to provide basic information on the development of nutrition education programs to improve the mealtime behavior of children with autism spectrum disorder (ASD) by investigating the mealtime behavior and food preferences of children with ASD through the perception of special education teachers. Surveys were given to 108 special education teachers in special education schools in Korea regarding the demographic characteristics, nutrition education support needs, mealtime behavior, and food preferences of children with ASD. Most of the special education teachers responded that nutrition education in special schools had not been conducted properly and nutrition education for ASD children is necessary. Mealtime behavior analysis classified the behavior into three clusters: cluster 1, 'less problematic mealtime behavior'; cluster 2, 'general feature of autism'; cluster 3, 'difficulty in self-directed diet'. The age, eating habits, and food preferences were different according to each mealtime behavior cluster. Therefore, it will be necessary to develop a nutrition education program based on the characteristics of mealtime behavior.

• Psychiatry investigation
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• v.15 no.12
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• pp.1144-1153
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• 2018

Objective One of the areas of social cognition is Theory of Mind (ToM) is defined as the capacity to interpret, infer and explain mental states underlying the behavior of others. When social cognition studies on neurodevelopmental disorders are examined, it can be seen that this skill has not been studied sufficiently in children with Specific Learning Disorder (SLD). Methods In this study, social cognition skills in children diagnosed with attention deficit hyperactivity disorder (ADHD), SLD or Autism Spectrum Disorder (ASD) evaluated before puberty and compared with controls. To evaluate the ToM skills, the first and second-order false belief tasks, the Hinting Task, the Faux Pas Test and the Reading the Mind in the Eyes Task were used. Results We found that children with neurodevelopmental disorders as ADHD, ASD, and SLD had ToM deficits independent of intelligence and language development. There was a significant correlation between social cognition deficits and problems experienced in many areas such as social communication and interaction, attention, behavior, and learning. Conclusion Social cognition is an important area of impairment in SLD and there is a strong relationship between clinical symptoms and impaired functionality.

• Clinical and Experimental Pediatrics
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• v.64 no.6
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• pp.286-292
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• 2021

Background: The effect of exposure to environmental factors on autism spectrum disorders (ASD), especially during pregnancy, is unclear. Purpose: This meta-analysis investigated the association between exposure to pesticides and polychlorinated biphenyls (PCBs) during pregnancy and ASD risk among children. Methods: We searched Scopus, PubMed, Web of Science, and ProQuest for articles published through September 2019. Random-effects models were used to examine the association among studies using pooled odds ratios (ORs) and their 95% confidence intervals (CI). I2 tests were used to measure interstudy heterogeneity. Results: The pooled OR indicated a significant association between PCB and pesticide exposure during pregnancy and ASD risk among children (OR, 1.80; 95% CI, 1.26-2.34; and OR, 1.20; 95% CI, 1.02-1.39), respectively. Conclusion: Findings of the present study indicate that exposure to pesticides and PCBs during pregnancy may affect the risk of ASD among children.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.31 no.3
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• pp.105-120
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• 2020

Although autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social impairments, patients with ASD frequently manifest atypical sensory behaviors. Recently, atypical sensory perception in ASD has received much attention, yet little is known about its cause or neurobiology. Herein, we review the findings from neuroimaging studies related to visual perception in ASD. Specifically, we examined the neural underpinnings of visual detection, motion perception, and face processing in ASD. Results from neuroimaging studies indicate that atypical visual perception in ASD may be influenced by attention or higher order cognitive mechanisms, and atypical face perception may be affected by disrupted social brain network. However, there is considerable evidence for atypical early visual processing in ASD. It is likely that visual perceptual abnormalities are independent of deficits of social functions or cognition. Importantly, atypical visual perception in ASD may enhance difficulties in dealing with complex and subtle social stimuli, or improve outstanding abilities in certain fields in individuals with Savant syndrome. Thus, future research is required to elucidate the characteristics and neurobiology of autistic visual perception to effectively apply these findings in the interventions of ASD.

• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.53-59
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• 2023

Detection of children with autism spectrum disorder (ASD) based on speech has relied on predefined feature sets due to their ease of use and the capabilities of speech analysis. However, clinical impressions may not be adequately captured due to the broad range and the large number of features included. This paper demonstrates that the knowledge-driven speech features (KDSFs) specifically tailored to the speech traits of ASD are more effective and efficient for detecting speech of ASD children from that of children with typical development (TD) than a predefined feature set, extended Geneva Minimalistic Acoustic Standard Parameter Set (eGeMAPS). The KDSFs encompass various speech characteristics related to frequency, voice quality, speech rate, and spectral features, that have been identified as corresponding to certain of their distinctive attributes of them. The speech dataset used for the experiments consists of 63 ASD children and 9 TD children. To alleviate the imbalance in the number of training utterances, a data augmentation technique was applied to TD children's utterances. The support vector machine (SVM) classifier trained with the KDSFs achieved an accuracy of 91.25%, surpassing the 88.08% obtained using the predefined set. This result underscores the importance of incorporating domain knowledge in the development of speech technologies for individuals with disorders.

• ETRI Journal
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• v.44 no.4
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.