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

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Background Children's motor development is a crucial tool for assessing developmental levels, identifying developmental disorders early, and taking appropriate action. Although the Korean Developmental Screening Test for Infants and Children (K-DST) can accurately assess childhood development, its dependence on parental surveys rather than reliable, professional observation limits it. This study constructed a dataset based on a skeleton of recordings of K-DST behaviors in children aged between 20 and 71 months, with and without developmental disorders. The dataset was validated using a child behavior artificial intelligence (AI) learning model to highlight its possibilities. Results The 339 participating children were divided into 3 groups by age. We collected videos of 4 behaviors by age group from 3 different angles and extracted skeletons from them. The raw data were used to annotate labels for each image, denoting whether each child performed the behavior properly. Behaviors were selected from the K-DST's gross motor section. The number of images collected differed by age group. The original dataset underwent additional processing to improve its quality. Finally, we confirmed that our dataset can be used in the AI model with 93.94%, 87.50%, and 96.31% test accuracy for the 3 age groups in an action recognition model. Additionally, the models trained with data including multiple views showed the best performance. Conclusion Ours is the first publicly available dataset that constitutes skeleton-based action recognition in young children according to the standardized criteria (K-DST). This dataset will enable the development of various models for developmental tests and screenings.

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Background Policy makers and practitioners in low- and middle-income countries (LMICs) are increasingly focusing on the effectiveness of digital devices in the delivery of medical and educational services to children under resource constraints. It is widely known that digital literacy can be fostered through exposure to and education regarding digital devices, which can improve children’s academic performance as well as their search and communication skills in the digital era. However, the correlation between the cognitive function of children and exposure and intensity of the exposure to digital devices has rarely been studied, and the association between digital device exposure and the socioeconomic characteristics and cognitive development of children in LMICs is unknown. Objective This study examines the association among exposure to digital devices, socioeconomic status, and cognitive function in children aged 3 to 9 years in Cambodia. Methods We used a survey of 232 children that gathered data on familiarity with digital devices, demographic characteristics, and socioeconomic status, as well as a Cambridge Neuropsychological Test Automated Battery test for cognitive function, to examine the association between possible barriers and factors that may influence the cognitive function of children in 2 Cambodian schools from April 22, 2019, to May 4, 2019. A comparative analysis was performed with and without digital exposure, and an association analysis was performed among the variables from the survey and cognitive function. Results Significant differences were observed in demographic and socioeconomic characteristics such as school location, family type, and family income according to digital device exposure. The results of the Cambridge Neuropsychological Test Automated Battery tests, except for 1 test related to executive function, indicated no significant differences (P>.05) between group A and group B or among the 4 subgroups. Pretest digital device experience and amount of time spent using digital devices during the test had no significant impacts on the cognitive development of the children. Conversely, the multivariate analyses showed that cognitive function was associated with educational expenses per child, school (location), family type, and family income. Conclusions These results provide evidence to policy makers and practitioners on the importance of improving socioeconomic conditions, leading to investment in education by implementing programs for children’s cognitive development through digital devices in LMICs.

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BACKGROUND Digital technologies such as mobile technology, the Internet of Things (IoT), artificial intelligence (AI), and virtual reality have revolutionized healthcare, and the COVID-19 pandemic has further highlighted the importance of implementing digital health in healthcare settings. However, the key indicators of digital health dissemination and application are yet to be identified, making it challenging to adopt digital health innovations in healthcare. Therefore, to promote digital health adoption, it is critical to identify the key indicators and applications of digital health-related technologies. OBJECTIVE This study aimed to identify the key indicators of digital health dissemination and application in healthcare settings by surveying digital health experts, workers in medical institutions, and industry experts regarding the priorities and demand for digital health adoption, and to determine if demand gaps exist. METHODS We surveyed 254 participants in South Korea between January 2022 and May 2022 to identify the key indicators, priorities, and demands related to digital health dissemination and application in healthcare settings. An Analytical Hierarchy Process (AHP) method was applied to derive the weight of the key indicators of digital health dissemination and application, and an online survey was conducted to obtain related information from workers in medical institutions and digital health-related fields. RESULTS Three surveys were conducted among 68 digital health experts and 186 medical workers to identify the key indicators of digital health dissemination and application. The results indicated that the standardization of healthcare information is essential for digital health adoption (AHP-weighted mean score: 78.11), with healthcare providers prioritizing digital health use for mental illness and chronic diseases. In addition, the findings revealed inconsistencies in the demand for digital health technology among digital health experts, workers in medical institutions, and workers in digital health-related industries. While digital health experts prioritized digital health systems, medical workers expressed a high demand for mobile healthcare and telemedicine. CONCLUSIONS Identifying the key indicators of digital health dissemination and application is crucial to facilitate the adoption of digital health. Our study found that the standardization of healthcare information is essential for digital health, with healthcare providers prioritizing its use in mental illness and chronic disease management. Prioritizing the dissemination and application of digital health at the point of care based on key indicators and the needs of healthcare providers is necessary for the successful implementation of digital health in the healthcare sector.