Ethics of the Use of Social Media as Training Data for AI Models Used for Digital Phenotyping.

Digital phenotyping, or personal sensing, is a field of research that seeks to quantify traits and characteristics of people using digital technologies, usually for health care purposes. In this commentary, we discuss emerging ethical issues regarding the use of social media as training data for artificial intelligence (AI) models used for digital phenotyping. In particular, we describe the ethical need for explicit consent from social media users, particularly in cases where sensitive information such as labels related to neurodiversity are scraped. We also advocate for the use of community-based participatory design principles when developing health care AI models using social media data.

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

[This corrects the article DOI: 10.2196/52660.].

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 (ß = 0; 95% CI, 0-0; P = .01) and Hispanic autistic children (ß = 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: ß = 0.05; <50% of the population: ß = 0.07; P = .002) or Hispanic autistic children (>50% of the population: ß = 0.04; <50% of the population: ß = 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 (ß = 0; 95% CI, 0-0; P < .001) and Hispanic autistic children (ß = 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 (ß = 0; 95% CI, 0-0; P = .005) and Hispanic autistic children (ß = 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.

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.