Forecasting High-Risk Behavioral and Medical Events in Children with Autism through Analysis of Digital Behavioral Records.

Individuals with Autism Spectrum Disorder may display interfering behaviors that limit their inclusion in educational and community settings, negatively impacting their quality of life. These behaviors may also signal potential medical conditions or indicate upcoming high-risk behaviors. This study explores behavior patterns that precede high-risk, challenging behaviors or seizures the following day. We analyzed an existing dataset of behavior and seizure data from 331 children with profound ASD over nine years. We developed a deep learning-based algorithm designed to predict the likelihood of aggression, elopement, and self-injurious behavior (SIB) as three high-risk behavioral events, as well as seizure episodes as a high-risk medical event occurring the next day. The proposed model attained accuracies of 78.4%, 80.68%, 85.43%, and 69.95% for predicting the next-day occurrence of aggression, SIB, elopement, and seizure episodes, respectively. The results were proven significant for more than 95% of the population for all high-risk event predictions using permutation-based statistical tests. Our findings emphasize the potential of leveraging historical behavior data for the early detection of high-risk behavioral and medical events, paving the way for behavioral interventions and improved support in both social and educational environments.

Off-body Sleep Analysis for Predicting Adverse Behavior in Individuals with Autism Spectrum Disorder.

Poor sleep quality in Autism Spectrum Disorder (ASD) individuals is linked to severe daytime behaviors. This study explores the relationship between a prior night's sleep structure and its predictive power for next-day behavior in ASD individuals. The motion was extracted using a low-cost near-infrared camera in a privacy-preserving way. Over two years, we recorded overnight data from 14 individuals, spanning over 2,000 nights, and tracked challenging daytime behaviors, including aggression, self-injury, and disruption. We developed an ensemble machine learning algorithm to predict next-day behavior in the morning and the afternoon. Our findings indicate that sleep quality is a more reliable predictor of morning behavior than afternoon behavior the next day. The proposed model attained an accuracy of 74% and a F1 score of 0.74 in target-sensitive tasks and 67% accuracy and 0.69 F1 score in target-insensitive tasks. For 7 of the 14, better-than-chance balanced accuracy was obtained (p-value<0.05), with 3 showing significant trends (p-value<0.1). These results suggest off-body, privacy-preserving sleep monitoring as a viable method for predicting next-day adverse behavior in ASD individuals, with the potential for behavioral intervention and enhanced care in social and learning settings.

Mindfulness and yoga therapy for acute pain in sickle cell disease.

There is a paucity of data regarding the use of non-pharmacologic therapies for pain in sickle cell disease. The purpose of this pilot study was to assess the acceptability and feasibility of video-guided mindfulness meditation, breathing exercises, and yoga, in addition to standard of care, during admission for painful vaso-occlusive crisis. Feasibility was demonstrated by the enrollment rate of > 90% and high level of participant engagement in the intervention. Acceptability was demonstrated by positive feedback obtained in post-intervention surveys and the majority of subjects who expressed interest in participating in future mindfulness and yoga therapy sessions.

Predicting Problematic Behavior in Autism Spectrum Disorder Using Medical History and Environmental Data.

Autism spectrum disorder (ASD), characterized by social, communication, and behavioral abnormalities, affects 1 in 36 children according to the CDC. Several co-occurring conditions are often associated with ASD, including sleep and immune disorders and gastrointestinal (GI) problems. ASD is also associated with sensory sensitivities. Some individuals with ASD exhibit episodes of challenging behaviors that can endanger themselves or others, including aggression and self-injurious behavior (SIB). In this work, we explored the use of artificial intelligence models to predict behavior episodes based on past data of co-occurring conditions and environmental factors for 80 individuals in a residential setting. We found that our models predict occurrences of behavior and non-behavior with accuracies as high as 90% for some individuals, and that environmental, as well as gastrointestinal, factors are notable predictors across the population examined. While more work is needed to examine the underlying connections between the factors and the behaviors, having reasonably accurate predictions for behaviors has the potential to improve the quality of life of some individuals with ASD.

Type 1 diabetes in North East England and North Cumbria: patterns and time trends in 0-14-year-olds from 2012 to 2020.

Introduction: It is important to understand patterns in the epidemiology of type 1 diabetes because they may provide insight into its etiology. We examined the incidence of type 1 diabetes in children aged 0-14 years, and patient demographics and clinical parameters at presentation, over the period 2012-2020 using the North East and North Cumbria Young Persons diabetes register. Methods: Patients up to the age of 14 years with type 1 diabetes, and their families- managed in a total of 18 young persons diabetes clinics-were approached in person at the time of clinic appointments or in the days following diagnosis and they consented to their data being included in the register. Data were submitted regionally to a central unit. Descriptive statistics including crude and age-specific incidence rates were calculated. Temporal trends were analyzed using Joinpoint regression. Comparisons in incidence rates were made between age, sex and areas of higher and lower affluence as measured by the Index of Multiple Deprivation (IMD). Results: A total of 943 cases were recorded between January 2012 and December 2020. Median age at diagnosis was 8.8 years (Q1: 5.3, Q3: 11.7). There were more males than females (54% male). The median HbA1c at diagnosis was 100 mmoL/L (IQR: 39) and over one third (35%) were in ketoacidosis (pH < 7.3). Crude incidence decreased from 25.5 (95% confidence interval [CI] 20.9, 29.9) in 2012 to 16.6 (95% CI: 13.0, 20.2) per 100,000 in 2020 (5.1% per annum, 95% CI 1.1, 8.8%). During the period of the study there was no evidence of any trends in median age, HbA1c, BMI or birthweight (p = 0.18, 0.80, 0.69, 0.32) at diagnosis. Higher rates were observed in males aged 10-14 years, but similar rates were found for both sexes aged 0-9 years and there was no difference between areas of higher or lower deprivation (p = 0.22). Conclusion: The incidence of diabetes in the young may be falling in the North East of England and North Cumbria. The reasons are unclear as there were no associations identified between levels of deprivation or anthropometric measurements. Potential mechanisms include alterations in socioeconomic background or growth pattern. Further research is needed to understand the reasons behind this finding.

Current Utilization of Qualitative Methodologies in Dermatology: A Scoping Review.

The focus of this review was to determine how qualitative methods are used in dermatology research and whether published manuscripts meet current standards for qualitative research. A scoping review of manuscripts published in English between January 1, 2016 and September 22, 2021 was conducted. A coding document was developed to collect information on authors, methodology, participants, research theme, and the presence of quality criteria as outlined by the Standards for Reporting Qualitative Research. Manuscripts were included if they described original qualitative research about dermatologic conditions or topics of primary interest to dermatology. An adjacency search yielded 372 manuscripts, and after screening, 134 met the inclusion criteria. Most studies utilized interviews or focus groups, and researchers predominantly selected participants on the basis of disease status, including over 30 common and rare dermatologic conditions. Research themes frequently included patient experience of disease, development of patient-reported outcomes, and descriptions of provider and caregiver experiences. Although most authors explained their analysis and sampling strategy and included empirical data, few referenced qualitative data reporting standards. Missed opportunities for qualitative methods in dermatology include examination of health disparities, exploration of surgical and cosmetic dermatology experiences, and determination of the lived experience of and provider attitudes toward diverse patient populations.

Cholera control and anti-Haitian stigma in the Dominican Republic: from migration policy to lived experience.

As cholera spread from Haiti to the Dominican Republic, Haitian migrants, a largely undocumented and stigmatized population in Dominican society, became a focus of public health concern. Concurrent to the epidemic, the Dominican legislature enacted new documentation requirements. This paper presents findings from an ethnographic study of anti-Haitian stigma in the Dominican Republic from June to August 2012. Eight focus group discussions (FGDs) were held with Haitian and Dominican community members. Five in-depth interviews were held with key informants in the migration policy sector. Theoretical frameworks of stigma's moral experience guided the analysis of how cholera was perceived, ways in which blame was assigned and felt and the relationship between documentation and healthcare access. In FGDs, both Haitians and Dominicans expressed fear of cholera and underscored the importance of public health messages to prevent the epidemic's spread. However, health messages also figured into experiences of stigma and rationales for blame. For Dominicans, failure to follow public health advice justified the blame of Haitians and seemed to confirm anti-Haitian sentiments. Haitians communicated a sense of powerlessness to follow public health messages given structural constraints like lack of safe water and sanitation, difficulty accessing healthcare and lack of documentation. In effect, by making documentation more difficult to obtain, the migration policy undermined cholera programs and contributed to ongoing processes of moral disqualification. Efforts to eliminate cholera from the island should consider how policy and stigma can undermine public health campaigns and further jeopardize the everyday 'being-in-the-world' of vulnerable groups.