Pediatric emergency department-based asthma education tools and parent/child asthma knowledge.

Asthma exacerbations are a leading cause of pediatric hospitalizations despite multiple efforts to educate patients and families on disease course and medication management. Asthma education in the pediatric emergency department (ED) is challenging, and although the use of written action plans has been associated with reduction in hospitalizations and ED visits, written tools may not be useful for individuals with low health literacy. Moreover, asthmatic children should participate in their asthma education. In this prospective randomized study of 53 families presenting to a pediatric ED with a child experiencing an asthma exacerbation, education on asthma was presented via an interactive mobile-based video-game versus a standard-of-care asthma education video (SAV). Median age was 10 years; 64% were males. Many patients had moderate-to-severe asthma, with 57% experiencing ≥ 2 asthma-related ED visits in the last year, 58% requiring hospitalization and 32% reporting a critical care admission. In this cohort, the mobile-based video-game was found to be a feasible, acceptable educational tool; 86% of parents and 96% of children liked the game, while 96% of parents and 76% of children preferred playing the game over watching a SAV. Despite a history of persistent asthma, only 34% of children used an inhaled corticosteroid while 70% required rescue inhaler use in the prior week. Basic asthma knowledge was sub-optimal with only 60% of parents and 43% of children correctly recognizing symptoms that should prompt immediate medical care. This reflects a major gap in asthma knowledge that coexists with parental misconceptions regarding optimal asthma management.

Intelligent Care Management for Diabetic Foot Ulcers: A Scoping Review of Computer Vision and Machine Learning Techniques and Applications.

Ten percent of adults in the United States have a diagnosis of diabetes and up to a third of these individuals will develop a diabetic foot ulcer (DFU) in their lifetime. Of those who develop a DFU, a fifth will ultimately require amputation with a mortality rate of up to 70% within five years. The human suffering, economic burden, and disproportionate impact of diabetes on communities of color has led to increasing interest in the use of computer vision (CV) and machine learning (ML) techniques to aid the detection, characterization, monitoring, and even prediction of DFUs. Remote monitoring and automated classification are expected to revolutionize wound care by allowing patients to self-monitor their wound pathology, assist in the remote triaging of patients by clinicians, and allow for more immediate interventions when necessary. This scoping review provides an overview of applicable CV and ML techniques. This includes automated CV methods developed for remote assessment of wound photographs, as well as predictive ML algorithms that leverage heterogeneous data streams. We discuss the benefits of such applications and the role they may play in diabetic foot care moving forward. We highlight both the need for, and possibilities of, computational sensing systems to improve diabetic foot care and bring greater knowledge to patients in need.

Detecting Clinically Relevant Emotional Distress and Functional Impairment in Children and Adolescents: Protocol for an Automated Speech Analysis Algorithm Development Study.

BACKGROUND: Even before the onset of the COVID-19 pandemic, children and adolescents were experiencing a mental health crisis, partly due to a lack of quality mental health services. The rate of suicide for Black youth has increased by 80%. By 2025, the health care system will be short of 225,000 therapists, further exacerbating the current crisis. Therefore, it is of utmost importance for providers, schools, youth mental health, and pediatric medical providers to integrate innovation in digital mental health to identify problems proactively and rapidly for effective collaboration with other health care providers. Such approaches can help identify robust, reproducible, and generalizable predictors and digital biomarkers of treatment response in psychiatry. Among the multitude of digital innovations to identify a biomarker for psychiatric diseases currently, as part of the macrolevel digital health transformation, speech stands out as an attractive candidate with features such as affordability, noninvasive, and nonintrusive. OBJECTIVE: The protocol aims to develop speech-emotion recognition algorithms leveraging artificial intelligence/machine learning, which can establish a link between trauma, stress, and voice types, including disrupting speech-based characteristics, and detect clinically relevant emotional distress and functional impairments in children and adolescents. METHODS: Informed by theoretical foundations (the Theory of Psychological Trauma Biomarkers and Archetypal Voice Categories), we developed our methodology to focus on 5 emotions: anger, happiness, fear, neutral, and sadness. Participants will be recruited from 2 local mental health centers that serve urban youths. Speech samples, along with responses to the Symptom and Functioning Severity Scale, Patient Health Questionnaire 9, and Adverse Childhood Experiences scales, will be collected using an Android mobile app. Our model development pipeline is informed by Gaussian mixture model (GMM), recurrent neural network, and long short-term memory. RESULTS: We tested our model with a public data set. The GMM with 128 clusters showed an evenly distributed accuracy across all 5 emotions. Using utterance-level features, GMM achieved an accuracy of 79.15% overall, while frame selection increased accuracy to 85.35%. This demonstrates that GMM is a robust model for emotion classification of all 5 emotions and that emotion frame selection enhances accuracy, which is significant for scientific evaluation. Recruitment and data collection for the study were initiated in August 2021 and are currently underway. The study results are likely to be available and published in 2024. CONCLUSIONS: This study contributes to the literature as it addresses the need for speech-focused digital health tools to detect clinically relevant emotional distress and functional impairments in children and adolescents. The preliminary results show that our algorithm has the potential to improve outcomes. The findings will contribute to the broader digital health transformation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46970.

Fully robotic social environment for teaching and practicing affective interaction: Case of teaching emotion recognition skills to children with autism spectrum disorder, a pilot study.

21st century brought along a considerable decrease in social interactions, due to the newly emerged lifestyle around the world, which became more noticeable recently of the COVID-19 pandemic. On the other hand, children with autism spectrum disorder have further complications regarding their social interactions with other humans. In this paper, a fully Robotic Social Environment (RSE), designed to simulate the needed social environment for children, especially those with autism is described. An RSE can be used to simulate many social situations, such as affective interpersonal interactions, in which observational learning can take place. In order to investigate the effectiveness of the proposed RSE, it has been tested on a group of children with autism, who had difficulties in emotion recognition, which in turn, can influence social interaction. An A-B-A single case study was designed to show how RSE can help children with autism recognize four basic facial expressions, i.e., happiness, sadness, anger, and fear, through observing the social interactions of two robots speaking about these facial expressions. The results showed that the emotion recognition skills of the participating children were improved. Furthermore, the results showed that the children could maintain and generalize their emotion recognition skills after the intervention period. In conclusion, the study shows that the proposed RSE, along with other rehabilitation methods, can be effective in improving the emotion recognition skills of children with autism and preparing them to enter human social environments.

Overcoming Clinician Technophobia: What We Learned from Our Mass Exposure to Telehealth During the COVID-19 Pandemic.

Mental health clinicians have migrated to telehealth during the COVID-19 pandemic and have reported their use of telehealth may be permanent. Understanding how stakeholders overcame hesitancy regarding the use of telehealth can potentially reveal how stakeholders can adopt future clinical technologies. The exposure therapy conceptual framework provides one explanation of how mental health clinicians can face their concerns about technologies that promise to improve clinical outcomes and worker well-being. We review available literature published since the start of the pandemic on the extent to which clinicians migrated to telehealth and their reactions to their transitions. In particular, we review available literature that describes negative attitudes and worries by clinicians as one of many barriers of telehealth implementation. We introduce the perspective that the necessary transition to telehealth at the start of the pandemic functioned as an exposure exercise that changed many clinicians' cognitive and emotional reactions to the use of telehealth technologies. Next, we provide guidance on how clinicians can continue taking an exposure approach to learning emerging technologies that are safe and can benefit all stakeholders. Clinicians can now reflect on how they overcame hesitancy regarding telehealth during the pandemic and identify how to build on that new learning by applying strategies used in exposure therapy. The future of clinical work will increasingly require mental health clinicians to better serve their patient populations and enhance their own well-being by overcoming technophobia, a broad term for any level of hesitancy, reluctance, skepticism, worry, anxiety, or fear of implementing technology.

Development of an interactive tool of early social responsiveness to track autism risk in infants and toddlers.

AIM: To evaluate the psychometric properties of a 4-minute assessment designed to identify early autism spectrum disorder (ASD) status through evaluation of early social responsiveness (ESR). METHOD: This retrospective, preliminary study included children between 13 and 24 months (78 males, 79 females mean age 19.4mo, SD 3.1) from two independent data sets (an experimental/training sample [n=120] and a validation/test sample [n=37]). The ESR assessment examined social behaviors (e.g. eye contact, smiling, ease-of-social-engagement) across five common play activities (e.g. rolling a ball, looking at a book). Data analyses examined reliability and accuracy of the assessment in identifying ESR abilities and in discriminating children with and without ASD. RESULTS: Results indicated adequate internal consistency and test-retest reliability of the ESR assessment. Receiver operator curve analysis identified a cutoff score that discriminated infants with ASD-risk from peers in the training sample. This score yielded moderate sensitivity and high specificity for best-estimate ASD diagnosis in the validation sample. INTERPRETATION: Preliminary findings indicated that brief, systematic observation of ESR may assist in discriminating infants with and without ASD, providing concrete evidence to validate or supplement parents', pediatricians', or clinicians' concerns. Future studies could examine the utility of ESR 'growth curves'.

Visual Categorization with Random Projection.

Humans learn categories of complex objects quickly and from a few examples. Random projection has been suggested as a means to learn and categorize efficiently. We investigate how random projection affects categorization by humans and by very simple neural networks on the same stimuli and categorization tasks, and how this relates to the robustness of categories. We find that (1) drastic reduction in stimulus complexity via random projection does not degrade performance in categorization tasks by either humans or simple neural networks, (2) human accuracy and neural network accuracy are remarkably correlated, even at the level of individual stimuli, and (3) the performance of both is strongly indicated by a natural notion of category robustness.

A Novel System for Supporting Autism Diagnosis Using Home Videos: Iterative Development and Evaluation of System Design.

BACKGROUND: Observing behavior in the natural environment is valuable to obtain an accurate and comprehensive assessment of a child's behavior, but in practice it is limited to in-clinic observation. Research shows significant time lag between when parents first become concerned and when the child is finally diagnosed with autism. This lag can delay early interventions that have been shown to improve developmental outcomes. OBJECTIVE: To develop and evaluate the design of an asynchronous system that allows parents to easily collect clinically valid in-home videos of their child's behavior and supports diagnosticians in completing diagnostic assessment of autism. METHODS: First, interviews were conducted with 11 clinicians and 6 families to solicit feedback from stakeholders about the system concept. Next, the system was iteratively designed, informed by experiences of families using it in a controlled home-like experimental setting and a participatory design process involving domain experts. Finally, in-field evaluation of the system design was conducted with 5 families of children (4 with previous autism diagnosis and 1 child typically developing) and 3 diagnosticians. For each family, 2 diagnosticians, blind to the child's previous diagnostic status, independently completed an autism diagnosis via our system. We compared the outcome of the assessment between the 2 diagnosticians, and between each diagnostician and the child's previous diagnostic status. RESULTS: The system that resulted through the iterative design process includes (1) NODA smartCapture, a mobile phone-based application for parents to record prescribed video evidence at home; and (2) NODA Connect, a Web portal for diagnosticians to direct in-home video collection, access developmental history, and conduct an assessment by linking evidence of behaviors tagged in the videos to the Diagnostic and Statistical Manual of Mental Disorders criteria. Applying clinical judgment, the diagnostician concludes a diagnostic outcome. During field evaluation, without prior training, parents easily (average rating of 4 on a 5-point scale) used the system to record video evidence. Across all in-home video evidence recorded during field evaluation, 96% (26/27) were judged as clinically useful, for performing an autism diagnosis. For 4 children (3 with autism and 1 typically developing), both diagnosticians independently arrived at the correct diagnostic status (autism versus typical). Overall, in 91% of assessments (10/11) via NODA Connect, diagnosticians confidently (average rating 4.5 on a 5-point scale) concluded a diagnostic outcome that matched with the child's previous diagnostic status. CONCLUSIONS: The in-field evaluation demonstrated that the system's design enabled parents to easily record clinically valid evidence of their child's behavior, and diagnosticians to complete a diagnostic assessment. These results shed light on the potential for appropriately designed telehealth technology to support clinical assessments using in-home video captured by families. This assessment model can be readily generalized to other conditions where direct observation of behavior plays a central role in the assessment process.

A Pilot Randomized Trial of Text-Messaging for Symptom Awareness and Diabetes Knowledge in Adolescents With Type 1 Diabetes.

Adolescents with type 1 diabetes typically receive clinical care every 3 months. Between visits, diabetes-related issues may not be frequently reflected, learned, and documented by the patients, limiting their self-awareness and knowledge about their condition. We designed a text-messaging system to help resolve this problem. In a pilot, randomized controlled trial with 30 adolescents, we examined the effect of text messages about symptom awareness and diabetes knowledge on glucose control and quality of life. The intervention group that received more text messages between visits had significant improvements in quality of life.