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Background: Diaper need is a lack of reliable access to a sufficient quantity of diaper supplies. It is an essential pediatric social determinant of health measure that is useful for exploring the effects of the COVID-19 pandemic on families of young children. Objective: This study aimed to identify the predictors of diaper need and the associations between diaper need and psychosocial outcomes among underresourced families with neurodiverse children. Methods : This cross-sectional study included 129 caregivers of children aged 0-36 months who completed an online survey in early 2021, during the height of the COVID-19 pandemic. Participants completed a diaper need assessment, the Hunger Vital Signs Food Insecurity Screener, the Perceived Stress Scale, and the Infant Behavioral Questionnaire subscale. Relationships were explored using independent-samples t -tests, chi-square tests, and multiple regression analyses. Results : Seventy-six percent of caregivers reported some diaper need, and 87.6% reported food insecurity. Greater than one third (39.5%) reported high diaper need. Common mitigation strategies included using a towel or other cloth like a diaper, not using a diaper, and keeping the child in the same diaper for longer than usual. In multivariate modeling, food insecurity, household size, and parent age were significantly associated with high diaper need. Families experiencing food insecurity were 4.24 times more likely to experience high diaper need than food-secure families. High diaper need compared to low or no diaper need was associated with increased parent perceived stress. Discussion : We found high levels of diaper need and food insecurity for families during the COVID-19 pandemic. The association of high diaper need and stress indicates an inadequate supply of diapers adversely affects parent stress. Nurses may consider including diaper need in social determinants of health screening and prioritize connecting families to appropriate resources. [ABSTRACT FROM AUTHOR]

This paper uses deep (machine) learning techniques to develop and test how motor behaviors, derived from location and movement sensor tracking data, may be associated with falls, delirium, and urinary tract infections (UTIs) in long-term care (LTC) residents. Longitudinal observational study. A total of 23 LTC residents (81,323 observations) with cognitive impairment or dementia in 2 northeast Department of Veterans Affairs LTC facilities. More than 18 months of continuous (24/7) monitoring of motor behavior and activity levels used objective radiofrequency identification sensor data to track and record movement data. Occurrence of acute events was recorded each week. Unsupervised deep learning models were used to classify motor behaviors into 5 clusters; supervised decision tree algorithms used these clusters to predict acute health events (falls, delirium, and UTIs) the week before the week of the event. Motor behaviors were classified into 5 categories (Silhouette score = 0.67), and these were significantly different from each other. Motor behavior classifications were sensitive and specific to falls, delirium, and UTI predictions 1 week before the week of the event (sensitivity range = 0.88–0.91; specificity range = 0.71–0.88). Intraindividual changes in motor behaviors predict some of the most common and detrimental acute events in LTC populations. Study findings suggest real-time locating system sensor data and machine learning techniques may be used in clinical applications to effectively prevent falls and lead to the earlier recognition of risk for delirium and UTIs in this vulnerable population. [ABSTRACT FROM AUTHOR]