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Autism is a heritable neurodevelopmental condition marked by impaired social interaction, repetitive behavior, and co-occurring conditions. Sleep disturbances are common in autism. This study uses low-cost wearable devices to compare sleep, physical activity, and circadian behavior in autistic adults and their non-autistic relatives. We recruited 318 autistic individuals and 130 family members, collecting accelerometer data over 3 weeks (8249 days). Using a data-driven approach, we identified actimetry-derived features associated with autism. We examined 308 traits using the elastic net algorithm and linear mixed effects regressions. We identified 52 actimetry measures associated with autism (area under the curve: 0.812; confidence interval: 0.761–0.862), validated in a test set (area under the curve: 0.756; confidence interval: 0.700–0.813). Both mean and day-to-day variability in several measures (e.g., time spent sedentary, total light physical activity) were associated with autism. In autistic individuals, reduced physical activity during wake was more strongly associated with shorter sleep time than in non-autistic relatives (likelihood ratio: 41.6; p = 1.13e−10). Reduced physical activity in autistic individuals was linked to increased social impairment, as measured by the Social Responsiveness Scale. Long inactivity periods and lower physical activity levels were associated with autism, correlating with less sleep and later sleep onset. Interventional studies are needed to explore if improving sleep and physical activity can improve the quality of life for autistic individuals. Lay Abstract Autistic individuals frequently report problems with their sleep, though what aspects of sleep are most affected is not well understood. In this study, we recruited 318 adult autistic participants without intellectual disability and 130 of their non-autistic family members to measure their sleep, physical activity, and daily routines. Study participants wore accelerometer-based wrist-worn devices over 3 consecutive weeks to record their movement and activity. In total, 154 distinct physical activity, sleep, and behavioral traits were identified from the recordings, 52 of which were found to associate with autism. Many of these traits were related to physical activity, where autistic individuals were more likely to be less active for longer periods and have lower overall physical activity levels. Long periods of inactivity also associated with less sleep, with a stronger association in those with autism. For example, for every hour of inactivity, autistic participants had on average ~23 min less of sleep compared to ~17 min in their family members. Autistic individuals with lower levels of physical activity showed higher social impairment as measured by the Social Responsiveness Scale. Overall, lower physical activity may impair sleep and worsen the core features of autism. Interventional studies aimed to increase physical activity may improve the quality of life of autistic individuals.

Introduction: Recipients of lung transplants are at heightened risk of developing low bone mineral density (BMD). They also have increased risk of developing osteoporosis-related fractures. This rate of BMD decline is not well characterized. The aim of this manuscript is to characterize the rate of BMD decrease after lung transplant. Methodology: This is a preliminary retrospective cohort study of 200 patients who received lung transplants. Each patient had pre-transplant dual energy X-ray absorptiometry (DXA) and post-transplant DXA scans on the same DXA scanner. The BMD at each lumbar vertebra, total hip, and femoral neck were recorded. Generalized linear mixed effects models with random intercepts and random slopes were used to model the rate of change of BMD after transplant, adjusting for time since transplant, sex, and sex*time interaction. Results: We found that men had higher baseline BMD than women at all sites (P<0.05 for all). The rate of BMD decrease was fastest at the femoral neck (P<0.05). Men lost BMD at a faster rate (-5.23 x10-5 g/cm2/day) than women at the femoral neck (-2.22 x10-5 g/cm2 /day). Conclusion: BMD decreases after lung transplant and occurs faster in men than women.

Patients with low bone mineral density (BMD) face an increased risk of fractures, yet are frequently undiagnosed. Consequently, it is imperative to have opportunistically screen for low BMD in patients undergoing other medical evaluations. This retrospective study encompassed 422 patients aged ≥ 50 who underwent both dual-energy X-ray absorptiometry (DXA) and hand radiographs (modality of digital X-ray) from three different vendors within a 12-month period. The dataset was randomly divided into training/validation (n=338) and test (n=84) datasets. we sought to predict osteoporosis/osteopenia and establish correlations between bone textural analysis and DXA measurements. Our results demonstrate that the deep learning model achieved an accuracy of 77.38%, sensitivity of 77.38%, specificity of 73.63%, and an area under the curve (AUC) of 83% in detecting osteoporosis/osteopenia. These findings suggest that hand radiographs can serve as a viable screening tool for identifying individuals warranting formal DXA assessment for osteoporosis/osteopenia.

Abstract Objective To evaluate whether the CT attenuation of bones seen on shoulder CT scans could be used to predict low bone mineral density (BMD) (osteopenia/osteoporosis), and to compare the performance of two machine learning models to predict low BMD. Methods In this study, we evaluated 194 patients aged 50 years or greater (69.2 ± 9.1 years; 170 females) who underwent unenhanced shoulder CT scans and dual-energy X-ray absorptiometry within 1 year of each other between January 1, 2010, and December 31, 2021. The CT attenuation of the humerus, glenoid, coracoid, acromion, clavicle, first, second, and third ribs was obtained using 3D-Slicer. Support vector machines (SVMs) and k-nearest neighbours (kNN) were used to predict low BMD. DeLong test was used to compare the areas under the curve (AUCs). Results A CT attenuation of 195.4 Hounsfield Units of the clavicle had a sensitivity of 0.577, specificity of 0.781, and AUC of 0.701 to predict low BMD. In the test dataset, the SVM had sensitivity of 0.686, specificity of 1.00, and AUC of 0.857, while the kNN model had sensitivity of 0.966, specificity of 0.200, and AUC of 0.583. The SVM was superior to the CT attenuation of the clavicle (P = .003) but not better than the kNN model (P = .098). Conclusion The CT attenuation of the clavicle was best for predicting low BMD; however, a multivariable SVM was superior for predicting low BMD. Advances in knowledge SVM utilizing the CT attenuations at many sites was best for predicting low BMD.