Search

Gregory Summers,1 Alison Booth,1 Katherine Brooke-Wavell,2 Tharaq Barami,1,3 Stacy Clemes21Department of Rheumatology, Royal Derby Hospital, Derby, UK; 2School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK; 3Kettering General Hospital, Kettering, UKObjective: In rheumatoid arthritis (RA) patients, low levels of physical activity (PA) and high levels of sedentary behavior (SB) may play a role in enhancing cardiovascular risk. We do not know how long-term control of disease activity impacts upon daily PA levels and if treated patients attain PA levels seen in healthy controls. We therefore compared habitual levels of PA and SB between female RA patients with low disease activity achieved by anti-tumor necrosis factor (TNF) therapy, those with active arthritis (aRA) and non-RA controls.Methods: We carried out a cross-sectional comparison of 40 RA patients on anti-TNF therapy for >2 years with DAS283.2 (aRA) and 34 healthy controls (C) with the groups matched for age and body mass index. PA was assessed using the ActiGraph accelerometer to determine step count and time spent in moderate-to-vigorous physical activity (MVPA), light activity and sedentary time.Results: Daily step count was 72% higher in tRA and 40% higher in C in comparison to aRA (p

Importance: Stereotypical motor movements (SMMs) are a form of restricted and repetitive behavior, which is a core symptom of autism spectrum disorder (ASD). Current quantification of SMM severity is extremely limited, with studies relying on coarse and subjective caregiver reports or laborious manual annotation of short video recordings., Objective: To assess the utility of a new open-source AI algorithm that can analyze extensive video recordings of children and automatically identify segments with heterogeneous SMMs, thereby enabling their direct and objective quantification., Design, Setting, and Participants: This retrospective cohort study included 241 children (aged 1.4 to 8.0 years) with ASD. Video recordings of 319 behavioral assessments carried out at the Azrieli National Centre for Autism and Neurodevelopment Research in Israel between 2017 and 2021 were extracted. Behavioral assessments included cognitive, language, and autism diagnostic observation schedule, 2nd edition (ADOS-2) assessments. Data were analyzed from October 2020 to May 2024., Exposures: Each assessment was recorded with 2 to 4 cameras, yielding 580 hours of video footage. Within these extensive video recordings, manual annotators identified 7352 video segments containing heterogeneous SMMs performed by different children (21.14 hours of video)., Main Outcomes and Measures: A pose estimation algorithm was used to extract skeletal representations of all individuals in each video frame and was trained an object detection algorithm to identify the child in each video. The skeletal representation of the child was then used to train an SMM recognition algorithm using a 3 dimensional convolutional neural network. Data from 220 children were used for training and data from the remaining 21 children were used for testing., Results: Among 319 behavioral assessment recordings from 241 children (172 [78%] male; mean [SD] age, 3.97 [1.30] years), the algorithm accurately detected 92.53% (95% CI, 81.09%-95.10%) of manually annotated SMMs in our test data with 66.82% (95% CI, 55.28%-72.05%) precision. Overall number and duration of algorithm-identified SMMs per child were highly correlated with manually annotated number and duration of SMMs (r = 0.8; 95% CI, 0.67-0.93; P < .001; and r = 0.88; 95% CI, 0.74-0.96; P < .001, respectively)., Conclusions and Relevance: This study suggests the ability of an algorithm to identify a highly diverse range of SMMs and quantify them with high accuracy, enabling objective and direct estimation of SMM severity in individual children with ASD.