Effect of Active Workstations on Neurocognitive Performance and Typing Skills: A Randomized Clinical Trial.

BACKGROUND: Extended sedentary behavior is a risk factor for chronic disease and mortality, even among those who exercise regularly. Given the time constraints of incorporating physical activity into daily schedules, and the high likelihood of sitting during office work, this environment may serve as a potentially feasible setting for interventions to reduce sedentary behavior. METHODS AND RESULTS: A randomized cross-over clinical trial was conducted at an employee wellness center. Four office settings were evaluated on 4 consecutive days: stationary or sitting station on day 1 (referent), and 3 subsequent active workstations (standing, walking, or stepper) in randomized order. Neurocognitive function (Selective Attention, Grammatical Reasoning, Odd One Out, Object Reasoning, Visuospatial Intelligence, Limited-Hold Memory, Paired Associates Learning, and Digit Span) and fine motor skills (typing speed and accuracy) were tested using validated tools. Average scores were compared among stations using linear regression with generalized estimating equations to adjust standard errors. Bonferroni method adjusted for multiple comparisons. Healthy subjects were enrolled (n=44), 28 (64%) women, mean±SD age 35±11 years, weight 75.5±17.1 kg, height 168.5±10.0 cm, and body mass index 26.5±5.2 kg/m2. When comparing active stations to sitting, neurocognitive test either improved or remained unchanged, while typing speed decreased without affecting typing errors. Overall results improved after day 1, suggesting habituation. We observed no major differences across active stations, except decrease in average typing speed 42.5 versus 39.7 words per minute with standing versus stepping (P=0.003). CONCLUSIONS: Active workstations improved cognitive performance, suggesting that these workstations can help decrease sedentary time without work performance impairment. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT06240286.

MedYOLO: A Medical Image Object Detection Framework.

Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to make voxel-accurate segmentations of the region of interest. However, the labels required to train these CNNs are time-consuming to generate and require attention from subject matter experts to ensure quality. For tasks where voxel-level precision is not required, object detection models offer a viable alternative that can reduce annotation effort. Despite this potential application, there are few options for general-purpose object detection frameworks available for 3-D medical imaging. We report on MedYOLO, a 3-D object detection framework using the one-shot detection method of the YOLO family of models and designed for use with medical imaging. We tested this model on four different datasets: BRaTS, LIDC, an abdominal organ Computed tomography (CT) dataset, and an ECG-gated heart CT dataset. We found our models achieve high performance on a diverse range of structures even without hyperparameter tuning, reaching mean average precision (mAP) at intersection over union (IoU) 0.5 of 0.861 on BRaTS, 0.715 on the abdominal CT dataset, and 0.995 on the heart CT dataset. However, the models struggle with some structures, failing to converge on LIDC resulting in a mAP@0.5 of 0.0.

Cardiovascular Complications of Coronavirus Disease-2019.

Cardiovascular complications associated with the severe acute respiratory syndrome coronavirus 2 infection are common and lead to high mortality in the acute phase and high morbidity in the chronic phase impacting an individual's quality of life and health outcomes. Patients afflicted with coronavirus disease-2019 (COVID-19) infection display an increased risk for myocarditis, dysrhythmia, pericarditis, ischemic heart disease, heart failure, and thromboembolism. Although cardiovascular complications are reported across all patients with COVID-19, hospitalized patients with severe infection are most vulnerable. The underline pathobiology remains poorly defined albeit complex. Following current guidelines in decision-making for evaluation and management in addition to the beginning or returning exercise is recommended.