Introduction: Men in racial and ethnic minority groups are less likely than non-Hispanic White men to participate in programs designed to improve health, despite having a higher prevalence of type 2 diabetes. We sought to understand 1) the interests and preferences of racial and ethnic minority men, with or at risk for type 2 diabetes, in programs designed to improve health and 2) factors that influence participation and health practices.
Methods: We designed a 43-question web-based survey on facilitators and barriers to participation in a healthy living program.
Background: Diabetes affects millions of people in the United States and poses significant health and economic challenges, but it can be prevented or managed through health behavior changes. Such changes might be aided by voice-activated personal assistants (VAPAs), which offer interactive and real-time assistance through features such as reminders, or obtaining health information. However, there are little data on interest and acceptability of integrating VAPAs into programs such as the National Diabetes Prevention Program (National DPP) or diabetes, self-management, education, and support (DSMES) services.
View Article and Find Full Text PDFObjectives: Higher prevalence of several chronic diseases occurs in men in the United States, including diabetes and prediabetes. Of the 34 million adults with diabetes and 88 million with prediabetes there is a higher prevalence of both conditions in men compared to women. Black, Hispanic, and American Indian men have some of the highest rates of diabetes and diabetes complications.
View Article and Find Full Text PDFOssabaw pigs (n = 11; 5-gilts, 6-barrows; age 15.6 ± 0.62 SD months) were exposed to a three-choice preference maze to evaluate preference for fermented sorghum teas (FSTs).
View Article and Find Full Text PDFPurpose: To evaluate the effectiveness of automated liver segmental volume quantification and calculation of the liver segmental volume ratio (LSVR) on a non-contrast T1-vibe Dixon liver MRI sequence using a deep learning segmentation pipeline.
Method: A dataset of 200 liver MRI with a non-contrast 3 mm T1-vibe Dixon sequence was manually labeledslice-by-sliceby an expert for Couinaud liver segments, while portal and hepatic veins were labeled separately. A convolutional neural networkwas trainedusing 170 liver MRI for training and 30 for evaluation.