In this study we examined the influence of ethnicity on weight, body image, and self-esteem among adult women while controlling for socioeconomic status (SES). Fifty women of African American (AA), European American (EA), and Mexican American (MA) descent completed interviews and questionnaires related to body size and self-esteem, and were measured for weight and height. AA women were significantly heavier than EA women, but MA women did not differ materially from either grouping. Regardless of ethnic descent, all reported congruence between real and ideal body size. In addition no differences were found in self-esteem. This new comparative study calls into question variations previously attributed to race or ethnicity. It also provides a view of middle-class women missing from recent research. Based on the outcomes of our research, we suggest that socioeconomic differences could be more important than ethnic background. Health educators and providers may find these data helpful when designing prevention and intervention strategies for middle-class women regardless of their ethnic origin.
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http://dx.doi.org/10.1080/073993302760190065 | DOI Listing |
Clin Interv Aging
January 2025
Department of Laboratory Medicine, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, Shandong, 253000, People's Republic of China.
Background: This study investigates the correlation between the weight-adjusted waist index (WWI) and cognitive performance in the senior American population, focusing on those without diabetes from 2011 to 2014.
Methods: We analyzed data from the 2011-2012 and 2013-2014 National Health and Nutrition Examination Surveys (NHANES), focusing on non-diabetic participants aged 60 and older who completed cognitive tests: Establish a Registry for Alzheimer's disease (CERAD), the Animal Fluency test (AFT), and Digit Symbol Substitution test (DSST). WWI was calculated using waist circumference divided by the square root of body weight.
Front Bioeng Biotechnol
January 2025
Center for Orthopaedic Biomechanics, University of Denver, Denver, CO, United States.
Introduction: Accurate prediction of knee biomechanics during total knee replacement (TKR) surgery is crucial for optimal outcomes. This study investigates the application of machine learning (ML) techniques for real-time prediction of knee joint mechanics.
Methods: A validated finite element (FE) model of the lower limb was used to generate a dataset of knee joint kinematics, kinetics, and contact mechanics.
Cureus
December 2024
Orthopedic Surgery, Middlebrook Family Medicine, Middlebrook, USA.
The treatment for osteoarthritis (OA) often requires total joint arthroplasty (TJA) when less invasive approaches fail. The annual incidence of TJA is rising. Metal-on-metal (MoM) hip and knee implants were widely used for TJA in the past, but complications have led to their decline.
View Article and Find Full Text PDFFront Vet Sci
January 2025
Divison of Small Animal Surgery, Department of Clinical Veterinary Medicine, Vetsuisse-Faculty, University of Bern, Bern, Switzerland.
Introduction: Sacroiliac luxation is a common traumatic feline injury, with the small size of the sacral body being a challenge for surgical stabilization. This study compared an innovative computer-guided drilling method with the conventional fluoroscopy-controlled freehand technique. Neuronavigation, using CT-based planning and real-time tracking, was evaluated against the freehand method for accuracy and time efficiency.
View Article and Find Full Text PDFFront Vet Sci
January 2025
College of Veterinary Medicine, China Agricultural University, Beijing, China.
Cows with high body condition scores experience more severe negative energy balance (NEB) and undergo mobilization of more body fat during the peripartum period, leading to more production of nonesterified fatty acids (NEFA) and -hydroxybutyric acid (BHBA). Postpartum insulin secretion is lower, and insulin resistance is stronger in obese cows. Exogenous insulin supplementation has been hypothesized as a key approach for regulating NEFA in these cows.
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