Purpose: To determine if obesity bias scores among nursing students changed after education was delivered and to what degree body mass index (BMI) was associated with a personal experience of bias during a student's healthcare history.
Methods: A quantitative, quasi-experimental design was used to analyze sociodemographic information and Implicit Association Test scores of junior, senior, and full-time accelerated coursework track nursing students.
Results: Generally, obesity bias declined among the group. However, bias increased regarding increasing BMI.
Conclusion: Obesity bias awareness and obesity education can decrease obesity bias among nursing students.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/01.NURSE.0000998024.65699.09 | DOI Listing |
Nutrients
January 2025
Grupo de Investigación en Calidad de Vida y Salud, Departamento de Ciencias de la Salud, Universidad Europea de Valencia, 03016 Alicante, Spain.
Introduction: Osteoarthritis (OA) is the most prevalent form of arthritis and affects over 528 million people worldwide. Degenerative joint disease involves cartilage degradation, subchondral bone remodeling, and synovial inflammation, leading to chronic pain, stiffness, and impaired joint function. Initially regarded as a "wear and tear" condition associated with aging and mechanical stress, OA is now recognized as a multifaceted disease influenced by systemic factors such as metabolic syndrome, obesity, and chronic low-grade inflammation.
View Article and Find Full Text PDFJ Surg Res
January 2025
School of Medicine, Tongji University, Shanghai, China; Department of Health Statistics, Navy Medical University, Shanghai, China. Electronic address:
Introduction: Body mass index (BMI) has been implicated in various cardiovascular conditions, but its association with peripheral artery disease (PAD) in both real-world and genetic studies have been contentious and debated.
Methods: This study enrolled 6707 individuals from the National Health and Nutrition Examination Survey database to investigate the association between BMI and the risk of PAD. The weighted logistic regression, restricted cubic spline, and subgroup analysis were performed using real-world data.
PLoS One
January 2025
Instituto de Cardiologia do Rio Grande do Sul/Fundação Universitária de Cardiologia (IC/FUC), Serviço de Nutrição e Dietética, Porto Alegre, Rio Grande do Sul, Brazil.
Background: Obesity is a risk factor for cardiovascular diseases and associated with reduced life expectancy metabolic bariatric surgery (MBS) is the treatment indicated when patients are unable to lose weight through lifestyle changes and medication alone. However, more evidence is necessary to show non-inferiority of e-health compared to in-person monitoring with regard to important parameters for the success of surgical treatment of obesity such as anthropometric changes.
Methods And Analyses: This review study will include cohort studies involving individuals with obesity and e-health or in-person patient monitoring before and after MBS.
Diabetologia
January 2025
MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Aims/hypothesis: UK standard care for type 2 diabetes is structured diabetes education, with no effects on HbA, small, short-term effects on weight and low uptake. We evaluated whether remotely delivered tailored diabetes education combined with commercial behavioural weight management is cost-effective compared with current standard care in helping people with type 2 diabetes to lower their blood glucose, lose weight, achieve remission and improve cardiovascular risk factors.
Methods: We conducted a pragmatic, randomised, parallel two-group trial.
Front Public Health
January 2025
Karolinska Institutet, Department of Medicine Solna, Division of Clinical Epidemiology, Stockholm, Sweden.
Background: Mexico has one of the highest global incidences of paediatric overweight and obesity. Public health interventions have shown only moderate success, possibly from relying on knowledge extracted using limited types of statistical data analysis methods.
Purpose: To explore if multimodal machine learning can enhance identifying predictive features from obesogenic environments and investigating complex disease or social patterns, using the Mexican National Health and Nutrition Survey.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!