Background: Overweight, obestity, and lack of fitness in America is reported to be increasing at an accelerated pace. This national trend has serious health and economic ramifications not only for the general population but also directly for industry. Obesity in the workplace has resulted in increased expenses to industry: directly through increased medical costs and indirectly through decreased productivity. The intent of this study is to define changes in fitness and body fat percentage in a working population in an isogeographical area over the past decade.
Methods: Data accumulated during a routine post-offer testing program were divided into two groups composed of new industry hires tested from 1990 to 1992 and from 2000 to 2002. Body fat was measured with a standard skinfold. The body fat percentage and body mass index (BMI) from year 1990 were compared to those from year 2000. Fitness was assessed through an individual's ability to perform a timed maximum number of sit-ups and squats.
Results: There was no significant difference in the BMI between the year 1990 group and the year 2000 group (P = 0.132 for females and P = 0.110 for males). There was a significant increase in body fat percentage from the 1990 group to the 2000 group (P = 0.000 for females and P = 0.000 for males). A significant decrease in fitness was observed for both males and females during the 10-year interval of the study.
Conclusions: Percent body fat appears to be a better indicator than BMI for utilization in industry for evaluation of obesity in the workplace. The differences in significance levels between BMI and percent body fat suggest that a decrease in leisure-time fitness activities could potentially contribute to the trend toward obesity. This study demonstrates that strength and/or fitness are important indicators that industry should monitor in order to improve the health and fitness of the workforce. Direct measurement of percent body fat and fitness indices are a more precise means than BMI for monitoring the workforce especially if employers are going to reward healthy behavior.
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http://dx.doi.org/10.1002/ajim.20279 | DOI Listing |
Cancer Med
January 2025
The Huntsman Cancer Institute at the University of Utah, Salt Lake City, Utah, USA.
Introduction: The purpose of this study was to evaluate the association between body composition, overall survival, odds of receiving treatment, and patient-reported outcomes (PROs) in individuals living with metastatic non-small-cell lung cancer (mNSCLC).
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Eur Heart J Cardiovasc Imaging
January 2025
Sorbonne Université, unité d'imagerie cardiovasculaire et thoracique, Hôpital La Pitié Salpêtrière (AP-HP), Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Institute of Cardiometabolism and Nutrition, ACTION Group, Paris, France.
Purpose: Epicardial adipose tissue (EAT) could contribute to the specific atherosclerosis profile observed in premature coronary artery disease (pCAD) characterized by accelerated plaque burden (calcified and non-calcified), high risk plaque features (HRP) and ischemic recurrence. Our aims were to describe EAT volume and density in pCAD compared to asymptomatic individuals matched on CV risk factors and to study their relationship with coronary plaque severity extension and vulnerability.
Materials And Methods: 208 patients who underwent coronary computed tomography angiography (CCTA) were analyzed.
EClinicalMedicine
October 2024
Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Center, St. Michael's Hospital, Unity Health Toronto, Toronto, ON M5B 1W8, Canada.
Background: Use of health applications (apps) to support healthy lifestyles has intensified. Different app features may support effectiveness, including gamification defined as the use of game elements in a non-game situation. Whether health apps with gamification can impact behaviour change and cardiometabolic risk factors remains unknown.
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View Article and Find Full Text PDFWorld J Clin Cases
January 2025
Department of Gastroenterology, Laiko General Hospital, National and Kapodistrian University of Athens, Athens 11527, Greece.
Machine learning (ML) is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis, thus creating machines that can complete tasks otherwise requiring human intelligence. Among its various applications, it has proven groundbreaking in healthcare as well, both in clinical practice and research. In this editorial, we succinctly introduce ML applications and present a study, featured in the latest issue of the .
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