Background: Evidence is building that strength training may reduce complications associated with cancer such as fatigue, muscle wasting, and lymphedema, particularly among breast and prostate cancer survivors. Population estimates are available for rates of aerobic physical activity; however, data on strength training in this population are limited. The objective of this study was to identify rates of meeting public health recommendations for strength training and aerobic activity among cancer survivors and individuals with no cancer history.
Methods: Data from the Health Information National Trends Survey (HINTS), Iteration 4 Cycle 1 and Cycle 2 were combined to conduct the analyses. Missing data were imputed, and weighted statistical analyses were conducted in SAS.
Results: The proportion of individuals meeting both strength training and aerobic guidelines were low for both cancer survivors and those without a history of cancer. The odds of meeting strength training guidelines were significantly lower for women with a history of any cancer except breast, compared with women with no history of cancer (OR: 0.70, 95% CI: 0.51-0.96).
Conclusions: More work needs to be done to understand why women with cancers other than breast, may be less inclined to engage in aerobic physical activity and strength training.
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http://dx.doi.org/10.1123/jpah.2014-0003 | DOI Listing |
J Sci Med Sport
January 2025
School of Exercise and Health, Shanghai University of Sport, China. Electronic address:
Objectives: This study aimed to evaluate the dose-response relationship between different exercise types and the alleviation of motor symptoms in Parkinson's Disease patients.
Design: A systematic review and network meta-analysis were conducted to compare the effects of 12 exercise types on motor symptoms in Parkinson's Disease patients using randomized controlled trials.
Methods: A systematic search was conducted across PubMed, Medline, Embase, PsycINFO, Cochrane Library, and Web of Science until September 10, 2024.
J Invertebr Pathol
January 2025
Laboratory of Molecular Entomology and Bee Pathology (L-MEB), Department of Biochemistry and Microbiology, Faculty of Sciences, Ghent University, Ghent, Belgium.
The ectoparasite Varroa destructor is a major contributor to the global decline of honeybee colonies (Apis mellifera), especially in the Northern Hemisphere. However, Varroa-resistant honeybee populations have been reported in various regions around the globe, including Europe and Africa. This resistance is primarily attributed to the trait known as Suppressed Mite Reproduction (SMR), which significantly reduces the reproductive success of Varroa mites within these colonies.
View Article and Find Full Text PDFAm J Vet Res
January 2025
Center for Animal Health and Food Safety, Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN.
Objective: Antimicrobial resistance (AMR), a global threat driven by factors such as improper antimicrobial use in humans and animals, is projected to cause 10 million annual deaths by 2050. For behavior change, public health messages must be tailored for diverse audiences. Generative AI may have the potential to create culturally and linguistically suited AMR awareness messages.
View Article and Find Full Text PDFAging Clin Exp Res
January 2025
Department of Physical Medicine and Rehabilitation, Kansai Medical University, Osaka, Japan.
Background: Falls on stairs are a major cause of severe injuries among older adults, with stair descent posing significantly greater risks than ascent. Variations in stair descent phenotypes may reflect differences in physical function and biomechanical stability, and their identification may prevent falls.
Aims: This study aims to classify stair descent phenotypes in older adults and investigate the biomechanical and physical functional differences between these phenotypes using hierarchical cluster analysis.
Physiol Rep
February 2025
Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
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