Background: Muscle-strengthening exercise (MSE) is a component of the World Health Organization's "2010 Global Recommendations on Physical Activity for Health." However, its participation trends are seldom examined in physical activity surveillance. This study describes the prevalence, trends, and correlates of MSE among a large sample of US adults.
Methods: The data were analyzed from the 2011, 2013, 2015, and 2017 US Behavioral Risk Factor Surveillance System surveys. Self-reported MSE participation was assessed using the same validated survey item. Population-weighted proportions were calculated for (1) "insufficient" (0-1 time/wk) or (2) "sufficient MSE" (≥2 times/wk). Prevalence ratios of those reporting sufficient MSE across sociodemographic characteristics were calculated using multivariate Poisson regression.
Results: The data were available for 1,735,626 participants (≥18 y). Over the 7-year monitoring period, the prevalence of sufficient MSE showed a small (1.2%) but statistically significant increase (2011 = 29.1%; 2013 = 29.4%; 2015 = 30.2%; and 2017 = 30.3%, P < .001 for linear trend). Older adults, women, and those with lower education/income were consistently less likely to report sufficient MSE, compared with their counterparts.
Conclusions: From 2011 to 2017, between 69.7% and 70.9% of US adults did not meet the MSE guidelines. Consistently low participation levels highlight the need to provide support for uptake of or adherence to MSE at the population level.
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http://dx.doi.org/10.1123/jpah.2019-0472 | DOI Listing |
PLoS One
December 2024
Faculty of Science, Department of Mathematics, Herat University, Herat, Afghanistan.
Gupta et al. suggested an improved estimator by using the Diana and Perri model in estimating the finite population variance using the single auxiliary variable. On the same lines, Saleem et al.
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October 2024
Department of Anesthesiology, Uniformed Services University of the Health Sciences, Bethesda, USA.
IEEE Nucl Sci Symp Conf Rec (1997)
September 2024
Yale University, Radiology and Biomedical Imaging, New Haven, Connecticut, United States of America.
Diffusion models (DM) built from a hierarchy of denoising autoencoders have achieved remarkable progress in image generation, and are increasingly influential in the field of image restoration (IR) tasks. In the meantime, its backbone of autoencoders also evolved from UNet to vision transformer, e.g.
View Article and Find Full Text PDFSci Rep
October 2024
Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India.
Recent developments indicate that malware programs present a significant risk in the security and privacy of cloud systems. Existing research in malware detection encounters numerous significant challenges due to the constantly changing and advanced characteristics of malware. Malware detection systems frequently experience high rates of false positives and false negatives, where legitimate applications are incorrectly identified as malware or actual malware remains undetected, which results in operational inefficiencies.
View Article and Find Full Text PDFHum Brain Mapp
August 2024
Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series.
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