The regular practice of physical activity helps in the prevention and control of several non-communicable diseases. However, evidence on the role of physical activity in mitigating worsening clinical outcomes in people with COVID-19 is still unclear. The aim of this study was to verify whether different levels of physical activity provide protection for clinical outcomes caused by SARS-CoV-2 infection. A cross-sectional study was conducted with 509 adults (43.8 ± 15.71 years; 61.1% female) with a positive diagnosis of COVID-19 residing in Ribeirão Preto, São Paulo, Brazil. Participants were interviewed by telephone to determine the severity of the infection and the physical activity performed. Binary logistic regression was used to indicate the odds ratio (OR) of active people reporting less harmful clinical outcomes from COVID-19. Active people had a lower chance of hospitalization, fewer hospitalization days, less respiratory difficulty and needed less oxygen support. The results suggest that active people, compared to sedentary people, have a lower frequency of hospitalization, length of stay, breathing difficulty and need for oxygen support. These results corroborate the importance of public policies to promote the practice of physical activity, in order to mitigate the severity of the clinical outcomes of COVID-19.
Control and monitor the training load program and training responses in athletes is important to avoid negative consequences of high training loads, such as injuries, illness and performance decay. Specifically in American football teams, there is a large number of athletes to monitor and depending on the sports team, they may or may not have easy access to a certain type of instrument. A reference material about instruments already used to monitor training load and training responses in American football athletes is interesting. This could help coaches and researchers to choose better instruments to use in their clinical practice or to cover some gap in the literature, advancing the state of the art. In this sense, the objective of this scope review is to indicate which tools have already been used to monitor training response and training load, thus indicating the limitations and gaps that researchers can advance. This study is a scoping review of the literature to be performed based on the stages proposed by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) for Scoping Reviews. The search for studies will be carried out in the following databases: Medical Literature Analysis and Retrieval System Online (MEDLINE [via: PubMed]), Web of Science, Excerpta Medica DataBase (EMBASE), Scopus, and SPORTDiscus. For Scopus and Embase we used the “title, abstract, keywords” filter. For the Web of Science we used the “topic” (title, abstract, author keywords, and Keywords Plus) filter. For SPORTDiscus we selected only "Academic Journals". The gray literature will be consulted using Google Scholar, Research Square, and MedRxiv. The search strategy will be developed from a combination of controlled descriptors and/or keywords related to the topic, without applying restrictions related to publication periods or language. The identified studies will be imported to EndNote Basic to remove the duplicates, and then imported into the Rayyan software. Studies without duplicates will then be evaluated and selected based on eligibility criteria by groups of two independent and blinded reviewers by reading the title and abstract of the studies (phase 1), followed by reading the full text of the selected studies in phase 1 (phase 2). Any disagreements in the process of study selection will be solved by a third reviewer. The lists of references cited by selected studies in phase 2 will be analyzed (hand search) to identify other eligible studies to be included in this review. The data of selected studies will be analyzed and collected by two independent and blinded reviewers, by filling out a characterization table in Microsoft Word software, which contains: characteristics of the study, characteristics of individuals, characteristics of instruments. At the end of this process, a cross-checking of all information retrieved from the studies will be carried out. The divergences will be resolved by a third reviewer.
People living with HIV (PWH) experience an accelerated reduction in bone mineral content (BMC), and a high risk of osteopenia and osteoporosis. Anthropometry is an accurate and low-cost method that can be used to monitor changes in body composition in PWH. To date, no studies have used anthropometry to estimate BMC in PWH. To propose and validate sex-specific anthropometric models to predict BMC in PWH. This cross-sectional study enrolled 104 PWH (64 males) aged >18 years at a local university hospital. BMC was measured using dual energy X-ray absorptiometry (DXA). Anthropometric measures were collected. We used linear regression analysis to generate the models. Cross-validations were conducted using the “leave one out”, from the predicted residual error sum of squares (PRESS) method. Bland–Altman plots were used to explore distributions of errors. We proposed models with high coefficient of determination and reduced standard error of estimate for males (r2 = 0.70; SEE = 199.97 g; Q2PRESS = 0.67; SEEPRESS = 208.65 g) and females (r2 = 0.65; SEE = 220.96 g; Q2PRESS = 0.62; SEEPRESS = 221.90 g). Our anthropometric predictive models for BMC are valid, practical, and a low-cost alternative to monitoring bone health in PWH.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.