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      The Association between Esports Participation, Health and Physical Activity Behaviour

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          Abstract

          We investigated the association between obesity, self-reported physical activity, cigarette smoking, alcohol consumption, and perceived health in esports players, and the influence of player in-game rank. Data was collected with an online survey with an international participant sample of esports players representing five esports and all skill levels ( n = 1772). Esports players were more likely to be categorized as normal weight, or obesity class 2 and 3 and as non-smokers (92%) and non-drinkers (65.1%) compared to international reference data. Esports players met international physical activity guidelines less than global general population. Esports players ranked in the top 10% were more physically active compared to the remaining esports players. As esports player in-game rank increased, so did the amount of time spent playing esports. Although esports players appear generally healthy, a small group was significantly obese and most esports players did not meet physical activity guidelines, indicating potential future health risks.

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          The role of deliberate practice in the acquisition of expert performance.

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            Sedentary behaviors and subsequent health outcomes in adults a systematic review of longitudinal studies, 1996-2011.

            To systematically review and provide an informative synthesis of findings from longitudinal studies published since 1996 reporting on relationships between self-reported sedentary behavior and device-based measures of sedentary time with health-related outcomes in adults. Studies published between 1996 and January 2011 were identified by examining existing literature reviews and by systematic searches in Web of Science, MEDLINE, PubMed, and PsycINFO. English-written articles were selected according to study design, targeted behavior, and health outcome. Forty-eight articles met the inclusion criteria; of these, 46 incorporated self-reported measures including total sitting time; TV viewing time only; TV viewing time and other screen-time behaviors; and TV viewing time plus other sedentary behaviors. Findings indicate a consistent relationship of self-reported sedentary behavior with mortality and with weight gain from childhood to the adult years. However, findings were mixed for associations with disease incidence, weight gain during adulthood, and cardiometabolic risk. Of the three studies that used device-based measures of sedentary time, one showed that markers of obesity predicted sedentary time, whereas inconclusive findings have been observed for markers of insulin resistance. There is a growing body of evidence that sedentary behavior may be a distinct risk factor, independent of physical activity, for multiple adverse health outcomes in adults. Prospective studies using device-based measures are required to provide a clearer understanding of the impact of sedentary time on health outcomes. Copyright © 2011 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
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              Normality Tests for Statistical Analysis: A Guide for Non-Statisticians

              Statistical errors are common in scientific literature and about 50% of the published articles have at least one error. The assumption of normality needs to be checked for many statistical procedures, namely parametric tests, because their validity depends on it. The aim of this commentary is to overview checking for normality in statistical analysis using SPSS.
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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                08 October 2020
                October 2020
                : 17
                : 19
                : 7329
                Affiliations
                [1 ]Faculty of Health, School of Exercise and Nutrition Sciences, Health Faculty, Queensland University of Technology, Brisbane QLD 4059, Australia; tristan.coulter@ 123456qut.edu.au (T.J.C.); d2.poulus@ 123456qut.edu.au (D.R.P.); remco.polman@ 123456qut.edu.au (R.P.)
                [2 ]Department of Psychology, Umeå University, 901 87 Umeå, Sweden; paul.davis@ 123456umu.se
                Author notes
                Author information
                https://orcid.org/0000-0003-4386-2033
                https://orcid.org/0000-0003-4502-6821
                https://orcid.org/0000-0003-2951-0904
                Article
                ijerph-17-07329
                10.3390/ijerph17197329
                7579013
                33049914
                f41d1e1b-1976-4f93-9024-8986267285ff
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 04 August 2020
                : 01 October 2020
                Categories
                Article

                Public health
                bmi,exercise,video gaming,alcohol use,smoking behaviour
                Public health
                bmi, exercise, video gaming, alcohol use, smoking behaviour

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