Peter D. Hart, Nestor Asiamah, Getu Teferi, Ivan Uher
{"title":"Relationships between physical activity and other health-related measures using state-based prevalence estimates","authors":"Peter D. Hart, Nestor Asiamah, Getu Teferi, Ivan Uher","doi":"10.34172/hpp.2023.36","DOIUrl":null,"url":null,"abstract":"Background: Both physical activity and muscle-strengthening activity have known relationships with other health-related variables such as alcohol and tobacco use, diet, and health-related quality of life (HRQOL). The purpose of this study was to explore and quantify the associations between physical activity measures and health-related variables at the higher state level. Methods: This cross-sectional study used data from the 2017 and 2019 Behavioral Risk Factor Surveillance System surveys. State-based prevalence (%) estimates were computed for meeting physical activity guidelines (PA), meeting muscle-strengthening activity guidelines (MS), both PA and MS (MB), drinking alcohol (D1), heavy alcohol drinking (HD), fruit consumption (F1), vegetable consumption (V1), good self-rated health (GH), overweight (OW), obesity (OB), current smoking (SN), and smokeless tobacco use (SL). Descriptive statistics, correlation coefficients, and data visualization methods were employed. Results: Strongest associations were seen between PA and F1 (2017: r=0.717 & 2019: r=0.695), MS and OB (2017: r=-0.781 & 2019: r=-0.599), PA and GH (2017: r=0.631 & 2019: r=0.649), PA and OB (2017: r=-0.645 & 2019: r=-0.763), and MB and SN (2017: r=-0.713 & 2019: r=-0.645). V1 was associated only with PA (2017: r=0.335 & 2019: r=0.357) whereas OW was not associated only with PA. Canonical correlation analysis showed the physical activity variables were directly related (r c=0.884, P<0.001) to the health variables. Conclusion: This study used high-level data to support the many known relationships between PA measures and health-related variables.","PeriodicalId":46588,"journal":{"name":"Health Promotion Perspectives","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Promotion Perspectives","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34172/hpp.2023.36","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Both physical activity and muscle-strengthening activity have known relationships with other health-related variables such as alcohol and tobacco use, diet, and health-related quality of life (HRQOL). The purpose of this study was to explore and quantify the associations between physical activity measures and health-related variables at the higher state level. Methods: This cross-sectional study used data from the 2017 and 2019 Behavioral Risk Factor Surveillance System surveys. State-based prevalence (%) estimates were computed for meeting physical activity guidelines (PA), meeting muscle-strengthening activity guidelines (MS), both PA and MS (MB), drinking alcohol (D1), heavy alcohol drinking (HD), fruit consumption (F1), vegetable consumption (V1), good self-rated health (GH), overweight (OW), obesity (OB), current smoking (SN), and smokeless tobacco use (SL). Descriptive statistics, correlation coefficients, and data visualization methods were employed. Results: Strongest associations were seen between PA and F1 (2017: r=0.717 & 2019: r=0.695), MS and OB (2017: r=-0.781 & 2019: r=-0.599), PA and GH (2017: r=0.631 & 2019: r=0.649), PA and OB (2017: r=-0.645 & 2019: r=-0.763), and MB and SN (2017: r=-0.713 & 2019: r=-0.645). V1 was associated only with PA (2017: r=0.335 & 2019: r=0.357) whereas OW was not associated only with PA. Canonical correlation analysis showed the physical activity variables were directly related (r c=0.884, P<0.001) to the health variables. Conclusion: This study used high-level data to support the many known relationships between PA measures and health-related variables.