The Difference in the Prevalence of Metabolic Syndrome According to Meeting Guidelines for Aerobic Physical Activity and Muscle-Strengthening Exercise: A Cross-Sectional Study Performed Using the Korea National Health and Nutrition Examination Survey, 2014-2019.
Du Ho KwonYoung Gyu ChoHyun-Ah ParkHo Seok KooPublished in: Nutrients (2022)
Physical activity and muscle strengthening are essential for preventing and managing metabolic syndrome. This study was conducted to investigate the relationship between the prevalence of metabolic syndrome and meeting the guidelines for aerobic physical activity (APA), muscle strengthening exercise (MSE), and combined exercise. We used data from 22,467 Koreans aged 40 years or older, who participated in in the Korea National Health and Nutrition Examination Survey (KNHANES) 2014-2019. We used the Global Physical Activity Questionnaire (GPAQ) to measure physical activity and surveyed frequency of MSE through a questionnaire. Metabolic syndrome was defined according to the American heart association and the National Heart, Lung, and Blood Institute. Compared with none exercise group, odds ratios of APA, MSE, and combined exercise group (CEG) on metabolic syndrome prevalence were 0.85 (95% confidence interval (CI), 0.74-0.98), 0.81 (95% CI, 0.67-0.99), and 0.65 (95% CI, 0.54-0.78) among men, respectively. Among women, ORs of APA, MSE, and CEG were 0.83 (95% CI, 0.73-0.93), 0.73 (95% CI, 0.58-0.91), and 0.74 (95% CI, 0.58-0.93), respectively. This study showed that meeting guidelines for APA and MSE was associated with lower prevalence of metabolic syndrome. Furthermore, subjects who met both APA and MSE had the lowest metabolic syndrome prevalence.
Keyphrases
- physical activity
- metabolic syndrome
- high intensity
- risk factors
- insulin resistance
- uric acid
- body mass index
- cardiovascular risk factors
- sleep quality
- heart failure
- clinical practice
- resistance training
- polycystic ovary syndrome
- pregnant women
- cross sectional
- cardiovascular disease
- tyrosine kinase
- type diabetes
- adipose tissue
- machine learning
- depressive symptoms
- middle aged
- artificial intelligence
- big data
- deep learning
- psychometric properties
- patient reported
- pregnancy outcomes