Effects of Recreational Physical Activity on Abdominal Obesity in Obese South Korean Adults.
Yoonmi LeeSungjung KwakJi Eun ShinPublished in: International journal of environmental research and public health (2022)
This study investigated the effects of general characteristics, health behaviors, and level of physical activity on abdominal obesity in obese adults (BMI (body mass index) ≥ 25 kg/m 2 ) using data from the seventh period (2016-2018) of the Korea National Health and Nutrition Examination Survey (KNHANES). We also prepared basic data on the improvement and management of abdominal obesity. The participants were 2343 obese adults (men, 1338; women, 1005) from the KNHANES. Factors relevant to abdominal obesity in obese Korean women were general characteristics (age, marital status, occupation, education, and menopause) and health behaviors (time for recreational physical activities and energy intake). In men, these factors tended to be health behaviors, including time spent on leisure physical activity, and sitting. It was found that over 600 Mets/week of recreational physical activity for both adult men and women reduced the incidence of abdominal obesity after adjusting for general characteristics and health behaviors (odds ratio (95% CI); men 0.69 (0.51-0.92); women, 0.61 (0.40-0.94)). Therefore, to prevent or improve abdominal obesity in obese adults in Korea, it is necessary to consider general characteristics and health behaviors according to sex. In addition, maintaining a physical activity rate of over 600 Mets/week is also recommended.
Keyphrases
- physical activity
- weight loss
- metabolic syndrome
- body mass index
- weight gain
- insulin resistance
- type diabetes
- healthcare
- public health
- bariatric surgery
- adipose tissue
- mental health
- high fat diet induced
- polycystic ovary syndrome
- health information
- obese patients
- sleep quality
- machine learning
- health promotion
- pregnant women
- middle aged
- pregnancy outcomes
- social media
- artificial intelligence
- risk factors
- quality improvement
- risk assessment
- cervical cancer screening
- deep learning