Eating Frequency Is Not Associated with Obesity in Chinese Adults.
Xinge ZhangYueqiao WangJason S BrinkleyTheresa M OniffreyRui ZhangGuoxun ChenRui LiJustin Brian MoorePublished in: International journal of environmental research and public health (2018)
The prevalence of overweight and obesity has been increasing globally. Recent studies suggest that eating frequency (EF) might be a factor influencing the development of overweight and obesity. This study aims to explore the association between eating frequency and obesity in Chinese adults. A cross-sectional study was conducted in Wuhan, China, from March to June 2016. A self-administered questionnaire and 24-h dietary recall were used to collect data on sociodemographic variables, lifestyle factors, nutrition knowledge, and eating frequency. Participants were divided into four groups according to eating frequency and meal timing: traditional time pattern (TTP), traditional time plus late snack pattern (TTLSP), irregular time pattern (ITP), and all-day pattern (ADP). We performed the chi-squared test and multiple logistic regression to assess associations among variables using JMP statistical software version 14.0.0 (SAS Institute Inc., Cary, NC, USA). Respondents were Chinese adults (N = 2290; range 29⁻74 years; 1162 men). Lower education level, higher food budget, and lower nutrition knowledge were associated with higher likelihood of irregular EF patterns (TTLSP, ITP, or ADP). Men, non-smokers, and participants with less physical activity, lower education level, or lower nutrition knowledge were more likely to be obese. Body mass index (BMI) categorization was significantly different among EF pattern groups (χ² = 25.40, p = 0.003); however, this association was no longer significant in the regression model after adjustment for age, sex, education, smoking, food budget, nutrition knowledge, and physical activity. Thus, EF is not associated with obesity in Chinese adults.
Keyphrases
- physical activity
- weight loss
- healthcare
- body mass index
- weight gain
- metabolic syndrome
- insulin resistance
- type diabetes
- bariatric surgery
- quality improvement
- sleep quality
- high fat diet induced
- smoking cessation
- cardiovascular disease
- coronavirus disease
- risk factors
- adipose tissue
- artificial intelligence
- big data
- climate change
- human health
- data analysis
- depressive symptoms
- obese patients