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Eating speed and obesity: A correlation analysis of cross-sectional data / 中华内分泌代谢杂志
Chinese Journal of Endocrinology and Metabolism ; (12): 186-189, 2022.
Article in Chinese | WPRIM | ID: wpr-933388
ABSTRACT

Objective:

To analysis the correlation of eating speed with obesity.

Methods:

A total of 644 people aged 40-65 from Caihe Community in Hangzhou were enrolled to collect clinical and demographic data, undergo extensive physical examination and laboratory tests. Participants were divided into two groups according to their eating speed (non-fast and fast). Obesity-related parameters were compared between two groups. Multivariable logistic regression was conducted to explore the relationship between eating speed and obesity after adjusting confounders.

Results:

Body mass index, waist circumference, and visceral fat area were greater in the fast eating group than non-fast eating group(all P<0.01). After adjusting for age, gender, smoking, alcohol drinking, physical activity level per week, and principal food intake, logistic regression analysis showed that eating fast was correlated with abdominal obesity( OR=1.66, 95% CI 1.11-2.48, P=0.014) and visceral obesity( OR=1.65, 95% CI 1.14-2.39, P=0.007). After stratified by gender, in the group of men, eating fast was correlated with abdominal obesity( OR=2.04, 95% CI 1.07-4.04, P=0.032) and visceral obesity( OR=1.85, 95% CI 1.04-3.31, P=0.037); In the group of women, eating fast was correlated with overweight and obesity( OR=1.59, 95% CI 1.04-2.42, P=0.031).

Conclusion:

Eating fast is positively associated with obesity. Interventions for reducing eating speed may be effective for weight control.

Full text: Available Index: WPRIM (Western Pacific) Type of study: Prevalence study / Risk factors Language: Chinese Journal: Chinese Journal of Endocrinology and Metabolism Year: 2022 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Type of study: Prevalence study / Risk factors Language: Chinese Journal: Chinese Journal of Endocrinology and Metabolism Year: 2022 Type: Article