ArticleFrontiers in public health2022
The relationship between anthropometric indicators and health-related quality of life in a community-based adult population: A cross-sectional study in Southern China.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Internet of health things and machine learning for continuous quality of life monitoring.Health and quality of life outcomes · 2025Article
- Relative Fat Mass and Psoriasis Risk: Evidence from NHANES 2009-2014.Journal of multidisciplinary healthcare · 2025Article
- Assessing health-related quality of life of Chinese population using CQ-11D.Health and quality of life outcomes · 2024Article
- Waist-to-height ratio and body roundness index: superior predictors of insulin resistance in Chinese adults and take gender and age into consideration.Frontiers in nutrition · 2024Article
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5 authors.
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Abstract
Background: This study was designed to analyze the relationship of waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), relative fat mass (RFM), lipid accumulation product (LAP) and health-related quality of life (HRQoL) in the community-dwelling population of southern China and to explore the independent contribution of socio-demographic characteristics, number of chronic diseases and anthropometric indicators to HRQoL in that population. Methods: This community-based cross-sectional survey studied 2,663 adults aged 18 years and older. HRQoL was assessed by the 3-level EuroQol 5-dimensional scale (EQ-5D-3L), and HRQoL were calculated using the Chinese EQ-5D-3L value set. The outcome variable was the EQ-5D-3L score (HRQoL). Cluster regression was used to analyse the independent contribution of each obesity indicator to HRQoL. Results: A total of 2,663 people participated in this study, and their mean EQ-5D-3L score was 0.938 ± 0.072. In this study, according to the results of the one-way ANOVA, HRQoL was significantly different between the groups of WHtR, WHR, RFM and LAP, respectively. The independent contributions of socio-demographic factors, number of chronic diseases and anthropometric measures to HRQoL in the whole population accounted for 76.2, 7.9, and 15.9% of the total effect, respectively. Conclusion: RFM and LAP were found to have a previously unreported negative impact on HRQoL in a community-dwelling population. In future studies, RFM and LAP could be used as new indicators of obesity to predict quality of life in humans.
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