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Psychometric network analysis of the Intuitive Eating Scale-2 in Chinese general adults.

Feng JiHongyi SunWesley R BarnhartTianxiang CuiShuqi CuiJihong ZhangJinbo He
Published in: Journal of clinical psychology (2024)
The Intuitive Eating Scale-2 (IES-2) is a measure of intuitive eating behaviors that has been extensively validated, with traditional latent variable modeling approaches, in youth and adults from many different populations, including college students in China. However, there is still a lack of research on the psychometric properties of the IES-2 in adults from the Chinese general population. Moreover, psychometric network analysis, as a complement to traditional latent variable modeling approaches, has not been used for examining the psychometric properties of the IES-2. Thus, the present study used a psychometric network approach to evaluate the psychometric properties of the IES-2 in Chinese adults from the general population. A sample of 700 Chinese general adults (50% women; M age  = 31.13 years, SD = 9.19) recruited online were included in the present study. Psychometric network analysis was performed. Exploratory graph analysis (EGA) identified four dimensions, which were well separated in the estimated network. The network structure showed excellent stability and metric measurement invariance (i.e., network loadings) across men and women. Furthermore, several items on the IES-2 were identified as key nodes in the network of the IES-2 that may be important for the development and maintenance of intuitive eating. For example, two items (i.e., "I trust my body to tell me when to eat," and "I trust my body to tell me when to stop eating") related to reliance on body cues were the most impactful nodes in the complete network. The findings of our study provide a greater understanding of the IES-2 from the perspective of network analysis and have implications for applications of intuitive eating interventions for general populations.
Keyphrases
  • network analysis
  • psychometric properties
  • physical activity
  • weight loss
  • squamous cell carcinoma
  • healthcare
  • mental health
  • multidrug resistant
  • metabolic syndrome
  • deep learning
  • pregnancy outcomes
  • breast cancer risk