Patterns of perceived parenting styles and associations with night eating syndrome symptoms and correlates among Chinese adolescents: a latent profile analysis
Abstract
Purpose: This paper aimed to explore the association between the patterns of perceived parenting styles and adolescents’ night eating syndrome symptoms and correlates, including sleep quality, weight status, loss of control over eating, and psychological distress.
Methods: A sample of 455 Chinese adolescents (54.5% females, aged 12–15 years) were included in the current study. Latent Profile Analysis (LPA) was adopted to examine the patterns of perceived parenting styles. The three-step approach was used to explore the differences in night eating syndrome symptoms and correlates between different profiles.
Results: A four-profile solution was found to fit the data best, and the four profiles were labeled as positive parenting, negative parenting, highly engaged parenting, and lowly engaged parenting. Subsequent analyses showed that adolescents across profiles exhibited significant differences in night eating syndrome symptoms and correlates. Specifically, adolescents in the positive parenting profile generally had the lowest scores in night eating and its correlates, while those in the negative parenting group reported the highest scores in night eating and its correlates.
Conclusion: Using a person-centered approach (i.e., LPA), the present study identified four distinct patterns of perceived parenting styles in a sample of Chinese adolescents, with night eating and related symptomatology differing across each profile. Future interventions targeting night eating among adolescents may consider the potential influence from the patterns of perceived parenting styles to have a better intervention outcome.
HE Jinbo
Assistant Professor in Applied Psychology
My research interests include the mental health of children and adolescents, obesity, eating behaviors, eating disorders, body image, and various advanced quantitative research methods (e.g., structural equation modeling, latent growth curve modeling, finite mixture modeling, meta-analysis).
