Frontiers in psychology · 2025
Latent depressive profiles and associated factors among overweight/obese individuals based on the socio-ecological model: a cross-sectional national survey in China.
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Yang Xiaoping, Chen Miaomiao, Liu Xiaohui, Wang Lijun, Wang Yanyun, Zheng Yingjie, Ma Shailing
Аннотация
BACKGROUND: Overweight/obesity is associated with an increased risk of depression, which compromises the mental health of affected individuals. This study aimed to identify distinct depressive subtypes among overweight/obese individuals and examine associated multilevel factors based on the socio-ecological model (SEM), for guiding interventions enhancing mental health in this population.
METHODS: Data were derived from the Psychology and Behavior Investigation of Chinese Residents in 2021 (PBICR 2021). Assessment instruments included a General Information Questionnaire, the Patient Health Questionnaire-9, the Eating Behavior Scale-Short Form, the Family Health Scale-Short Form, and the Perceived Social Support Scale. Latent profile analysis (LPA) was employed to identify depressive subtypes, and multinomial logistic regression was used to examine associated multilevel factors across the identified subtypes. Analyses were conducted using SPSS 24.0 and Mplus 8.3.
RESULTS: This study included 2,588 participants classified into low-level (52.3%), moderate-level (36.6%), and high-level depression (11.1%) groups. Compared to the low-level group, high-level depression was significantly associated with age (18-45 years), current medication count (≥3, excl. supplements), out-of-pocket medical expenditures, higher abnormal eating behavior scores, and lower family health and social support scores. Similarly, moderate-level depression showed significant associations with female gender, age (18-45 years), having chronic conditions, current medication count (≥3, excl. supplements), out-of-pocket medical expenditures, higher abnormal eating behavior scores, and lower family health and social support scores.
CONCLUSION: Depression demonstrates significant heterogeneity in overweight/obese individuals, with three distinct latent profiles identified. These findings highlight the need for future primary healthcare to prioritize personalized, depression subtype-specific interventions for overweight/obese individuals, guided by multidimensional factors identified through SEM, to improve mental health.
METHODS: Data were derived from the Psychology and Behavior Investigation of Chinese Residents in 2021 (PBICR 2021). Assessment instruments included a General Information Questionnaire, the Patient Health Questionnaire-9, the Eating Behavior Scale-Short Form, the Family Health Scale-Short Form, and the Perceived Social Support Scale. Latent profile analysis (LPA) was employed to identify depressive subtypes, and multinomial logistic regression was used to examine associated multilevel factors across the identified subtypes. Analyses were conducted using SPSS 24.0 and Mplus 8.3.
RESULTS: This study included 2,588 participants classified into low-level (52.3%), moderate-level (36.6%), and high-level depression (11.1%) groups. Compared to the low-level group, high-level depression was significantly associated with age (18-45 years), current medication count (≥3, excl. supplements), out-of-pocket medical expenditures, higher abnormal eating behavior scores, and lower family health and social support scores. Similarly, moderate-level depression showed significant associations with female gender, age (18-45 years), having chronic conditions, current medication count (≥3, excl. supplements), out-of-pocket medical expenditures, higher abnormal eating behavior scores, and lower family health and social support scores.
CONCLUSION: Depression demonstrates significant heterogeneity in overweight/obese individuals, with three distinct latent profiles identified. These findings highlight the need for future primary healthcare to prioritize personalized, depression subtype-specific interventions for overweight/obese individuals, guided by multidimensional factors identified through SEM, to improve mental health.