# Negative emotional symptoms and e-Health literacy among Chinese college students: a latent profile analysis

## TL;DR

This study identifies two groups of Chinese college students with different levels of negative emotions and finds that lower e-Health literacy is linked to higher emotional symptoms.

## Contribution

The study uses latent profile analysis to uncover distinct emotional symptom profiles and their associations with e-Health literacy among Chinese college students.

## Key findings

- Two groups of students were identified: low/no negative emotional symptoms (61.49%) and high negative emotional symptoms (38.51%).
- Higher negative emotional symptoms were associated with being female, low income, lack of exercise, and alcohol consumption.
- E-Health literacy was significantly lower in students with high negative emotional symptoms.

## Abstract

Negative Emotional symptoms such as depression and anxiety do not exist independently, often co-occurring in the same individual, and heterogeneity exists between individuals suffering from depression and anxiety; however, prior research has rarely investigated heterogeneity in a person-centered manner and from the perspective of college students. The main purpose of this study was to explore this heterogeneity and its association with e-Health literacy (e-HL) using Latent profile analysis (LPA), a person-centered statistical method.

A total of 7,503 Chinese college students from 10 regions (including Guangdong Province, Shanghai Municipality, and Jiangsu Province) were surveyed using the Generalized Anxiety Disorder Scale (GAD-7) and Patient Health Questionnaire (PHQ-9) to assess anxiety and depressive symptoms. LPA was employed to identify potential profiles of negative emotional symptoms and validate their robustness; binary logistic regression was used to explore differences in demographic characteristics (sex, grade ranking), sociological factors (family residential background, per capita monthly family income), and lifestyle factors (adherence to physical activity, smoking status, alcohol consumption) across profiles; analysis of variance (ANOVA) was applied to compare e-HL levels among different profiles.

The two-class model was identified as the optimal classification of negative emotional symptoms: low/no negative emotional symptoms (61.49%) and high negative emotional symptoms (38.51%). Female college students, those with low per capita monthly family income, lack of regular physical exercise, and alcohol consumption habits were more likely to be categorized into the high negative emotional symptoms group (all p < 0.001). E-Health literacy levels were significantly negatively correlated with the severity of negative emotional symptoms (F = 212.661, p < 0.001), with the low/no negative emotional symptoms group showing higher average e-HL scores (30.11 ± 7.004 vs. 27.80 ± 5.837).

Reliance on self-report measures may lead to recall bias and social desirability bias; the cross-sectional design cannot establish causal relationships between variables; digital addiction, a potential confounding factor that may co-occur with negative emotional symptoms and influence e-HL, was not included in the analysis.

This study identified two distinct latent profiles of negative emotional symptoms among Chinese college students and their key predictive factors using LPA. The findings highlight the need for stratified early screening for high-risk groups (females, low-income families, inactive individuals, and drinkers) and the development of targeted interventions. Enhancing e-HL could be a potential pathway to improve mental health outcomes, providing actionable insights for scientific and effective mental health management in colleges and universities.