Gender difference in prevalence and network structure of subclinical Hikikomori and depression among college students.
Wei Zhang, Meng-Yi Chen, Li-Ya A, Yuan-Yuan Jiang, Hui-Ting Huang, Shou Liu, Yi Ma, Zhaohui Su, Teris Cheung, Gabor S Ungvari, Todd Jackson, Yu-Tao Xiang
Author Information
Wei Zhang: Unit of Psychiatry, Department of Public Health and Medicinal Administration & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China. ORCID
Meng-Yi Chen: Unit of Psychiatry, Department of Public Health and Medicinal Administration & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China.
Li-Ya A: Unit of Psychiatry, Department of Public Health and Medicinal Administration & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China.
Yuan-Yuan Jiang: Unit of Psychiatry, Department of Public Health and Medicinal Administration & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China. ORCID
Hui-Ting Huang: Unit of Psychiatry, Department of Public Health and Medicinal Administration & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China.
Shou Liu: Department of Public Health, Medical College, Qinghai University, Xining, Qinghai, China.
Yi Ma: Faculty of Medicine, Macau University of Science and Technology, Taipa, Macao SAR, China.
Zhaohui Su: School of Public Health, Southeast University, Nanjing, China. ORCID
Teris Cheung: School of Nursing, Hong Kong Polytechnic University, Hong Kong SAR, China.
Gabor S Ungvari: Section of Psychiatry, University of Notre Dame Australia, Fremantle, WA, Australia.
Todd Jackson: Department of Psychology, University of Macau, Macao SAR, China.
Yu-Tao Xiang: Unit of Psychiatry, Department of Public Health and Medicinal Administration & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China.
BACKGROUND: Subclinical Hikikomori and depression are common among college students, yet gender differences in their prevalence and interrelationships are under-explored. This study evaluated gender differences in prevalence and symptom networks of these disturbances. METHODS: A large-scale, multi-center study was conducted across Xinjiang, Qinghai, and Guangdong provinces, China between September and December 2023. Subclinical Hikikomori and depression were assessed with the 1-month 25-item Hikikomori Questionnaire (HQ-25M) and the Patient Health Questionnaire-9 (PHQ-9), respectively. Gender differences in prevalence were tested with univariate analyses, while network analyses assessed symptom structures within each gender. Expected Influence (EI) identified the most central symptoms, with higher EI indicating greater impact. Bridge EI identified specific symptoms that linked Hikikomori and depression symptom communities. RESULTS: Among 6,222 college students, no significant gender differences were found in the prevalence of subclinical Hikikomori (males: 11.4% and females: 13.3%) or depression (males: 19.1% and females: 18.3%). Network analysis revealed 'I avoid talking with other people' (HQ18) as the most central symptom for both males (EI���=���1.60) and females (EI���=���1.73), followed by 'It is hard for me to join in groups' (HQ13, EI���=���1.442) and 'I have little contact with other people' (HQ19, EI���=���1.437) in males, and followed by 'Loss of energy' (PHQ4, EI���=���1.17) and 'I have little contact with other people' (HQ19, EI���=���1.09) in females. The key bridge symptoms were identified as 'Guilt feelings' (PHQ6) for males (Bridge EI���=���0.14) and 'Suicidal ideation' (PHQ9) for females (Bridge EI���=���0.13). Significant overall gender differences in networks were observed (���=���0.12, ���=���.01). CONCLUSION: Depression and subclinical Hikikomori are common among Chinese college students although we observed no significant gender differences in its prevalence. The most influential central and bridge symptoms from network models are viable targets for intervention for both genders.
