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Determination the cut-off point for the Bergen social media addiction (BSMAS): Diagnostic contribution of the six criteria of the components model of addiction for social media disorder

OBJECTIVE: Social media disorder (SMD) is an increasing problem, especially in adolescents. The lack of a consensual classification for SMD hinders the further development of the research field. The six components of Griffiths’ biopsychosocial model of addiction have been the most widely used criter...

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Detalles Bibliográficos
Autores principales: Luo, Tao, Qin, Lixia, Cheng, Limei, Wang, Sheng, Zhu, Zijun, Xu, Jiabing, Chen, Haibo, Liu, Qiaosheng, Hu, Maorong, Tong, Jianqin, Hao, Wei, Wei, Bo, Liao, Yanhui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Akadémiai Kiadó 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8996805/
https://www.ncbi.nlm.nih.gov/pubmed/34010148
http://dx.doi.org/10.1556/2006.2021.00025
Descripción
Sumario:OBJECTIVE: Social media disorder (SMD) is an increasing problem, especially in adolescents. The lack of a consensual classification for SMD hinders the further development of the research field. The six components of Griffiths’ biopsychosocial model of addiction have been the most widely used criteria to assess and diagnosis SMD. The Bergen social media addiction scale (BSMAS) based on Griffiths’ six criteria is a widely used instrument to assess the symptoms and prevalence of SMD in populations. This study aims to: (1) determine the optimal cut-off point for the BSMAS to identify SMD among Chinese adolescents, and (2) evaluate the contribution of specific criteria to the diagnosis of SMD. METHOD: Structured diagnostic interviews in a clinical sample (n = 252) were performed to determine the optimal clinical cut-off point for the BSMAS. The BSMAS was further used to investigate SMD in a community sample of 21,375 adolescents. RESULTS: The BSMAS score of 24 was determined as the best cut-off score based on the gold standards of clinical diagnosis. The estimated 12-month prevalence of SMD among Chinese adolescents was 3.5%. According to conditional inference trees analysis, the criteria “mood modification”, “conflict”, “withdrawal”, and “relapse” showed the higher predictive power for SMD diagnosis. CONCLUSIONS: Results suggest that a BSMAS score of 24 is the optimal clinical cut-off score for future research that measure SMD and its impact on health among adolescents. Furthermore, criteria of “mood modification”, “conflict”, “withdrawal”, and “relapse” are the most relevant to the diagnosis of SMA in Chinese adolescents.