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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


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          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.


          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.


          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.


          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.

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          Most cited references40

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          Society and the Adolescent Self-Image

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            A ‘components’ model of addiction within a biopsychosocial framework

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              Is Open Access

              Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: An Interaction of Person-Affect-Cognition-Execution (I-PACE) model.

              Within the last two decades, many studies have addressed the clinical phenomenon of Internet-use disorders, with a particular focus on Internet-gaming disorder. Based on previous theoretical considerations and empirical findings, we suggest an Interaction of Person-Affect-Cognition-Execution (I-PACE) model of specific Internet-use disorders. The I-PACE model is a theoretical framework for the processes underlying the development and maintenance of an addictive use of certain Internet applications or sites promoting gaming, gambling, pornography viewing, shopping, or communication. The model is composed as a process model. Specific Internet-use disorders are considered to be the consequence of interactions between predisposing factors, such as neurobiological and psychological constitutions, moderators, such as coping styles and Internet-related cognitive biases, and mediators, such as affective and cognitive responses to situational triggers in combination with reduced executive functioning. Conditioning processes may strengthen these associations within an addiction process. Although the hypotheses regarding the mechanisms underlying the development and maintenance of specific Internet-use disorders, summarized in the I-PACE model, must be further tested empirically, implications for treatment interventions are suggested.

                Author and article information

                J Behav Addict
                J Behav Addict
                Journal of Behavioral Addictions
                Akadémiai Kiadó (Budapest )
                18 May 2021
                July 2021
                July 2021
                : 10
                : 2
                : 281-290
                [1 ]The Treatment Center for Addiction, Jiangxi Mental Hospital , Nanchang, Jiangxi, 330029, P. R. China
                [2 ]Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University , Changsha, 410078, P. R. China
                [3 ]Department of Psychology, Hospital of Tsinghua University , Beijing, 100084, P. R. China
                [4 ] Department of Psychology Yingtan People’s Hospital, Yingtan, 335000, P. R. China
                [5 ] Department of Psychology, Jiangxi Mental Hospital, Nanchang, Jiangxi, 330029, P. R. China
                [6 ] Department of Psychiatry, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, P. R. China
                [7 ]Department of Psychiatry, the Second Xiangya Hospital, Central South University , Changsha, Hunan, 410011, P. R. China
                [8 ]Department of Psychiatry, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University , Hangzhou, Zhejiang, 310016, P. R. China
                [9 ]Key Laboratory of Medical Neurobiology of Zhejiang Province , Hangzhou, Zhejiang, 310016, P. R. China
                Author notes
                Author information
                © 2021 The Author(s)

                Open Access. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium for non-commercial purposes, provided the original author and source are credited, a link to the CC License is provided, and changes – if any – are indicated.

                : 25 August 2020
                : 1 January 2021
                : 23 March 2021
                Page count
                Figures: 2, Tables: 6, Equations: 1, References: 41, Pages: 10
                Funded by: Natural Science Foundation of Jiangxi Province of China
                Award ID: 20192BAB205037
                Funded by: Zhejiang University

                social media disorder (smd),cut-off score,bergen social media addiction scale (bsmas),latent profile analysis


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