Skip to main content

A rise in social media use in adolescents during the COVID-19 pandemic: the French validation of the Bergen Social Media Addiction Scale in a Canadian cohort



Social media use has grown dramatically since its inception in the early 2000s and has further increased during the COVID-19 pandemic. Problematic use of social media (PUSM) is a type of behavioural addiction which has generated increasing interest among mental health clinicians and scholars in the last decade. PUSM is associated with multiple psychiatric conditions and is known to interfere with patients’ daily functioning. There is no single accepted definition of PUSM, nor means of measuring it, in the literature. The Bergen Social Media Addiction Scale (BSMAS) is a helpful tool for identifying PUSM. This paper aims to validate BSMAS and to translate it from English into French, with the goal of making this clinical screening tool for PUSM available in French-language contexts.


This study explored the psychometric validity of the French version of the BSMAS in a sample of 247 adolescents, who were either psychiatric inpatients (the hospitalized group, n = 123) or recruited in local high schools (the community group, n = 124).


The adolescents in the sample reported an increase in their social media use during the COVID-19 pandemic. This increase was more pronounced in the hospitalized group. Confirmatory factorial analysis showed an excellent fit, very good internal consistency and established convergent validity for the French version of the BSMAS. A total of 15.4% of the hospitalization group and 6.5% of the community group met the recommended clinical cutoff of 24 on the BSMAS, suggesting problematic use of social media.


The French version of BSMAS is a psychometrically validated and clinically useful tool to screen for PUSM in adolescents.


The Bergen Social Media Addiction Scale is valid for use in a French-speaking population.

Social media use increased more in adolescent inpatients than in community during the COVID-19 pandemic.

Adolescent inpatients should be screened for problematic use of social media.

Peer Review reports


Social media (SM) use has seen a major increase in use worldwide in the past years. According to, as of February 2022, there are 4.65 billion users of social media worldwide, the most popular platform being Facebook©. In Canada, 96% of adolescents use SM [1]. As with other online behaviours, some adolescents are heavy users of SM, leading experts to argue that they might present a problematic use of social media (PUSM) [2]. Internet Gaming Disorder (IGD), another online behavioural addiction, has been recognized by the DSM-5-TR as a condition requiring further study to be included in the psychiatric classification [3]. There is a need for more evidence on other online behavioural addictions and their consequences on youth.

It has been estimated that 7.38% of European adolescents [4] present with PUSM. The prevalence in a large cohort of Hungarian adolescents was 4.5% [5]. Prevalence estimates might vary according to the samples studied, the classification used, and cultural and geographics factors [6]. The prevalence might also increase with the adolescents’ age [7]. Social media use has been associated with various psychiatric conditions, such as depression [8], poor sleep quality [9, 10], social anxiety [11], internalizing problems [12] and suicidal behaviours [13, 14]. There is also a relationship between IGD and PUSM, with an apparent gender difference: boys are more represented in IGD and girls are more represented in PUSM [15]. In another study, social media addiction was also weakly to moderately linked to selfitis, showing that even if both behaviours occur on social media, they are merely related and seem to reflect different addictive behaviours [16]. Evidence indicates that adolescents with poor mental health might be greater users of social media networks [17]. He et al. [18] also demonstrated that social networking site addiction was associated with potential brain structural alterations in the gray matter of the amygdala, the anterior cingulate cortex and the midcingulate cortex, which is consistent with alterations also found in other addictions. However, expected changes in nucleus accumbens were not observed, suggesting that PUSM may be distinct from other addictions. It is important to underscore that social media use also has multiple positive effects on the adolescents’ wellbeing [19, 20].

Conceptual approaches to PUSM vary greatly in the literature, as it is the case for other behavioural addictions. The component model of addiction, proposed by Griffiths [21], remains the most established. In the component model, behavioural addiction should be understood in a biopsychosocial framework, and a common etiology should be sought for all potentially addictive behaviours. He suggested a model encompassing commonalities of these conditions, including salience (the behaviour becomes the most important activity in the person’s life), mood modification (subjective consequences of engaging the behaviour), tolerance (to achieve the same effect, the activity must be engaged in for longer periods or at a greater intensity), withdrawal symptoms (such as anxiety and irritability when the activity is discontinued for a certain amount of time), conflict (interpersonal or intrapsychic) and relapse (repetition of the problematic behaviour). These components are also seen in other addiction disorders, such as the substance-use disorders. Other models exist in the field, and there are multiple scales to measure PUSM (see Cataldo et al. [22] for an extensive review). This plurality of models reflects the ever-changing nature of SM platforms, which continue to grow and adapt to the needs of the users. Some authors are critical of the status of PUSM, noting that social network sites are comprising a larger proportion of the communication tools in the modern society and cautioning against overpathologization of new behaviours. These authors suggest we should focus on SM’s negative effects without the need to classify them as true addictive disorders [23]. To consider a problematic behaviour for addictive potential, Brand et al. have suggested the use of three meta-level criteria, namely (1) scientific evidence for clinical relevance, (2) theoretical embedding and (3) empirical evidence for underlying mechanisms. There is growing evidence supporting that the above-mentioned criteria can be applied to social-network-use disorder [24].

As seen during the COVID-19 pandemic, SM use has increased in adolescents. This increase could be understood as a coping strategy used to combat the loneliness experienced during lockdowns [25], and not necessarily as an increase in the incidence of PUSM.

Aside from the conceptual framework, child and adolescent mental health professionals are confronted with youth who are struggling with behaviours related to SM, that can interfere with their functioning and healthy behaviours [26]. There is a clinical need to develop and adapt tools to screen for these youth and, ultimately, offer them resources to develop a more balanced use of social media. In this article, we aim to translate and validate the Bergen Social Media Addiction Scale (BSMAS) in a cohort of French Canadian adolescents.



Participants were recruited in two settings. The first group (the hospitalized group) was composed of adolescent psychiatric inpatients at the CIUSSS de l’Estrie-CHUS, Fleurimont Hospital in Sherbrooke, Quebec, Canada. The second group (the community group) was composed of adolescents recruited in four high schools in the region. Three of these were public schools and one was a private institution. Adolescents from 7th to 11th grade were recruited, and the target population was aged from 12 to 17 years old, inclusively.


A convenience sample was recruited from February 2021 to May 2022. Informed written consent was obtained from the adolescents and from their parent(s) or legal guardian(s). All participants were informed of the study details, the advantages and the potential risks of the study, and were advised that they were free to participate or not. The study was approved by the Ethic and Scientific committees of the CIUSSS de l’Estrie-CHUS and the Université de Sherbrooke in January 2021.

In the hospitalized group, adolescents were recruited on a specialized adolescent psychiatric ward. Adolescents of the community group were recruited after a visit by the research team to the classrooms (in person or virtually) to explain the study. After receiving the consent forms signed by the student and their parents, participants received a link to complete their questionnaire. Participants had to complete the questionnaire on LimeSurvey© V5.3.5, an online survey software. Participants who completed the questionnaire were enlisted in a draw, with prizes values which complied with the ethics guidelines of the Université de Sherbrooke.

Measures and analysis

Socio-demographic data such as age and gender were collected. Participants were also questioned on their SM use, including the frequency of use, perceived use of SM (positive, negative or neutral), and the changes in use during the COVID-19 pandemic.

To assess PUSM, we used a French translation of Bergen Social Media Addiction Scale (BSMAS). The 6-item scale is adapted from the Bergen Facebook Addiction Scale [27] and encompasses the six domains of the component addiction model described above, rated on a five-point Likert scale ranging from 1 (very rarely) to 5 (very often). It has been translated into multiple languages [5, 28,29,30,31]. It was previously validated in a cohort of adolescents, with a threshold of 19 or more suggested to classify a youth as having PUSM, based on a latent profile analysis [5]. A recent paper suggested a cut-off of 24 to retain an “SM disorder”, based on a clinical sample of adolescents who were diagnosed by certified psychiatrists and a set of clinical criteria based on IGD found in the DSM-5 [32]. We retained a clinical cutoff of 24 in this study to suggest an adolescent had PUSM. French version of BSMAS is presented in Table 1.

Table 1 French version of the Bergen Social Media Addiction Scale. Échelle de dépendance aux médias sociaux de Bergen (BSMAS) – Version française Directive : Les questions qui suivent portent sur votre rapport aux médias sociaux (Facebook, Twitter, Instagram et autres plateformes semblables) et l’utilisation que vous en faites. Pour chacune d’elles, veuillez cocher la réponse qui décrit le mieux votre situation

The back-translation method of Vallerand transcultural translation [33] was performed by two independent translators. Two experts (V.B. and R.D.R.) compared the original scale with the translated one and made the necessary adjustments. The translated scale was administered to a pilot group of ten French-speaking participants to ensure that all items of the scale were understandable. Internal validity was measured using Cronbach’s α statistic.

Concurrent validity between the Bergen Social Media Addiction Scale (BSMAS), Problematic Use of Internet Questionnaire (PIUQ), Adolescents et Substances Psychoactives (ADOSPA) and Patient Health Questionnaire (PHQ-9) scores was assessed using correlation matrix with Spearman’s rho coefficients, with a level of statistical significance set at a p value of < 0.05. Considering the distribution of the variables, we found that Spearman’s rho rank coefficients was more appropriate. Assumptions for Pearson’s correlation coefficients are more restraining. Hence, we opted for a non-parametric coefficient and a uniform presentation. PIUQ, a 12-item scale [34] measuring Internet addiction, translated into French [35] and validated in an adolescent cohort [36], was used for concurrent validity. Problematic use of Internet can be understood as an umbrella concept encompassing multiple online behavioural addictions, including PUSM [37]. For the PIUQ in our study, the Cronbach’s α was 0.88 (95% CI [0.86–0.90]). PHQ-9 was used to measure depression level in the sample. It is a nine-item self-administered scale rated on a four-point Likert-scale ranging from 0 (never) to 3 (almost every day) that can be used to define the severity of the depression (minimal to severe) [38]. Depression is related to PUSM [15] and hence can be viewed as an indicator of concurrent validity. For the PHQ-9, the Cronbach’s α was 0.93 (95% CI [0.91–0.94]). ADOSPA is a French translation of the CRAFFT questionnaire [39]. It includes six questions, with one point given for each positive answer. A cut-off score of 2 or more indicates a risk of substance use disorder. It is used here as a marker of substance abuse, which has been associated with PUSM [40]. For the ADOSPA, the Cronbach’s α was 0.83 (95% CI [0.77–0.87]).

Part of the sample (n = 101) answered questions regarding their perception of their own use of SM and the impact on their functioning, relationships and behaviours. Items were based on the Deba-Internet Scale [41] and used to document the self-perceived consequences on participants from their PUSM.

For construct validity analysis, a confirmatory factor analysis (CFA) was performed. A series of parameters assessing goodness of fit were obtained. Chi-square test with its degree of freedom (χ2/df), with non-significance at p > 0.05 was used, as it is more sensitive for larger samples [42]. Other indices used were the Comparative fit index (CFI) and Tucker-Lewis indices (TLI), with cut-off value of more than 0.9 suggesting an acceptable level of goodness of fit between model and date [43]. Root mean square error of approximation (RMSEA) with 95% confidence interval (CI) and the standardized root mean square residual (SRMR) are also provided, with a satisfactory fit indicated by a value of 0.08 or less [44]. Measurement invariance (MI) across gender was examined. MI for age was not included as the age range was limited in a sample of adolescents. We tested for configural, metric and scalar invariance. There is a debate as which fit statistics should be used to measure fitness of the MI, but the majority of experts recommend comparing the fit of two nested models by computing the difference between fit indices, with change criteria as follow: ∆ χ2 (having the same restriction as stated above concerning larger sample), ∆ CFI < 0.01, ∆ SRMR < 0.030 (for metric invariance) and < 0.015 (for scalar invariance), and ∆ RMSEA < 0.015 [45].

Convergent validity was assessed by computing the average variance extracted (AVE), using a cutoff of 0.5 [46], and with Composite reliability (CR), with a threshold of 0.6 [46]. Standard error of measurement was computed, with an acceptable cut-off of < SD/2. Statistical analysis were performed using IBM SPSS V28 and R V.4.05, using lavaan, bluegrafir and semTools packages.


Descriptive statistics

A total of 247 adolescents participated in the study, from which 123 were from the hospitalization group (HG; 49.8%) and 124 from the community group (CG; 50.2%). Pertinent demographic and social media use data are presented in Table 2 pertaining to the hospitalized, community and total sample. 97.6% of the cohort used at least one social media platform (96.0% for CG and 99.2% for HG), which is similar to the Canadian prevalence of use [1]. When looking at power analysis, with a total sample size of 247 participants, we obtain a 41:1 subject to item ratio regarding core factors for BSMAS, which is well above the recommended minimum 20:1 ratio for computing confirmatory factor analysis [47].

Table 2 Descriptive results on demographics, social media (SM) use and Bergen Social Media Addiction Scale (BSMAS) scores, with respect to hospitalized, community and total samples

Construct and internal validity

Internal consistency of the scale was very good (Cronbach’s α = 0.84; 95% CI [0.80–0.87]). As expected, a strong positive association was observed between BSMAS and PIUQ scores (0.773; p < 0.0001), which assess similar constructs. Moderate and weak [48] positive associations (Spearman’s Rho coefficients) were detected with PHQ-9 (0.476; p < 0.0001) and ADOSPA (0.243; p < 0.01) scores respectively, showing sufficient concurrent validity, as depression symptoms [49] and substance use [50] are known comorbidities of PUSM but not similar constructs. SM self-perceived use consequences were all at least moderately correlated to BSMAS score (Spearman’s Rho coefficients from 0.421 to 0.620), which indicated a good concurrent validity between BSMAS score and self-perceived consequences of SM use (see Table 3).

Table 3 Social media self-perceived consequences with respect to hospitalized, community and total samples

Confirmatory factorial analysis

Parameters of goodness of fit and reliability are presented in Table 4 and were on or above all the recommended thresholds. AVE didn’t reach the cut-off point (0.47), but CR (0.63) and Standard error of measurement (0.95) were in range of the recommended parameters. Factor loading of the model and associated item-total correlation coefficients are shown in Fig. 1. Factor loading (lambda values) were all above the recommended cutoff (> 0.50) and similar to other studies aforementioned [28,29,30,31].

Table 4 Confirmatory factor analysis parameters and measurement of invariance across sex for the Bergen Social Media Addiction Scale (BSMAS), French version
Fig. 1
figure 1

Standardized loading and Item-total correlations of the French version of the Bergen Social Media Addiction Scale (BSMAS).


The objective of the present study was to validate the Bergen Social Media Addiction Scale in a cohort of French-speaking Canadian adolescents. This scale is a useful and easy-to-use tool to screen for PUSM and has been validated in multiple other languages using a similar statistical method [5, 28,29,30,31]. Confirmatory factorial analysis and invariance measurement showed a good fitness of the model, with convergent validity also demonstrating positive associations with other scales measuring associated concepts or consequences of PUSM. AVE was the only parameter not reaching the recommended threshold. This implies that the variance is explained more by measurement error than by the construct, but as the reliability parameter (CR) was reached, convergent validity of the construct can still be considered acceptable [51].

One strength of this validation is that part of the sample included psychiatric adolescent inpatients. To date and to our knowledge, most of the validation studies of the BSMAS used a sample of adolescents from the community, which diminishes generalizability of use to a clinical population. It has been shown that, in a hospitalized population, SM use in vulnerable youth was commonly associated with negative emotional experience [52], with sleep disturbance hypothesized as a mediator between negative emotional response to SM and higher symptoms severity [53]. SM use in adolescent psychiatric inpatients was also associated with greater risk of self-injurious behaviours [54]. It is worth emphasizing that, for some inpatient adolescents, SM use can also bring benefits, such as a sense of social support (from peers and family), access to positive content and access to mental health resources [55]. It is still not clear in the literature which adolescents may benefit from SM use, but we can hypothesize that adolescents with PUSM may be at an increased risk of experiencing negative effects from their SM use compared to other adolescents, as shown by the differences in BSMAS scores and consequences of use between the community and hospitalized groups.

A significant part of the sample reported an increase in their use of SM during the pandemic, which was more pronounced in the hospitalization group. This is in phase with worldwide data showing an increase in online activities during the pandemic [56]. Adolescents also experienced a dramatic increase in mental health issues and substance use [57, 58] during that period. Throughout the COVID-19 lockdowns, SM served as a substitute for face-to-face interactions and may have had protective influence against social deprivation in adolescents [59]. It is unknown if the rate of PUSM increased during the pandemic and this study was not designed to assess changes in prevalence of PUSM during this period.

As stated above, there is ongoing debate regarding the definitions and tools that should be used to evaluate PUSM in adolescents. In a rapidly evolving technological society, there is a risk of over-pathologizing adolescents who are using SM or other online services, as behavioural addictions often are time-limited and contextual [60]. Therefore, scales designed to measure PUSM, such as the BSMAS, should not be used alone in investigating problematic SM use. Rather, they should be used with caution and in combination with a thorough clinical assessment that takes into account all relevant factors of the biopsychosocial evaluation.

In comparison to other studies of validation, our sample size was modest, which may diminish the accuracy of the scale. Our study used convenience sampling, as do most studies in the field of PUSM [24], which can be a limitation when interpreting results. As aforementioned, there was a significant difference in PUSM rate between adolescents in the community group and those in the hospitalized group, but we cannot infer the direction of the relation between PUSM and psychiatric comorbidities (which are naturally over-represented in the hospitalized group).

Our study demonstrated that the French version of the Bergen Social Media Addiction Scale (BSMAS) is a psychometrically validated and clinically useful tool to screen for Problematic Use Of Social Media (PUSM) in adolescents, including adolescents hospitalized in psychiatry. Future studies should attempt to investigate PUSM risk factors and explore the relationship between psychiatric symptoms and PUSM in adolescents. Therapeutic interventions for adolescents struggling with PUSM and longitudinal evolution of these patients should also be further examined.

Data Availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.


  1. Statistique Canada. Un portrait des jeunes Canadiens. 2018; Available from:

  2. Kuss DJ, Griffiths MD. Social networking sites and addiction: Ten lessons learned. Int J Environ Res Public Health. 2017;14(3).

  3. Crocq M-A, Guelfi J-D, Boyer P, Pull C-B, Pull-Erpelding M-C, American Psychiatric Association. DSM-5 : manuel diagnostique et statistique des troubles mentaux [Internet]. 5th edition. American Psychiatric Association Publishing; 2015. 1275 p.

  4. van den Boer M, Boniel-Nissim M, Wong SL, Inchley JC, Badura P, et al. Adolescents’ intense and problematic Social Media Use and their Well-Being in 29 countries. J Adolesc Heal. 2020;66(6):89–99.

    Article  Google Scholar 

  5. Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, et al. Problematic social media use: results from a large-scale nationally representative adolescent sample. PLoS ONE. 2017;12(1):10–4.

    Article  Google Scholar 

  6. Cheng C, Lau Y, ching, Chan L, Luk JW. Prevalence of social media addiction across 32 nations: Meta-analysis with subgroup analysis of classification schemes and cultural values.Addict Behav. 2021;117(November 2020).

  7. Paakkari L, Tynjälä J, Lahti H, Ojala K, Lyyra N. Problematic social media use and health among adolescents. Int J Environ Res Public Health. 2021;18(4):1–11.

    Article  Google Scholar 

  8. Vidal C, Lhaksampa T, Miller L, Platt R. Social media use and depression in adolescents: a scoping review. Int Rev Psychiatry [Internet]. 2020 Apr 2;32(3):235–53. Available from:

  9. Alonzo R, Hussain J, Stranges S, Anderson KK. Interplay between social media use, sleep quality, and mental health in youth: A systematic review.Sleep Med Rev. 2021;56.

  10. Shimoga SV, Erlyana E, Rebello V. Associations of social media use with physical activity and sleep adequacy among adolescents: cross-sectional survey. J Med Internet Res. 2019;21(6):2014–5.

    Article  Google Scholar 

  11. Stănculescu E, Griffiths MD. Social media addiction profiles and their antecedents using latent profile analysis: The contribution of social anxiety, gender, and age.Telemat Informatics. 2022;74(August).

  12. Riehm KE, Feder KA, Tormohlen KN, Crum RM, Young AS, Green KM, et al. Associations between Time spent using social media and internalizing and externalizing problems among US Youth. JAMA Psychiatry. 2019;76(12):1266–73.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Sedgwick R, Epstein S, Dutta R, Ougrin D. Social media, internet use and suicide attempts in adolescents. Curr Opin Psychiatry. 2019;32(6):534–41.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Memon AM, Sharma SG, Mohite SS, Jain S. The role of online social networking on deliberate self-harm and suicidality in adolescents: a systematized review of literature. Indian J Psychiatry. 2018;59(4):2017–8.

    Google Scholar 

  15. Andreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, et al. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychol Addict Behav. 2016;30(2):252–62.

    Article  Google Scholar 

  16. Lin CY, Lin CK, Imani V, Griffiths MD, Pakpour AH. Evaluation of the Selfitis Behavior Scale Across two persian-speaking countries, Iran and Afghanistan: Advanced Psychometric Testing in a large-scale sample. Int J Ment Health Addict. 2020;18(1):222–35.

    Article  Google Scholar 

  17. Sampasa-Kanyinga H, Lewis RF. Frequent Use of Social Networking Sites Is Associated with Poor Psychological Functioning Among Children and Adolescents. Cyberpsychology, Behav Soc Netw [Internet]. 2015;18(7):380–5. Available from:

  18. He Q, Turel O, Bechara A. Brain anatomy alterations associated with Social Networking Site (SNS) addiction. Sci Rep [Internet]. 2017;7(February):1–8. Available from:

  19. Best P, Manktelow R, Taylor B. Online communication, social media and adolescent wellbeing: A systematic narrative review. Child Youth Serv Rev [Internet]. 2014;41:27–36. Available from:

  20. Richards D, Caldwell PHY, Go H. Impact of social media on the health of children and young people. J Paediatr Child Health. 2015;51(12):1152–7.

    Article  PubMed  Google Scholar 

  21. Griffiths M. A “components” model of addiction within a biopsychosocial framework. J Subst Use. 2005;10(4):191–7.

    Article  Google Scholar 

  22. Cataldo I, Billieux J, Esposito G, Corazza O. Assessing problematic use of social media: where do we stand and what can be improved? Curr Opin Behav Sci [Internet]. 2022 Jun;45:101145. Available from:

  23. Carbonell X, Panova T. A critical consideration of social networking sites’ addiction potential. Addict Res Theory. 2017;25(1):48–57.

    Article  Google Scholar 

  24. Brand M, Rumpf HJ, Demetrovics Z, Müller A, Stark R, King DL, et al. Which conditions should be considered as disorders in the International classification of Diseases (ICD-11) designation of “other specified disorders due to addictive behaviors”? J Behav Addict. 2022;11(2):150–9.

    Google Scholar 

  25. Cauberghe V, Van Wesenbeeck I, De Jans S, Hudders L, Ponnet K. How Adolescents Use Social Media to Cope with Feelings of Loneliness and Anxiety during COVID-19 Lockdown. Cyberpsychology, Behav Soc Netw. 2021;24(4):250–7.

  26. Buda G, Lukoševičiūtė J, Šalčiūnaitė L, Šmigelskas K. Possible Effects of Social Media use on adolescent health behaviors and perceptions. Psychol Rep. 2021;124(3):1031–48.

    Article  PubMed  Google Scholar 

  27. Andreassen CS, TorbjØrn T, Brunborg GS, Pallesen S. Development of a facebook addiction scale. Psychol Rep. 2012;110(2):501–17.

    Article  PubMed  Google Scholar 

  28. Stănculescu E. The Bergen Social Media Addiction Scale Validity in a Romanian Sample Using Item Response Theory and Network Analysis. Int J Ment Health Addict. 2022

  29. Monacis L, De Palo V, Griffiths MD, Sinatra M. Social networking addiction, attachment style, and validation of the italian version of the Bergen Social Media Addiction Scale. J Behav Addict. 2017;6(2):178–86.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Leung H, Pakpour AH, Strong C, Lin YC, Tsai MC, Griffiths MD et al. Measurement invariance across young adults from Hong Kong and Taiwan among three internet-related addiction scales: Bergen Social Media Addiction Scale (BSMAS), Smartphone Application-Based Addiction Scale (SABAS), and Internet Gaming Disorder Scale-Short.Addict Behav. 2020;101(October 2018).

  31. Lin CY, Broström A, Nilsen P, Griffiths MD, Pakpour AH. Psychometric validation of the persian bergen social media addiction scale using classic test theory and rasch models. J Behav Addict. 2017;6(4):620–9.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Luo T, Qin L, Cheng L, Wang S, Zhu Z, Xu J, et al. 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. J Behav Addict. 2021;10(2):281–90.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Vallerand RJ. Vers une méthodologie de validation trans-culturelle de questionnaires psychologiques: implications pour la recherche en langue française. Can Psychol Can. 1989;30(4):662–80.

    Article  Google Scholar 

  34. Demetrovics Z, Szeredi B, Rózsa S. The three-factor model of internet addiction: the development of the problematic internet use Questionnaire. Behav Res Methods. 2008;40(2):563–74.

    Article  PubMed  Google Scholar 

  35. Kern L, Acier D. Adaptation française de l’échelle problematic internet use Questionnaire French adaptation of the problematic. Internet Use Questionnaire. 2013;78:357–71.

    Google Scholar 

  36. Demetrovics Z, Király O, Koronczai B, Griffiths MD, Nagygyörgy K, Elekes Z, et al. Psychometric properties of the problematic internet use Questionnaire Short-Form (PIUQ-SF-6) in a nationally representative sample of adolescents. PLoS ONE. 2016;11(8):1–12.

    Article  Google Scholar 

  37. Fineberg NA, Demetrovics Z, Stein DJ, Ioannidis K, Potenza MN, Grünblatt E, et al. Manifesto for a european research network into problematic usage of the internet. Eur Neuropsychopharmacol. 2018;28(11):1232–46.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Richardson LP, McCauley E, Grossman DC, McCarty CA, Richards J, Russo JE, et al. Evaluation of the patient health questionnaire-9 item for detecting major depression among adolescents. Pediatrics. 2010;126(6):1117–23.

    Article  PubMed  Google Scholar 

  39. Karila L, Legleye S, Beck F, Corruble E, Falissard B, Reynaud M. Validation d’un questionnaire de repérage de l’usage nocif d’alcool et de cannabis dans la population générale : le CRAFFT-ADOSPA. Press Medicale. 2007;36(4 I):582–90.

    Article  Google Scholar 

  40. Buja A, Gallimberti L, Chindamo S, Lion C, Terraneo A, Rivera M et al. Problematic social networking site usage and substance use by young adolescents. BMC Pediatr [Internet]. 2018 Dec 23;18(1):367. Available from:

  41. Dufour M, Tremblay J, Blanchette-Martin N, Ferland F, Goyette M, Turcotte S, Khazaal Y, Brunelle N, Gagnon SR, Tétrault-Beaudoin CS, Genois R. et Légaré A-A. Dépistage/Évaluation du Besoin d’Aide – Internet (DÉBA-Internet). 2019.

  42. Alavi M, Visentin DC, Thapa DK, Hunt GE, Watson R, Cleary M. Chi-square for model fit in confirmatory factor analysis. J Adv Nurs. 2020;76(9):2209–11.

    Article  PubMed  Google Scholar 

  43. Brown TA. Confirmatory factor analysis for applied research. 2nd ed. New York: Guilford Press; 2015.

    Google Scholar 

  44. Boateng GO, Neilands TB, Frongillo EA, Melgar-Quiñonez HR, Young SL. Best Practices for developing and validating Scales for Health, Social, and behavioral research: a primer. Front Public Heal. 2018;6(June):1–18.

    Google Scholar 

  45. Putnick DL, Bornstein MH. Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Dev Rev [Internet]. 2016 Sep;41(5):71–90. Available from:

  46. Hair JF. Multivariate Data Analysis. Upper Saddle River, N.J.: Prentice Hall; 1998.

    Google Scholar 

  47. Costello AB, Osborne JW. Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis.Pract Assessment, Res Eval. 2005;10(7).

  48. Schober P, Boer C, Schwarte LA. Correlation Coefficients. Anesth Analg [Internet]. 2018 May;126(5):1763–8. Available from:

  49. Cunningham S, Hudson CC, Harkness K. Social Media and Depression Symptoms: a Meta-Analysis. Res Child Adolesc Psychopathol [Internet]. 2021 Feb 6;49(2):241–53. Available from:

  50. Vannucci A, Simpson EG, Gagnon S, Ohannessian CM. Social media use and risky behaviors in adolescents: A meta-analysis. J Adolesc [Internet]. 2020 Feb;79(1):258–74. Available from:

  51. Fornell C, Larcker DF. Evaluating Structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39.

    Article  Google Scholar 

  52. Nesi J, Wolff JC, Hunt J. Patterns of Social Media Use among Adolescents who are psychiatrically hospitalized. J Am Acad Child Adolesc Psychiatry. 2019;58(6):635–639e1.

    Article  PubMed  Google Scholar 

  53. Nesi J, Burke TA, Extein J, Kudinova AY, Fox KA, Hunt J, et al. Social media use, sleep, and psychopathology in psychiatrically hospitalized adolescents. J Psychiatr Res. 2021;144(March):296–303.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Shafi RMA, Nakonezny PA, Romanowicz M, Nandakumar AL, Suarez L, Croarkin PE. Suicidality and self-injurious behavior among adolescent social media users at psychiatric hospitalization. CNS Spectr [Internet]. 2021 Jun 27;26(3):275–81. Available from:

  55. Weinstein E, Kleiman EM, Franz PJ, Joyce VW, Nash CC, Buonopane RJ, et al. Positive and negative uses of social media among adolescents hospitalized for suicidal behavior. J Adolesc. 2021;87(December):63–73.

    Article  PubMed  Google Scholar 

  56. Marciano L, Ostroumova M, Schulz PJ, Camerini A-L. Digital Media Use and Adolescents’ Mental Health During the Covid-19 Pandemic: A Systematic Review and Meta-Analysis. Front Public Heal [Internet]. 2022 Feb 1;9(February). Available from:

  57. Craig SG, Ames ME, Bondi BC, Pepler DJ. Canadian adolescents’ mental health and substance use during the COVID-19 pandemic: Associations with COVID-19 stressors. Can J Behav Sci / Rev Can des Sci du Comport [Internet]. 2022 Mar 7; Available from:

  58. Gadermann AC, Thomson KC, Richardson CG, Gagné M, Mcauliffe C, Hirani S, et al. Examining the impacts of the COVID-19 pandemic on family mental health in Canada: findings from a national cross-sectional study. BMJ Open. 2021;11(1):1–11.

    Article  Google Scholar 

  59. Orben A, Tomova L, Blakemore S. The effects of social deprivation on adolescent development and mental health. Lancet Child Adolesc Heal [Internet]. 2020 Aug;4(8):634–40. Available from:

  60. Billieux J, Schimmenti A, Khazaal Y, Maurage P, Heeren A. Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research. J Behav Addict. 2015;4(3):119–23.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


The authors would like to thank all the participants and participating high schools, the statistical team (Samuel Lemaire-Paquette) from the Unité de Recherche Clinique et Épidémiologique, the entire team of dedicated professionals working at «L’unité des adolescents» for their support, Allyson Reid-Taylor and Drs Véronique Anctil, Stéphanie Mailloux and Jean-François Trudel for their precious assistance, and finally Pr. Éric Marsault, PhD, for his lifetime work and continuing legacy to science.


This study was supported by funds from the department of psychiatry of the Université de Sherbrooke.

Author information

Authors and Affiliations



Dr Raphaël Dufort Rouleau: Conceptualization, Methodology, Investigation, Writing – Original Draft, Visualization.

Dr Carmen Beauregard: Investigation, Resources, Writing – Review and Editing,

Dr Vincent Beaudry: Conceptualization, Methodology, Investigation, Writing – Review and Editing, Supervision.

All authors reviewed the final manuscript.

Corresponding author

Correspondence to Vincent Beaudry.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Ethics and Scientific committees of the CIUSSS de l’Estrie-CHUS and the Université de Sherbrooke in January 2021. Our study was prepared in accordance with the ethical principles stated in the Declaration of Helsinki. Informed consent was obtained from the adolescents and from their parent(s) or legal guardian(s).

Consent for publication

Not applicable.

Competing Interest

The authors declare no competing interests with respect to the research, authorship, and/or publication of this article.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rouleau, R.D., Beauregard, C. & Beaudry, V. A rise in social media use in adolescents during the COVID-19 pandemic: the French validation of the Bergen Social Media Addiction Scale in a Canadian cohort. BMC Psychol 11, 92 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: