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Pauline Billaux Louvain Experimental Psychopathology Research Group (LEP), Psychological Science Research Institute, UCLouvain, Louvain-la-Neuve, Belgium

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Joël Billieux Institute of Psychology, University of Lausanne, Lausanne, Switzerland

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Stéphanie Baggio Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
Population Health Laboratory (#PopHealthLab), University of Fribourg, Fribourg, Switzerland

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Pierre Maurage Louvain Experimental Psychopathology Research Group (LEP), Psychological Science Research Institute, UCLouvain, Louvain-la-Neuve, Belgium

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Maèva Flayelle Institute of Psychology, University of Lausanne, Lausanne, Switzerland

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Abstract

While applying a diagnostic approach (i.e., comparing “clinical” cases with “healthy” controls) is part of our methodological habits as researchers and clinicians, this approach has been particularly criticized in the behavioral addictions research field, in which a lot of studies are conducted on “emerging” conditions. Here we exemplify the pitfalls of using a cut-off-based approach in the context of binge-watching (i.e., watching multiple episodes of series back-to-back) by demonstrating that no reliable cut-off scores could be determined with a widely used assessment instrument measuring binge-watching.

Abstract

While applying a diagnostic approach (i.e., comparing “clinical” cases with “healthy” controls) is part of our methodological habits as researchers and clinicians, this approach has been particularly criticized in the behavioral addictions research field, in which a lot of studies are conducted on “emerging” conditions. Here we exemplify the pitfalls of using a cut-off-based approach in the context of binge-watching (i.e., watching multiple episodes of series back-to-back) by demonstrating that no reliable cut-off scores could be determined with a widely used assessment instrument measuring binge-watching.

The recent expansion of the behavioral addiction research field is a concern (Billieux, Flayelle, & King, 2022). Binge-watching (i.e., watching multiple episodes of series in one session) research exemplifies this phenomenon through the development of various assessment tools reclaiming traditional substance-use disorder criteria. This trend led to the conceptualization of binge-watching as a potential addictive behavior (e.g., Forte, Favieri, Tedeschi, & Casagrande, 2021; Orosz, Bőthe, & Tóth-Király, 2016; Paschke, Napp, & Thomasius, 2022; Starosta, Izydorczyk, & Lizińczyk, 2019). Other studies, however, insisted on the need to distinguish elevated (but non-harmful) binge-watching from problematic binge-watching in order to prevent over-pathologization (Flayelle et al., 2022; Steins-Loeber, Reiter, Averbeck, Harbarth, & Brand, 2020; Töth-Király, Böthe, Töth-Fáber, Gÿozö, & Orosz, 2017). The Binge-Watching Engagement and Symptoms Questionnaire (BWESQ; Flayelle et al., 2019) is a quantitative tool that assesses this dual nature of binge-watching (i.e., healthy vs. problematic) by measuring both healthy engagement (e.g., positive emotions, pleasure preservation) and symptoms of problematic binge-watching (e.g., loss of control, dependency). As the BWESQ is increasingly used in various contexts (e.g., Alfonsi et al., 2022; Boursier et al., 2021; Costa, Bugatti, & Lucchini, 2022; Demir & Batik, 2020; Gabbiadini, Baldissarri, Valtorta, Durante, & Mari, 2021; Munawar & Siraj, 2022; Tolba & Zoghaib, 2022), dozens of researchers have recently requested cut-off scores to identify problematic binge-watching. However, although following a diagnostic approach (i.e., comparing “clinical” cases with “healthy” controls) is core to psychiatry research and clinical practice, such an approach has been criticized in relation to putative behavioral addictions (Billieux, Schimmenti, Khazaal, Maurage, & Heeren, 2015), especially because these behaviors concern daily life activities and leisure, which can be performed at high levels of engagement without involving negative consequences and functional impairment (Bőthe, T.th-Kir.ly, Orosz, Potenza, & Demetrovics, 2020; Brevers, Maurage, Kohut, Perales, & Billieux, 2022; Charlton & Danforth, 2007; Whelan, Laato, Islam, & Billieux, 2021). Although the BWESQ was not developed as a diagnostic tool, we addressed this request by exploring whether reliable BWESQ cut-off scores could be determined.

We capitalized on an international data set comprising 12,616 BWESQ answers from series viewers (Flayelle, Castro-Calvo, et al., 2020). We applied the criteria from prior work on binge-watching (Billaux, Billieux, Gärtner, Maurage, & Flayelle, 2022; Flayelle, Verbruggen, et al., 2020)1 to distinguish three groups: 1) non-binge-watchers (n = 2,642), with a typical viewing session comprising less than three episodes and lasting for less than 2 h, with neither a reported functional impact caused by series watching nor self-identification as problematic series viewers; 2) trouble-free binge-watchers (n = 2,345), with a typical viewing session comprising three or more episodes and lasting at least 2 h per viewing session without reporting a functional impact caused by series watching and without self-identifying as problematic series viewers; and 3) problematic binge-watchers (n = 2,996), with a typical viewing session comprising three or more episodes and lasting at least 2 h, with a reported functional impact caused by series watching. This classification approach resulted in a final sample size of 7,983 participants (AgeM(SD) = 24.19 (7.91), 70.90% female). We thus excluded the remaining 4,633 participants who did not fulfill the criteria related to any of the three groups (e.g., participants who typically watched less than two episodes but for more than 2 h). However, because cut-off scores aim at dissociating clinical from non-clinical populations, we gathered non-binge-watchers and trouble-free binge-watchers into one group of non-problematic TV series viewers (n = 4,987, AgeM(SD) = 24.74 (8.49), 67.70% female), in opposition to the group of problematic binge-watchers (n = 2,996, AgeM(SD) = 23.28 (6.74), 76.30% female).

We conducted accuracy analyses for each of the seven BWESQ facets: binge-watching (e.g., “I always need to watch more episodes to feel satisfied”), dependency (e.g., “I am usually in a bad mood, sad, depressed or annoyed when I can't watch any TV series, and I feel better when I am able to watch them again”), desire/savoring (e.g., “I get really excited when a new episode is released”), engagement (e.g., “In my opinion, TV series are a part of my life and they contribute to my welfare”), loss of control (e.g., “I watch more TV series than I should”), pleasure preservation (e.g., “I worry about getting spoiled”), and positive emotions (e.g., “Watching TV series is a cause for joy and enthusiasm in my life”).

Using SPSS 27.0 (IBM, Corp.), we first assessed the diagnostic accuracy with area under the curve (AUC) analyses of receiver operating characteristics (ROC) curves, following diagnostic accuracy guidelines (i.e., AUC <0.70 implying low accuracy, AUC ≥0.70 and <0.90 indicating moderate diagnostic accuracy, and AUC ≥0.90 corresponding to high diagnostic accuracy; Swets, 2014). Results indicated low or close to low accuracy for the following five facets: engagement (AUC = 0.70), dependency (AUC = 0.68), desire/savoring (AUC = 0.72), positive emotions (AUC = 0.66) and pleasure preservation (AUC = 0.62). Because loss of control (AUC = 0.82) and binge-watching (AUC = 0.81) had moderate diagnostic accuracy, we conducted further accuracy analyses: specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). As observed in Figs 1 and 2, and based on accuracy indices for each of the curve coordinates (see Appendixes A and B), a cut-off score of 15.50 (corresponding to an actual score of 16) optimizes the accuracy of both subscales, ensuring a minimization of false positives (contrarily to the values inferior to the 15.50 cut-off score). For the loss of control facet, this threshold yields a poor sensitivity score of 54.40% (yielding a rate of 45.60% false negatives), a more than acceptable specificity score of 89.30%, a medium PPV of 75.30%, and a medium NPV of 76.50%. Regarding the binge-watching facet, this threshold is related to poor sensitivity (56.10%, yielding 43.90% false negatives), a good specificity score (86.30%), and a medium PPV (71.20%) and NPV (76.60%). This implies that if clinicians were to use either the binge-watching or loss of control subscale for screening purposes, approximately 30% of respondents labeled as presenting problematic binge-watching would be misclassified (Maraz, Király, & Demetrovics, 2015). Considering such a substantial likelihood of generating false positives, we therefore cannot reasonably recommend the use of cut-off values for the binge-watching and loss of control facets of the BWESQ.

Fig. 1.
Fig. 1.

Frequency curves of scores of non-problematic TV series viewers and problematic binge-watchers for the binge-watching facet of the BWESQ Note. BWESQ: Binge-Watching Engagement and Symptoms Questionnaire; PBW: problematic binge-watchers; NPTSV: non-problematic TV series viewers; Std. Dev.: standard deviation

Citation: Journal of Behavioral Addictions 12, 2; 10.1556/2006.2023.00032

Fig. 2.
Fig. 2.

Frequency curves of scores of non-problematic TV series viewers and problematic binge-watchers for the loss of control facet of the BWESQ Note. BWESQ: Binge-Watching Engagement and Symptoms Questionnaire; PBW: problematic binge-watchers; NPTSV: non-problematic TV series viewers; Std. Dev.: standard deviation

Citation: Journal of Behavioral Addictions 12, 2; 10.1556/2006.2023.00032

In summary, the current results indicate that no reliable BWESQ cut-off scores could be determined to accurately discriminate problematic from non-problematic binge-watchers. They also point to the notion that applying such a diagnostic approach might not be the most relevant in the context of binge-watching behaviors. Notably, since most putative behavioral addictions (except gambling and gaming disorders) are not yet recognized as such in international diagnostic classifications, the current lack of established diagnostic criteria for problematic and potentially addictive engagement in these activities prevents the generation of reliable cut-off scores. This is why researchers and clinicians should, at this stage, refrain from proposing cut-off scores in new scales that assess emerging problematic behaviors, including the binge-watching research field as well as other emerging conditions. Indeed, previous attempts to suggest cut-offs for such scales (e.g., in the context of “Internet addiction”) resulted in unrealistic prevalence rates (up to 10%–20% of “pathological cases”; e.g., Kuss, Griffiths, Karila, & Billieux, 2014), thus promoting over-pathologization, stigmatization, and moral panic. Efforts should instead be focused on developing a strong research base to clarify where the dividing line between elevated but non-harmful and problematic patterns of engagement resides. Clinically useful assessment criteria could then be derived, thus allowing for the generation of valid cut-off scores in terms of measurement instruments specially designed for this purpose.

It is worth noting that determining reliable cut-off scores for self-reported screening tools (such as the BWESQ) requires a gold standard (e.g., a diagnostic interview administered by a certified clinician), which was not possible in the present context as binge-watching is not a recognized condition. We also want to point-out that the identification of problematic behaviors should go beyond the use of a single cut-off, and that different cut-offs could be used for different purposes. For example, we could opt for a different cut-off if our aim is to diminish the number of false positives to avoid over-pathologization effects, or if, in contrast, our objective is to reduce as far as possible false negatives to ensure that most persons in need of help are correctly identified via the screening instrument. Finally, future studies could also apply other statistical approaches (e.g., supervised machine learning) to identify optimal cut-off scores based on a selection of theoretically informed variables.

Authors’ contribution

MF, JB, SB and PB designed the statistical analysis strategy. PB ran the statistical analyses. MF, JB, SB and PB interpreted the results. PB wrote the initial draft of the commentary under the supervision of MF and JB. MF, JB, SB and PM reviewed the initial draft and participated in the writing of the final draft. All authors approved the final version of the manuscript.

Conflict of interest

PM is funded by the Belgian Fund for Scientific Research (FRS-FNRS, Belgium).

Acknowledgments

The authors would like to warmly thank Robert Astur, Rafael Ballester-Arnal, Jesús Castro-Calvo, Gaëlle Challet-Bouju, Matthias Brand, Georgina Cárdenas, Gaëtan Devos, Hussien Elkholy, Marie Grall-Bronnec, Richard J.E. James, Martha Jiménez-Martínez, Yasser Khazaal, Saeideh Valizadeh-Haghi, Daniel L. King, Yueheng Liu, Christine Lochner, Sabine Steins-Loeber, Jiang Long, Marc N. Potenza, Shahabedin Rahmatizadeh, Adriano Schimmenti, Dan J. Stein, István Tóth-Király, Richard Tunney, Yingying Wang and Zu Wei Zhai for their precious support in collecting the data used in the present commentary.

References

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Appendix A

Table A1.

Curve coordinates and associated sensitivity, specificity, positive and negative predictive values and accuracy of the binge-watching facet of the BWESQ

ValueSensitivitySpecificityPPVNPVAccuracy
5.0001.0000.0000.375/0.375
6.5000.9960.0910.3970.9740.431
7.5000.9920.1530.4130.9700.468
8.5000.9850.2340.4360.9620.516
9.5000.9720.3130.4590.9480.560
10.5000.9460.3940.4840.9240.601
11.5000.9030.4840.5130.8920.641
12.5000.8450.6050.5630.8670.695
13.5000.7700.7060.6110.8360.730
14.5000.6750.7990.6690.8040.752
15.5000.5610.8630.7120.7660.750
16.5000.4330.9140.7510.7290.733
17.5000.3180.9530.8030.6990.715
18.5000.2230.9730.8320.6760.691
19.5000.1590.9840.8560.6610.674
20.5000.1090.9920.8910.6500.661
21.5000.0720.9960.9110.6410.649
22.5000.0440.9980.9420.6350.640
23.5000.0210.9990.9550.6300.632
25.0000.0001.000/0.3750.375

Note. BWESQ: Binge-Watching Engagement and Symptoms Questionnaire; NPV: negative predictive value; PPV: positive predictive value.

Appendix B

Table B1.

Curve coordinates and associated sensitivity, specificity, positive and negative predictive values and accuracy of the loss of control facet of the BWESQ

ValueSensitivitySpecificityPPVNPVAccuracy
6.0001.0000.0000.375/0.375
7.5000.9830.1880.4210.9470.486
8.5000.9680.2950.4520.9390.547
9.5000.9390.4050.4860.9140.604
10.5000.8960.5010.5190.8890.650
11.5000.8500.5910.5560.8680.688
12.5000.7900.6730.5920.8420.717
13.5000.7260.7460.6320.8190.739
14.5000.6390.8350.7000.7940.761
15.5000.5440.8930.7530.7650.762
16.5000.4600.9320.8020.7420.755
17.5000.3660.9580.8400.7150.736
18.5000.2940.9700.8530.6960.716
19.5000.2250.9790.8660.6780.696
20.5000.1670.9850.8720.6630.678
21.5000.1150.9940.9150.6520.664
22.5000.0860.9960.9250.6450.655
23.5000.0600.9980.9420.6390.646
24.5000.0400.9990.9760.5100.639
25.5000.0260.9990.9520.6310.634
26.5000.0140.9990.9330.6280.630
27.5000.0081.000.9200.6260.627
29.0000.0001.000/0.6250.625

Note. BWESQ: Binge-Watching Engagement and Symptoms Questionnaire; NPV: negative predictive value; PPV: positive predictive value.

1

See Flayelle, Verbruggen, et al. (2020) for the rationale behind the selection of criteria used for creating the three groups.

  • Alfonsi, V., Varallo, G., Scarpelli, S., Gorgoni, M., Filosa, M., De Gennaro, L., … Franceschini, C. (2022). ‘This is the last episode': The association between problematic binge-watching and loneliness, emotion regulation, and sleep-related factors in poor sleepers. Journal of Sleep Research, e13747. Advance online publication https://doi.org/10.1111/jsr.13747.

    • Search Google Scholar
    • Export Citation
  • Billaux, P., Billieux, J., Gärtner, L., Maurage, P., & Flayelle, M. (2022). Negative affect and problematic binge-watching: The mediating role of unconstructive ruminative thinking style. Psychologica Belgica, 62(1), 272285. https://doi.org/10.5334/pb.1163.

    • Search Google Scholar
    • Export Citation
  • Billieux, J., Flayelle, M., & King, D. L. (2022). Addiction: Expand diagnostic borders with care (correspondence). Nature, 611(7937), 665. https://doi.org/10.1038/d41586-022-03760-y.

    • Search Google Scholar
    • Export Citation
  • Billieux, J., Schimmenti, A., Khazaal, Y., Maurage, P., & Heeren, A. (2015). Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research. Journal of Behavioral Addictions, 4(3), 119123. https://doi.org/10.1556/2006.4.2015.009.

    • Search Google Scholar
    • Export Citation
  • Bőthe, B., T.th-Kir.ly, I., Orosz, G., Potenza, M. N., & Demetrovics, Z. (2020). High-frequency pornography use may not always be problematic. Journal of Sexual Medicine, 17(4), 793811. https://doi.org/10.1016/j.jsxm.2020.01.007.

    • Search Google Scholar
    • Export Citation
  • Boursier, V., Musetti, A., Gioia, F., Flayelle, M., Billieux, J., & Schimmenti, A. (2021). Is watching TV series an adaptive coping strategy during the COVID-19 pandemic? Insights from an Italian community sample. Frontiers in Psychiatry, 12, 554. https://doi.org/10.3389/fpsyt.2021.599859.

    • Search Google Scholar
    • Export Citation
  • Brevers, D., Maurage, P., Kohut, T., Perales, J. C., & Billieux, J. (2022). On the pitfalls of conceptualizing excessive physical exercise as an addictive disorder: Commentary on Dinardi et al. (2021). Journal of Behavioral Addictions, 11(2), 234239. https://doi.org/10.1556/2006.2022.00001.

    • Search Google Scholar
    • Export Citation
  • Charlton, J. P., & Danforth, I. D. W. (2007). Distinguishing addiction and high engagement in the context of online game playing. Computers in Human Behavior, 23(3), 15311548. https://doi.org/10.1016/j.chb.2005.07.002.

    • Search Google Scholar
    • Export Citation
  • Costa, A., Bugatti, A., & Lucchini, G. (2022). Il fenomeno del binge watching tra gli adolescenti: Uno studio osservazionale descrittivo. Il fenomeno del Binge Watching tra gli adolescenti: uno studio osservazionale descrittivo, 80108.

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  • Demir, M., & Batik, M. V. (2020). Dizi İzleme Nedenleri Ölçeği ile Problemli Dizi İzleme ve Belirtileri Ölçeği’nin Geçerlik ve Güvenirlik Çalışması. Online Journal of Technology Addiction and Cyberbullying, 7(2), 131.

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  • Flayelle, M., Canale, N., Vögele, C., Karila, L., Maurage, P., & Billieux, J. (2019). Assessing binge-watching behaviors: Development and validation of the “watching TV series motives” and “binge-watching engagement and symptoms” questionnaires. Computers in Human Behavior, 90, 2636. https://doi.org/10.1016/j.chb.2018.08.022.

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  • Flayelle, M., Castro-Calvo, J., Vögele, C., Astur, R., Ballester-Arnal, R., Challet-Bouju, G, … Billieux, J. (2020). Towards a cross-cultural assessment of binge-watching: Psychometric evaluation of the “watching TV series motives” and “binge-watching engagement and symptoms” questionnaires across nine languages. Computers in Human Behavior, 111, 106410. https://doi.org/10.1016/j.chb.2020.106410.

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  • Flayelle, M., Elhai, J. D., Maurage, P., Vögele, C., Brevers, D., Baggio, S., & Billieux, J. (2022). Identifiying the psychological processes delineating non-harmful from problematic binge-watching: A machine learning analytical approach. Telematics and Informatics, 74, 101880. https://doi.org/10.1016/j.tele.2022.101880.

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  • Flayelle, M., Verbruggen, F., Schiel, J., Vögele, C., Maurage, P., & Billieux, J. (2020). Non‐problematic and problematic binge‐watchers do not differ on prepotent response inhibition: A preregistered pilot experimental study. Human Behavior and Emerging Technologies, 2(3), 259268. https://doi.org/10.1002/hbe2.194.

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  • Forte, G., Favieri, F., Tedeschi, D., & Casagrande, M. (2021). Binge-Watching: Development and validation of the binge-watching addiction questionnaire. Behavioral Sciences, 11(2), 27. https://doi.org/10.3390/bs11020027.

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  • Gabbiadini, A., Baldissarri, C., Valtorta, R. R., Durante, F., & Mari, S. (2021). Loneliness, escapism, and identification with media characters: An exploration of the psychological factors underlying binge-watching tendency. Frontiers in Psychology, 12.

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  • Kuss, D. J., Griffiths, M. D., Karila, L., & Billieux, J. (2014). Internet addiction: A systematic review of epidemiological research for the last decade. Current Pharmaceutical Design, 20(25), 40264052. https://doi.org/10.2174/13816128113199990617.

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  • Maraz, A., Király, O., & Demetrovics, Z. (2015). Commentary on: Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research. The diagnostic pitfalls of surveys: If you score positive on a test of addiction, you still have a good chance not to be addicted. Journal of Behavioral Addictions, 4(3), 151154. https://doi.org/10.1556/2006.4.2015.026.

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  • Munawar, K., & Siraj, S. A. (2022). Problematic symptoms among binge watchers in Islamabad and Rawalpindi, Pakistan: Analysis from uses, gratification, and dependency perspectives (pp. 120). Media Asia.

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  • Orosz, G., Bőthe, B., & Tóth-Király, I. (2016). The development of the problematic series Watching Scale (PSWS). Journal of Behavioral Addictions, 5(1), 144150. https://doi.org/10.1556/2006.5.2016.011.

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  • Paschke, K., Napp, A. K., & Thomasius, R. (2022). Applying ICD-11 criteria of Gaming Disorder to identify problematic video streaming in adolescents: Conceptualization of a new clinical phenomenon. Journal of Behavioral Addictions, 11(2), 451466. https://doi.org/10.1556/2006.2022.00041.

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  • Starosta, J., Izydorczyk, B., & Lizińczyk, S. (2019). Characteristics of people’s binge-watching behavior in the “entering into early adulthood” period of life. Health Psychology Report, 7(2), 149164. https://doi.org/10.5114/hpr.2019.83025.

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  • Töth-Király, I., Böthe, B., Töth-Fáber, E., Gÿozö, H., & Orosz, G. (2017). Connected to TV series: Quantifying series watching engagement. Journal of Behavioral Addictions, 6, 472489. https://doi.org/10.1556/2006.6.2017.083.

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  • Whelan, E., Laato, S., Islam, A. K. M. N., & Billieux, J. (2021). A casino in my pocket: Gratifications associated with obsessive and harmonious passion for mobile gambling. Plos One, 16(2), e0246432. https://doi.org/10.1371/journal.pone.0246432.

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The author instructions are available in PDF.
Please, download the file from HERE

 

Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

Indexing and Abstracting Services:

  • Web of Science [Science Citation Index Expanded (also known as SciSearch®)
  • Journal Citation Reports/Science Edition
  • Social Sciences Citation Index®
  • Journal Citation Reports/ Social Sciences Edition
  • Current Contents®/Social and Behavioral Sciences
  • EBSCO
  • GoogleScholar
  • PsycINFO
  • PubMed Central
  • SCOPUS
  • Medline
  • CABI
  • CABELLS Journalytics

2022  
Web of Science  
Total Cites
WoS
5713
Journal Impact Factor 7.8
Rank by Impact Factor

Psychiatry (SCIE) 18/155
Psychiatry (SSCI) 13/144

Impact Factor
without
Journal Self Cites
7.2
5 Year
Impact Factor
8.9
Journal Citation Indicator 1.42
Rank by Journal Citation Indicator

Psychiatry 35/264

Scimago  
Scimago
H-index
69
Scimago
Journal Rank
1.918
Scimago Quartile Score Clinical Psychology Q1
Medicine (miscellaneous) Q1
Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 990 EUR/article for articles submitted after 30 April 2023 (850 EUR for articles submitted prior to this date)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%.
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Stephanie ANTONS (Universitat Duisburg-Essen, Germany)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Ruth J. van HOLST (Amsterdam UMC, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Gyöngyi KÖKÖNYEI (ELTE Eötvös Loránd University, Hungary)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)

Editorial Board

  • Sophia ACHAB (Faculty of Medicine, University of Geneva, Switzerland)
  • Alex BALDACCHINO (St Andrews University, United Kingdom)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Maria BELLRINGER (Auckland University of Technology, Auckland, New Zealand)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Damien BREVERS (University of Luxembourg, Luxembourg)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Julius BURKAUSKAS (Lithuanian University of Health Sciences, Lithuania)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Silvia CASALE (University of Florence, Florence, Italy)
  • Luke CLARK (University of British Columbia, Vancouver, B.C., Canada)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Geert DOM (University of Antwerp, Belgium)
  • Nicki DOWLING (Deakin University, Geelong, Australia)
  • Hamed EKHTIARI (University of Minnesota, United States)
  • Jon ELHAI (University of Toledo, Toledo, Ohio, USA)
  • Ana ESTEVEZ (University of Deusto, Spain)
  • Fernando FERNANDEZ-ARANDA (Bellvitge University Hospital, Barcelona, Spain)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Sally GAINSBURY (The University of Sydney, Camperdown, NSW, Australia)
  • Belle GAVRIEL-FRIED (The Bob Shapell School of Social Work, Tel Aviv University, Israel)
  • Biljana GJONESKA (Macedonian Academy of Sciences and Arts, Republic of North Macedonia)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Joshua GRUBBS (University of New Mexico, Albuquerque, NM, USA)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Zsolt HORVÁTH (Eötvös Loránd University, Hungary)
  • Susana JIMÉNEZ-MURCIA (Clinical Psychology Unit, Bellvitge University Hospital, Barcelona, Spain)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Chih-Hung KO (Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Taiwan)
  • Shane KRAUS (University of Nevada, Las Vegas, NV, USA)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Bernadette KUN (Eötvös Loránd University, Hungary)
  • Katerina LUKAVSKA (Charles University, Prague, Czech Republic)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Gemma MESTRE-BACH (Universidad Internacional de la Rioja, La Rioja, Spain)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Ståle PALLESEN (University of Bergen, Norway)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Michael SCHAUB (University of Zurich, Switzerland)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Hermano TAVARES (Instituto de Psiquiatria do Hospital das Clínicas da Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN (Ariel University, Israel)
  • Anise WU (University of Macau, Macao, China)

 

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