View More View Less
  • 1 German Center for Addiction Research in Childhood and Adolescence (DZSKJ), University Medical Center Hamburg-Eppendorf (UKE), Martinistrasse 52, D-20246, Hamburg, Germany
Open access

Abstract

Background and aims

Internet video streaming (VS) has become a popular leisure activity among the majority of adolescents, especially under the COVID-19 pandemic. Research on binge watching patterns in adults suggests an addictive potential of VS. To date, no unified conceptualization on problematic VS and no standardized assessment tools for adolescents exist even though they might be especially vulnerable.

Methods

STREDIS-A is based on the ICD-11 criteria of gaming disorder. It was validated in a representative sample of 959 dyads of 10- to 17-year old adolescents with frequent VS and a respective parent using standardized questionnaires on Internet addiction, depressive and anxiety symptoms, insomnia, loneliness, and academic performance in an online survey. Item structure was investigated by factorial analyses. Cutoffs were estimated and latent profile analysis was performed.

Results

The two-factorial structure of STREDIS-A describes cognitive-behavioral symptoms and negative consequences of VS. Internal consistency and criterion validity were good to excellent. It could excellently discriminate between affected and non-affected adolescents.

Discussion and conclusions

The present study makes a significant contribution to the conceptualization of a new phenomenon. It provides the very first tool to assess streaming disorder in adolescents for clinical and research settings. Clinical validation is highly warranted.

Abstract

Background and aims

Internet video streaming (VS) has become a popular leisure activity among the majority of adolescents, especially under the COVID-19 pandemic. Research on binge watching patterns in adults suggests an addictive potential of VS. To date, no unified conceptualization on problematic VS and no standardized assessment tools for adolescents exist even though they might be especially vulnerable.

Methods

STREDIS-A is based on the ICD-11 criteria of gaming disorder. It was validated in a representative sample of 959 dyads of 10- to 17-year old adolescents with frequent VS and a respective parent using standardized questionnaires on Internet addiction, depressive and anxiety symptoms, insomnia, loneliness, and academic performance in an online survey. Item structure was investigated by factorial analyses. Cutoffs were estimated and latent profile analysis was performed.

Results

The two-factorial structure of STREDIS-A describes cognitive-behavioral symptoms and negative consequences of VS. Internal consistency and criterion validity were good to excellent. It could excellently discriminate between affected and non-affected adolescents.

Discussion and conclusions

The present study makes a significant contribution to the conceptualization of a new phenomenon. It provides the very first tool to assess streaming disorder in adolescents for clinical and research settings. Clinical validation is highly warranted.

Introduction

In the last decade, video streaming (VS) services such as Netflix, YouTube, and Twitch have experienced tremendous growth worldwide, especially since the beginning of the COVID-19 pandemic (statista, 2022). Among German adolescents, internet-based VS is taking on increasing priority over traditional television (Die medienanstalten, 2021) and about half of them are daily users (Feierabend, Rathgeb, Kheredmand, & Glöckler, 2021). VS is characterized by “the possibility for time-shifting and on-demand retrieval, access to vast libraries of content and limitless number of channels, and the use of multiple devices for retrieval […] to choose what and when to watch” (Spilker & Colbjørnsen, 2020). The authors distinguish professional vs. user-generated streaming multipurpose platforms and live streaming service vs. on-demand videos. Streaming on multipurpose platforms like YouTube and TikTok can be separated from social media activities on these sites (Balakrishnan & Griffiths, 2017; Smith & Short, 2022) by a passive consuming pattern in contrast to active participation via direct interactions (Spilker, Ask, & Hansen, 2020).

To date, VS has been mainly addressed scientifically in the context of binge watching, i.e. watching multiple episodes of a televison series online in a row (Starosta & Izydorczyk, 2020). Accordingly, motives for binge watching include enjoyment and entertainment, recreation, social aspects, information, and control but also escaping from reality and dealing with lonelinesss (Panda & Pandey, 2017; Shim & Kim, 2018; Starosta, Izydorczyk, & Lizińczyk, 2019, 2021). In the systematic reviews of Flayelle et al. (2020) and Starosta and Izydorczyk (2020) a specific streaming pattern is described which is characterized by multiple symptoms of behavioral addictions including loss of control, neglect of other activities and duties, feelings of guilt, lying, withdrawal symptoms, and negative social, work-related, and health consequences. Associations between problematic binge watching and loneliness as well as symptoms of depression, anxiety, and insomnia have been found in studies with (young) adults (Exelmans & Van den Bulck, 2017; Starosta, Izydorczyk, & Wontorczyk, 2021; Sun & Chang, 2021). Moreover, problematic patterns were related to more frequent and longer usage times (Flayelle, Canale, et al., 2019; Ort, Wirz, & Fahr, 2021). Assessment of binge watching mainly focused on motives and engagement or on general criteria of addictions in adults only (cf. six core components of addiction model; Griffiths, 2005) and includes the adapted Television Viewing Motivation Scale and Questionnaire of Excessive Binge-Watching Behaviors (Starosta et al., 2019), Problematic Series Watching Scale (Orosz, Bőthe, & Tóth-Király, 2016), Series Watching Engagement Scale (Tóth-Király, Bőthe, Tóth-Fáber, Hága, & Orosz, 2017), Watching TV Series Motives Questionnaire and Binge-Watching Engagement and Symptoms Questionnaire (Flayelle, Canale, et al., 2019), and Binge-Watching Addiction Questionnaire (Forte, Favieri, Tedeschi, & Casagrande, 2021).

Although problematic users have been described with characteristics similar to established behavioral addiction concepts as specified in the ICD-11 for gaming disorder (GD; WHO, 2018), these have not yet been applied in a structured and thus reproducible manner. Accordingly, problematic users could be characterized by an impaired control over streaming, increasing priority given to streaming over other activities and the continuation or escalation of streaming despite the occurrence of negative consequences over a period of at least 12 months. Importantly, the streaming behavior need to result in clinically significant distress or impairment of personal, social, educational, work-related, and financial functions.

Flayelle et al. (2020) argue for a major need for the conceptualization of problematic forms of binge watching to ensure comparability of constructs. However, reducing VS to series binge watching seems to be artificial since the key characteristic of the user's control of content and time does apply to all streaming offers (Spilker & Colbjørnsen, 2020). Moreover, as streaming is a leisure activity for the majority of young people, a vague and inconsistent definition of streaming patterns as in “binge” does not seem to be useful. The focus should be laid on the usage pattern as suggested by the term “problematic” (Forte et al., 2021; Orosz et al., 2016; Starosta et al., 2021; Sun & Chang, 2021) and should be preferred over the focus on the usage time as suggested by the term “excessive” (Starosta et al., 2019) to avoid over-pathologizing an everyday activity (Billieux, Schimmenti, Khazaal, Maurage, & Heeren, 2015).

To the best of our knowledge, no study on problematic or even disordered VS in children and adolescents has been published and no specific assessment tool is available yet despite high public, clinical, and research concerns (Matrix, 2014; Starosta & Izydorczyk, 2020). This study aims to close this significant gap by (1) developing a screening instrument to assess streaming disorder (StrD) based on the ICD-11 criteria of GD in adolescents (Streaming Disorder Scale for Adolescents, STREDIS-A), (2) exploring the psychometric properties of the new scale, and (3) validating it in a representative sample of 10- to 17-year-old frequent VS users and a respective parent.

Methods

Participants and procedure

1,128 households with children between 10 and 17 years and a respective parent were included in an online survey between May 19 and June 06, 2021 by the German market research and opinion polling company forsa. The sample was representative regarding residence region, age, and gender. Figure 1 shows the recruitment process. For more details on the recruitment and sampling method see Paschke, Austermann, and Thomasius (2021a).

Fig. 1.
Fig. 1.

Flow chart of sample recruitment. Abbreviations: VS = video streaming

Citation: Journal of Behavioral Addictions 11, 2; 10.1556/2006.2022.00041

Measures

Video streaming patterns

VS was defined as passively retrieving videos from professional and/or user-generated streaming platforms including on-demand and live streaming services as well as mono- and/or multipurpose platforms (e.g., Netflix, YouTube, Twitch, TikTok) without providing, sharing, commenting on, or liking content. Problematic VS was assessed by the Streaming Disorder Scale for Adolescents (STREDIS-A) which was developed by clinical and scientific experts in the field of behavioral addictions in adolescence. It was adapted from the ICD-11-based Gaming Disorder Scale for Adolescents (GADIS-A; Paschke, Austermann, & Thomasius, 2020; WHO, 2018) and already successfully applied to the problematic use of social media (Social Media Use Disorder Scale for Adolescents [SOMEDIS-A], Paschke, Austermann, & Thomasius, 2021b). Adolescents were asked to indicate their agreement with nine statements by choosing one out of five (Likert-scale) response options when thinking of the past 12 months (strongly disagree[0]—strongly agree[4]). Higher scores suggested more problems. Frequency of problems, conflicts, or difficulties due to VS was acquired by item 10 with four response options (not at all[0]—nearly daily[3]) with a score of ≥2 considered significant regarding the ICD-11-time criterion. Table 1 depicts STREDIS-A items matched with the ICD-11 criteria.

Table 1.

STREDIS-A items with corresponding ICD-11 criteria and YDQ items

ICD-11 criteriaSTREDIS-A items
(Corresponding YDQ Items)Thinking of the last 12 months, how strongly do you agree with the following statements?
  1. A.Impaired control over VS (e.g., onset, frequency, intensity, duration, termination, context).
  1. 1.I often use VS services more frequently and longer than I planned to or agreed upon with my parents.a
(Have you repeatedly made unsuccessful efforts to control, cut back, or stop Internet use?

Do you stay online longer than originally intended?)
  1. 2.I often cannot stop VS even though it would be sensible to do so or, for example, my parents have told me to stop.a
  1. B.Increasing priority given to VS to the extent that VS takes precedence over other life interests and daily activities.
  1. 3.I often do not pursue interests outside the digital world because I prefer VS. For example, I do not meet with friends/my partner in real life, do not attend sports clubs/societies, do not read books or make music because of VS.a
  1. 4.I neglect daily duties because I prefer VS. Daily duties include, e.g., doing grocery shopping, cleaning, tidying up after myself, tidying up my room, fulfilling obligations for school/apprenticeship/job.a
  1. C.Continuation or escalation of VS despite the occurrence of negative consequences.
  1. 5.I often continue using VS services even though it causes me stress with others. This means, e.g., stress with my parents, siblings, friends, partner or teachers because of VS.a
(Have you jeopardized or risked the loss of a significant relationship, job, educational, or career opportunity because of the Internet?)
  1. 6.I continue VS although it harms my performance at school (or apprenticeship/job). For example, I'm late, I do not participate in class, I neglect homework and I get worse grades because of VS.a
  1. D.The behavior pattern is of sufficient severity to result in significant impairment in personal, family, social, educational, occupational or other important areas of functioning.
  1. 7.Due to VS, I neglect my appearance, personal hygiene, and/or my health. For instance, I sleep less, eat unhealthy, and/or exercise less because of streaming.a
  1. 8.Due to VS, I risk losing important relationships or have lost them already. This includes contacts with partners, friends, acquaintances or family.a
  1. 9.Due to VS, I have disadvantages at school/apprenticeship/job. For example, I got bad (final) grades, I'm unable to continue to next grade or do not graduate, I have no place for training or studying, and/or I got a poor reference or a warning/dismissal as a result of VS.a
  1. E.The pattern of VS behavior may be continuous or episodic and recurrent and normally evident over a period of at least 12 months.
  1. 10.In the past year, how often did you experience the conflicts or difficulties described in the statements 1 to 9 due to VS? Did this only occur on single days, during longer periods of several weeks to months, or was it almost daily?b

Notes: STREDIS-A = Streaming Disorder Scale for Adolescents; YDQ = Young Diagnostic Questionnaire; ICD-11 = 11th revision of the International Classification of Diseases; VS = video streaming.

a response options: 5-point Likert-Scale: “strongly disagree” – “strongly agree”; b response options: “not at all”, “only on single days”, “during longer periods”, “almost daily”.

Since no standardized instrument to exclusively assess problematic VS in adolescents is available yet, the Young Diagnostic Questionnaire in its self- (YDQ; Wartberg et al., 2017; Young, 1998) and parental-judgement version (PYDQ; Wartberg, Kriston, Kegel, & Thomasius, 2016) was used as a comparative measure. It is based on the criteria for pathological gambling as described in the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; APA, 1994). To distinguish the assessed VS patterns from the use of other Internet applications, the questionnaires were preceded by the instruction to think of VS only. Both one-factorial polythetic scales consist of eight items on Internet use with a dichotomous response format (yes[1]/no[0]) and a higher total score indicating more problems. Young (1998) proposed a cutoff ≥5 which was applied in the majority of studies (e.g., Li, Zhang, Lu, Zhang, & Wang, 2014). However, alternative approaches were suggested to allow a better differentiation and avoid overestimation of problematic patterns (e.g., Beard & Wolf, 2001). Items 3, 5 and 6 of the YDQ are related to ICD-11 GD criteria (Table 1). We therefore applied a YDQ cutoff ≥5 and the condition that items 3 and/or 5 and item 6 were answered with yes. In the current sample both scales showed acceptable internal consistency (YDQ: Cronbach's α = 0.76; PYDQ: α = 0.79).

Additionally, the average number of VS days per week and the average VS time on week (school) days and weekend (leisure) days were evaluated. Out of the latter two, a mean daily VS time was computed.

Psychological measurements

Participants reported depressive symptoms on the 9-item Patient Health Questionnaire (PHQ-9) adapted for adolescents (Kroenke, Spitzer, & Williams, 2001; Paschke et al., 2021b) and anxiety symptoms by the 2-item version of the Generalized Anxiety Disorder Scale (GAD-2) with higher scores indicating more symptoms (Kroenke, Spitzer, Williams, Monahan, & Löwe, 2007; Parodi et al., 2022). Additionally, insomnia symptoms were measured with the 7-item Insomnia Severity Index (ISI; Bastien, Vallières, & Morin, 2001; Gerber et al., 2016) and loneliness with a 6-item version of the Revised UCLA Loneliness Scale (R-ULS; Hudiyana et al., 2021; Neto, 1992; Russell, Peplau, & Cutrona, 1980). Internal consistencies were good to acceptable in the present sample (PHQ-9: Cronbach's α = 0.89; GAD-2: α = 0.82; ISI: α = 0.71; R-ULS: α = 0.87).

Moreover, adolescents reported their past-term final grades (very good[1]—failed[6]) in the subjects German, Mathematics, and first foreign language out of which a grade sum was calculated with lower scores indicating better performance.

Statistical analysis

All analyses were performed with the statistical program R (R Core Team, 2019).

Data management

1,001 children and adolescents (88.74%) reported VS on a weekly basis. After exclusion of dyads with severe missing data on VS patterns, a final sample of N = 959 adolescent-parent dyads resulted (Fig. 1). Response patterns on standardized scales with non-severe missings were replaced by multiple imputations (mice; Buuren & Groothuis-Oudshoorn, 2011). The data was revised for normality distribution if appropriate. Absolute values of skewness >2.0 and kurtosis >7.0 indicated substantial univariate non-normality (Kim, 2013). Multivariate normality was explored by Mardia's test (QuantPsyc; Fletcher, 2012).

Factor analyses

Factor structure was investigated by exploratory factor analysis (EFA) with promax and confirmatory factor analysis (CFA) with diagonally weighted least squares on split-half samples (lavaan, psych, rsample; Kuhn, Chow, & Wickham, 2019; Revelle, 2018; Rosseel, 2012). Suitability of EFA was tested by the Kaiser-Meyer-Olkin (KMO) criterion and Bartlett's test of sphericity. Visual scree test, parallel analysis, and the Wayne Velicer's Minimum Average Partial (MAP) criterion were calculated in order to determine the appropriate number of factors (Velicer, Eaton, & Fava, 2000). Item representation on each factor was evaluated as suggested by Howard (2016) considering a minimal factor loading of 0.4 on the primary factor, a maximum factor loading of 0.3 on alternative factors, and factor loading differences of at least 0.2. The goodness of CFA model fit was supposed as follows: χ2/df ratio<5, root mean square error of approximation (RMSEA)<0.08, standardized root mean squared residual (SRMR)<0.08, Tucker-Lewis Index (TLI)≥0.95, comparative fit index (CFI)≥0.95 (Hooper, Coughlan, & Mullen, 2008). Model fits were compared by the mean adjusted χ2-difference statistic (Satorra & Bentler, 2001).

Internal consistency

Internal consistency coefficients were determined by Cronbach's α and McDonald's ω (Nunnally & Bernstein, 1994; Watkins, 2017).

Criterion validity

As the sum scores of the comparable scales GADIS-A and SOMEDIS-A could be shown to be a reliable measure for assessing criterion validity, STREDIS-A sum score was correlated with VS days per week and mean VS time per day, total scores of (P)YDQ, PHQ-9, GAD-2, R-ULS, ISI, and grades sum. Pearson or Spearman rank correlation tests were conducted (Cohen, 1988; Dancey & Reidy, 2011).

Sensitivity and specificity

Based on the YDQ classification of problematic VS users, receiver operating characteristic (ROC) curve analyses was performed to assess sensitivity and specificity across STREDIS-A subscale scores according to EFA/CFA factors (pROC; Robin et al., 2011). 95% confidence intervals (CI) were computed with 999 bootstrapping replications. Cutoffs for StrD were estimated applying Youden's criterion. The area under curve (AUC) value was calculated as measure of goodness of differentiation (Allgaier, 2014). Adolescents were classified as with or without StrD based on cutoffs and ICD-11-time criterion. Prevalences were estimated with 95% CI. Both groups were compared according to sex proportion by χ 2 test and Cramer's V, and according to age, VS days per week, mean VS time per day, grades sum, (P)YDQ and PHQ-9, GAD-2, R-ULS, and ISI total scores by MANOVA with post-hoc Scheffé tests and Cohen's d (Cohen, 1988; Ellis, 2010).

Classification

To further investigate construct validity and allow comparisons with other ICD-11 based instruments to assess GD (GADIS-A; Paschke et al., 2020) and social media use disorder (SMUD; SOMEDIS-A; Paschke et al., 2021b), VS patterns were analyzed by latent-profile analysis (LPA) on STREDIS-A factor sum scores and STREDIS-A time criterion (mclust; Scrucca, Fop, Murphy, & Raftery, 2016). Robustness of the LPA results was checked by 999 non-parametric bootstrapping operations to account for multivariate non-normality. The ideal number of subgroup profiles k was determined by the model with lowest values on Akaike information criterion (AIC), Bayesian information criterion (BIC), and integrated completed likelihood (ICL; Jedidi, Jagpal, & DeSarbo, 1997; Yang, 2006). Additionally, k-1 bootstrap likelihood ratio tests (LRT) were performed which support the model before the first non-significant test result (Nylund, Asparouhov, & Muthén, 2007).

Profile groups were investigated regarding sex proportions by χ 2 tests and Cramer's V, and regarding STREDIS-A scores, age, VS days per week, mean VS time per day, grades sum, (P)YDQ, PHQ-9, GAD-2, R-ULS, and ISI total scores by MANOVA with post-hoc Scheffé tests and Cohen's d.

Ethics

The study was conducted in accordance with the relevant national and institutional committees on human experimentation, complied with the Declaration of Helsinki, and was approved by the Local Psychological Ethics Commission at the Center for Psychosocial Medicine of the University Medical Center Hamburg-Eppendorf. Each participant gave informed consent prior to enrolment and could withdraw from the study at any time for any reason. Parental consent was sought for the adolescents.

Results

Sample description

Characteristics of the final adolescent-parent dyad sample are depicted in Table 2.

Table 2.

Sociodemographic characteristics of final samplea

Variables/categoriesAdolescents N [% (95% CI)]/mean

(SD; range)
Parents N [% (95% CI)]/mean

(SD; range)
Absolute frequency959959
Gender
 Male509 [53.08 (49.91–56.22)]465 [48.54 (45.37–51.65)]
 Female450 [46.92 (43.78–50.09)]494 [51.51 (48.35–54.66)]
 Age in years13.55 (2.16; 10–17)45.53 (7.27; 28–72)
Relationship status
 Biological child886 [92.39 (90.54–93.90)]
 Adoptive child6 [0.63 (0.29–1.36)]
 Stepchild43 [4.48 (3.35–5.99)]
 Otherb24 [2.50 (1.69–3.70)]
Education levelc,d
 High504 [56.12 (52.86–59.34)]234 [24.87 (22.21–27.73)]
 Medium325 [36.19 (33.11–39.39)]641 [68.12 (65.07–71.02)]
 Low69 [7.68 (6.12–9.61)]66 [7.01 (5.55–8.83)]
Occupatione
 Full time employment/school attendance910 [95.49 (93.98–96.63)]581 [60.58 (57.45–63.63)]
 Part time employment/apprenticeship32 [3.36 (2.39–4.70)]289 [30.14(27.32–33.11)]
 Other f11 [1.15 (0.65–2.05)]89 [9.28 (7.60–11.28)]
Psychological measures
 GAD-2 sum score0.92 (1.34; 0–6)
 ISI sum score8.54 (6.45; 0–28)
 PHQ-9 sum score4.69 (4.94; 0–27)
 R-ULS sum score11.54 (4.37; 6–24)
 YDQ sum score1.8 (1.87; 0–8)

Notes: N = absolute frequency; CI = confidence interval; SD = standard deviation; GAD = Generalized Anxiety Disorder Scale; ISI = Insomnia Severity Index; PHQ = Patient Health Questionnaire; R-ULS = Revised UCLA Loneliness Scale; YDQ = Young Diagnostic Questionnaire; VS = video streaming.

a dyads with frequently VS adolescents, i.e. adolescents use VS at least once a week; b not specified; c for parents: highest level achieved - high = bachelor/master's degree to doctorate (Ph.D); medium = secondary school leaving certificate (Realschulabschluss)/university entry qualification (Abitur)/completed apprenticeship; low = no or lower school-leaving certificate (Hauptschulabschluss); for adolescents: (prospective) school leaving certificate (based on the current school performance) - high = university entry qualification (Abitur), medium = secondary school certificate (Realschulabschluss), low = no/special-school (Förderschulabschluss)/lower school certificate (Hauptschulabschluss); d no response adolescents n = 61, no response parents n = 1, not specified parents n = 17; e Item not presented to adolescents younger than 14 years, no response adolescents n = 6; f for adolescents: university students, in voluntary service, military service, other occupation, or unemployed; for parents: unemployed, job seeking, welfare recipient, pensioners, disabled, trainee, student, no specification.

Factor structure

A (very) good suitability of the data for EFA could be demonstrated by Bartlett's test (χ 2(36) = 2528.94, P < 0.001) on STREDIS-A items and KMO criterion of 0.9 overall and 0.84–0.94 for individual items (Tabachnik & Fidell, 2013, p. 6). Visual scree test and MAP criterion indicated that two factors should be retained (eigenvaluefactor1 = 5.22, eigenvaluefactor2 = 1.1; minimum Velicer MAP of 0.05). Item communalities were 0.5–0.86. Two factors showed an intercorrelation of r = 0.67 and explained a cumulative variance of 0.62 (variancefactor1 = 0.4). Factor loadings ranged from 0.53 to 0.9 (factor1) and 0.47 to 1.0 (factor2).

CFA on the 2-factorial model returned mixed results of excellent fit by CFI (0.993) and TLI (0.990), acceptable fit by SRMR (0.056), and poor fit by χ 2/df ratio (5.04; (χ 2(26) = 131.08, P < 0.001)) and RMSEA (0.092). However, a two-factorial solution showed a significantly better fit to the data than a single-factor model (χ 2diff(1) = 40.82, P < 0.001; χ 2/df ratio = 7.75 [χ 2(27) = 209.37, P < 0.001]; CFI = 0.988; TLI = 0.984; SRMR = 0.076; RMSEA = 0.119). The two factors correlated by r = 0.84. All standardized coefficients were significantly positive ranging from 0.74 to 0.93.

STREDIS-A items 3, 4, and 7 to 9 loaded highest on factor1 mirroring impending or manifest consequences due to VS. STREDIS-A items 1, 2, and 5 loaded highest on factor2 resembling cognitive-behavioral symptoms of StrD (Fig. 2, Table 3). However, item 5 also loaded substantially on factor1 and thus was no clearly representative for factor2. Moderate correlations were found between items and time criterion (0.42 ≤ r ≤ 0.57, Table 4). Time criterion strongly correlated with total STREDIS-A (r = 0.66) and factor2-based subscale score (r = 0.66). Its correlation with subscale 1 was moderate (r = 0.58). Relative item-response frequencies are shown in Table 5.

Fig. 2.
Fig. 2.

Two-factorial structure of STREDIS-A and criterion validity

Individual STREDIS-A items are depicted on the left. Their EFA factor loadings with STREDIS-A factor 1 (negative consequences) and factor 2 (cognitive-behavioral symptoms of VS) are represented by different line types (thickest line: factor loading >0.9; medium line: factor loading >0.7; thinnest line: factor loading >0.5; dashed line: equivocal factor loading; for exact values see Table 3). The proportions of explained variance of both factors based on the EFA are given in the center. All EFA calculations are based on half of the sample. Significant Pearson correlation coefficients of the STREDIS-A sum score with all criteria except for Average VS frequency (per week) are shown on the right. For the frequency criterion, the Spearman rank correlation coefficient is presented. Correlation calculations were performed on the complete sample. All factor loadings and correlations were significant (P < 0.001)

Abbreviations: VS = video streaming; STREDIS-A = Streaming Disorder Scale for Adolescents; (P)YDQ = (Parental) Young Diagnostic Questionnaire; PHQ = Patient Health Questionnaire; GAD = Generalized Anxiety Disorder Scale; R-ULS= Revised UCLA Loneliness Scale; ISI = Insomnia Severity Index; EFA = Exploratory Factor Analysis

Citation: Journal of Behavioral Addictions 11, 2; 10.1556/2006.2022.00041

Table 3.

Factorial analyses of STREDIS-A items

STREDIS-A itemaFactor 1 bFactor 2 bCommunalities
Item 1 EFA0.230.730.50
CFA-0.74-
Item 2 EFA−0.151.00.86
CFA-0.81-
Item 3 EFA0.530.180.44
CFA0.79--
Item 4 EFA0.560.280.61
CFA0.81-
Item 5 EFA0.400.470.63
CFA-0.93-
Item 6 EFA0.730.10.65
CFA0.83--
Item 7 EFA0.820.230.60
CFA0.76--
Item 8 EFA0.9−0.110.68
CFA0.85--
Item 9 EFA0.9−0.130.67
CFA0.86--

Notes: STREDIS-A = Streaming Disorder Scale for Adolescents; EFA = Explanatory Factor Analysis with promax rotation (based on split-half sub-sample of n1 = 479 dyads); CFA = Confirmatory Factor Analysis (based on split-half sub-sample of n2 = 480 dyads); factor 1 = negative consequences; factor 2 = cognitive-behavioral symptoms.

a for the description of the items, refer to Table 1; b (standardized) factor loadings are depicted.

Table 4.

Inter-item Pearson correlation of STREDIS-A itemsa

Notes: STREDIS-A = Streaming Disorder Scale for Adolescents.

a based on total sample of N = 959 adolescents; b for the description of items, refer to Table 1. The items of factor 2 are highlighted in gray.

Table 5.

Relative item-response frequency of STREDIS-A items (in %)a

Notes: STREDIS-A = Streaming Disorder Scale for Adolescents.

a based on the total sample of N = 959 adolescents; b for the description of items, refer to Table 1. The items of factor 2 are highlighted in gray.

Internal consistency

Total STREDIS-A showed excellent (Cronbach's α = 0.9, McDonald's ω = 0.93) and factor-based subscales good internal consistency (subscale 1: α = 0.89, ω = 0.91; subscale 2: α = 0.82, ω = 0.83).

Criterion validity

Strong positive correlations were found between STREDIS-A and (P)YDQ as well as PHQ-9 total scores. STREDIS-A total score correlated with mean daily VS time, GAD-2, and R-ULS total score in a moderate positive manner. Between STREDIS-A total score and weekly VS days, ISI total score, and grades sum, weak positive correlations were calculated. All correlations were significant with P < 0.001 (Fig. 2; right column).

Sensitivity and specificity

According to ROC curve analyses results on the two STREDIS-A subscales, the optimal cutoff for subscale 1 was 11.5 (95% CI 6.5–11.5) with a specificity of 90.7% (95% CI 73.85–93.17), sensitivity of 86.36% (95% CI 72.73–100.0), and AUC value of 92.1% (95% CI 87.3–96.9). Accuracy was 91.35%. For subscale 2 a cut-off value of 5.5 (95% CI 5.5–8.5) with a specificity of 64.03% (95% CI 59.02–90.39), sensitivity of 90.91% (95% CI 68.18–100.0), and AUC value of 84.8% (95% CI 75.8–93.8) was estimated based on Youden's criterion. However, due to low specificity and accuracy of only 62.25%, the more conservative cutoff of 6.5 was chosen with a specificity of 72.79%, sensitivity of 81.82%, and accuracy of 80.0% to avoid overestimation. AUC values indicated good to excellent differentiation.

Classification by cutoff values

4.7% (95% CI 3.4-6.0; N = 45) of the adolescent VS users were classified with StrD based on estimated cutoffs and ICD-11-time criterion. As expected per definition, adolescents with StrD differed from those without regarding their scores on STREDIS-A subscale 1 and 2 as well as the time criterion with large effect sizes. Except for age and number of VS days per week, all dependent variables ((P)YDQ, PHQ-9, GAD-2, ISI, R-ULS total scores and grades sum) were significant in the MANOVA on the two classified VS groups (Pillai score(1,782) = 0.24, F(10,773) = 24.65, P < 0.001; Table 6). Post-hoc Scheffé tests revealed large differences between both groups according to the mean VS time per day and the total scores of (P)YDQ, PHQ-9, GAD-2, and R-ULS with significantly higher values in the StrD group. Medium effect sizes were found for the significantly larger ISI total score and for grades sum in the StrD group.

Table 6.

Frequencies and statistical comparisons of adolescents with and without StrD

Variables

Frequency [95% CI]

Mean (SE)
No StrDStrDF-value (MANOVA)χ2/post-hoc Scheffé testsEffect sizea
absolute frequency91445---
relative frequency95.31 [93.97; 96.65]4.69 [3.35; 6.03]---
female sex in %47.59 [44.36; 50.83]33.33 [19.56; 47.11]-0.02 NS (P = 0.89)0.06
STREDIS-A subscale 1 sum score4.1 (0.13)15.6 (0.46)-11.5***2.96
STREDIS-A subscale 2 sum score4.51 (0.09)9.64 (0.27)-5.13***1.84
STREDIS-A time criterion score0.8 (0.02)2.24 (0.06)-1.45***2.52
age13.55 (0.07)13.53 (0.3)0.0 NS (P = 0.99)-0.01
YDQ sum score1.65 (0.06)5.12 (0.31)158.65***3.47***2.01
PYDQ sum score1.78 (0.06)5.38 (0.31)128.04***3.59***1.89
average number of VS days per week5.49 (0.06)5.87 (0.23)1.96 NS (P = 0.16)-0.21
average VS time per day [in minutes] b198.95 (18.84)337.67 (23.29)38.37***138.71***1.08
PHQ-9 sum score4.3 (0.15)12.44 (0.99)127.06***8.14***1.76
GAD-2 sum score0.83 (0.04)2.79 (0.31)96.02***1.96***1.53
R-ULS sum score11.34 (0.14)15.53 (0.62)37.16***4.19***0.98
ISI sum score8.32 (0.21)13.05 (1.02)17.91***4.73***0.74
grades sumcd6.34 (0.36)7.92 (0.47)16.26***1.58***0.65

Notes: StrD = Streaming Disorder; MANOVA = Multivariate Analysis of Variance; χ 2 = chi-square; CI = confidence interval; NS = not significant; SE = standard error of means; VS = video streaming; STREDIS-A = Streaming Disorder Scale for Adolescents; subscale 1 = negative consequences; subscale 2 = cognitive-behavioral symptoms; (P)YDQ = (Parental) Young Diagnostic Questionnaire; PHQ = Patient Health Questionnaire; GAD = Generalized Anxiety Disorder Scale; R-ULS = Revised UCLA Loneliness Scale; ISI = Insomnia Severity Index.

a based on Cramer's V for female sex and Cohen's d for all other variables; b Mean of VS per week (school) day and weekend (leisure) day [in minutes]; c Sum of school grades in mathematics, German, and first foreign language (each ranging 1–6, with higher scores indicating worse performance); d during past school term.

*** P ≤ 0.001.

Classification by LPA

LPA on STREDIS-A-based VS patterns with an ellipsoidal, equal volume and shape model preferred a model with three mutually exclusive and exhaustive latent profiles with lowest AIC, absolute BIC, and ICL values (Table 7). Moreover, a four-profile model did not fit the data significantly better than the three-profile one based on LRT. Both log likelihood values were identical. Thus, the null hypothesis stating that the smaller model was the best model, was not rejected.

Table 7.

Comparison of latent profile models

Notes: AIC = Akaike information criterion; BIC = Bayesian information criterion; ICL = Integrated Completed Likelihood; LRTS = likelihood ratio test score.

*** P ≤ 0.001 (based on bootstrapping with 999 replications).

Accordingly, the majority of frequent VS users was classified in profile 2 (Nprofile2 = 594; 61.9%), about one quarter in profile 3 (Nprofile3 = 258; 26.9%), and the smallest proportion in profile 1 (Nprofile1 = 59; 11.2%).

Except for age, the three profiles significantly differed regarding their VS patterns (as assessed by STREDIS-A and (P)YDQ) and VS time, their grades sum, as well as their total scores on PHQ-9, GAD-2, ISI, and R-ULS (MANOVA: Pillai score(2,781) = 1.09, Fapprox(26,1540) = 71.57, P < 0.001; Table 8). Moreover, profile1 showed a significantly lower proportion of girls compared to profile 3 with weak effect size whereas no significant difference was found for the other profile comparisons.

Table 8.

MANOVA and post-hoc tests on the three VS profiles based on LPAa

Variables

Frequency [95% CI]

Mean (SE)
Problematic streamers (PS)Intensive streamers (IS)Light streamers (LS)F-valueχ2/post-hoc Scheffé testsaEffect sizeb
absolute frequency107594258---
relative frequency in %11.16 [9.16; 13.15]61.94 [58.87; 65.01]26.9 [24.1; 29.71]---
--
--
female sex in %36.45 [27.33; 45.57]46.63 [42.62; 50.64]51.94 [45.84; 58.03]-3.4 NS (P = 0.07)0.07
6.67**0.14
1.82 NS (P = 0.18)0.05
STREDIS-A subscale 1 sum score11.1 (0.48)4.83 (0.16)1.52 (0.13)222.44***−6.27***1.52
−9.58***2.95
−3.31***0.94
STREDIS-A subscale 2 sum score8.29 (0.23)5.39 (0.09)1.82 (0.12)345.09***−2.9***1.25
−6.47***3.08
−3.57***1.62
STREDIS-A time criterion score2.19 (0.04)1 (0)0 (0)9827.96***−1.19***7.78
−2.19***10.33
−1***Inf
age13.22 (0.19)13.51 (0.09)13.77 (0.14)0.82 NS (P = 0.44)-0.13

0.25

0.12
YDQ sum score4.18 (0.21)1.89 (0.07)0.64 (0.07)180.79***−2.29***1.33-
−3.55***2.36
−1.25***0.83
PYDQ sum score4.14 (0.24)1.99 (0.08)0.96 (0.09)99.87***−2.15***1.11
−3.19***1.74
−1.03***0.59
number of VS days per week5.77 (0.15)5.71 (0.07)4.94 (0.13)14.89***−0.06 NS (P = 0.96)0.03
−0.83***0.43
−0.77***0.43
mean VS time per day [in minutes] c285.61 (14.16)209.21 (5.31)164.81 (6.95)34.81***−76.4***0.58
−120.8***0.98
−44.4***0.36
PHQ-9 sum score9.69 (0.59)4.69 (0.19)2.6 (0.2)84.22***−5***1.02
−7.09***1.64
−2.09***0.49
GAD-2 sum score2.17 (0.17)0.87 (0.05)0.5 (0.06)55.5***−1.29***0.97
−1.66***1.3
−0.37***0.32
R-ULS sum score14.34 (0.44)11.77 (0.18)9.83 (0.23)−2.56***0.59
46.14***−4.51***1.14
−1.94***0.47
ISI sum score11.34 (0.62)7.95 (0.25)8.71 (0.42)−3.39***0.54
8.01***−2.63**0.39
0.76 NS (P = 0.28)0.12
grades sumde7.23 (0.29)6.45 (0.1)6 (0.14)−0.78*0.31
8.67***−1.23***0.5
−0.45 NS (P = 0.07)0.19

Notes: MANOVA = Multivariate Analysis of Variance; VS = video streaming; LPA = Latent Profile Analysis; CI = confidence interval; NS = not significant; SE = standard error of the means: STREDIS-A=Streaming Disorder Scale for Adolescents; STREDIS-A subscale 1 = negative consequences; STREDIS-A subscale 2 = cognitive-behavioral symptoms; (P)YDQ = (Parental) Young Diagnostic Questionnaire; PHQ = Patient Health Questionnaire; GAD = Generalized Anxiety Disorder Scale; R-ULS = Revised UCLA Loneliness Scale; ISI = Insomnia Severity Index.

a post-hoc tests reported in the following sequence: PS- IS, PS – LS, IS – LS; b based on Cramer's V for female sex and Cohen's d for all other variables; c Mean of VS per week (school) day and weekend (leisure) day [in minutes]; d Sum of grades in mathematics, German, and first foreign language (each ranging 1–6, with higher scores indicating worse performance); e during past school term.

*** P ≤ 0.001, ** P ≤ 0.01, * P ≤ 0.05.

Of all profiles, profile1 had the highest scores on the STREDIS-A subscales which on average were both above the estimated cutoffs for StrD and reported problems with their VS patterns for longer periods or daily. All StrD values were significantly higher in profile1 compared to profile2 and 3. Profile3 showed significantly lower values than profile2 with large effect sizes. The same pattern could be found for (P)YDQ and the time of daily VS as well as for PHQ-9, GAD-2, and R-ULS total scores with medium to large effect sizes. On average, adolescents of profile3 significantly used VS services one day less than those of profiles1 and 2 with small effect sizes while no difference was found between profiles1 and 2. Significantly higher ISI total scores were revealed for adolescents of profile1 compared to profile2 and 3 with small to medium effect sizes while no difference was found between profiles2 and 3. Adolescents of profile1 showed the worst school performance compared to profile2 (small effect size) and profile3 (medium effect size) whereas profiles2 and 3 did not differ.

Discussion

The present study is the first to provide conceptualization for problematic streaming patterns in adolescents by applying the ICD-11 criteria of GD. As the first available tool for the assessment of StrD, the 10-item STREDIS-A was successfully validated in a representative adolescent-parent sample. It showed good to excellent internal consistency, criterion validity, and discriminatory power.

As in the original GADIS-A (Paschke et al., 2020) for the assessment of GD and the adapted SOMEDIS-A (Paschke et al., 2021b) for SMUD, the two-factorial structure of negative consequences (factor1) and cognitive-behavioral symptoms (factor2) could be replicated. Yet, item four (neglect of daily duties) did no longer load highest on factor2 but was assigned factor1. Moreover, although loading highest on factor2, item5 could not be unequivocally assigned to it. This suggests the loss of control over VS to be the main cognitive-behavioral symptom of StrD, while the increased priority given to VS, the acceptance of associated problems and the occurrence of impairments is rather a consequence covered by factor1. However, the lack of separation between attitudes towards VS and resulting impairments might be also due to item wording which does not distinguish between current and long-term perspectives.

In line with the altered factor definition, cutoff values changed towards higher thresholds for factor1 compared to factor2. Diverging cutoffs between instruments assessing usage patterns of different digital media are expected according to the concept of separate entities of behavioral addictions (Király et al., 2014). Since frequent VS is a leisure activity of almost 90% of 10- to 17-year-old adolescents, aspects on the control of usage alone are of limited use when trying to identify problematic VS patterns. As it is known from binge watching research, watching multiple series in a row (i.e, to the cost of control) is a typical viewing habit in the majority of users and, thus, not necessarily dysfunctional or an indicator of addictive viewing behavior (Flayelle, Maurage, Karila, Vögele, & Billieux, 2019; Tóth-Király et al., 2017). Noteworthy, the intensity of VS has even increased during the on-going COVID-19 pandemic (Dixit, Marthoenis, Arafat, Sharma, & Kar, 2020). Therefore, more emphasis needs to be laid on the occurrence of negative consequences to identify potential StrD. The two-factorial solution is in line with the biaxial model of addiction and the ICD-11 approach (Reed et al., 2019; Wartberg et al., 2017). Accordingly, a disordered usage pattern is characterized by “an impaired-control motivational dysfunction and a harmful consequence component involving negative social, psychological, and physical consequences of excessive use” (Wakefield, 2015). However, despite the strong clinical plausibility and the factorial results, the two factors are strongly correlated and therefore only limitedly distinct from a psychometric point of view. This favors the use of the total STREDIS-A score in multivariate analyses.

Based on the two-factorial concept, 4.69% (95% CI 3.35–6.03%) of adolescents with regular VS were classified with StrD. This prevalence is comparable to GD (3.7%, 95% CI 2.4–5.0%; Paschke et al., 2020) and SMUD (3.33%, 95% CI 2.18–4.48%; Paschke et al., 2021b) but also common adolescent mental disorders like anxieties (6.5%, 95% CI 4.7–9.1%), depression (2.6%, 95% CI 1.7–3.9%), attention deficit hyperactivity disorder (3.4%, 95% CI 2.6–4.5%), and disruptive disorders (5.7%, 95% CI 4.0–8.1%; Polanczyk, Salum, Sugaya, Caye, & Rohde, 2015). The StrD group streamed more than two hours longer per day than nonaffected regular users. Higher VS intensities have been reported for adult problematic binge-watchers (Flayelle, Canale, et al., 2019). Adolescents with StrD showed a moderate depressive symptom expression (Richardson et al., 2010), more symptoms of anxiety and insomnia and reported more loneliness and worse school performance compared to those without StrD. This is line with studies with (problematic) binge watchers (Anghelcev, Sar, Martin, & Moultrie, 2020; Flayelle et al., 2020; Raza et al., 2021; Starosta & Izydorczyk, 2020; Steins-Loeber, Reiter, Averbeck, Harbarth, & Brand, 2020) and studies with adolescents with problematic social media and Internet use (Barry, Sidoti, Briggs, Reiter, & Lindsey, 2017; Marttila, Koivula, & Räsänen, 2021; Pontes et al., 2021; Tsitsika et al., 2014) as well as higher screen times (Adelantado-Renau et al., 2019; Hale & Guan, 2015; Shenoi et al., 2022; Tremblay et al., 2011). Comparable results, although to a smaller extend, were found for problematic streamers according to the LPA results. This group made up 11.16% (95% CI 9.16–13.15%) of all regular streamers and included at-risk and disordered usage patterns. It showed lower scores on the negative-consequences subscale 1 suggesting at-risk VS to be a potential precursor to StrD.

Adolescents seem to be especially vulnerable to developing StrD due to immature cognitive-control abilities (Casey & Jones, 2010). Adolescent gamers with GD show a larger cognitive-affective imbalance than unaffected gamers (Schettler, Thomasius, & Paschke, 2021). This might be also the case in adolescents with StrD although further studies, including neuroimaging are highly warranted.

STREDIS-A is the first tool to reliably and validly screen for StrD to support standardized approaches in epidemiological, clinical, and neuroscientific research fields. It can be easily and cost-effectively used, e.g., prior to clinical appointments to induce proper treatments with the goal of reducing symptoms and preventing severe secondary impairments, comorbidities, or even chronicity.

Limitations

One major strength of this study is the large representative adolescent-parent sample. However, only households with Internet access (92%, of German households, Statista, 2022) and sufficient knowledge of the German language were included. Furthermore, missing data is a common problem in large online surveys, especially with younger adolescents, that might have affected representativity. No objective markers such as logged streaming times could be considered in the validation process. Since no standardized questionnaire to assess StrD in adolescents as a comparative measure of the new scale has been available yet, one of the most established instruments on the general concept of Internet addiction was applied: (P)YDQ has been broadly used in adolescents to also assess specific use patterns like problematic gaming and shows large similarities to DSM-5 internet gaming disorder criteria (Strittmatter et al., 2015). However, verification by a specialized clinician as the gold standard for concordant validity would have been desirable. Yet, bearing in mind, that this study is the first attempt to conceptualize problematic streaming patterns in adolescents, the newly introduced questionnaire provides a good starting point for further research. Future studies should enhance validation and add the evaluation of re-test reliability and potential cause-and-effect relationships by a longitudinal design. Moreover, they should further investigate the influence of different VS patterns (i.e., purely passive vs. mixed passive-interactive) and the use of different VS services (i.e., mono- vs. multipurpose, professional vs. user generated, live vs. on-demand).

Conclusions

The current study has the potential to make significant contributions to the conceptualization of addictive VS among adolescents. Based on the ICD-11 criteria of GD and the two-factorial approach of cognitive-behavioral symptoms and their negative consequences, STREDIS-A provides the first standardized measure to excellently distinguish StrD from unproblematic VS patterns with good to excellent internal consistency, reliability and criterion validity. This is of particular importance, on the one hand, to avoid the pathologization of a regular leisure activity in the majority of adolescents, and, on the other hand, to identify suffering adolescents in need of treatment at a stage of life that is particularly prone to negative consequences, mental disorders, and chronification.

Funding sources

The current study is part of a parent-child survey that was financially supported by the German health insurance company DAK Gesundheit.

Authors’ contribution

KP conceptualized and designed the study. KP and RT decided to publish the paper. KP performed the statistical analysis. All authors interpreted the data. KP drafted the manuscript. AKN performed literature review and prepared tables and figures, and formatting. All authors critically revised the draft for important intellectual content. All authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors gave final approval for the article to be published.

Conflict of interest

DAK Gesundheit had no role in the design of the study, collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results. The authors declare no conflict of interest.

Acknowledgements

We thank Maria Austermann for her efforts during study conceptualization. We thank Johanna Philippi for her support on preparing the data set.

References

  • Adelantado-Renau, M. , Moliner-Urdiales, D. , Cavero-Redondo, I. , Beltran-Valls, M. R. , Martínez-Vizcaíno, V. , & Álvarez-Bueno, C. (2019). Association between screen media use and academic performance among children and adolescents: A systematic review and meta-analysis. JAMA Pediatrics, 173(11), 1058. https://doi.org/10.1001/jamapediatrics.2019.3176.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Allgaier, A.-K. (2014). Diagnostische Güte von Testverfahren [Accuracy of diagnostic tests]. PPmP - Psychotherapie · Psychosomatik · Medizinische Psychologie, 64(02), 8687. https://doi.org/10.1055/s-0033-1360057.

    • Search Google Scholar
    • Export Citation
  • Anghelcev, G. , Sar, S. , Martin, J. , & Moultrie, J. L. (2020). Is heavy binge-watching a socially driven behaviour? Exploring differences between heavy, regular and non-binge-watchers. Journal of Digital Media & Policy, 121. https://doi.org/10.1386/jdmp_00035_1.

    • Search Google Scholar
    • Export Citation
  • APA (1994). Diagnostic and statistical Manual of mental disorders (DSM-IV) (5. Aufl.). American Psychiatric Association.

  • Balakrishnan, J. , & Griffiths, M. D. (2017). Social media addiction: What is the role of content in YouTube? Journal of Behavioral Addictions, 6(3), 364377. https://doi.org/10.1556/2006.6.2017.058.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Barry, C. T. , Sidoti, C. L. , Briggs, S. M. , Reiter, S. R. , & Lindsey, R. A. (2017). Adolescent social media use and mental health from adolescent and parent perspectives. Journal of Adolescence, 61, 111. https://doi.org/10.1016/j.adolescence.2017.08.005.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Bastien, C. , Vallières, A. , & Morin, C. M. (2001). Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Medicine, 2(4), 297307. https://doi.org/10.1016/S1389-9457(00)00065-4.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Beard, K. W. , & Wolf, E. M. (2001). Modification in the proposed diagnostic criteria for internet addiction. CyberPsychology & Behavior, 4(3), 377383. https://doi.org/10.1089/109493101300210286.

    • 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.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Buuren, S. van. , & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 167. https://doi.org/10.18637/jss.v045.i03.

    • Search Google Scholar
    • Export Citation
  • Casey, B. J. , & Jones, R. M. (2010). Neurobiology of the adolescent brain and behavior: Implications for substance use disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 49(12), 11891201. https://doi.org/10.1016/j.jaac.2010.08.017.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Cohen, J. (1988). The effect size index: D. Statistical power analysis for the behavioral sciences (Second Edition, S. 284–288). Lawrence Erlbaum Associates.

    • Search Google Scholar
    • Export Citation
  • Dancey, P. C. , & Reidy, J. (2011). Statistics without maths for psychology (5 edition). Prentice Hall.

  • Die medienanstalten (Hrsg.). (2021). Digitalisierungsbericht 2021: Video. die medienanstalten - ALM GbR. https://www.die-medienanstalten.de/publikationen/digitalisierungsbericht-video?tx_news_pi1%5Bnews%5D=4966&cHash=d01bfbc9aa8a91d2e71be1683396db46.

    • Search Google Scholar
    • Export Citation
  • Dixit, A. , Marthoenis, M. , Arafat, S. M. Y. , Sharma, P. , & Kar, S. K. (2020). Binge watching behavior during COVID 19 pandemic: A cross-sectional, cross-national online survey. Psychiatry Research, 289, 113089. https://doi.org/10.1016/j.psychres.2020.113089.

    • Search Google Scholar
    • Export Citation
  • Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge University Press. https://doi.org/10.1017/CBO9780511761676.

    • Search Google Scholar
    • Export Citation
  • Exelmans, L. , & Van den Bulck, J. (2017). Binge viewing, sleep, and the role of Pre-sleep arousal. Journal of Clinical Sleep Medicine, 13(08), 10011008. https://doi.org/10.5664/jcsm.6704.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Feierabend, S. , Rathgeb, T. , Kheredmand, H. , & Glöckler, S. (2021). JIM-Studie 2021 Medien—Basisstudie zum Medienumgang 12- bis 19-Jähriger in Deutschland. Medienpädagogischer Forschungsverbund Südwest. https://www.mpfs.de/fileadmin/files/Studien/JIM/2021/JIM-Studie_2021_barrierefrei.pdf.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • Flayelle, M. , Maurage, P. , Di Lorenzo, K. R. , Vögele, C. , Gainsbury, S. M. , & Billieux, J. (2020). Binge-Watching: What do we know so far? A first systematic review of the evidence. Current Addiction Reports, 7(1), 4460. https://doi.org/10.1007/s40429-020-00299-8.

    • Search Google Scholar
    • Export Citation
  • Flayelle, M. , Maurage, P. , Karila, L. , Vögele, C. , & Billieux, J. (2019). Overcoming the unitary exploration of binge-watching: A cluster analytical approach. Journal of Behavioral Addictions, 8(3), 586602. https://doi.org/10.1556/2006.8.2019.53.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Fletcher, T. D. (2012). QuantPsyc: Quantitative psychology tools. R package version 1.5. https://CRAN.R-project.org/package=QuantPsyc.

  • 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.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Gerber, M. , Lang, C. , Lemola, S. , Colledge, F. , Kalak, N. , Holsboer-Trachsler, E. , … Brand, S. (2016). Validation of the German version of the insomnia severity index in adolescents, young adults and adult workers: Results from three cross-sectional studies. BMC Psychiatry, 16(1), 174. https://doi.org/10.1186/s12888-016-0876-8.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Griffiths, M. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10(4), 191197. https://doi.org/10.1080/14659890500114359.

    • Search Google Scholar
    • Export Citation
  • Hale, L. , & Guan, S. (2015). Screen time and sleep among school-aged children and adolescents: A systematic literature review. Sleep Medicine Reviews, 21, 5058. https://doi.org/10.1016/j.smrv.2014.07.007.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Hooper, D. , Coughlan, J. , & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 5360.

    • Search Google Scholar
    • Export Citation
  • Howard, M. C. (2016). A review of exploratory factor Analysis decisions and overview of current practices: What we are doing and how can we improve? International Journal of Human-Computer Interaction, 32(1), 5162. https://doi.org/10.1080/10447318.2015.1087664.

    • Search Google Scholar
    • Export Citation
  • Hudiyana, J. , Lincoln, T. M. , Hartanto, S. , Shadiqi, M. A. , Milla, M. N. , Muluk, H. , & Jaya, E. S. (2021). How Universal Is a Construct of Loneliness? Measurement Invariance of the UCLA Loneliness Scale in Indonesia, Germany, and the United States. Assessment. https://doi.org/10.1177/10731911211034564.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Jedidi, K. , Jagpal, H. S. , & DeSarbo, W. S. (1997). Finite-Mixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Science, 16(1), 3959. https://doi.org/10.1287/mksc.16.1.39.

    • Search Google Scholar
    • Export Citation
  • Kim, H.-Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52. https://doi.org/10.5395/rde.2013.38.1.52.

    • Search Google Scholar
    • Export Citation
  • Király, O. , Griffiths, M. D. , Urban, R. , Farkas, J. , Kokonyei, G. , Elekes, Z. , & Demetrovics, Z. (2014). Problematic internet use and problematic online gaming are not the same: Findings from a large nationally representative adolescent sample. Cyberpsychology, Behavior and Social Networking, 17(12), 749754. https://doi.org/10.1089/cyber.2014.0475.

    • Search Google Scholar
    • Export Citation
  • Kroenke, K. , Spitzer, R. L. , & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Kroenke, K. , Spitzer, R. L. , Williams, J. B. W. , Monahan, P. O. , & Löwe, B. (2007). Anxiety disorders in primary care: Prevalence, impairment, comorbidity, and detection. Annals of Internal Medicine, 146(5), 317. https://doi.org/10.7326/0003-4819-146-5-200703060-00004.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Kuhn, M. , Chow, F. , & Wickham, H. (2019). rsample: General resampling infrastructure. https://CRAN.R-project.org/package=rsample.

  • Li, Y. , Zhang, X. , Lu, F. , Zhang, Q. , & Wang, Y. (2014). Internet addiction among elementary and middle school students in China: A nationally representative sample study. Cyberpsychology, Behavior, and Social Networking, 17(2), 111116. https://doi.org/10.1089/cyber.2012.0482.

    • Search Google Scholar
    • Export Citation
  • Marttila, E. , Koivula, A. , & Räsänen, P. (2021). Does excessive social media use decrease subjective well-being? A longitudinal analysis of the relationship between problematic use, loneliness and life satisfaction. Telematics and Informatics, 59, 101556. https://doi.org/10.1016/j.tele.2020.101556.

    • Search Google Scholar
    • Export Citation
  • Matrix, S. (2014). The Netflix effect: Teens, binge watching, and on-demand digital media trends. Jeunesse: Young People, Texts, Cultures, 6(1), 119138. https://doi.org/10.1353/jeu.2014.0002.

    • Search Google Scholar
    • Export Citation
  • Neto, F. (1992). Loneliness among Portuguese adolescents. Social Behavior and Personality, 20(1), 1521. https://doi.org/10.2224/sbp.1992.20.1.15.

    • Search Google Scholar
    • Export Citation
  • Nunnally, J. C. , & Bernstein, I. H. (1994). Psychometric theory. McGraw-Hill.

  • Nylund, K. L. , Asparouhov, T. , & Muthén, B. O. (2007). Deciding on the number of classes in latent class Analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535569. https://doi.org/10.1080/10705510701575396.

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

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Ort, A. , Wirz, D. S. , & Fahr, A. (2021). Is binge-watching addictive? Effects of motives for TV series use on the relationship between excessive media consumption and problematic viewing habits. Addictive Behaviors Reports, 13, 100325. https://doi.org/10.1016/j.abrep.2020.100325.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Panda, S. , & Pandey, S. C. (2017). Binge watching and college students: Motivations and outcomes. Young Consumers, 18(4), 425438. https://doi.org/10.1108/YC-07-2017-00707.

    • Search Google Scholar
    • Export Citation
  • Parodi, K. B. , Holt, M. K. , Green, J. G. , Porche, M. V. , Koenig, B. , & Xuan, Z. (2022). Time trends and disparities in anxiety among adolescents, 2012–2018. Social Psychiatry and Psychiatric Epidemiology, 57(1), 127137. https://doi.org/10.1007/s00127-021-02122-9.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Paschke, K. , Austermann, M. I. , & Thomasius, R. (2020). Assessing ICD-11 gaming disorder in adolescent gamers: Development and validation of the gaming disorder scale for adolescents (GADIS-A). Journal of Clinical Medicine, 9(4), 993. https://doi.org/10.3390/jcm9040993.

    • Search Google Scholar
    • Export Citation
  • Paschke, K. , Austermann, M. I. , & Thomasius, R. (2021a). Assessing ICD-11 gaming disorder in adolescent gamers by parental ratings: Development and validation of the Gaming Disorder Scale for Parents (GADIS-P). Journal of Behavioral Addictions, 10(1), 159168. https://doi.org/10.1556/2006.2020.00105.

    • Search Google Scholar
    • Export Citation
  • Paschke, K. , Austermann, M. I. , & Thomasius, R. (2021b). ICD-11-Based assessment of social media use disorder in adolescents: Development and validation of the social media use disorder scale for adolescents. Frontiers in Psychiatry, 12, 661483. https://doi.org/10.3389/fpsyt.2021.661483.

    • Search Google Scholar
    • Export Citation
  • Polanczyk, G. V. , Salum, G. A. , Sugaya, L. S. , Caye, A. , & Rohde, L. A. (2015). Annual research review: A meta-analysis of the worldwide prevalence of mental disorders in children and adolescents. Journal of Child Psychology and Psychiatry, 56(3), 345365. https://doi.org/10.1111/jcpp.12381.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Pontes, H. M. , Schivinski, B. , Sindermann, C. , Li, M. , Becker, B. , Zhou, M. , & Montag, C. (2021). Measurement and conceptualization of gaming disorder according to the world health organization framework: The development of the gaming disorder test. International Journal of Mental Health and Addiction, 19, 508528. https://doi.org/10.1007/s11469-019-00088-z.

    • Search Google Scholar
    • Export Citation
  • R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/.

    • Search Google Scholar
    • Export Citation
  • Raza, S. H. , Yousaf, M. , Sohail, F. , Munawar, R. , Ogadimma, E. C. , & Marisa Lim Dao Siang, J. (2021). Investigating binge-watching adverse mental health outcomes during covid-19 pandemic: Moderating role of screen time for web series using online streaming. Psychology Research and Behavior Management, 14, 16151629. https://doi.org/10.2147/PRBM.S328416.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Reed, G. M. , First, M. B. , Kogan, C. S. , Hyman, S. E. , Gureje, O. , Gaebel, W. , …, Saxena, S. (2019). Innovations and changes in the ICD-11 classification of mental, behavioural and neurodevelopmental disorders. World Psychiatry, 18(1), 319. https://doi.org/10.1002/wps.20611.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Revelle, W. (2018). psych: Procedures for psychological, psychometric, and personality research. Northwestern University. https://CRAN.R-project.org/package=psych.

    • Search Google Scholar
    • Export Citation
  • Richardson, L. P. , McCauley, E. , Grossman, D. C. , McCarty, C. A. , Richards, J. , Russo, J. E. , … Katon, W. (2010). Evaluation of the Patient health questionnaire-9 item for detecting major depression among adolescents. Pediatrics, 126(6), 11171123. https://doi.org/10.1542/peds.2010-0852.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Robin, X. , Turck, N. , Hainard, A. , Tiberti, N. , Lisacek, F. , Sanchez, J.-C. , & Müller, M. (2011). PROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, 77. https://doi.org/10.1186/1471-2105-12-77.

    • Search Google Scholar
    • Export Citation
  • Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 136.

  • Russell, D. , Peplau, L. A. , & Cutrona, C. E. (1980). The revised UCLA Loneliness Scale: Concurrent and discriminant validity evidence. Journal of Personality and Social Psychology, 39(3), 472480. https://doi.org/10.1037/0022-3514.39.3.472.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Satorra, A. , & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507514. https://doi.org/10.1007/BF02296192.

    • Search Google Scholar
    • Export Citation
  • Schettler, L. , Thomasius, R. , & Paschke, K. (2021). Neural correlates of problematic gaming in adolescents: A systematic review of structural and functional magnetic resonance imaging studies. Addiction Biology. https://doi.org/10.1111/adb.13093.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Scrucca, L. , Fop, M. , Murphy, T. B. , & Raftery, A. E. (2016). Mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models. The R Journal, 8(1), 205233.

    • Search Google Scholar
    • Export Citation
  • Shenoi, R. P. , Linakis, J. G. , Bromberg, J. R. , Casper, T. C. , Richards, R. , Chun, T. H. , & Spirito, A. (2022). Association of physical activity, sports, and screen time with adolescent behaviors in youth who visit the Pediatric emergency department. Clinical Pediatrics. https://doi.org/10.1177/00099228221075094.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Shim, H. , & Kim, K. J. (2018). An exploration of the motivations for binge-watching and the role of individual differences. Computers in Human Behavior, 82, 94100. https://doi.org/10.1016/j.chb.2017.12.032.

    • Search Google Scholar
    • Export Citation
  • Smith, T. , & Short, A. (2022). Needs affordance as a key factor in likelihood of problematic social media use: Validation, latent Profile analysis and comparison of TikTok and Facebook problematic use measures. Addictive Behaviors, 129, 107259. https://doi.org/10.1016/j.addbeh.2022.107259.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Spilker, H. S. , Ask, K. , & Hansen, M. (2020). The new practices and infrastructures of participation: How the popularity of Twitch.tv challenges old and new ideas about television viewing. Information, Communication & Society, 23(4), 605620. https://doi.org/10.1080/1369118X.2018.1529193.

    • Search Google Scholar
    • Export Citation
  • Spilker, H. S. , & Colbjørnsen, T. (2020). The dimensions of streaming: Toward a typology of an evolving concept. Media, Culture & Society, 42(7–8), 12101225. https://doi.org/10.1177/0163443720904587.

    • Search Google Scholar
    • Export Citation
  • Starosta, J. , & Izydorczyk, B. (2020). Understanding the phenomenon of binge-watching—a systematic review. International Journal of Environmental Research and Public Health, 17(12), 4469. https://doi.org/10.3390/ijerph17124469.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • Starosta, J. , Izydorczyk, B. , & Wontorczyk, A. (2021). Anxiety-depressive syndrome and binge-watching among young adults. Frontiers in Psychology, 12, 689944. https://doi.org/10.3389/fpsyg.2021.689944.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • statista (2022 , Januar 24). Internetzugang in deutschen Haushalten [Internet Access in German households]. Anteil der Haushalte in Deutschland mit Internetzugang von 2002 bis 2021. https://de.statista.com/statistik/daten/studie/153257/umfrage/haushalte-mit-internetzugang-in-deutschland-seit-2002/.

    • Search Google Scholar
    • Export Citation
  • Statista (2022 , Januar). Anzahl der zahlenden Streaming-Abonnenten von Netflix weltweit vom 3. Quartal 2011 bis zum 4. Quartal 2021. https://de.statista.com/statistik/daten/studie/196642/umfrage/abonnenten-von-netflix-quartalszahlen/.

    • Search Google Scholar
    • Export Citation
  • Steins-Loeber, S. , Reiter, T. , Averbeck, H. , Harbarth, L. , & Brand, M. (2020). Binge-watching behaviour: The role of impulsivity and depressive symptoms. European Addiction Research, 26(3), 141150. https://doi.org/10.1159/000506307.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Sun, J.-J. , & Chang, Y.-J. (2021). Associations of problematic binge-watching with depression, social interaction anxiety, and loneliness. International Journal of Environmental Research and Public Health, 18(3), 1168. https://doi.org/10.3390/ijerph18031168.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Tabachnik, B. G. , & Fidell, L. S. (2013). Using multivariate statistics. Harlow: Pearson Education Limited, 6.

  • Tóth-Király, I. , Bőthe, B. , Tóth-Fáber, E. , Hága, G. , & Orosz, G. (2017). Connected to TV series: Quantifying series watching engagement. Journal of Behavioral Addictions, 6(4), 472489. https://doi.org/10.1556/2006.6.2017.083.

    • Search Google Scholar
    • Export Citation
  • Tremblay, M. S. , LeBlanc, A. G. , Kho, M. E. , Saunders, T. J. , Larouche, R. , Colley, R. C. , … Gorber, S. (2011). Systematic review of sedentary behaviour and health indicators in school-aged children and youth. International Journal of Behavioral Nutrition and Physical Activity, 8(1), 98. https://doi.org/10.1186/1479-5868-8-98.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Tsitsika, A. K. , Tzavela, E. C. , Janikian, M. , Ólafsson, K. , Iordache, A. , Schoenmakers, T. M. , … Richardson, C. (2014). Online social networking in adolescence: Patterns of use in six European countries and links with Psychosocial functioning. Journal of Adolescent Health, 55(1), 141147. https://doi.org/10.1016/j.jadohealth.2013.11.010.

    • Search Google Scholar
    • Export Citation
  • Velicer, W. F. , Eaton, C. A. , & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative Procedures for determining the number of factors or components. In R. D. Goffin , & E. Helmes (Hrsg.), (Eds.), Problems and Solutions in human assessment: Honoring Douglas N. Jackson at Seventy (S. 41–71). Springer US. https://doi.org/10.1007/978-1-4615-4397-8_3.

    • Search Google Scholar
    • Export Citation
  • Wakefield, J. C. (2015). DSM-5 substance use disorder: How conceptual missteps weakened the foundations of the addictive disorders field. Acta Psychiatrica Scandinavica, 132(5), 327334. https://doi.org/10.1111/acps.12446.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Wartberg, L. , Durkee, T. , Kriston, L. , Parzer, P. , Fischer-Waldschmidt, G. , Resch, F. , … Kaess, M. (2017). Psychometric properties of a German version of the young diagnostic questionnaire (YDQ) in two independent samples of adolescents. International Journal of Mental Health, 15(1), 182190. https://doi.org/10.1007/s11469-016-9654-6.

    • Search Google Scholar
    • Export Citation
  • Wartberg, L. , Kriston, L. , Kegel, K. , & Thomasius, R. (2016). Adaptation and psychometric evaluation of the young diagnostic questionnaire (YDQ) for parental assessment of adolescent problematic internet use. Journal of Behavioral Addictions, 5(2), 311317. https://doi.org/10.1556/2006.5.2016.049.

    • Search Google Scholar
    • Export Citation
  • Watkins, M. W. (2017). The reliability of multidimensional neuropsychological measures: From alpha to omega. The Clinical Neuropsychologist, 31(6–7), 11131126. https://doi.org/10.1080/13854046.2017.1317364.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • World Health Organization (2018). International classification of diseases for mortality and morbidity statistics (11th Revision). https://icd.who.int/browse11/l-m/en.

    • Search Google Scholar
    • Export Citation
  • Yang, C.-C. (2006). Evaluating latent class analysis models in qualitative phenotype identification. Computational Statistics & Data Analysis, 50(4), 10901104. https://doi.org/10.1016/j.csda.2004.11.004.

    • Search Google Scholar
    • Export Citation
  • Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior, 1(3), 237244. https://doi.org/10.1089/cpb.1998.1.237.

    • Search Google Scholar
    • Export Citation
  • Adelantado-Renau, M. , Moliner-Urdiales, D. , Cavero-Redondo, I. , Beltran-Valls, M. R. , Martínez-Vizcaíno, V. , & Álvarez-Bueno, C. (2019). Association between screen media use and academic performance among children and adolescents: A systematic review and meta-analysis. JAMA Pediatrics, 173(11), 1058. https://doi.org/10.1001/jamapediatrics.2019.3176.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Allgaier, A.-K. (2014). Diagnostische Güte von Testverfahren [Accuracy of diagnostic tests]. PPmP - Psychotherapie · Psychosomatik · Medizinische Psychologie, 64(02), 8687. https://doi.org/10.1055/s-0033-1360057.

    • Search Google Scholar
    • Export Citation
  • Anghelcev, G. , Sar, S. , Martin, J. , & Moultrie, J. L. (2020). Is heavy binge-watching a socially driven behaviour? Exploring differences between heavy, regular and non-binge-watchers. Journal of Digital Media & Policy, 121. https://doi.org/10.1386/jdmp_00035_1.

    • Search Google Scholar
    • Export Citation
  • APA (1994). Diagnostic and statistical Manual of mental disorders (DSM-IV) (5. Aufl.). American Psychiatric Association.

  • Balakrishnan, J. , & Griffiths, M. D. (2017). Social media addiction: What is the role of content in YouTube? Journal of Behavioral Addictions, 6(3), 364377. https://doi.org/10.1556/2006.6.2017.058.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Barry, C. T. , Sidoti, C. L. , Briggs, S. M. , Reiter, S. R. , & Lindsey, R. A. (2017). Adolescent social media use and mental health from adolescent and parent perspectives. Journal of Adolescence, 61, 111. https://doi.org/10.1016/j.adolescence.2017.08.005.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Bastien, C. , Vallières, A. , & Morin, C. M. (2001). Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Medicine, 2(4), 297307. https://doi.org/10.1016/S1389-9457(00)00065-4.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Beard, K. W. , & Wolf, E. M. (2001). Modification in the proposed diagnostic criteria for internet addiction. CyberPsychology & Behavior, 4(3), 377383. https://doi.org/10.1089/109493101300210286.

    • 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.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Buuren, S. van. , & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 167. https://doi.org/10.18637/jss.v045.i03.

    • Search Google Scholar
    • Export Citation
  • Casey, B. J. , & Jones, R. M. (2010). Neurobiology of the adolescent brain and behavior: Implications for substance use disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 49(12), 11891201. https://doi.org/10.1016/j.jaac.2010.08.017.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Cohen, J. (1988). The effect size index: D. Statistical power analysis for the behavioral sciences (Second Edition, S. 284–288). Lawrence Erlbaum Associates.

    • Search Google Scholar
    • Export Citation
  • Dancey, P. C. , & Reidy, J. (2011). Statistics without maths for psychology (5 edition). Prentice Hall.

  • Die medienanstalten (Hrsg.). (2021). Digitalisierungsbericht 2021: Video. die medienanstalten - ALM GbR. https://www.die-medienanstalten.de/publikationen/digitalisierungsbericht-video?tx_news_pi1%5Bnews%5D=4966&cHash=d01bfbc9aa8a91d2e71be1683396db46.

    • Search Google Scholar
    • Export Citation
  • Dixit, A. , Marthoenis, M. , Arafat, S. M. Y. , Sharma, P. , & Kar, S. K. (2020). Binge watching behavior during COVID 19 pandemic: A cross-sectional, cross-national online survey. Psychiatry Research, 289, 113089. https://doi.org/10.1016/j.psychres.2020.113089.

    • Search Google Scholar
    • Export Citation
  • Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge University Press. https://doi.org/10.1017/CBO9780511761676.

    • Search Google Scholar
    • Export Citation
  • Exelmans, L. , & Van den Bulck, J. (2017). Binge viewing, sleep, and the role of Pre-sleep arousal. Journal of Clinical Sleep Medicine, 13(08), 10011008. https://doi.org/10.5664/jcsm.6704.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Feierabend, S. , Rathgeb, T. , Kheredmand, H. , & Glöckler, S. (2021). JIM-Studie 2021 Medien—Basisstudie zum Medienumgang 12- bis 19-Jähriger in Deutschland. Medienpädagogischer Forschungsverbund Südwest. https://www.mpfs.de/fileadmin/files/Studien/JIM/2021/JIM-Studie_2021_barrierefrei.pdf.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • Flayelle, M. , Maurage, P. , Di Lorenzo, K. R. , Vögele, C. , Gainsbury, S. M. , & Billieux, J. (2020). Binge-Watching: What do we know so far? A first systematic review of the evidence. Current Addiction Reports, 7(1), 4460. https://doi.org/10.1007/s40429-020-00299-8.

    • Search Google Scholar
    • Export Citation
  • Flayelle, M. , Maurage, P. , Karila, L. , Vögele, C. , & Billieux, J. (2019). Overcoming the unitary exploration of binge-watching: A cluster analytical approach. Journal of Behavioral Addictions, 8(3), 586602. https://doi.org/10.1556/2006.8.2019.53.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Fletcher, T. D. (2012). QuantPsyc: Quantitative psychology tools. R package version 1.5. https://CRAN.R-project.org/package=QuantPsyc.

  • 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.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Gerber, M. , Lang, C. , Lemola, S. , Colledge, F. , Kalak, N. , Holsboer-Trachsler, E. , … Brand, S. (2016). Validation of the German version of the insomnia severity index in adolescents, young adults and adult workers: Results from three cross-sectional studies. BMC Psychiatry, 16(1), 174. https://doi.org/10.1186/s12888-016-0876-8.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Griffiths, M. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10(4), 191197. https://doi.org/10.1080/14659890500114359.

    • Search Google Scholar
    • Export Citation
  • Hale, L. , & Guan, S. (2015). Screen time and sleep among school-aged children and adolescents: A systematic literature review. Sleep Medicine Reviews, 21, 5058. https://doi.org/10.1016/j.smrv.2014.07.007.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Hooper, D. , Coughlan, J. , & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 5360.

    • Search Google Scholar
    • Export Citation
  • Howard, M. C. (2016). A review of exploratory factor Analysis decisions and overview of current practices: What we are doing and how can we improve? International Journal of Human-Computer Interaction, 32(1), 5162. https://doi.org/10.1080/10447318.2015.1087664.

    • Search Google Scholar
    • Export Citation
  • Hudiyana, J. , Lincoln, T. M. , Hartanto, S. , Shadiqi, M. A. , Milla, M. N. , Muluk, H. , & Jaya, E. S. (2021). How Universal Is a Construct of Loneliness? Measurement Invariance of the UCLA Loneliness Scale in Indonesia, Germany, and the United States. Assessment. https://doi.org/10.1177/10731911211034564.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Jedidi, K. , Jagpal, H. S. , & DeSarbo, W. S. (1997). Finite-Mixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Science, 16(1), 3959. https://doi.org/10.1287/mksc.16.1.39.

    • Search Google Scholar
    • Export Citation
  • Kim, H.-Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52. https://doi.org/10.5395/rde.2013.38.1.52.

    • Search Google Scholar
    • Export Citation
  • Király, O. , Griffiths, M. D. , Urban, R. , Farkas, J. , Kokonyei, G. , Elekes, Z. , & Demetrovics, Z. (2014). Problematic internet use and problematic online gaming are not the same: Findings from a large nationally representative adolescent sample. Cyberpsychology, Behavior and Social Networking, 17(12), 749754. https://doi.org/10.1089/cyber.2014.0475.

    • Search Google Scholar
    • Export Citation
  • Kroenke, K. , Spitzer, R. L. , & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Kroenke, K. , Spitzer, R. L. , Williams, J. B. W. , Monahan, P. O. , & Löwe, B. (2007). Anxiety disorders in primary care: Prevalence, impairment, comorbidity, and detection. Annals of Internal Medicine, 146(5), 317. https://doi.org/10.7326/0003-4819-146-5-200703060-00004.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Kuhn, M. , Chow, F. , & Wickham, H. (2019). rsample: General resampling infrastructure. https://CRAN.R-project.org/package=rsample.

  • Li, Y. , Zhang, X. , Lu, F. , Zhang, Q. , & Wang, Y. (2014). Internet addiction among elementary and middle school students in China: A nationally representative sample study. Cyberpsychology, Behavior, and Social Networking, 17(2), 111116. https://doi.org/10.1089/cyber.2012.0482.

    • Search Google Scholar
    • Export Citation
  • Marttila, E. , Koivula, A. , & Räsänen, P. (2021). Does excessive social media use decrease subjective well-being? A longitudinal analysis of the relationship between problematic use, loneliness and life satisfaction. Telematics and Informatics, 59, 101556. https://doi.org/10.1016/j.tele.2020.101556.

    • Search Google Scholar
    • Export Citation
  • Matrix, S. (2014). The Netflix effect: Teens, binge watching, and on-demand digital media trends. Jeunesse: Young People, Texts, Cultures, 6(1), 119138. https://doi.org/10.1353/jeu.2014.0002.

    • Search Google Scholar
    • Export Citation
  • Neto, F. (1992). Loneliness among Portuguese adolescents. Social Behavior and Personality, 20(1), 1521. https://doi.org/10.2224/sbp.1992.20.1.15.

    • Search Google Scholar
    • Export Citation
  • Nunnally, J. C. , & Bernstein, I. H. (1994). Psychometric theory. McGraw-Hill.

  • Nylund, K. L. , Asparouhov, T. , & Muthén, B. O. (2007). Deciding on the number of classes in latent class Analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535569. https://doi.org/10.1080/10705510701575396.

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

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Ort, A. , Wirz, D. S. , & Fahr, A. (2021). Is binge-watching addictive? Effects of motives for TV series use on the relationship between excessive media consumption and problematic viewing habits. Addictive Behaviors Reports, 13, 100325. https://doi.org/10.1016/j.abrep.2020.100325.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Panda, S. , & Pandey, S. C. (2017). Binge watching and college students: Motivations and outcomes. Young Consumers, 18(4), 425438. https://doi.org/10.1108/YC-07-2017-00707.

    • Search Google Scholar
    • Export Citation
  • Parodi, K. B. , Holt, M. K. , Green, J. G. , Porche, M. V. , Koenig, B. , & Xuan, Z. (2022). Time trends and disparities in anxiety among adolescents, 2012–2018. Social Psychiatry and Psychiatric Epidemiology, 57(1), 127137. https://doi.org/10.1007/s00127-021-02122-9.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Paschke, K. , Austermann, M. I. , & Thomasius, R. (2020). Assessing ICD-11 gaming disorder in adolescent gamers: Development and validation of the gaming disorder scale for adolescents (GADIS-A). Journal of Clinical Medicine, 9(4), 993. https://doi.org/10.3390/jcm9040993.

    • Search Google Scholar
    • Export Citation
  • Paschke, K. , Austermann, M. I. , & Thomasius, R. (2021a). Assessing ICD-11 gaming disorder in adolescent gamers by parental ratings: Development and validation of the Gaming Disorder Scale for Parents (GADIS-P). Journal of Behavioral Addictions, 10(1), 159168. https://doi.org/10.1556/2006.2020.00105.

    • Search Google Scholar
    • Export Citation
  • Paschke, K. , Austermann, M. I. , & Thomasius, R. (2021b). ICD-11-Based assessment of social media use disorder in adolescents: Development and validation of the social media use disorder scale for adolescents. Frontiers in Psychiatry, 12, 661483. https://doi.org/10.3389/fpsyt.2021.661483.

    • Search Google Scholar
    • Export Citation
  • Polanczyk, G. V. , Salum, G. A. , Sugaya, L. S. , Caye, A. , & Rohde, L. A. (2015). Annual research review: A meta-analysis of the worldwide prevalence of mental disorders in children and adolescents. Journal of Child Psychology and Psychiatry, 56(3), 345365. https://doi.org/10.1111/jcpp.12381.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Pontes, H. M. , Schivinski, B. , Sindermann, C. , Li, M. , Becker, B. , Zhou, M. , & Montag, C. (2021). Measurement and conceptualization of gaming disorder according to the world health organization framework: The development of the gaming disorder test. International Journal of Mental Health and Addiction, 19, 508528. https://doi.org/10.1007/s11469-019-00088-z.

    • Search Google Scholar
    • Export Citation
  • R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/.

    • Search Google Scholar
    • Export Citation
  • Raza, S. H. , Yousaf, M. , Sohail, F. , Munawar, R. , Ogadimma, E. C. , & Marisa Lim Dao Siang, J. (2021). Investigating binge-watching adverse mental health outcomes during covid-19 pandemic: Moderating role of screen time for web series using online streaming. Psychology Research and Behavior Management, 14, 16151629. https://doi.org/10.2147/PRBM.S328416.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Reed, G. M. , First, M. B. , Kogan, C. S. , Hyman, S. E. , Gureje, O. , Gaebel, W. , …, Saxena, S. (2019). Innovations and changes in the ICD-11 classification of mental, behavioural and neurodevelopmental disorders. World Psychiatry, 18(1), 319. https://doi.org/10.1002/wps.20611.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Revelle, W. (2018). psych: Procedures for psychological, psychometric, and personality research. Northwestern University. https://CRAN.R-project.org/package=psych.

    • Search Google Scholar
    • Export Citation
  • Richardson, L. P. , McCauley, E. , Grossman, D. C. , McCarty, C. A. , Richards, J. , Russo, J. E. , … Katon, W. (2010). Evaluation of the Patient health questionnaire-9 item for detecting major depression among adolescents. Pediatrics, 126(6), 11171123. https://doi.org/10.1542/peds.2010-0852.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Robin, X. , Turck, N. , Hainard, A. , Tiberti, N. , Lisacek, F. , Sanchez, J.-C. , & Müller, M. (2011). PROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, 77. https://doi.org/10.1186/1471-2105-12-77.

    • Search Google Scholar
    • Export Citation
  • Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 136.

  • Russell, D. , Peplau, L. A. , & Cutrona, C. E. (1980). The revised UCLA Loneliness Scale: Concurrent and discriminant validity evidence. Journal of Personality and Social Psychology, 39(3), 472480. https://doi.org/10.1037/0022-3514.39.3.472.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Satorra, A. , & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507514. https://doi.org/10.1007/BF02296192.

    • Search Google Scholar
    • Export Citation
  • Schettler, L. , Thomasius, R. , & Paschke, K. (2021). Neural correlates of problematic gaming in adolescents: A systematic review of structural and functional magnetic resonance imaging studies. Addiction Biology. https://doi.org/10.1111/adb.13093.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Scrucca, L. , Fop, M. , Murphy, T. B. , & Raftery, A. E. (2016). Mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models. The R Journal, 8(1), 205233.

    • Search Google Scholar
    • Export Citation
  • Shenoi, R. P. , Linakis, J. G. , Bromberg, J. R. , Casper, T. C. , Richards, R. , Chun, T. H. , & Spirito, A. (2022). Association of physical activity, sports, and screen time with adolescent behaviors in youth who visit the Pediatric emergency department. Clinical Pediatrics. https://doi.org/10.1177/00099228221075094.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Shim, H. , & Kim, K. J. (2018). An exploration of the motivations for binge-watching and the role of individual differences. Computers in Human Behavior, 82, 94100. https://doi.org/10.1016/j.chb.2017.12.032.

    • Search Google Scholar
    • Export Citation
  • Smith, T. , & Short, A. (2022). Needs affordance as a key factor in likelihood of problematic social media use: Validation, latent Profile analysis and comparison of TikTok and Facebook problematic use measures. Addictive Behaviors, 129, 107259. https://doi.org/10.1016/j.addbeh.2022.107259.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Spilker, H. S. , Ask, K. , & Hansen, M. (2020). The new practices and infrastructures of participation: How the popularity of Twitch.tv challenges old and new ideas about television viewing. Information, Communication & Society, 23(4), 605620. https://doi.org/10.1080/1369118X.2018.1529193.

    • Search Google Scholar
    • Export Citation
  • Spilker, H. S. , & Colbjørnsen, T. (2020). The dimensions of streaming: Toward a typology of an evolving concept. Media, Culture & Society, 42(7–8), 12101225. https://doi.org/10.1177/0163443720904587.

    • Search Google Scholar
    • Export Citation
  • Starosta, J. , & Izydorczyk, B. (2020). Understanding the phenomenon of binge-watching—a systematic review. International Journal of Environmental Research and Public Health, 17(12), 4469. https://doi.org/10.3390/ijerph17124469.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • Starosta, J. , Izydorczyk, B. , & Wontorczyk, A. (2021). Anxiety-depressive syndrome and binge-watching among young adults. Frontiers in Psychology, 12, 689944. https://doi.org/10.3389/fpsyg.2021.689944.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • statista (2022 , Januar 24). Internetzugang in deutschen Haushalten [Internet Access in German households]. Anteil der Haushalte in Deutschland mit Internetzugang von 2002 bis 2021. https://de.statista.com/statistik/daten/studie/153257/umfrage/haushalte-mit-internetzugang-in-deutschland-seit-2002/.

    • Search Google Scholar
    • Export Citation
  • Statista (2022 , Januar). Anzahl der zahlenden Streaming-Abonnenten von Netflix weltweit vom 3. Quartal 2011 bis zum 4. Quartal 2021. https://de.statista.com/statistik/daten/studie/196642/umfrage/abonnenten-von-netflix-quartalszahlen/.

    • Search Google Scholar
    • Export Citation
  • Steins-Loeber, S. , Reiter, T. , Averbeck, H. , Harbarth, L. , & Brand, M. (2020). Binge-watching behaviour: The role of impulsivity and depressive symptoms. European Addiction Research, 26(3), 141150. https://doi.org/10.1159/000506307.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Sun, J.-J. , & Chang, Y.-J. (2021). Associations of problematic binge-watching with depression, social interaction anxiety, and loneliness. International Journal of Environmental Research and Public Health, 18(3), 1168. https://doi.org/10.3390/ijerph18031168.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Tabachnik, B. G. , & Fidell, L. S. (2013). Using multivariate statistics. Harlow: Pearson Education Limited, 6.

  • Tóth-Király, I. , Bőthe, B. , Tóth-Fáber, E. , Hága, G. , & Orosz, G. (2017). Connected to TV series: Quantifying series watching engagement. Journal of Behavioral Addictions, 6(4), 472489. https://doi.org/10.1556/2006.6.2017.083.

    • Search Google Scholar
    • Export Citation
  • Tremblay, M. S. , LeBlanc, A. G. , Kho, M. E. , Saunders, T. J. , Larouche, R. , Colley, R. C. , … Gorber, S. (2011). Systematic review of sedentary behaviour and health indicators in school-aged children and youth. International Journal of Behavioral Nutrition and Physical Activity, 8(1), 98. https://doi.org/10.1186/1479-5868-8-98.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Tsitsika, A. K. , Tzavela, E. C. , Janikian, M. , Ólafsson, K. , Iordache, A. , Schoenmakers, T. M. , … Richardson, C. (2014). Online social networking in adolescence: Patterns of use in six European countries and links with Psychosocial functioning. Journal of Adolescent Health, 55(1), 141147. https://doi.org/10.1016/j.jadohealth.2013.11.010.

    • Search Google Scholar
    • Export Citation
  • Velicer, W. F. , Eaton, C. A. , & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative Procedures for determining the number of factors or components. In R. D. Goffin , & E. Helmes (Hrsg.), (Eds.), Problems and Solutions in human assessment: Honoring Douglas N. Jackson at Seventy (S. 41–71). Springer US. https://doi.org/10.1007/978-1-4615-4397-8_3.

    • Search Google Scholar
    • Export Citation
  • Wakefield, J. C. (2015). DSM-5 substance use disorder: How conceptual missteps weakened the foundations of the addictive disorders field. Acta Psychiatrica Scandinavica, 132(5), 327334. https://doi.org/10.1111/acps.12446.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Wartberg, L. , Durkee, T. , Kriston, L. , Parzer, P. , Fischer-Waldschmidt, G. , Resch, F. , … Kaess, M. (2017). Psychometric properties of a German version of the young diagnostic questionnaire (YDQ) in two independent samples of adolescents. International Journal of Mental Health, 15(1), 182190. https://doi.org/10.1007/s11469-016-9654-6.

    • Search Google Scholar
    • Export Citation
  • Wartberg, L. , Kriston, L. , Kegel, K. , & Thomasius, R. (2016). Adaptation and psychometric evaluation of the young diagnostic questionnaire (YDQ) for parental assessment of adolescent problematic internet use. Journal of Behavioral Addictions, 5(2), 311317. https://doi.org/10.1556/2006.5.2016.049.

    • Search Google Scholar
    • Export Citation
  • Watkins, M. W. (2017). The reliability of multidimensional neuropsychological measures: From alpha to omega. The Clinical Neuropsychologist, 31(6–7), 11131126. https://doi.org/10.1080/13854046.2017.1317364.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • World Health Organization (2018). International classification of diseases for mortality and morbidity statistics (11th Revision). https://icd.who.int/browse11/l-m/en.

    • Search Google Scholar
    • Export Citation
  • Yang, C.-C. (2006). Evaluating latent class analysis models in qualitative phenotype identification. Computational Statistics & Data Analysis, 50(4), 10901104. https://doi.org/10.1016/j.csda.2004.11.004.

    • Search Google Scholar
    • Export Citation
  • Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior, 1(3), 237244. https://doi.org/10.1089/cpb.1998.1.237.

    • Search Google Scholar
    • Export Citation
The author instruction is 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

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 850 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
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

  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Luke CLARK (University of British Columbia, Canada)
  • Daniel KING (Flinders University, Australia)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • H. N. Alexander LOGEMANN (ELTE Eötvös Loránd University, Hungary)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)
  • Attila SZABÓ (ELTE Eötvös Loránd University, Hungary)
  • Róbert URBÁN (ELTE Eötvös Loránd University, Hungary)
  • Aviv M. WEINSTEIN (Ariel University, Israel)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • 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)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Humboldt-Universität zu Berlin, Germany)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • 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)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Kimberly YOUNG (Center for Internet Addiction, USA)