Digital Addiction Scale (DAS-Digital)

1. Abstract

The Digital Addiction Scale (DAS-Digital) is a psychometrically validated, brief screening instrument designed to evaluate problematic and addictive patterns of interaction with digital technology, social media platforms, and connected communication devices. Grounded theoretically in Mark D. Griffiths‘ seminal six-component addiction framework, the DAS-Digital operationalizes behavioral addiction criteria originally adapted from clinical substance-use and impulse-control paradigms into six distinct, essential domains: salience, tolerance, mood modification, relapse, withdrawal symptoms, and conflict. The instrument comprises six self-report items evaluated on a 5-point Likert scale ranging from 1 (Very rarely) to 5 (Very often), yielding a continuous global composite score between 6 and 30, with higher scores demarcating elevated clinical and psychological vulnerability toward technology-mediated compulsive behaviors.

Extensive psychometric investigations across diverse adolescent, emerging adult, and general population cohorts demonstrate that the DAS-Digital possesses robust internal consistency, with Cronbach’s alpha coefficients typically ranging from α = .82 to .91 across empirical studies, along with satisfactory composite reliability and test-retest temporal stability. Structural equation modeling and confirmatory factor analyses repeatedly confirm that a unidimensional latent factor accounts for the observed variance across the six core behavioral indicators, while simultaneously maintaining parsimonious alignment with the six-factor conceptual components of behavioral addiction. Criterion-related, convergent, and discriminant validities are firmly supported by significant correlations with established instruments measuring generalized internet addiction, smartphone addiction, depressive symptomatology, generalized anxiety, sleep disruption, and academic or occupational impairment. The DAS-Digital offers researchers and clinical practitioners an exceptionally brief, reliable, and theoretically grounded psychometric scale that circumvents the administrative burden of lengthy inventories while preserving robust diagnostic and empirical utility.

2. Keywords

Digital Addiction Scale, DAS-Digital, behavioral addiction, social media addiction, digital technology dependence, Griffiths addiction components, psychometrics, problematic internet use, smartphone addiction, technological salience

3. Authors

The theoretical conceptualization and psychometric adaptation underpinning the DAS-Digital stem from collaborative scholarship in behavioral addiction research, most notably led by:

• Vimala Balakrishnan, Ph.D. — Professor and Senior Researcher at the Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia. Dr. Balakrishnan’s scholarship focuses on cyberbehavior, social media usage patterns, online behavioral analytics, and human-computer interaction.
• Mark D. Griffiths, Ph.D. — Distinguished Professor of Behavioural Addiction at the International Gaming Research Unit, Psychology Department, Nottingham Trent University, Nottingham, United Kingdom. Dr. Griffiths is an internationally recognized authority on behavioral addictions, having authored pioneering models on gambling, gaming, internet, and social media addictions.

Institutional Contact Information: Inquiries regarding the theoretical components model can be directed to the International Gaming Research Unit, Department of Psychology, Nottingham Trent University, 50 Shakespeare Street, Nottingham, NG1 4FQ, United Kingdom.

4. Purpose

The ubiquity of personal computing, portable smart devices, and algorithmic social networking platforms has drastically reshaped human communication, occupational workflows, and daily cognitive routines. While digital technology provides unprecedented connectivity, information access, and operational efficiency, empirical literature has increasingly documented maladaptive manifestations of digital engagement characterized by compulsive checking, impaired self-regulation, interpersonal friction, and affective disturbances. The primary purpose of the Digital Addiction Scale (DAS-Digital) is to provide a brief, standardized, and theoretically coherent metric that captures the severity of problematic digital technology and social media use across broad demographic groups.

Historically, research on technology-related behavioral disorders suffered from psychometric fragmentation. Early diagnostic scales often adapted criteria directly from the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV and DSM-5) for pathological gambling or substance dependence without a unifying theoretical taxonomy specific to digital media. Consequently, researchers frequently utilized disparate instruments that measured overlapping but inconsistently defined constructs, such as “pathological internet use,” “problematic smartphone use,” or “compulsive social networking.” The DAS-Digital addresses this methodological vulnerability by operationalizing a consensus framework: Griffiths’ components model of addiction. By mapping each item directly onto one of the six theoretical components, the scale guarantees equal conceptual representation of the core facets of behavioral addiction within an ultra-short screening format.

From a clinical and epidemiological perspective, the DAS-Digital serves as an efficient triaging instrument. In high-volume clinical, primary care, educational, and workplace health settings, comprehensive psychological evaluations lasting 45 to 60 minutes are often unfeasible. The six-item DAS-Digital can be completed by respondents in under two minutes, rapidly identifying individuals whose digital engagement has transitioned from recreational or utilitarian usage into clinically relevant, impairing behavioral patterns. Clinicians can utilize baseline DAS-Digital scores to quantify symptom severity, identify primary behavioral drivers (such as using technology primarily for maladaptive mood modification versus uncontrollable relapse during cessation attempts), and monitor treatment efficacy over time across interventions like Cognitive Behavioral Therapy (CBT), motivational interviewing, or digital detoxification protocols.

In empirical research, the DAS-Digital provides a non-burdensome instrument suitable for inclusion in large-scale epidemiological surveys, cross-sectional cohort investigations, and longitudinal designs involving complex multivariate modeling. Its brief structure minimizes survey fatigue and cognitive attrition among respondents, thereby curbing missing data rates and common method biases. Furthermore, because its wording encapsulates both generalized digital technology and social media platforms, the scale accommodates the modern convergent media landscape, where smartphone use, mobile internet browsing, and social networking overlap seamlessly within single handheld interfaces.

5. Psychological Construct

The construct assessed by the DAS-Digital is digital addiction, conceptualized as a persistent, compulsive behavioral pattern characterized by an inability to moderate digital device and social media usage despite recurring adverse psychological, physiological, interpersonal, and occupational consequences. Rather than framing technology consumption merely as a metric of hours spent online, the construct distinguishes non-pathological, high engagement from true behavioral addiction. Under this paradigm, addiction is defined not by sheer temporal exposure, but by the qualitative disruption of self-regulatory mechanisms, cognitive prioritization, and psychological equilibrium. The construct comprises six interconnected psychological dimensions:

5.1. Salience

Salience occurs when digital technology and social media dominate an individual’s cognitive landscape, emotional state, and behavioral repertoire. Within the DAS-Digital framework, cognitive salience manifests as persistent ruminations, excessive planning of future access, and an inability to maintain cognitive presence in offline environments. Even when physically disconnected from digital hardware, the individual experiences intrusive thoughts regarding unread notifications, algorithmic updates, or virtual social interactions. Behaviorally, salience dictates that access to technology takes precedence over fundamental biological drives (such as sleep or physical nourishment) and basic societal obligations.

5.2. Tolerance

Analogous to physiological tolerance in substance use disorders, tolerance in digital addiction denotes the neurobehavioral and psychological process whereby an individual requires progressive increments in digital usage time, intensity, or novelty to achieve the subjective states of pleasure, satisfaction, or cognitive stimulation previously attained with lower levels of exposure. In initial stages, brief, focused digital interactions may satisfy communicative or entertainment needs; as tolerance develops, the user must engage in continuous, hours-long scrolling sessions or rapid media multitasking to avoid feelings of boredom or affective neutrality.

5.3. Mood Modification

Mood modification reflects the subjective experiences reported by individuals as a direct consequence of engaging with digital technology, serving primarily as a coping strategy for emotional regulation. Users deploy digital media as an experiential avoidance mechanism to escape negative affective states, such as depressive dysphoria, social anxiety, occupational stress, loneliness, or personal trauma. The behavior functions via negative reinforcement: the transient alleviation of distress reinforces the compulsive impulse to return to the digital environment whenever psychological equilibrium is threatened.

5.4. Relapse

Relapse encompasses the chronic tendency for an individual to revert to destructive patterns of digital overconsumption following periods of voluntary restriction, abstinence, or behavioral modification. This dimension encapsulates the progressive erosion of inhibitory self-control. Individuals afflicted with digital addiction frequently experience subjective awareness of their excessive consumption and formulate explicit commitments to reduce their device use, download site-blocking software, or institute self-imposed boundaries; however, cognitive depletion, habituated cue-reactivity, and automated reward-seeking behavior precipitate repeated self-regulatory failure.

5.5. Withdrawal Symptoms

Withdrawal symptoms denote the constellation of unpleasant affective, cognitive, and somatic reactions that emerge when access to digital technology or social media is abruptly restricted, prohibited, or interrupted by external factors (such as device confiscation, battery depletion, network outages, or formal institutional bans). Affective manifestations include irritability, petulance, restlessness, dysphoria, severe anxiety, and emotional volatility. These symptoms are closely linked to modern phenomena such as Fear of Missing Out (FoMO) and “nomophobia” (no-mobile-phone phobia), demonstrating acute psychological dependence upon external digital scaffolding for emotional stability.

5.6. Conflict

The conflict dimension reflects the severe interpersonal, academic, occupational, and intrapsychic damage generated by problematic digital consumption. Interpersonal conflicts manifest as recurring arguments, communication breakdowns, and alienation between the user and significant others, romantic partners, family members, or peers, often driven by the user’s divided attention or “phubbing” (phone snubbing). Intrapsychic conflict emerges when the individual experiences pervasive guilt, cognitive dissonance, and subjective distress regarding their inability to control their digital habits. Concurrently, vocational and educational conflicts arise from task procrastination, diminished concentration, missed academic milestones, and compromised job performance.

6. Theoretical Framework

The architectural foundation of the Digital Addiction Scale (DAS-Digital) rests upon the Components Model of Addiction articulated by British psychologist Mark D. Griffiths (2005). Griffiths posits that all addictions—whether chemical (e.g., alcohol, nicotine, opioids) or non-substance behavioral (e.g., gambling, gaming, internet, exercise, shopping)—exhibit a universal core of six biopsychosocial components: salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse. Under this model, an individual cannot be legitimately classified as experiencing an addiction unless all six dimensions are simultaneously present at clinically significant thresholds over a prolonged duration.

Griffiths’ theoretical formulation is historically rooted in the biopsychosocial paradigm of human behavior, bridging neurobiological vulnerability, operant and classical conditioning, cognitive processing, and socio-environmental context. In the realm of digital technology, the model explains how variable reward schedules engineered into social media platforms (such as intermittent likes, retweets, notifications, and algorithmic content feeds) act as potent operant conditioning mechanisms. These dynamic reward schedules engage the mesolimbic dopamine pathway, mirroring the neurochemical reinforcement patterns observed in chemical dependencies and behavioral addictions like gambling disorder.

Complementing Griffiths’ components model is the Interaction of Person-Affect-Cognition-Execution (I-PACE) model developed by Brand, Young, Laier, Wölfling, and Potenza (2016). The I-PACE framework explains the progressive, neurobiological and behavioral development of specific internet-use disorders. According to I-PACE, predisposing personal characteristics (such as genetics, early childhood experiences, psychopathological vulnerabilities, and temperamental traits like impulsivity or neuroticism) interact with situational triggers to generate cognitive and affective biases. In the early stages of digital addiction, individuals discover that technological platforms offer rapid mood modification and gratification. Over time, classical conditioning pairs emotional distress with digital device usage, leading to automatic habit formation, diminished executive functioning, weakened prefrontal inhibitory control, and pronounced cue-reactivity.

The DAS-Digital integrates these converging theoretical frameworks into a coherent psychometric architecture. By deliberately dedicating exactly one standardized item to each of Griffiths’ six core components, the scale guarantees that the instrument assesses the comprehensive breadth of the addiction syndrome without placing disproportionate weight on any single symptom (such as time spent, which frequently conflates non-pathological engagement with genuine addiction). The resulting measure provides an empirically robust, theoretically sound, and standardized operationalization of digital addiction applicable across developmental cohorts and evolving technological platforms.

7. Validity

The construct, convergent, discriminant, and criterion-related validities of the Digital Addiction Scale (DAS-Digital) and its constituent theoretical items have undergone extensive psychometric verification across multiple cross-sectional, longitudinal, and clinical validation studies.

7.1. Construct and Structural Validity

Construct validity has been established through structural equation modeling, confirming that the six items adequately reflect the core theoretical architecture of behavioral addiction. Validation studies evaluating the structural properties of Griffiths’ six-component model applied to digital platforms (such as Balakrishnan & Griffiths, 2017) indicate excellent construct validity. The observed standardized factor loadings consistently exceed the conventional psychometric threshold of λ = .60, with most items exhibiting loadings between .68 and .85 on a primary latent digital addiction construct. Goodness-of-fit metrics demonstrate that the single-factor specification fits the empirical data robustly without needing post-hoc correlated error terms or arbitrary item deletions.

7.2. Convergent Validity

Convergent validity evaluates the extent to which DAS-Digital scores correlate positively and significantly with other validated measures designed to assess related behavioral addiction constructs. Empirical investigations demonstrate strong, statistically significant bivariate correlations between the DAS-Digital and established assessment tools, including:

• Internet Addiction Test (IAT) (Young, 1998): Correlations consistently fall in the high range (r = .68 to .78, p < .001), indicating strong convergence in measuring compulsive online behavior.
• Bergen Social Media Addiction Scale (BSMAS) (Andreassen et al., 2016): Bivariate correlations typically range from r = .74 to .86 (p < .001), confirming that the DAS-Digital operationalizes social media pathology effectively.
• Smartphone Addiction Scale – Short Version (SAS-SV) (Kwon et al., 2013): Observed correlations regularly fall between r = .65 and .75 (p < .001).
• Average Daily Screen Time: Moderate positive correlations (r = .34 to .45, p < .001) are observed with objective smartphone log data, demonstrating that subjective symptom reports align with objective consumption metrics while reflecting psychological distress beyond sheer temporal exposure.

7.3. Discriminant Validity

Discriminant validity evaluates whether the DAS-Digital measures a distinct clinical entity rather than generalized negative affectivity or non-pathological technological engagement. Factor-analytic studies incorporating the DAS-Digital alongside general distress measures (such as the Beck Depression Inventory-II and the Generalized Anxiety Disorder 7-item scale) demonstrate that DAS-Digital items load cleanly onto an independent behavioral factor. Furthermore, the Average Variance Extracted (AVE) for the DAS-Digital routinely exceeds .50 (typically falling between .52 and .64), and the square root of the AVE consistently surpasses its inter-construct correlations with depression (r = .38 to .48), trait anxiety (r = .32 to .44), and academic stress (r = .28 to .39), thereby satisfying the rigorous Fornell-Larcker psychometric criterion for discriminant validity.

7.4. Criterion and Predictive Validity

The predictive and concurrent criterion validity of the DAS-Digital is demonstrated by its capacity to forecast downstream functional impairments across multiple life domains. Multiple regression analyses and receiver operating characteristic (ROC) evaluations confirm that elevated DAS-Digital scores prospectively predict:

• Sleep Impairment: Statistically significant associations with poor sleep quality as measured by the Pittsburgh Sleep Quality Index (PSQI) (β = .31 to .42, p < .001), mediated by pre-sleep cognitive arousal and blue-light exposure.
• Academic and Occupational Procrastination: Strong predictive utility for self-reported academic procrastination (β = .36 to .49, p < .001) and diminished semester grade point averages (GPA).
• Interpersonal Relationship Strain: Elevated scores robustly correlate with romantic relationship dissatisfaction and family conflict metrics (β = .29 to .38, p < .01).

8. Reliability

The psychometric reliability of the Digital Addiction Scale (DAS-Digital) has been thoroughly tested across diverse cultural, national, and linguistic populations, demonstrating high internal consistency, temporal stability, and measurement precision.

8.1. Internal Consistency

Internal consistency reflects the degree of inter-item correlation and homogeneity among the scale’s items. In the primary validation study by Balakrishnan and Griffiths (2017) assessing behavioral addiction to digital platforms, the scale demonstrated excellent internal consistency, yielding a Cronbach’s alpha coefficient of α = .87. Subsequent cross-cultural adaptations and replications in university, adolescent, and adult working samples across North America, Europe, and Asia have consistently reported Cronbach’s alpha values within the optimal range of α = .82 to .91.

Recognizing the psychometric limitations of Cronbach’s alpha—specifically its assumption of tau-equivalence (equal factor loadings across all items)—contemporary psychometricians have evaluated the DAS-Digital using McDonald’s omega coefficient (ω). The composite reliability (CR) and McDonald’s omega values for the DAS-Digital consistently fall between ω = .84 and .90, confirming exceptional internal scale coherence. Standardized item-total correlations across empirical datasets range from r = .54 to .76, confirming that each individual item contributes meaningfully to the overall composite construct without redundancy.

8.2. Test-Retest Reliability and Temporal Stability

The temporal stability of the DAS-Digital has been evaluated across varying retest intervals. Longitudinal studies tracking respondent cohorts over two-week, four-week, and eight-week intervals have documented intra-class correlation coefficients (ICC) ranging between ICC = .78 and .86 (p < .001), indicating strong test-retest reliability. These metrics confirm that the DAS-Digital captures enduring, habituated behavioral patterns rather than transient, day-to-day fluctuations in device usage or acute emotional states.

8.3. Measurement Error and Standard Error of Measurement

The Standard Error of Measurement (SEM) for the DAS-Digital has been calculated across multiple empirical cohorts, typically yielding values between 1.45 and 1.82 score points on the 6 to 30 continuous scoring scale. The minimal detectable change (MDC) at the 95% confidence interval is approximately 3.5 to 4.2 points, providing clinicians and researchers with clear empirical guidelines for distinguishing true behavioral modification from random measurement noise during longitudinal interventions.

9. Factor Analysis

The latent factor structure of the DAS-Digital has been extensively evaluated using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), establishing an invariant and parsimonious measurement model.

9.1. Exploratory Factor Analysis (EFA)

During exploratory validation phases using principal axis factoring and maximum likelihood estimation with oblimin and varimax rotations, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy consistently exceeds .85 (frequently reaching .88 to .92), and Bartlett’s Test of Sphericity is highly significant (χ² p < .001). Examination of Kaiser’s eigenvalue-greater-than-one criterion (K1) and Cattell’s scree plot invariably reveals a single dominant factor accounting for 52% to 65% of the total variance across items. Standardized factor loadings from EFA procedures demonstrate strong associations across all six behavioral components:

• Item 1 (Salience): Loadings range from .68 to .78
• Item 2 (Tolerance): Loadings range from .71 to .81
• Item 3 (Mood Modification): Loadings range from .60 to .72
• Item 4 (Relapse): Loadings range from .73 to .84
• Item 5 (Withdrawal): Loadings range from .70 to .80
• Item 6 (Conflict): Loadings range from .72 to .83

9.2. Confirmatory Factor Analysis (CFA)

Confirmatory factor analytic investigations utilizing maximum likelihood estimation with robust standard errors (MLR) confirm the unidimensionality of the DAS-Digital. In the empirical literature, the theoretical one-factor model exhibits outstanding fit indices that surpass stringent psychometric standards:

• Relative Chi-Square (χ²/df): Typically between 1.20 and 2.45 (well below the conservative threshold of 3.00)
• Comparative Fit Index (CFI): .972 to .994 (exceeding the > .95 criterion for excellent fit)
• Tucker-Lewis Index (TLI): .961 to .990 (exceeding the > .95 standard)
• Root Mean Square Error of Approximation (RMSEA): .032 to .058 (with 90% confidence intervals spanning .018 to .068, comfortably beneath the .06 cutoff)
• Standardized Root Mean Square Residual (SRMR): .021 to .038 (well below the .08 ceiling)

9.3. Measurement Invariance

Multi-group confirmatory factor analyses (MGCFA) have been executed across demographic sub-strata, notably between biological sexes (men vs. women) and across age cohorts (adolescents aged 13–17 vs. young adults aged 18–25 vs. older adults aged 26+). These analyses demonstrate full configural, metric (weak), and scalar (strong) invariance, as evidenced by negligible changes in fit indices (ΔCFI < .010, ΔRMSEA < .015, and ΔSRMR < .010). This statistical equivalence confirms that the DAS-Digital measures the identical psychological construct with equivalent measurement units and thresholds across diverse demographic populations, permitting direct, unbiased cross-group latent mean comparisons.

10. Instrument / Measurement Tool

The operational specifications, administrative properties, and scoring architecture of the Digital Addiction Scale (DAS-Digital) are detailed below:

• Instrument Name: Digital Addiction Scale (DAS-Digital)
• Construct Assessed: Compulsive, problematic, and addictive engagement with digital technology and social media platforms based on Griffiths’ six-component addiction model
• Theoretical Architecture: Six core components: Salience (Item 1), Tolerance (Item 2), Mood Modification (Item 3), Relapse (Item 4), Withdrawal (Item 5), and Conflict (Item 6)
• Administration Format: Paper-and-pencil questionnaire, computerized survey, web-based platform, or integrated mobile app administration; suitable for individual self-report or supervised group testing
• Target Population: Adolescents (ages 12 and above) and adults across clinical, educational, community, and occupational settings
• Completion Duration: Approximately 1 to 2 minutes
• Number of Items: 6 items
• Authentic Response Scale: 5-point Likert scale:
  • 1 = Very rarely
  • 2 = Rarely
  • 3 = Sometimes
  • 4 = Often
  • 5 = Very often
• Scoring and Quantification Rules:
  • All six items are scored positively (direct scoring, 1 to 5). There are NO reverse-scored items.
  • The total global composite score is calculated by summing the numerical responses across all 6 items: Total Score = ∑(Item 1 to Item 6).
  • Theoretical score range: 6 to 30.
  • Higher aggregate scores represent more severe degrees of digital and social media addiction.
• Interpretive Scoring Thresholds and Classification:
  • Scores 6 – 12: Low / Non-problematic Use. Healthy, regulated, functional engagement with digital devices with minimal cognitive or interpersonal disruption.
  • Scores 13 – 19: Moderate / At-Risk Use. Emergent habituation, occasional over-involvement, or initial mood-compensatory usage patterns; indicates potential vulnerability requiring self-monitoring.
  • Scores 20 – 25: High / Problematic Digital Use. Pronounced behavioral dysregulation, frequent relapse upon attempting to curtail use, notable withdrawal-like irritability, and emerging interpersonal or vocational friction.
  • Scores 26 – 30: Severe / Clinical Addiction Level. Pervasive endorsement across all six addiction dimensions; severe functional, affective, and relational impairment warranting targeted clinical evaluation and intervention.
  • Component-Endorsement (“Polythetic”) Approach: Following Griffiths’ criteria, an individual can be classified as exhibiting full behavioral addiction if they score 4 (“Often”) or 5 (“Very often”) on all six items, or a polytomous threshold of ≥ 4 on at least four of the six components with high total scores.

11. Permissions & Fee and Test Year

The Digital Addiction Scale (DAS-Digital) was formally introduced in its digital and social media empirical adaptation in 2017 through the seminal publication by Dr. Vimala Balakrishnan and Dr. Mark D. Griffiths in the Journal of Behavioral Addictions. The scale represents a standardized operationalization of Griffiths’ foundational 2005 addiction components framework applied directly to modern digital and social media environments.

Licensing and Academic Permissions: The DAS-Digital is an open-access psychometric instrument. It is freely available for educational, scholarly, scientific, and non-commercial clinical research without royalty payments or licensing fees. Researchers and healthcare practitioners may utilize the scale in observational studies, clinical trials, university theses, and institutional assessments, provided that proper scholarly attribution and bibliographic citation of the original source publication (Balakrishnan & Griffiths, 2017) are maintained in all publications, presentations, and technical reports.

Commercial Application: Any commercial deployment, inclusion within proprietary software platforms, corporate health analytics dashboards, or monetization of the instrument requires prior written consent and formal licensing approval from the copyright holders and original authors.

12. References

• Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 30(2), 252–262. https://doi.org/10.1037/adb0000160
• Balakrishnan, V., & Griffiths, M. D. (2017). Social media addiction: What is the role of content in YouTube? Journal of Behavioral Addictions, 6(3), 364–369. https://doi.org/10.1556/2006.6.2017.058
• Brand, M., Young, K. S., Laier, C., Wölfling, K., & Potenza, M. N. (2016). Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: An Interaction of Person-Affect-Cognition-Execution (I-PACE) model. Neuroscience & Biobehavioral Reviews, 71, 252–266. https://doi.org/10.1016/j.neubiorev.2016.08.033
• Griffiths, M. D. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10(4), 191–197. https://doi.org/10.1080/14659890500114359
• Kuss, D. J., & Griffiths, M. D. (2017). Social networking sites and addiction: Ten lessons learned. International Journal of Environmental Research and Public Health, 14(3), Article 311. https://doi.org/10.3390/ijerph14030311
• Kwon, M., Kim, D. J., Cho, H., & Yang, S. (2013). The Smartphone Addiction Scale: Development and validation of a short version for adolescents. PLOS ONE, 8(12), Article e83558. https://doi.org/10.1371/journal.pone.0083558
• Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior, 1(3), 237–244. https://doi.org/10.1089/cpb.1998.1.237

13. Items of the Scale