Smartphone Addiction Scale – Short Version

1. Abstract

The Smartphone Addiction Scale – Short Version (SAS-SV) is a standardized psychometric screening instrument developed by Min Kwon and colleagues in 2013 at the Catholic Medical Center in South Korea. Created in response to the escalating public health crisis surrounding excessive mobile device usage among youth, the SAS-SV condenses the original 33-item multidimensional Smartphone Addiction Scale (SAS) into a highly efficient 10-item self-report questionnaire. The scale assesses the severity of smartphone dependency and its associated functional impairments, operating as a unidimensional assessment tool designed specifically for adolescents aged approximately 14 to 15 years, while maintaining extensive utility across older adolescent and adult populations internationally.

Each of the 10 items is rated on a 6-point Likert scale ranging from 1 (“Strongly disagree”) to 6 (“Strongly agree”), producing a composite score that ranges from 10 to 60, with higher scores reflecting elevated levels of compulsive smartphone involvement. Psychometric evaluation of the scale demonstrates outstanding internal consistency, evidenced by a Cronbach's alpha coefficient of 0.91 and corrected item-total correlations spanning 0.50 to 0.80. The SAS-SV exhibits robust content validity (mean Item-Content Validity Index of 0.943), concurrent validity with existing behavioral addiction instruments, and remarkable diagnostic efficiency. Receiver Operating Characteristic (ROC) curve analysis against gold-standard clinical psychological evaluations yielded an Area Under the Curve (AUC) of 0.963 for boys and 0.947 for girls. Consequently, empirically validated clinical cut-off scores were established at 31 for boys (sensitivity: 0.77, specificity: 0.89) and 33 for girls (sensitivity: 0.88, specificity: 0.89). By delivering high diagnostic accuracy within a 2- to 5-minute administration window, the SAS-SV represents a foundational instrument for epidemiological surveillance, school-based screening, and clinical psychological assessment.

2. Keywords

Smartphone Addiction, Behavioral Addiction, Smartphone Addiction Scale – Short Version, SAS-SV, Adolescent Psychometrics, Problematic Mobile Phone Use, Screening Tool, Receiver Operating Characteristic Analysis, Clinical Cut-offs, Digital Technology Dependence, Non-Substance Addiction, Validation Study

3. Authors

The Smartphone Addiction Scale – Short Version was developed and validated by a multidisciplinary team of psychiatric, medical, and psychological researchers affiliated with the Catholic University of Korea:

• Min Kwon, Ph.D. — Department of Psychiatry, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea.
• Dai-Jin Kim, M.D., Ph.D. — Department of Psychiatry, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea.
• Hyun Cho, M.S. — Department of Psychiatry, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea.
• Soo Yang, Ph.D., R.N. (Corresponding Author) — College of Nursing, The Catholic University of Korea, Seoul, Republic of Korea. Contact: [email protected].

4. Purpose

The primary rationale underlying the development of the Smartphone Addiction Scale – Short Version was the urgent need for a brief, psychometrically sound, and developmentally attuned screening tool capable of identifying problematic smartphone usage among adolescents. While the original 33-item Smartphone Addiction Scale (Kwon et al., 2013) provided an extensive assessment of smartphone addiction across six distinct sub-dimensions, its primary normative data were gathered from adult and university cohorts. Furthermore, the 33-item scale lacked empirically derived, gender-differentiated clinical cut-off thresholds validated against formal clinical diagnoses, which limited its direct utility in triage environments.

Adolescents inhabit a distinct neurodevelopmental period marked by heightened neural reward sensitivity, underdeveloped executive functioning within the prefrontal cortex, and intense vulnerability to peer pressure and social exclusion. These developmental dynamics make teenagers uniquely susceptible to the rapid feedback loops engineered into mobile applications, algorithmic social feeds, and mobile games. Given that comprehensive diagnostic interviews and lengthy psychometric batteries are frequently impractical in primary educational and community health settings due to time constraints, respondent fatigue, and cognitive load, the SAS-SV was engineered to provide an accessible alternative that takes less than five minutes to administer.

In research contexts, the SAS-SV enables large-scale epidemiological investigations exploring the prevalence of technology dependency, longitudinal developmental trajectories, and cross-cultural comparisons of behavioral addiction. In clinical, pediatric, and psychiatric workflows, the tool functions as an initial screening device to rapidly identify youth exhibiting maladaptive reliance on mobile technology. School counselors and clinical psychologists utilize the instrument to establish baseline symptom profiles, benchmark therapeutic progress, and assess post-treatment recovery trajectories.

5. Psychological Construct

The psychological construct operationalized by the SAS-SV is smartphone addiction, conceptualized as a non-substance or behavioral addiction characterized by compulsive use, cognitive preoccupation, tolerance, withdrawal, and pervasive functional impairment. Rather than treating smartphone usage as a benign communication habit or merely a secondary conduit for generalized internet dependence, the construct reflects a primary, pathological syndrome driven by mobile affordances, including ubiquitous connectivity, intermittent reward schedules, and portable social networks.

Although psychometrically organized as a unidimensional composite score, the items incorporated into the SAS-SV systematically sample the primary behavioral domains identified in the addiction literature:

• Daily-Life Disturbance: Refers to direct negative consequences on physical well-being, academic productivity, occupational functioning, and domestic responsibilities. It encompasses musculoskeletal discomfort (such as neck stiffness and repetitive strain wrist injuries) and cognitive interference (such as academic distraction and dereliction of planned tasks).
• Withdrawal: Represents acute negative affective and psychological states experienced during periods of involuntary separation from or unavailability of the mobile device. This manifests clinically as restlessness, agitation, dysphoria, severe anxiety, and persistent irritability.
• Tolerance: Defined by the progressive escalation of screen engagement time required to attain the baseline subjective state of satisfaction, alongside unsuccessful efforts to curtail device interaction or accurately monitor time spent on the device.
• Cyberspace-Oriented Relationship Preference: The psychological tendency to substitute real-world interpersonal dynamics with asynchronous, digitally mediated communications, manifesting as compulsive checking of social networking applications (such as Twitter, Instagram, or Facebook) driven by social anxiety or Fear of Missing Out (FoMO).
• Positive Anticipation and Preoccupation: The pervasive intrusive presence of the device in conscious thought, where the user continuously ruminates about notifications, incoming messages, or digital updates even when engaged in offline endeavors.
• Loss of Control and Continued Use Despite Adverse Consequences: The inability to moderate device engagement, coupled with an explicit, persistent refusal or incapacity to reduce screen time even when aware of its tangible negative toll on health, interpersonal relations, and daily functioning.

6. Theoretical Framework

The SAS-SV is anchored within the broader theoretical paradigm of Behavioral Addiction, predominantly shaped by the diagnostic taxonomies of Isaac Marks (1990) and Mark Griffiths' Component Model of Addiction (2005). Griffiths posited that all addictions—whether chemical or behavioral—share six core clinical components: salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse. The SAS-SV maps these core criteria into the domain of mobile computing. Salience is expressed when the smartphone dominates an individual's cognitive processing; mood modification occurs when users seek the device for hedonic regulation; tolerance and withdrawal appear through escalating usage demands and separation anxiety; conflict manifests in academic and domestic impairment; and relapse is seen in the chronic inability to moderate engagement.

Furthermore, the scale integrates cognitive-behavioral and neurobiological paradigms, such as the Interaction of Person-Affect-Cognition-Execution (I-PACE) model formulated by Brand et al. (2016). According to the I-PACE framework, technological addictions emerge through dynamic interactions between core person-level characteristics (neurobiological vulnerabilities, coping deficits, and psychopathological traits), affective and cognitive processing biases (diminished executive control, attentional capture, and implicit cognitive associations), and executive dysfunction. The persistent tactile, auditory, and visual cue-reactivity provided by mobile notifications triggers dopaminergic reinforcement pathways, reinforcing operant conditioning loops that circumvent conscious inhibitory regulation.

7. Validity

The psychometric validation of the SAS-SV was conducted across several methodological domains, demonstrating exceptional construct, concurrent, convergent, and criterion-related validity.

Content Validity

The selection of the final 10 items from the parent 33-item scale was guided by rigorous expert consensus. A panel of psychiatric and psychological specialists evaluated the original items using the Content Validity Index (CVI). Only items that achieved an individual Item-CVI (I-CVI) higher than 0.78 were retained, ensuring that the selected subset retained high conceptual fidelity. The mean I-CVI across the retained 10 items was 0.943, indicating high professional consensus regarding item relevance and representativeness.

Concurrent and Convergent Validity

Concurrent validity was established through bivariate correlational analyses against existing validated psychometric instruments. The SAS-SV exhibited strong positive correlations with the original full-length Smartphone Addiction Scale (r > 0.70) and the Korean Smartphone Addiction Proneness Scale (SAPS; r > 0.70). In contrast, the scale demonstrated moderate correlations with the Internet Addiction Proneness Scale-Short Form (KS-scale; r = 0.611 for boys, r = 0.375 for girls). This moderate convergence confirms divergent validity, demonstrating that smartphone addiction is not merely synonymous with generalized internet addiction, but constitutes an independent diagnostic construct characterized by pervasive mobility and continuous social connectivity.

Criterion-Related Validity and ROC Analysis

Criterion-related validity was established by benchmarking self-reported SAS-SV scores against blind diagnostic clinical evaluations conducted by licensed clinical psychologists. Receiver Operating Characteristic (ROC) curve analysis demonstrated exceptional discriminative accuracy, with an Area Under the Curve (AUC) of 0.963 (95% CI: 0.944–0.982) for boys and 0.947 (95% CI: 0.916–0.978) for girls. Based on optimal Youden index calculations, the diagnostic cut-off thresholds were identified as follows:

• Boys: Cut-off score of 31 (Sensitivity: 0.772; Specificity: 0.893; Positive Predictive Value: 0.725; Negative Predictive Value: 0.916).
• Girls: Cut-off score of 33 (Sensitivity: 0.884; Specificity: 0.887; Positive Predictive Value: 0.792; Negative Predictive Value: 0.941).

8. Reliability

The SAS-SV demonstrates outstanding internal consistency and structural reliability. In the initial validation cohort of 540 South Korean junior high school students (mean age = 14.5 years), the overall scale achieved an empirical Cronbach's alpha coefficient of 0.91. This indicates strong inter-item covariance and minimal measurement error, which is particularly notable for an abbreviated 10-item instrument.

Corrected item-total correlation coefficients ranged from 0.50 to 0.80 across the ten items, confirming that each item contributes substantive variance toward measuring the core latent construct. Alpha-if-item-deleted analyses verified that eliminating any single item caused the overall Cronbach's alpha to decline below the 0.90 threshold, demonstrating the psychometric indispensability of each included question. Cross-cultural adaptations globally—spanning Spanish, Italian, Turkish, Arabic, French, and Chinese cohorts—have consistently replicated internal consistency values ranging from α = 0.84 to α = 0.92, alongside robust test-retest reliability intraclass correlation coefficients (ICC > 0.82 across two- to four-week administration windows).

9. Factor Analysis

Unlike conventional scale development methodologies that construct factor structures de novo via exploratory factor analysis (EFA), the SAS-SV was derived through targeted item-distillation from the validated six-factor structure of the 33-item parent instrument (Daily-Life Disturbance, Positive Anticipation, Withdrawal, Cyberspace-Oriented Relationship, Overuse, and Tolerance). By selecting the items with the highest content validity and discriminative capacity across these dimensions, the SAS-SV was optimized to function as a parsimonious, unidimensional screening instrument.

Subsequent confirmatory factor analyses (CFA) across numerous international validation studies have evaluated the structural integrity of this unidimensional model. Standard structural equation modeling consistently yields acceptable goodness-of-fit parameters for the single-factor configuration, with comparative fit index (CFI) values exceeding 0.92, Tucker-Lewis Index (TLI) values surpassing 0.90, and Root Mean Square Error of Approximation (RMSEA) values remaining between 0.05 and 0.08. Standardized factor loadings across the 10 items typically range from 0.55 to 0.82, confirming that a single overarching latent factor captures the observed variance in smartphone addiction symptomatology.

10. Instrument / Measurement Tool

• Test Type: Self-report screening questionnaire.
• Format: 10 items, 6-point Likert scale.
• Response Options: 1 = Strongly disagree, 2 = Disagree, 3 = Weakly disagree, 4 = Weakly agree, 5 = Agree, 6 = Strongly agree.
• Item Count: 10 items.
• Target Population: Adolescents (validated originally for ages 14–15, widely applied across ages 12–25).
• Administration Time: Approximately 2 to 5 minutes.
• Scoring Procedure: Items are summed directly to produce a total score ranging from 10 to 60.
• Reverse Scoring: No reverse-scored items; all 10 items are positively keyed.
• Diagnostic Cut-off Scores:
  • Boys / Males: A score of 31 or higher indicates high risk of smartphone addiction.
  • Girls / Females: A score of 33 or higher indicates high risk of smartphone addiction.
• Normative Reference Values (Original Validation Study):
  • Boys: Mean = 23.75 (SD = 8.87).
  • Girls: Mean = 27.89 (SD = 9.87).

11. Permissions & Fee and Test Year

The Smartphone Addiction Scale – Short Version was developed and published in 2013. The validation study was published in PLoS ONE, an open-access peer-reviewed journal. The instrument is distributed under the terms of the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

The scale is free of charge for academic, educational, and clinical research purposes. Commercial entities, pharmaceutical organizations, or platforms wishing to incorporate the instrument into proprietary applications should contact the corresponding author, Dr. Soo Yang ([email protected]), and Catholic Medical Center for explicit licensing agreements.

12. References

Billieux, J., Van der Linden, M., & Rochat, L. (2008). The role of impulsivity in actual and problematic use of the mobile phone. Applied Cognitive Psychology, 22(9), 1195–1210. https://doi.org/10.1002/acp.1429

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. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10(4), 191–197. https://doi.org/10.1080/14659890500114359

Kim, D. I., Chung, Y. J., Lee, J. Y., Kim, M. C., Lee, Y. H., Kang, E. B., & Keum, C. M. (2008). Development of internet addiction proneness scale-short form (KS scale). The Korea Journal of Counseling, 9(4), 1703–1722. https://doi.org/10.15703/kjc.9.4.200812.1703

Kuss, D. J., & Griffiths, M. D. (2011). Online social networking and addiction—a review of the psychological literature. International Journal of Environmental Research and Public Health, 8(9), 3528–3552. https://doi.org/10.3390/ijerph8093528

Kwon, M., Kim, D. J., Cho, H., & Yang, S. (2013). The Smartphone Addiction Scale: Development and validation of a short version for adolescents (SAS-SV). PLoS ONE, 8(12), Article e83558. https://doi.org/10.1371/journal.pone.0083558

Kwon, M., Lee, J. Y., Won, W. Y., Park, J. W., Min, J. A., Hahn, C., Gu, X., Choi, S. W., & Kim, D. J. (2013). Development and validation of a Smartphone Addiction Scale (SAS). PLoS ONE, 8(2), Article e56936. https://doi.org/10.1371/journal.pone.0056936

Marks, I. (1990). Behavioural (non-chemical) addictions. British Journal of Addiction, 85(11), 1389–1394. https://doi.org/10.1111/j.1360-0443.1990.tb01618.x

National Information Society Agency. (2011). Development of Korean Smartphone Addiction Proneness Scale for youth and adults. National Information Society Agency, Seoul, Korea, pp. 85–86.

Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what's being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489–497. https://doi.org/10.1002/nur.20147

13. Items of the Scale