Smartphone Application-Based Addiction Scale: Psychometric Evidence Across Nine Asian Regions Using Advanced Analytic Methods

Overview

Journal Brain Behav

Specialty Psychology

Date 2024 Nov 17

PMID 39551939

Authors

I-Hua Chen

Iqbal Pramukti

Wan Ying Gan

Kamolthip Ruckwongpatr

Le An Pham

Po-Ching Huang

Mohammed A Mamun

Irfan Ullah

Haitham A Jahrami

Chung-Ying Lin

Amir H Pakpour

Affiliations

Soon will be listed here.

Abstract

Introduction: A smartphone is a device with various functions, including wifi, application functions, mobile networks, ease of mobility, and the capability of using mobile data. Because of the aforementioned functions, people may use smartphones frequently. The Smartphone Application-Based Addiction Scale (SABAS) is a six-item questionnaire assessing smartphone addiction with promising psychometric properties. However, it is unclear if the SABAS possesses the strong psychometric properties across Asian regions. The present study aimed to examine the factor structure of the SABAS across nine Asian regions.

Methods: Using datasets collected from Asian regions of Bangladesh, China, Indonesia, Iran, Malaysia, Pakistan, Taiwan, Thailand, and Vietnam, data from 10,397 participants (mean age = 22.40 years; 44.8% men) were used for analyses. All participants completed the SABAS using an online survey or paper-and-pencil mode.

Results: Findings from confirmatory factor analysis, Rasch analysis, and network analysis all indicate a one-factor structure for the SABAS. Moreover, the one-factor structure of the SABAS was measurement invariant across age (21 years or less vs. above 21 years) and gender (men vs. women) in metric, scalar, and strict invariance. The one-factor structure was invariant across regions in metric but not scalar or strict invariance.

Conclusion: The present study findings showed that the SABAS possesses a one-factor structure across nine Asian regions; however, noninvariant findings in scalar and strict levels indicate that people in the nine Asian regions may interpret the importance of each SABAS item differently. Age group and gender group comparisons are comparable because of the invariance evidence for the SABAS found in the present study. However, cautions should be made when comparing SABAS scores across Asian regions.

References

Lin C, Tsai M, Koos M, Nagy L, Kraus S, Demetrovics Z . The short version of the Sexual Distress Scale (SDS-3): Measurement invariance across countries, gender identities, and sexual orientations. Int J Clin Health Psychol. 2024; 24(2):100461. PMC: 11067538. DOI: 10.1016/j.ijchp.2024.100461. View

Chang C, Chen J, Huang S, Potenza M, Su J, Chang K . Problematic smartphone use and two types of problematic use of the internet and self-stigma among people with substance use disorders. Addict Behav. 2023; 147:107807. DOI: 10.1016/j.addbeh.2023.107807. View

Chen I, Chang K, Chang C, Huang S, Potenza M, Pakpour A . Temporal associations between problematic use of the internet and self-stigma among people with substance use disorders: A cross-lagged model across one year. J Psychiatr Res. 2022; 156:339-348. DOI: 10.1016/j.jpsychires.2022.10.044. View

Fan C, Li Y, Chang C, Hu F, Lin C . The psychometric properties of the Physical Resilience Instrument for Older Adults (PRIFOR): a Rasch analysis. Aging Clin Exp Res. 2023; 35(11):2721-2728. DOI: 10.1007/s40520-023-02547-z. View

Montag C, Bey K, Sha P, Li M, Chen Y, Liu W . Is it meaningful to distinguish between generalized and specific Internet addiction? Evidence from a cross-cultural study from Germany, Sweden, Taiwan and China. Asia Pac Psychiatry. 2014; 7(1):20-6. DOI: 10.1111/appy.12122. View

Fournier L, Schimmenti A, Musetti A, Boursier V, Flayelle M, Cataldo I . Further evidence for the bidimensionality of the components model of addiction: a reply to Amendola (2023). Addict Behav. 2023; 150:107914. DOI: 10.1016/j.addbeh.2023.107914. View

Fournier L, Schimmenti A, Musetti A, Boursier V, Flayelle M, Cataldo I . Deconstructing the components model of addiction: an illustration through "addictive" use of social media. Addict Behav. 2023; 143:107694. DOI: 10.1016/j.addbeh.2023.107694. View

Chen I, Pakpour A, Leung H, Potenza M, Su J, Lin C . Comparing generalized and specific problematic smartphone/internet use: Longitudinal relationships between smartphone application-based addiction and social media addiction and psychological distress. J Behav Addict. 2020; 9(2):410-419. PMC: 8939406. DOI: 10.1556/2006.2020.00023. View

Souza M, Coster W, Mancini M, Dutra F, Kramer J, Sampaio R . Rasch analysis of the participation scale (P-scale): usefulness of the P-scale to a rehabilitation services network. BMC Public Health. 2017; 17(1):934. PMC: 5721585. DOI: 10.1186/s12889-017-4945-9. View

10.

Tung S, Gan W, Chen J, Ruckwongpatr K, Pramukti I, Nadhiroh S . Internet-Related Instruments (Bergen Social Media Addiction Scale, Smartphone Application-Based Addiction Scale, Internet Gaming Disorder Scale-Short Form, and Nomophobia Questionnaire) and Their Associations with Distress among Malaysian University.... Healthcare (Basel). 2022; 10(8). PMC: 9408412. DOI: 10.3390/healthcare10081448. View

11.

Callan J, Sereika S, Cui R, Tamres L, Tarneja M, Greene B . Cognitive Behavioral Therapy (CBT) Telehealth Augmented With a CBT Smartphone Application to Address Type 2 Diabetes Self-Management: A Randomized Pilot Trial. Sci Diabetes Self Manag Care. 2022; 48(6):492-504. DOI: 10.1177/26350106221133027. View

12.

Yang Y, Su J, Pimsen A, Chen J, Potenza M, Pakpour A . Validation of the Thai Assessment of Criteria for Specific Internet-use Disorders (ACSID-11) among young adults. BMC Psychiatry. 2023; 23(1):819. PMC: 10633967. DOI: 10.1186/s12888-023-05210-z. View

13.

Kim H, Hodgins D . Component Model of Addiction Treatment: A Pragmatic Transdiagnostic Treatment Model of Behavioral and Substance Addictions. Front Psychiatry. 2018; 9:406. PMC: 6127248. DOI: 10.3389/fpsyt.2018.00406. View

14.

Kwon M, Kim D, Cho H, Yang S . The smartphone addiction scale: development and validation of a short version for adolescents. PLoS One. 2014; 8(12):e83558. PMC: 3877074. DOI: 10.1371/journal.pone.0083558. View

15.

Borsboom D, Cramer A . Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol. 2013; 9:91-121. DOI: 10.1146/annurev-clinpsy-050212-185608. View

16.

Nurmala I, Nadhiroh S, Pramukti I, Tyas L, Zari A, Griffiths M . Reliability and validity study of the Indonesian Smartphone Application-Based Addiction Scale (SABAS) among college students. Heliyon. 2022; 8(8):e10403. PMC: 9449775. DOI: 10.1016/j.heliyon.2022.e10403. View

17.

Liu W, Chen J, Gan W, Poon W, Tung S, Lee L . Associations of Problematic Internet Use, Weight-Related Self-Stigma, and Nomophobia with Physical Activity: Findings from Mainland China, Taiwan, and Malaysia. Int J Environ Res Public Health. 2022; 19(19). PMC: 9566738. DOI: 10.3390/ijerph191912135. View

18.

Lee C, Lin C, Koos M, Nagy L, Kraus S, Demetrovics Z . The eleven-item Alcohol, Smoking and Substance Involvement Screening Test (ASSIST-11): Cross-cultural psychometric evaluation across 42 countries. J Psychiatr Res. 2023; 165:16-27. DOI: 10.1016/j.jpsychires.2023.06.033. View

19.

Lin C, Mamun M, Al Mamun F, Ullah I, Hosen I, Malik N . A phubbing scale tested in Bangladesh, Iran, and Pakistan: confirmatory factor, network, and Rasch analyses. BMC Psychiatry. 2023; 23(1):763. PMC: 10583412. DOI: 10.1186/s12888-023-05251-4. View

20.

Peng P, Chen Z, Ren S, Liu Y, He R, Liang Y . Determination of the cutoff point for Smartphone Application-Based Addiction Scale for adolescents: a latent profile analysis. BMC Psychiatry. 2023; 23(1):675. PMC: 10504767. DOI: 10.1186/s12888-023-05170-4. View