Development and Validation of a New Gadget Addiction Scale (Screen Dependency Scale) Among Pre-School Children in Malaysia

Overview

Journal Int J Environ Res Public Health

Publisher MDPI

Specialties Environmental Health
Public Health

Date 2022 Dec 23

PMID 36554796

Authors

Azwanis Abdul Hadi

Siti Ruziana Roslan

Edre Mohammad Aidid

Nurzulaikha Abdullah

Ramli Musa

Affiliations

Soon will be listed here.

Abstract

Background: Excessive screen time in young children is associated with many harmful consequences including screen dependency. Research has shown a worrying prevalence of media-related dependency among adolescents and pre-school children. There are a few available questionnaires among adolescents but none for pre-school children. This study aimed to design and validate a questionnaire to assess screen dependency among pre-school children aged 4 to 6 years old.

Methodology: A cross-sectional two-phase study was carried out to develop the scale. In phase 1, a preliminary parent-report measure questionnaire was developed in Bahasa Malaysia. Later, it was sent to four experts for content validity followed by face validity. In Phase 2, a total of 386 parents of pre-school children aged 4 to 6 years old, split into two samples, were involved in the field study for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).

Result: Sample 1 was used to perform EFA to determine the factorial structure of the SDS. All items with a factor loading of >0.4 were included. Sample 2 was used to perform the CFA. RMSEA and CFI analysis showed that the SDS has a good fit and confirms the dimensional structure found via EFA. The final questionnaire consists of 15 items with a 4 factors' structure and has excellent internal consistency reliability.

Conclusions: The Screen Dependency Scale (SDS) is a reliable and valid questionnaire to detect screen dependency among pre-school children aged 4 to 6 years old in Malaysia.

References

Wang Z, Wu L, Yuan K, Hu Y, Zheng H, Du X . Cortical thickness and volume abnormalities in Internet gaming disorder: Evidence from comparison of recreational Internet game users. Eur J Neurosci. 2018; . DOI: 10.1111/ejn.13987. View

. Media and Young Minds. Pediatrics. 2016; 138(5). DOI: 10.1542/peds.2016-2591. View

Zimmerman F, Christakis D . Children's television viewing and cognitive outcomes: a longitudinal analysis of national data. Arch Pediatr Adolesc Med. 2005; 159(7):619-25. DOI: 10.1001/archpedi.159.7.619. View

Knudsen E . Sensitive periods in the development of the brain and behavior. J Cogn Neurosci. 2004; 16(8):1412-25. DOI: 10.1162/0898929042304796. View

Radesky J, Schumacher J, Zuckerman B . Mobile and interactive media use by young children: the good, the bad, and the unknown. Pediatrics. 2014; 135(1):1-3. DOI: 10.1542/peds.2014-2251. View

Domoff S, Harrison K, Gearhardt A, Gentile D, Lumeng J, Miller A . Development and Validation of the Problematic Media Use Measure: A Parent Report Measure of Screen Media "Addiction" in Children. Psychol Pop Media Cult. 2019; 8(1):2-11. PMC: 6411079. DOI: 10.1037/ppm0000163. View

Ulbricht C, Chrysanthopoulou S, Levin L, Lapane K . The use of latent class analysis for identifying subtypes of depression: A systematic review. Psychiatry Res. 2018; 266:228-246. PMC: 6345275. DOI: 10.1016/j.psychres.2018.03.003. View

Zou K, OMalley A, Mauri L . Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation. 2007; 115(5):654-7. DOI: 10.1161/CIRCULATIONAHA.105.594929. View

Madigan S, Browne D, Racine N, Mori C, Tough S . Association Between Screen Time and Children's Performance on a Developmental Screening Test. JAMA Pediatr. 2019; 173(3):244-250. PMC: 6439882. DOI: 10.1001/jamapediatrics.2018.5056. View

10.

Yilmaz E, Griffiths M, Kan A . Development and Validation of Videogame Addiction Scale for Children (VASC). Int J Ment Health Addict. 2017; 15(4):869-882. PMC: 5529472. DOI: 10.1007/s11469-017-9766-7. View

11.

Pless C, Pless I . How well they remember. The accuracy of parent reports. Arch Pediatr Adolesc Med. 1995; 149(5):553-8. DOI: 10.1001/archpedi.1995.02170180083016. View

12.

Mandrekar J . Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010; 5(9):1315-6. DOI: 10.1097/JTO.0b013e3181ec173d. View

13.

Ruscio J, Ruscio A . Categories and Dimensions Advancing Psychological Science Through the Study of Latent Structure. Curr Dir Psychol Sci. 2011; 17(3):203-207. PMC: 2736067. DOI: 10.1111/j.1467-8721.2008.00575.x. View

14.

Alavi S, Ferdosi M, Jannatifard F, Eslami M, Alaghemandan H, Setare M . Behavioral Addiction versus Substance Addiction: Correspondence of Psychiatric and Psychological Views. Int J Prev Med. 2012; 3(4):290-4. PMC: 3354400. View

15.

Zimmerman F, Christakis D, Meltzoff A . Television and DVD/video viewing in children younger than 2 years. Arch Pediatr Adolesc Med. 2007; 161(5):473-9. DOI: 10.1001/archpedi.161.5.473. View

16.

Goodman A . Addiction: definition and implications. Br J Addict. 1990; 85(11):1403-8. DOI: 10.1111/j.1360-0443.1990.tb01620.x. View

17.

Safari S, Baratloo A, Elfil M, Negida A . Evidence Based Emergency Medicine; Part 5 Receiver Operating Curve and Area under the Curve. Emerg (Tehran). 2016; 4(2):111-3. PMC: 4893763. View

18.

Griffiths M . Nicotine, tobacco and addiction. Nature. 1996; 384(6604):18. DOI: 10.1038/384018a0. View

19.

Hawi N, Samaha M, Griffiths M . The Digital Addiction Scale for Children: Development and Validation. Cyberpsychol Behav Soc Netw. 2019; 22(12):771-778. DOI: 10.1089/cyber.2019.0132. View

20.

Lee D, Namkoong K, Lee J, Jung Y . Preliminary evidence of altered gray matter volume in subjects with internet gaming disorder: associations with history of childhood attention-deficit/hyperactivity disorder symptoms. Brain Imaging Behav. 2018; 13(3):660-668. DOI: 10.1007/s11682-018-9872-6. View