The SAMS: Smartphone Addiction Management System and Verification
While the popularity of smartphones has given enormous convenience to our lives, their pathological use has created a new mental health concern among the community. Hence, intensive research is being conducted on the etiology and treatment of the condition. However, the traditional clinical approach based surveys and interviews has serious limitations: health professionals cannot perform continual assessment and intervention for the affected group and the subjectivity of assessment is questionable. To cope with these limitations, a comprehensive ICT (Information and Communications Technology) system called SAMS (Smartphone Addiction Management System) is developed for objective assessment and intervention. The SAMS system consists of an Android smartphone application and a web application server. The SAMS client monitors the user’s application usage together with GPS location and Internet access location, and transmits the data to the SAMS server. The SAMS server stores the usage data and performs key statistical data analysis and usage intervention according to the clinicians’ decision. To verify the reliability and efficacy of the developed system, a comparison study with survey-based screening with the K-SAS (Korean Smartphone Addiction Scale) as well as self-field trials is performed. The comparison study is done using usage data from 14 users who are 19 to 50 year old adults that left at least 1 week usage logs and completed the survey questionnaires. The field trial fully verified the accuracy of the time, location, and Internet access information in the usage measurement and the reliability of the system operation over more than 2 weeks. The comparison study showed that daily use count has a strong correlation with K-SAS scores, whereas daily use times do not strongly correlate for potentially addicted users. The correlation coefficients of count and times with total K-SAS score are CC = 0.62 and CC =0.07, respectively, and the t-test analysis for the contrast group of potential addicts and the values for the non-addicts were p = 0.047 and p = 0.507, respectively.
KeywordsObjective assessment Smartphone addiction Statistical analysis ICT system
This study was supported by a grant of the Korea Healthcare Technology R&D Project, Ministry for Health Welfare, the Republic of Korea (A120157).
Conflicts of interest
- 1.Zheng, P., Lionel, N., Smart phone and next generation mobile computing, Morgan Kaufmann. 2010.Google Scholar
- 2.Porter, G. Alleviating the dark side of smart phone use. In: Technology and Society (ISTAS), 2010 I.E. International Symposium; June 7–9, Rutgers. Conference Publications 435–440, 2010.Google Scholar
- 3.Kim, D. I., Chung, Y. J., Lee, J. Y., et al., Development of smartphone addiction proneness scale for adults: Self-report. Korean J. Couns. 13(2):629–644, 2012.Google Scholar
- 7.Young, K. S., Internet addiction: symptoms, evaluation, and treatment. In VandeCreek, L., and Jackson, T. (eds.), Innovations in Clinical Practice: A Source Book, 17:19–31.Google Scholar
- 8.Berg, M., Arts, J., and Lei, J., ICT in health care: Socio-technical approaches. Methods Inf. Med. 42(4):297–301, 2003.Google Scholar
- 9.Gustafson, D. H., Boyle, M. G., Shaw, B. R., et al., An e-health solution for people with alcohol problems. Alcohol Res. Health 33(4):327–337, 2011.Google Scholar
- 10.Brady, P., Android anatomy and physiology. In Google IO developer conference. 2008.Google Scholar
- 12.Pautasso, C., Zimmermann, O., Leymann, F., Restful web services vs. big web services: Making the right architectural decision. In Proc. of ACM the 17th Int’l conference on World Wide Web: 805–814. 2008.Google Scholar
- 13.Cattell, R., Scalable SQL and NoSQL data stores. ACM SIGMOD Record39, no. 4, 12–27, 2011.Google Scholar
- 16.Dennison, L., Morrison, L., Conway, G., Yardley, L. Opportunities and challenges for smartphone applications in supporting health behaviors change: qualitative study. J. Med. Internet Res. 15(4) 2013.Google Scholar