Skip to main content

Smart Healthcare Systems and Precision Medicine

  • Chapter
  • First Online:
Frontiers in Psychiatry

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1192))

Abstract

This article gives an overview of the concept and brain mechanisms of Internet game and smartphone addiction and the applicability of precision medicine and smart healthcare system. Internet game and smartphone addiction are categorized as behavioral addictions, which share similar phenomenology and neurobiological underpinnings with substance addictions. Neuroimaging studies revealed the alteration in the functional activity and structure of individuals with Internet game and smartphone addiction, which also can be potent biomarkers. Precision medicine is defined as treatments targeted to the individual patients on the basis of genetic, biomarker, phenotypic or psychosocial characteristics. Recent advances in high-throughput technology and bioinformatics have enabled us to integrate these big data with behavioral data collected from smartphones or other wearable devices. Data collected via smart devices can be transferred to medical institute and integrated in order to diagnose current status precisely and to provide optimal intervention. The feedbacks of intervention are sent back to the medical provider via self-reports or objective measures to evaluate the appropriateness of the intervention. In conclusion, Internet game and smartphone addiction can be diagnosed precisely using high-throughput technology and optimally managed via smart healthcare system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Penetration rate of gamers among the general population in the United States from 2013 to 2018. https://www.statista.com/statistics/748835/us-gamers-penetration-rate/.

  2. Game users survey report 2016. Korea Creative Content Agency. 2016. http://www.kocca.kr/cop/bbs/view/B0000147/1831102.do?menuNo=200904.

  3. D Griffiths M, J Kuss D, L King D. Video game addiction: past, present and future. Curr Psychiatry Rev. 2012;8(4):308–18.

    Google Scholar 

  4. Soper WB, Miller MJ. Junk-time junkies: an emerging addiction among students. Sch Couns. 1983;31(1):40–3.

    Google Scholar 

  5. Yee N. The demographics, motivations, and derived experiences of users of massively multi-user online graphical environments. Presence: Teleoperators Virtual Environ. 2006;15(3):309–29.

    Article  Google Scholar 

  6. Holden C. ‘Behavioral’ addictions: do they exist? American Association for the Advancement of Science; 2001.

    Google Scholar 

  7. O’brien C. Addiction and dependence in DSM‐V. Addiction. 2011;106(5):866–7.

    Article  PubMed  Google Scholar 

  8. Young KS. Internet addiction: The emergence of a new clinical disorder. Cyberpsychol & Behav. 1998;1(3):237–44.

    Article  Google Scholar 

  9. Van Rooij AJ, Schoenmakers TM, Van de Eijnden RJ, Van de Mheen D. Compulsive internet use: the role of online gaming and other internet applications. J Adolesc Health. 2010;47(1):51–7.

    Article  PubMed  Google Scholar 

  10. Király O, Nagygyörgy K, Griffiths MD, Demetrovics Z. Problematic online gaming. Behav Addict: Elsevier; 2014. p. 61–97.

    Google Scholar 

  11. Lemmens JS, Valkenburg PM, Peter J. Development and validation of a game addiction scale for adolescents. Media Psychol. 2009;12(1):77–95.

    Article  Google Scholar 

  12. Tejeiro Salguero RA, Morán RMB. Measuring problem video game playing in adolescents. Addiction. 2002;97(12):1601–6.

    Article  PubMed  Google Scholar 

  13. American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Association Publishing; 2013.

    Google Scholar 

  14. World Health Organization. ICD-11 for mortality and morbidity statistics DRAFT. 2018. https://icd.who.int/browse11/l-m/en#/http%3a%2f%2fid.who.int%2ficd%2fentity%2f1448597234.

  15. Kwon M, Nam K, Seo B. A survey on internet overdependence; 2015. Ministry of Science: ICT, and Future Planning, Korea; 2016.

    Google Scholar 

  16. İNal EE, Demİrcİ k, Çetİntürk A, Akgönül M, Savaş S. Effects of smartphone overuse on hand function, pinch strength, and the median nerve. Muscle & Nerve. 2015;52(2):183–8.

    Google Scholar 

  17. Cazzulino F, Burke RV, Muller V, Arbogast H, Upperman JS. Cell phones and young drivers: a systematic review regarding the association between psychological factors and prevention. Traffic Inj Prev. 2014;15(3):234–42.

    Article  PubMed  Google Scholar 

  18. Kim H-J, Min J-Y, Kim H-J, Min K-B. Accident risk associated with smartphone addiction: a study on university students in Korea. J Behav Addict. 2017;6(4):699–707.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Hawi NS, Samaha M. To excel or not to excel: strong evidence on the adverse effect of smartphone addiction on academic performance. Comput Educ. 2016;98:81–9.

    Article  Google Scholar 

  20. Boumosleh JM, Jaalouk D. Depression, anxiety, and smartphone addiction in university students-a cross sectional study. PLoS ONE. 2017;12(8):e0182239.

    Article  CAS  Google Scholar 

  21. Demirci K, Akgönül M, Akpinar A. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. J Behav Addict. 2015;4(2):85–92.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kwon M, Lee J-Y, Won W-Y, Park J-W, Min J-A, Hahn C, et al. Development and validation of a smartphone addiction scale (SAS). PLoS ONE. 2013;8(2):e56936.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Billieux J, Van der Linden M, Rochat L. The role of impulsivity in actual and problematic use of the mobile phone. Appl Cogn Psychol: Off J Soc Appl Res Mem Cogn. 2008;22(9):1195–210.

    Article  Google Scholar 

  24. Ministry of Science and ICT NISA. A survey on smartphone overdependence 2017. 2018.

    Google Scholar 

  25. Lopez-Fernandez O, Kuss DJ, Romo L, Morvan Y, Kern L, Graziani P, et al. Self-reported dependence on mobile phones in young adults: a European cross-cultural empirical survey. J Behav Addict. 2017;6(2):168–77.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Panova T, Carbonell X. Is smartphone addiction really an addiction? J Behav Addict. 2018:1–8.

    Google Scholar 

  27. Zhang Y, Ndasauka Y, Hou J, Chen J, Wang Y, Han L, et al. Cue-induced behavioral and neural changes among excessive internet gamers and possible application of cue exposure therapy to internet gaming disorder. Front Psychol. 2016;7:675.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Yao Y-W, Chen P-R, Li S, Wang L-J, Zhang J-T, Yip SW, et al. Decision-making for risky gains and losses among college students with Internet gaming disorder. PLoS ONE. 2015;10(1):e0116471.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Choi J-S, Park SM, Roh M-S, Lee J-Y, Park C-B, Hwang JY, et al. Dysfunctional inhibitory control and impulsivity in internet addiction. Psychiatry Res. 2014;215(2):424–8.

    Article  PubMed  Google Scholar 

  30. Walton E, Turner JA, Ehrlich S. Neuroimaging as a potential biomarker to optimize psychiatric research and treatment. Int Rev Psychiatry. 2013;25(5):619–31.

    Article  PubMed  Google Scholar 

  31. Bullmore E. The future of functional MRI in clinical medicine. Neuroimage. 2012;62(2):1267–71.

    Article  PubMed  Google Scholar 

  32. Mitterschiffthaler MT, Ettinger U, Mehta MA, Mataix-Cols D, Williams SC. Applications of functional magnetic resonance imaging in psychiatry. J Magn Reson Imaging: Off J Int Soc Magn Reson Med. 2006;23(6):851–61.

    Article  Google Scholar 

  33. Chen CY, Huang MF, Yen JY, Chen CS, Liu GC, Yen CF, et al. Brain correlates of response inhibition in internet gaming disorder. Psychiatry Clin Neurosci. 2015;69(4):201–9.

    Article  PubMed  Google Scholar 

  34. Ding W-n, Sun J-h, Sun Y-w, Chen X, Zhou Y, Zhuang Z-g, et al. Trait impulsivity and impaired prefrontal impulse inhibition function in adolescents with internet gaming addiction revealed by a Go/No-Go fMRI study. Behav Brain Funct. 2014;10(1):20.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Dong G, Huang J, Du X. Enhanced reward sensitivity and decreased loss sensitivity in Internet addicts: an fMRI study during a guessing task. J Psychiatr Res. 2011;45(11):1525–9.

    Article  PubMed  Google Scholar 

  36. Dong G, Hu Y, Lin X. Reward/punishment sensitivities among internet addicts: Implications for their addictive behaviors. Prog Neuropsychopharmacol Biol Psychiatry. 2013;46:139–45.

    Article  PubMed  Google Scholar 

  37. Ko C-H, Liu G-C, Hsiao S, Yen J-Y, Yang M-J, Lin W-C, et al. Brain activities associated with gaming urge of online gaming addiction. J Psychiatr Res. 2009;43(7):739–47.

    Article  PubMed  Google Scholar 

  38. Ko CH, Liu GC, Yen JY, Chen CY, Yen CF, Chen CS. Brain correlates of craving for online gaming under cue exposure in subjects with Internet gaming addiction and in remitted subjects. Addict Biol. 2013;18(3):559–69.

    Article  PubMed  Google Scholar 

  39. Han DH, Hwang JW, Renshaw PF. Bupropion sustained release treatment decreases craving for video games and cue-induced brain activity in patients with Internet video game addiction. 2011.

    Google Scholar 

  40. Chun J, Choi J, Cho H, Lee S, Kim D. Dysfunction of the frontolimbic region during swear word processing in young adolescents with Internet gaming disorder. Transl Psychiatry. 2015;5(8):e624.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Sepede G, Tavino M, Santacroce R, Fiori F, Salerno RM, Di Giannantonio M. Functional magnetic resonance imaging of internet addiction in young adults. World J Radiol. 2016;8(2):210.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Kim H, Kim YK, Gwak AR, Lim J-A, Lee J-Y, Jung HY, et al. Resting-state regional homogeneity as a biological marker for patients with Internet gaming disorder: a comparison with patients with alcohol use disorder and healthy controls. Prog Neuropsychopharmacol Biol Psychiatry. 2015;60:104–11.

    Article  PubMed  Google Scholar 

  43. Dong G, Huang J, Du X. Alterations in regional homogeneity of resting-state brain activity in internet gaming addicts. Behav Brain Funct. 2012;8(1):41.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Meng Y, Deng W, Wang H, Guo W, Li T. The prefrontal dysfunction in individuals with Internet gaming disorder: a meta-analysis of functional magnetic resonance imaging studies. Addict Biol. 2015;20(4):799–808.

    Article  PubMed  Google Scholar 

  45. Park Ch, Chun JW, Cho H, Jung YC, Choi J, Kim DJ. Is the I nternet gaming-addicted brain close to be in a pathological state? Addict Biol. 2017;22(1):196–205.

    Article  CAS  PubMed  Google Scholar 

  46. Wang H, Jin C, Yuan K, Shakir TM, Mao C, Niu X, et al. The alteration of gray matter volume and cognitive control in adolescents with internet gaming disorder. Front Behav Neurosci. 2015;9:64.

    PubMed  PubMed Central  Google Scholar 

  47. Lin X, Dong G, Wang Q, Du X. Abnormal gray matter and white matter volume in ‘Internet gaming addicts’. Addict Behav. 2015;40:137–43.

    Article  PubMed  Google Scholar 

  48. Yuan K, Cheng P, Dong T, Bi Y, Xing L, Yu D, et al. Cortical thickness abnormalities in late adolescence with online gaming addiction. PLoS ONE. 2013;8(1):e53055.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Choi J, Cho H, Kim J-Y, Jung DJ, Ahn KJ, Kang H-B, et al. Structural alterations in the prefrontal cortex mediate the relationship between internet gaming disorder and depressed mood. Sci Rep. 2017;7(1):1245.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Park C-h, Chun J-W, Cho H, Kim D-J. Discriminating pathological and non-pathological internet gamers using sparse neuroanatomical features. Front Psychiatry. 2018;9:291.

    Google Scholar 

  51. Lee J, Cho B, Kim Y, Noh J. Smartphone addiction in university students and its implication for learning. Emerging issues in smart learning. Springer; 2015. p. 297–305.

    Google Scholar 

  52. Stothart C, Mitchum A, Yehnert C. The attentional cost of receiving a cell phone notification. J Exp Psychol Hum Percept Perform. 2015;41(4):893.

    Article  PubMed  Google Scholar 

  53. Uncapher MR, Thieu MK, Wagner AD. Media multitasking and memory: differences in working memory and long-term memory. Psychon Bull Rev. 2016;23(2):483–90.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Moisala M, Salmela V, Hietajärvi L, Salo E, Carlson S, Salonen O, et al. Media multitasking is associated with distractibility and increased prefrontal activity in adolescents and young adults. NeuroImage. 2016;134:113–21.

    Article  CAS  PubMed  Google Scholar 

  55. Hu Y, Long X, Lyu H, Zhou Y, Chen J. Alterations in white matter integrity in young adults with smartphone dependence. Front Hum Neurosci. 2017;11:532.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Chun J-W, Choi J, Cho H, Choi M-R, Ahn K-J, Choi J-S, et al. Role of frontostriatal connectivity in adolescents with excessive smartphone use. Front Psychiatry. 2018;9:437.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Chun J-W, Choi J, Kim J-Y, Cho H, Ahn K-J, Nam J-H, et al. Altered brain activity and the effect of personality traits in excessive smartphone use during facial emotion processing. Sci Rep. 2017;7(1):12156.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Jameson JL, Longo DL. Precision medicine—personalized, problematic, and promising. Obstet Gynecol Surv. 2015;70(10):612–4.

    Article  Google Scholar 

  59. Insel TR. The NIMH research domain criteria (RDoC) project: precision medicine for psychiatry. Am J Psychiatry. 2014;171(4):395–7.

    Article  PubMed  Google Scholar 

  60. Chamorro AJ, Marcos M, Mirón-Canelo JA, Pastor I, González-Sarmiento R, Laso FJ. Association of µ-opioid receptor (OPRM1) gene polymorphism with response to naltrexone in alcohol dependence: a systematic review and meta-analysis. Addict Biol. 2012;17(3):505–12.

    Article  CAS  PubMed  Google Scholar 

  61. Kim D-J, Choi I-G, Park BL, Lee B-C, Ham B-J, Yoon S, et al. Major genetic components underlying alcoholism in Korean population. Hum Mol Genet. 2007;17(6):854–8.

    Article  PubMed  CAS  Google Scholar 

  62. Farris SP, Pietrzykowski AZ, Miles MF, O’Brien MA, Sanna PP, Zakhari S, et al. Applying the new genomics to alcohol dependence. Alcohol. 2015;49(8):825–36.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Agrawal A, Bierut LJ. Identifying genetic variation for alcohol dependence. Alcohol Res Curr Rev. 2012;34(3):274.

    Google Scholar 

  64. Gorini G, Harris RA, Mayfield RD. Proteomic approaches and identification of novel therapeutic targets for alcoholism. Neuropsychopharmacology. 2014;39(1):104.

    Article  CAS  PubMed  Google Scholar 

  65. Mostafa H, Amin AM, Teh C-H, Murugaiyah V, Arif NH, Ibrahim B. Metabolic phenotyping of urine for discriminating alcohol-dependent from social drinkers and alcohol-naive subjects. Drug Alcohol Depend. 2016;169:80–4.

    Article  CAS  PubMed  Google Scholar 

  66. Moeller SJ, Paulus MP. Toward biomarkers of the addicted human brain: Using neuroimaging to predict relapse and sustained abstinence in substance use disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2018;80:143–54.

    Article  CAS  PubMed  Google Scholar 

  67. Schacht JP, Anton RF, Myrick H. Functional neuroimaging studies of alcohol cue reactivity: a quantitative meta-analysis and systematic review. Addict Biol. 2013;18(1):121–33.

    Article  PubMed  Google Scholar 

  68. Justice AC, McGinnis KA, Tate JP, Xu K, Becker WC, Zhao H, et al. Validating harmful alcohol use as a phenotype for genetic discovery using phosphatidylethanol and a polymorphism in ADH 1B. Alcohol: Clin Exp Res. 2017;41(5):998–1003.

    Google Scholar 

  69. Connor J, Symons M, Feeney G, Young RM, Wiles J. The application of machine learning techniques as an adjunct to clinical decision making in alcohol dependence treatment. Subst Use Misuse. 2007;42(14):2193–206.

    Article  CAS  PubMed  Google Scholar 

  70. Sivagami S, Revathy D, Nithyabharathi L. Smart health care system implemented using IoT. Int J Contemp Res Comput Sci Technol. 2016;2(3).

    Google Scholar 

  71. Rooke S, Thorsteinsson E, Karpin A, Copeland J, Allsop D. Computer-delivered interventions for alcohol and tobacco use: a meta-analysis. Addiction. 2010;105(8):1381–90.

    Article  PubMed  Google Scholar 

  72. Free C, Knight R, Robertson S, Whittaker R, Edwards P, Zhou W, et al. Smoking cessation support delivered via mobile phone text messaging (txt2stop): a single-blind, randomised trial. The Lancet. 2011;378(9785):49–55.

    Article  Google Scholar 

  73. Rodgers A, Corbett T, Bramley D, Riddell T, Wills M, Lin R-B, et al. Do u smoke after txt? Results of a randomised trial of smoking cessation using mobile phone text messaging. Tob Control. 2005;14(4):255–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Buller DB, Borland R, Bettinghaus EP, Shane JH, Zimmerman DE. Randomized trial of a smartphone mobile application compared to text messaging to support smoking cessation. Telemed E-Health. 2014;20(3):206–14.

    Article  Google Scholar 

  75. Gustafson DH, Shaw BR, Isham A, Baker T, Boyle MG, Levy M. Explicating an evidence-based, theoretically informed, mobile technology-based system to improve outcomes for people in recovery for alcohol dependence. Subst Use Misuse. 2011;46(1):96–111.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Gustafson DH, McTavish FM, Chih M-Y, Atwood AK, Johnson RA, Boyle MG, et al. A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA Psychiatry. 2014;71(5):566–72.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Greenfield TK, Bond J, Kerr WC. Biomonitoring for improving alcohol consumption surveys: the new gold standard? Alcohol Res Curr Rev. 2014;36(1):39.

    Google Scholar 

  78. Sakai JT, Mikulich-Gilbertson SK, Long RJ, Crowley TJ. Validity of transdermal alcohol monitoring: fixed and self-regulated dosing. Alcohol: Clin Exp Res. 2006;30(1):26–33.

    Article  CAS  Google Scholar 

  79. Swift R. Transdermal alcohol measurement for estimation of blood alcohol concentration. Alcohol: Clin Exp Res. 2000;24(4):422–3.

    Article  CAS  Google Scholar 

  80. Barnett NP, Celio MA, Tidey JW, Murphy JG, Colby SM, Swift RM. A preliminary randomized controlled trial of contingency management for alcohol use reduction using a transdermal alcohol sensor. Addiction. 2017;112(6):1025–35.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Barnett NP, Tidey J, Murphy JG, Swift R, Colby SM. Contingency management for alcohol use reduction: a pilot study using a transdermal alcohol sensor. Drug Alcohol Depend. 2011;118(2–3):391–9.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Choi J, Rho MJ, Kim Y, Yook IH, Yu H, Kim D-J, et al. Smartphone dependence classification using tensor factorization. PLoS ONE. 2017;12(6):e0177629.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Rho MJ, Jeong J-E, Chun J-W, Cho H, Jung DJ, Choi IY, et al. Predictors and patterns of problematic Internet game use using a decision tree model. J Behav Addict. 2016;5(3):500–9.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55(1):68.

    Article  CAS  PubMed  Google Scholar 

  85. Pingree S, Hawkins R, Baker T, DuBenske L, Roberts LJ, Gustafson DH. The value of theory for enhancing and understanding e-health interventions. Am J Prev Med. 2010;38(1):103–9.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dai-Jin Kim .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Paik, SH., Kim, DJ. (2019). Smart Healthcare Systems and Precision Medicine. In: Kim, YK. (eds) Frontiers in Psychiatry. Advances in Experimental Medicine and Biology, vol 1192. Springer, Singapore. https://doi.org/10.1007/978-981-32-9721-0_13

Download citation

Publish with us

Policies and ethics