Realizing the Potential of Mobile Mental Health: New Methods for New Data in Psychiatry

Abstract

Smartphones are now ubiquitous and can be harnessed to offer psychiatry a wealth of real-time data regarding patient behavior, self-reported symptoms, and even physiology. The data collected from smartphones meet the three criteria of big data: velocity, volume, and variety. Although these data have tremendous potential, transforming them into clinically valid and useful information requires using new tools and methods as a part of assessment in psychiatry. In this paper, we introduce and explore numerous analytical methods and tools from the computational and statistical sciences that appear readily applicable to psychiatric data collected using smartphones. By matching smartphone data with appropriate statistical methods, psychiatry can better realize the potential of mobile mental health and empower both patients and providers with novel clinical tools.

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References

Papers of particular interest, published recently, have been highlighted as: •• Of major importance

  1. 1.

    Takayanagi Y, Spira AP, Roth KB, Gallo JJ, Eaton WW, Mojtabai R. Accuracy of reports of lifetime mental and physical disorders: results from the Baltimore epidemiological catchment area study. JAMA Psychiatr. 2014;71(3):273–80. http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=24402003&retmode=ref&cmd=prlinks.

    Article  Google Scholar 

  2. 2.

    BinDhim NF, Shaman AM, Trevena L, Basyouni MH, Pont LG, Alhawassi TM. Depression screening via a smartphone app: cross-country user characteristics and feasibility. J Am Med Inform Assoc. 2015;22:29–34. doi:10.1136/amiajnl-2014-002840.

    PubMed  Google Scholar 

  3. 3.

    Faurholt-Jepsen M, Frost M, Vinberg M, Christensen EM, Bardram JE, Kessing LV. Smartphone data as objective measures of bipolar disorder symptoms. Psychiatry Res. 2014;217:124–7. http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=24679993&retmode=ref&cmd=prlinks.

    PubMed  Article  Google Scholar 

  4. 4.••

    Ben-Zeev D, Brenner CJ, Begale M, Duffecy J, Mohr DC, Mueser KT. Feasibility, acceptability, and preliminary efficacy of a smartphone intervention for schizophrenia. Schizophr Bull. 2014:1–10. doi:10.1093/schbul/sbu033. This is a significant paper as it demonstrates high levels of patient engagement with a smartphone apps and the immediate clinical applicability of current smartphone technology.

  5. 5.

    Pramana G, Parmanto B, Kendall PC, Silk JS. The SmartCAT: an m-health platform for ecological momentary intervention in child anxiety treatment. Telemed J E Health. 2013;20:419–27. doi:10.1089/tmj.2013.0214.

    Article  Google Scholar 

  6. 6.••

    Gustafson DH, McTavish FM, Chih M-Y, et al. A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA Psychiatr. 2014;71:566–72. doi:10.1001/jamapsychiatry.2013.4642. This is a significant paper as it is one of the largest and most methodologically rigorous studies demonstrating the clinical applicability of smartphones and their ability to collect high volume, variety and velocity data related to clinical care.

    Article  Google Scholar 

  7. 7.

    Glenn T, Monteith S. New measures of mental state and behavior based on data collected from sensors, smartphones, and the Internet. Curr Psychiatry Rep. 2014. doi:10.1007/s11920-014-0523-3.

    Google Scholar 

  8. 8.

    Miller G. The smartphone psychology manifesto. Perspect Psychol Sci. 2012;7(3):221–37. doi:10.1177/1745691612441215.

    PubMed  Article  Google Scholar 

  9. 9.

    Torous J, Friedman R, Keshvan M. Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions. J Med Internet Res. 2014;16. doi:10.2196/mhealth.2994.

  10. 10.••

    Torous J, Chan SR, Tan SY, et al. Patient smartphone ownership and interest in mobile apps to monitor symptoms of mental health conditions: a survey in four geographically distinct psychiatric clinics corresponding author. JMIR Ment Heal. 2014;1:1–7. doi:10.2196/mental.4004. This paper provides the largest dataset, stratified by age and clinic setting, regarding mental health patients’ interest in smartphone monitoring.

    Article  Google Scholar 

  11. 11.

    Donker T, Petrie K, Proudfoot J, Clarke J, Birch MR, Christensen H. Smartphones for smarter delivery of mental health programs: a systematic review. J Med Internet Res. 2013;15. doi:10.2196/jmir.2791.

  12. 12.

    Boudreaux ED, Waring ME, Hayes RB, Sadasivam RS, Mullen S, Pagoto S. Evaluating and selecting mobile health apps: strategies for healthcare providers and healthcare organizations. Transl Behav Med. 2014;4:363–71. doi:10.1007/s13142-014-0293-9.

    PubMed Central  PubMed  Article  Google Scholar 

  13. 13.

    Bush NE, Skopp N, Smolenski D, Crumpton R, Fairall J. Behavioral screening measures delivered with a smartphone app: psychometric properties and user preference. J Nerv Ment Dis. 2013;201:991–5. doi:10.1097/NMD.0000000000000039.

    PubMed  Article  Google Scholar 

  14. 14.

    Press G. 12 big data definitions: what’s yours? Forbes. 2014. http://www.forbes.com/sites/gilpress/2014/09/03/12-big-data-definitions-whats-yours/. Accessed 3 Jan 2015.

  15. 15.

    Markowetz A, Błaszkiewicz K, Montag C, Switala C, Schlaepfer TE. Psycho-informatics: big data shaping modern psychometrics. Med Hypotheses. 2014;82:405–11. doi:10.1016/j.mehy.2013.11.030.

    PubMed  Article  Google Scholar 

  16. 16.

    Selby EA, Yen S, Spirito A. Time varying prediction of thoughts of death and suicidal ideation in adolescents: weekly ratings over 6-month follow-up. J Clin Child Adolesc Psychol. 2013;42:481–95. doi:10.1080/15374416.2012.736356.

    PubMed Central  PubMed  Article  Google Scholar 

  17. 17.

    Ou J, Xie L, Jin C, et al. Characterizing and differentiating brain state dynamics via hidden Markov models. Brain Topogr. 2014. doi:10.1007/s10548-014-0406-2.

    PubMed  Google Scholar 

  18. 18.

    Ip EH, Zhang Q, Rejeski WJ, Harris TB, Kritchevsky S. Partially ordered mixed hidden Markov model for the disablement process of older adults. J Am Stat Assoc. 2013;108(2015):370–80. doi:10.1080/01621459.2013.770307.

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  19. 19.

    Elliot RJ, Aggoun L, Moore JB. Hidden Markov models. 2008.

  20. 20.

    Grunerbl A, Muaremi A, Osmani V, et al. Smart-phone based recognition of states and state changes in bipolar disorder patients. IEEE J Biomed Health Inform. 2014. doi:10.1109/JBHI.2014.2343154.

    PubMed  Google Scholar 

  21. 21.

    Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. Elements. 2009;1:337–87. doi:10.1007/b94608.

    Google Scholar 

  22. 22.••

    James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning. New York: Springer; 2013. This book summarizes a wide variety of the best and most basic methods available to predict and interpret data, written for non-specialists with a focus on application.

    Google Scholar 

  23. 23.

    Burke J a, Estrin D, Hansen M, et al. Participatory sensing. WSW’06 at SenSys’06. 2006.

  24. 24.

    Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT. A survey of mobile phone sensing. IEEE Commun Mag. 2010;48(September):140–50. doi:10.1109/MCOM.2010.5560598.

    Article  Google Scholar 

  25. 25.

    De Boeck P, Wilson M. Explanatory item response models.; 2004:382 pages. doi:10.1007/978-1-4757-3990-9.

  26. 26.

    Liu LC, Hedeker D. A mixed-effects regression model for longitudinal multivariate ordinal data. Biometrics. 2006;62(March):261–8. doi:10.1111/j.1541-0420.2005.00408.x.

    PubMed  Article  Google Scholar 

  27. 27.

    Liu LC, Hedeker D, Mermelstein RJ. Modeling nicotine dependence: an application of a longitudinal IRT model for the analysis of adolescent nicotine dependence syndrome scale. Nicotine Tob Res. 2012;15:326–33. doi:10.1093/ntr/nts125.

    PubMed Central  PubMed  Article  Google Scholar 

  28. 28.

    Iani L, Lauriola M, Costantini M. A confirmatory bifactor analysis of the hospital anxiety and depression scale in an Italian community sample. Health Qual Life Outcomes. 2014;12(1):84. doi:10.1186/1477-7525-12-84.

    PubMed Central  PubMed  Article  Google Scholar 

  29. 29.

    Klasnja P, Pratt W. Healthcare in the pocket: mapping the space of mobile-phone health interventions. J Biomed Inform. 2012;45(1):184–98. doi:10.1016/j.jbi.2011.08.017.

    PubMed Central  PubMed  Article  Google Scholar 

  30. 30.

    Kotsiantis SB. Decision trees: a recent overview. Artif Intell Rev. 2013;39(June 2011):261–83. doi:10.1007/s10462-011-9272-4.

    Article  Google Scholar 

  31. 31.

    Mun M, Reddy S, Shilton K, Yau N. PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. MobiSys. 2009:55–68. doi:10.1145/1555816.1555823.

  32. 32.

    Wu M-J, Wu HE, Mwangi B, et al. Prediction of pediatric unipolar depression using multiple neuromorphometric measurements: a pattern classification approach. J Psychiatr Res. 2015. doi:10.1016/j.jpsychires.2015.01.015.

    Google Scholar 

  33. 33.

    Patel MJ, Andreescu C, Price JC, Edelman KL, Reynolds CF, Aizenstein HJ. Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int J Geriatr Psychiatry. 2015. doi:10.1002/gps.4262.

    PubMed  Google Scholar 

  34. 34.

    Burns MN, Begale M, Duffecy J, et al. Harnessing context sensing to develop a mobile intervention for depression. J Med Internet Res. 2011;13(3):e55. http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=21840837&retmode=ref&cmd=prlinks.

  35. 35.

    Curran PJ, McGinley JS, Bauer DJ, et al. A moderated nonlinear factor model for the development of commensurate measures in integrative data analysis. Multivar Behav Res. 2014;49(February 2015):214–31. doi:10.1080/00273171.2014.889594.

    Article  Google Scholar 

  36. 36.

    Perera C, Zaslavsky A, Christen P, Georgakopoulos D. Context aware computing for the Internet of things: a survey. IEEE Commun Surv Tutorials. 2014;16(1):414–54. doi:10.1109/SURV.2013.042313.00197.

    Article  Google Scholar 

  37. 37.

    Rosenquist JN, Fowler JH, Christakis NA. Social network determinants of depression. Mol Psychiatry. 2011;16(3):273–81. doi:10.1038/mp.2010.48.

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Christakis NA, Fowler JH. Social contagion theory: examining dynamic social networks and human behavior. Stat Med. 2013;32(November 2011):556–77. doi:10.1002/sim.5408.

    PubMed  Article  Google Scholar 

  39. 39.

    Rivlin A, Hawton K, Marzano L, Fazel S. Psychosocial characteristics and social networks of suicidal prisoners: towards a model of suicidal behaviour in detention. PLoS One. 2013;8. doi:10.1371/journal.pone.0068944.

  40. 40.

    Kennedy DP, Adolphs R. The social brain in psychiatric and neurological disorders. Trends Cogn Sci. 2012;16(11):559–72. doi:10.1016/j.tics.2012.09.006.

    PubMed Central  PubMed  Article  Google Scholar 

  41. 41.

    Barabási A-L, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12:56–68. doi:10.1038/nrg2918.

    PubMed Central  PubMed  Article  Google Scholar 

  42. 42.

    Bullmore E, Sporns O. The economy of brain network organization. Nat Rev Neurosci. 2012. doi:10.1038/nrn3214.

    PubMed  Google Scholar 

  43. 43.

    Rubinov M, Bullmore E. Schizophrenia and abnormal brain network hubs. Dialogues Clin Neurosci. 2013;15:339–49.

    PubMed Central  PubMed  Google Scholar 

  44. 44.••

    Onnela J-P, Saramäki J, Hyvönen J, et al. Structure and tie strengths in mobile communication networks. Proc Natl Acad Sci U S A. 2007;104:7332–6. doi:10.1073/pnas.0610245104. This was the first paper to demonstrate the construction of social networks from mobile phone communication data, also known as call detail records (CDRs), which is a form of high-volume passive data collected by telecom operators for billing purposes worldwide.

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  45. 45.

    Onnela J-P, Saramäki J, Hyvönen J, et al. Analysis of a large-scale network of one-to-one human communication. New Journal of Physics. 2007; 9 (9): 179–179. doi:10.1088/1367-2630/9/6/179.

  46. 46.

    Onnela J-P, Arbesman S, Gonzalez M, et al. Geographic constraints on social network groups. PLoS ONE. 2011;6(4), e16939. doi:10.1371/journal.pone.0016939.

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  47. 47.••

    Eagle N, Pentland A. Reality mining: sensing complex social systems. Pers Ubiquit Comput. 2006;10:255–68. doi:10.1007/s00779-005-0046-3. This is a significant paper as it was one of the first to propose mobile phone sensing in computer science in the academic research community.

    Article  Google Scholar 

  48. 48.

    Baller RD, Richardson KK. The “dark side” of the strength of weak ties: the diffusion of suicidal thoughts. J Health Soc Behav. 2009;50:261–76. doi:10.1177/002214650905000302.

    PubMed  Article  Google Scholar 

  49. 49.

    Torous J, Staples P, Shanahan M, Charlie L, Pamela P, Keshavan M, et al. Utilizing a personal mobile phone custom app to assess the Patient Health Questionnaire-9 depressive symptoms in patients with major depressive disorder. JMIR Ment Heal. 2015;2(1):e8.

    Article  Google Scholar 

  50. 50.

    Powell AC, Landman AB, Bates DW. In search of a few good apps. JAMA. 2014;311(18):1851–2.

    CAS  PubMed  Article  Google Scholar 

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Acknowledgments

PS is supported by NIH-95T32ES0071429-32 (PI Coull) and JPO by NIH-1DP2MH1039099-01 (PI Onnela).

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Conflict of Interest

John Torous, Patrick Staples, and Jukka-Pekka Onnela declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

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Correspondence to John Torous.

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This article is part of the Topical Collection on Psychiatry in the Digital Age

John Torous and Patrick Staples contributed equally and are co-first authors.

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Torous, J., Staples, P. & Onnela, J. Realizing the Potential of Mobile Mental Health: New Methods for New Data in Psychiatry. Curr Psychiatry Rep 17, 61 (2015). https://doi.org/10.1007/s11920-015-0602-0

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Keywords

  • Smartphones
  • Mobile
  • Big data
  • Technology
  • Statistical methods