Realizing the Potential of Mobile Mental Health: New Methods for New Data in Psychiatry
- 1.6k Downloads
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.
KeywordsSmartphones Mobile Big data Technology Statistical methods
PS is supported by NIH-95T32ES0071429-32 (PI Coull) and JPO by NIH-1DP2MH1039099-01 (PI Onnela).
Compliance with Ethics Guidelines
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.
Papers of particular interest, published recently, have been highlighted as: •• Of major importance
- 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.CrossRefGoogle Scholar
- 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.PubMedCrossRefGoogle Scholar
- 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.
- 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. CrossRefGoogle Scholar
- 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.••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. CrossRefGoogle Scholar
- 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.
- 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.
- 19.Elliot RJ, Aggoun L, Moore JB. Hidden Markov models. 2008.Google Scholar
- 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. CrossRefGoogle Scholar
- 23.Burke J a, Estrin D, Hansen M, et al. Participatory sensing. WSW’06 at SenSys’06. 2006.Google Scholar
- 25.De Boeck P, Wilson M. Explanatory item response models.; 2004:382 pages. doi: 10.1007/978-1-4757-3990-9.
- 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.
- 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.
- 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.
- 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. PubMedCentralPubMedCrossRefGoogle Scholar
- 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.
- 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. CrossRefGoogle Scholar