Among individuals diagnosed, hospitalized, and treated for schizophrenia, up to 40% of those discharged may relapse within 1 year even with appropriate treatment. Passively collected smartphone behavioral data present a scalable and at present underutilized opportunity to monitor patients in order to identify possible warning signs of relapse. Seventeen patients with schizophrenia in active treatment at a state mental health clinic in Boston used the Beiwe app on their personal smartphone for up to 3 months. By testing for changes in mobility patterns and social behavior over time as measured through smartphone use, we were able to identify statistically significant anomalies in patient behavior in the days prior to relapse. We found that the rate of behavioral anomalies detected in the 2 weeks prior to relapse was 71% higher than the rate of anomalies during other time periods. Our findings show how passive smartphone data, data collected in the background during regular phone use without active input from the subjects, can provide an unprecedented and detailed view into patient behavior outside the clinic. Real-time detection of behavioral anomalies could signal the need for an intervention before an escalation of symptoms and relapse occur, therefore reducing patient suffering and reducing the cost of care.
Access optionsAccess options
Subscribe to Journal
Get full journal access for 1 year
only $43.69 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Emsley R, Chiliza B, Asmal L, Harvey BH. The nature of relapse in schizophrenia. BMC Psychiatry. 2013;13:50.
Almond S, Knapp M, Francois C, Toumi M, Brugha T. Relapse in schizophrenia: costs, clinical outcomes and quality of life. Br J Psychiatry. 2004;184:346–51.
Remington G, Foussias G, Agid O, Fervaha G, Takeuchi H, Hahn M. The neurobiology of relapse in schizophrenia. Schizophr Res. 2014;152:381–90.
Tibbo P, Malla A, Manchanda R, Williams R, Joober R. Relapse risk assessment in early phase psychosis: the search for a reliable and valid tool. Can J Psychiatry. 2014;59:655–8.
Španiel F, Vohlídka P, Hrdlička J, Kožený J, Novák T, Motlová L, et al. ITAREPS: information technology aided relapse prevention programme in schizophrenia. Schizophr Res. 2008;98:312–7.
Torous J, Firth J, Mueller N, Onnela J-P, Baker JT. Methodology and reporting of mobile heath and smartphone application studies for schizophrenia. Harv Rev Psychiatry. 2017;25:146–54.
Torous J, Onnela J-P, Keshavan M. New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Transl Psychiatry. 2017;7:e1053.
Onnela J-P, Rauch SL. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology. 2016;41:1691–6.
Firth J, Cotter J, Torous J, Bucci S, Firth JA, Yung AR. Mobile phone ownership and endorsement of “mHealth” among people with psychosis: a meta-analysis of cross-sectional studies. Schizophr Bull. 2015;42:448–55.
Gay K, Torous J, Joseph A, Pandya A, Duckworth K. Digital technology use among individuals with schizophrenia: results of an online survey. JMIR Ment Health. 2016;3:e15.
Johnson J. What’s holding fitness wearables back? Brodeur Partners, 2015. https://www.prnewswire.com/news-releases/whats-holding-fitness-wearables-back-300150936.html
Ennis L, Rose D, Denis M, Pandit N, Wykes T. Can’t surf, won’t surf: the digital divide in mental health. J Ment Health. 2012;21:395–403.
Schlosser D, Campellone T, Kim D, Truong B, Vergani S, Ward C et al. Feasibility of PRIME: a cognitive neuroscience-informed mobile app intervention to enhance motivated behavior and improve quality of life in recent onset schizophrenia. JMIR Res Protoc. 2016;5:e77.
Grünerbl A, Muaremi A, Osmani V, Bahle G, Oehler S, Tröster G, et al. Smartphone-based recognition of states and state changes in bipolar disorder patients. IEEE J Biomed Health Inform. 2015;19:140–8.
Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP et al. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J Med Internet Res. 2015;17:e175.
Wang R, Aung MS, Abdullah S, Brian R, Campbell AT, Choudhury T, Hauser M, Kane J, Merrill M, Scherer EA, Tseng VW. CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM; 2016.
Faherty LJ, Hantsoo L, Appleby D, Sammel MD, Bennett IM, Wiebe DJ. Movement patterns in women at risk for perinatal depression: use of a mood-monitoring mobile application in pregnancy. J Am Med Inform Assoc. 2017;24:746–753
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;40:1244–53.
Torous J, Kiang MV, Lorme J, Onnela J-P. New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR Ment Health 2016;3:e16.
Torous J, Staples P, Onnela J-P. Realizing the potential of mobile mental health: new methods for new data in psychiatry. Curr Psychiatry Rep. 2015;17:1–7.
Csernansky JG, Mahmoud R, Brenner R. A comparison of risperidone and haloperidol for the prevention of relapse in patients with schizophrenia. N Engl J Med. 2002;346:16–22.
Shin IRNM, Lee SHNK, Chong S. Human mobility patterns and their impact on routing in human-driven mobile networks. HotNets-VI, 2017.
Barnett I, Onnela J-P. Inferring mobility measures from GPS traces with missing data. arXiv preprint arXiv:160606328 2016.
Filzmoser P. A multivariate outlier detection method, vol. 1. Proceedings of the Seventh International Conference on Computer Data Analysis and Modeling; 2004.
Li J, Pedrycz W, Jamal I. Multivariate time series anomaly detection: a framework of hidden Markov models. Applied Soft Computing; 2017.
Idé T, Papadimitriou S, Vlachos M. Computing correlation anomaly scores using stochastic nearest neighbors. Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on IEEE; 2007.
Qiu H, Liu Y, Subrahmanya NA, Li W. Granger causality for time-series anomaly detection. Data Mining (ICDM), 2012 IEEE 12th International Conference on IEEE; 2012.
Cheng H, Tan PN, Potter C, Klooster S. Detection and characterization of anomalies in multivariate time series. Proceedings of the 2009 SIAM International Conference on Data Mining. SIAM; 2009.
Arora S, Ford K, Terp S, Abramson T, Ruiz R, Camilon M, et al. Describing the evolution of mobile technology usage for Latino patients and comparing findings to national mHealth estimates. J Am Med Inform Assoc. 2016;23:979–83.
Blumberg SJ, Luke, Julian V. Wireless substitution: early release of estimates from the National Health Interview Survey, January–June 2016. National Center for Health Statistics; 2016;201:1-17.
Adler NE, Newman K. Socioeconomic disparities in health: pathways and policies. Health Aff. 2002;21:60–76.
Chen E, Miller GE. Socioeconomic status and health: mediating and moderating factors. Annu Rev Clin Psychol. 2013;9:723–49.
Osmani V. Smartphones in mental health: detecting depressive and manic episodes. IEEE Pervasive Comput. 2015;14:10–13.
Goasduff LF, Forni AA. Gartner says worldwide sales of smartphones grew 7 percent in the fourth quarter of 2016. Stamford: Gartner; 2017. https://www.gartner.com/newsroom/id/3609817.
Canzian L, Musolesi M. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. ACM; 2015..
IB, PS, and J-PO are supported by NIH/NIMH 1DP2MH103909 (PI: J-PO) and the Harvard McLennan Dean’s Challenge Program (PI: J-PO). JT, LS, and MK are supported by the Natalia Mental Health Foundation. JT is also supported by a Dupont-Warren Fellowship from the Harvard Medical School Department of Psychiatry as well as a Young Investigator Grant form the Brain and Behavior Research Foundation.
Electronic supplementary material
About this article
A qualitative exploration of service user views about using digital health interventions for self-management in severe mental health problems
BMC Psychiatry (2019)
Evaluating digital medicine ingestion data from seriously mentally ill patients with a Bayesian Hybrid Model
npj Digital Medicine (2019)
Digital phenotyping approaches and mobile devices enhance CNS biopharmaceutical research and development