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Relapse prediction in schizophrenia through digital phenotyping: a pilot study


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.

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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.

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The authors declare no competing interests.

Correspondence to Ian Barnett.

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