Relapse prediction in schizophrenia through digital phenotyping: a pilot study

Abstract

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.

Nonsocial and social cognition in schizophrenia: current evidence and future directions

“…Passive data collection, on the other hand, does not require actions from participants. These include using the global positioning system (GPS) functions to estimate the number of locations or the amount of distance traveled by participants. The ability to collect passive data, for long periods of time and with no effort on the part of the subjects, opens up a new world of information derived from big data.…”

A qualitative exploration of service user views about using digital health interventions for self-management in severe mental health problems

“…Interventions delivered via other technologies such as video-conferencing, telephone and virtual reality were, therefore, not considered. Additionally, researchers are beginning to explore how relapse can be predicted via passively collected smartphone data (digital phenotyping) [61]. Future research should explore both clinician and service user views towards passive data collection methods to determine its acceptability.…”

Understanding the quality, effectiveness and attributes of top-rated smartphone health apps

“…From apps that collect smartphone sensor data that researcher can use to predict relapse in schizophrenia6 to others used to monitor heart conditions in real time,7 the potential of apps across healthcare is vast. However, the majority of apps featured in the research literature are often not available to the public 8.…”