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.

中文翻译：

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通过数字表型在精神分裂症中的复发预测：一项初步研究。

在诊断，住院和治疗过精神分裂症的个体中，即使接受适当治疗，出院者中有高达40％可能在1年内复发。被动收集的智能手机行为数据为监测患者提供了可扩展且目前未充分利用的机会，以识别可能的复发征兆。在波士顿的一家州精神健康诊所接受积极治疗的精神分裂症患者共有17名，使用其个人智能手机上的Beiwe应用程序长达3个月。通过测试随着时间推移（通过使用智能手机进行测量）的出行方式和社交行为的变化，我们能够确定复发前几天患者行为的统计学显着异常。我们发现，在复发前2周内发现的行为异常发生率比其他时间段内的异常发生率高71％。我们的研究结果表明，无源智能手机数据（在常规电话使用过程中在后台收集的数据，而没有受试者的积极输入）如何能够提供前所未有的详细视图，了解诊所外的患者行为。行为异常的实时检测可以表明在症状升级和复发之前需要进行干预，从而减少了患者的痛苦并降低了护理成本。可以对诊所外的患者行为提供前所未有的详细视图。行为异常的实时检测可以表明在症状升级和复发发生之前需要进行干预，从而减少了患者的痛苦并降低了护理成本。可以对诊所外的患者行为提供前所未有的详细视图。行为异常的实时检测可以表明在症状升级和复发之前需要进行干预，从而减少了患者的痛苦并降低了护理成本。