Relapse prediction in schizophrenia through digital phenotyping: a pilot study
Relapse prediction in schizophrenia through digital phenotyping: a pilot study
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DOI：
10.1038/s41386-018-0030-z
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发表时间：
2018-07-01
影响因子：
7.6
通讯作者：
Onnela, Jukka-Pekka
中科院分区：
文献类型：
--
作者：
Barnett, Ian;Torous, John;Onnela, Jukka-Pekka
在因精神分裂症被诊断、住院和治疗的个体中，即使接受适当的治疗，高达 40% 的出院患者可能会在 1 年内复发。被动收集的智能手机行为数据提供了一个可扩展且目前未充分利用的机会来监测患者，以识别可能的复发警告信号。波士顿一家州立心理健康诊所的 17 名精神分裂症患者正在接受积极治疗，他们在个人智能手机上使用 Beiwe 应用程序长达 3 个月。通过测试通过智能手机使用测量的移动模式和社交行为随时间的变化，我们能够识别出复发前几天患者行为的统计上显着的异常情况。我们发现，复发前 2 周内检测到的行为异常率比其他时间段的异常率高 71%。我们的研究结果表明，被动智能手机数据（即常规手机使用过程中在后台收集的数据，无需受试者主动输入）如何能够提供对诊所外患者行为的前所未有的详细了解。实时检测行为异常可以表明需要在症状升级和复发之前进行干预，从而减少患者的痛苦并降低护理成本。
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.