Smartphone relapse prediction in serious mental illness: a pathway towards personalized preventive care.
Smartphone relapse prediction in serious mental illness: a pathway towards personalized preventive care.
复制标题
智能手机对严重精神疾病的复发预测：个性化预防护理的途径。
DOI：
10.1002/wps.20805
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Kane,John
中科院分区：
文献类型：
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作者：
Torous,John;Choudhury,Tanzeem;Barnett,Ian;Keshavan,Matcheri;Kane,John
想象一下，一款智能手机应用程序可以根据地理位置数据（表明靠近酒类商店）和实时调查（表明渴望增加）来了解患者何时有复发的风险。智能手机检测到这种迫在眉睫的风险，提醒临床医生，病人在几分钟内收到个人检查。这样的系统在2020年听起来并不未来，十年前也不是，当时进行了上述酒精综合健康增强支持系统（A-CHESS）研究。十年后，智能手机复发预测系统正在催化精神卫生保健的范式转变，而现在COVID-19大流行进一步加速了这一转变。随着这些方法继续使动态和纵向建模的风险，个性化的预防保健是触手可及的。智能手机对主要精神疾病复发预测的证据令人印象深刻。如今，可以使用智能手机和可穿戴设备为症状、功能、认知和生理学建立动态数字代理-通常称为数字表型分析2。举例来说：来自全球定位系统（GPS）等传感器的被动智能手机数据可以提供位置信息;加速度计可以提供睡眠信息;来自调查（通常称为生态瞬时评估）的主动数据可以捕获真实的时间症状;来自手机交互的元数据可以表征认知;来自可穿戴设备的数据可以提供生理测量信息。捕获这些不同的数据流是非常可行的。mindLAMP等开源和免费平台允许世界各地的团队参与这项工作2。使用这些数字数据流的不同组合，研究显示了对精神分裂症3、抑郁症4、双相情感障碍5和物质滥用1复发风险的临床可操作评估。此外，有关口语和书面语言以及社交媒体使用（通常通过智能手机访问）的数据也在增强复发预测。至少自2018年以来，人们一直在努力通过真实的时间自然语言处理来预测美国的自杀企图。
Imagine a smartphone app that knows when a patient is at risk of relapsing on alcohol use based on geolocation data indicating proximity to a liquor store and real-time surveys suggesting elevated craving. The smartphone detects this imminent risk, alerts a clinician, and the patient receives a personal check-in within minutes. Such a system does not sound futuristic in 2020, neither was it a decade ago, when the Alcohol-Comprehensive Health Enhancement Support System (A-CHESS) study, described above, was conducted1. Ten years later, smartphone relapse prediction systems are catalyzing a paradigm shift in mental health care that is now further accelerated by the COVID-19 pandemic. As these approaches continue to enable dynamic and longitudinal modeling of risk, personalized preventive care is within reach. The evidence for smartphone relapse prediction across major mental disorders is impressive. Today it is possible to build dynamic digital proxies for symptoms, functioning, cognition and physiology using smartphones and wearables–often referred to as digital phenotyping2. For example: passive smartphone data from sensors like global positioning system (GPS) can inform about location; accelerometer about sleep; active data from surveys (often referred to as ecological momentary assessment) can capture real time symptoms; metadata from phone interactions can characterize cognition; and data from wearables can inform on physiological measures.Capturing these diverse data streams is highly feasible. Opensource and free platforms such as mindLAMP have permitted teams across the world to engage in this work2. Using varying combinations of these digital data streams, studies have shown clinically actionable assessment of relapse risk in schizophrenia3, depression4, bipolar disorder5 and substance abuse1. Furthermore, data around spoken and written language as well as social media use (often accessed via smartphones) is also augmenting relapse prediction. Since at least 2018, an effort has been made to predict suicide attempts in the US through real time natural language processing6.