Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health.
Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health.
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DOI：
10.1037/prj0000130
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发表时间：
2015-09
影响因子：
1.9
通讯作者：
Campbell AT
中科院分区：
文献类型：
--
作者：
Ben-Zeev D;Scherer EA;Wang R;Xie H;Campbell AT
最佳的精神卫生保健取决于对精神卫生问题的敏感和早期发现。目前的研究引入了一种最先进的远程行为监测方法，将评估从诊所转移到个人日常生活的环境中。本研究的目的是研究多模态智能手机传感器捕获的信息是否可以作为一个人心理健康的行为标记。我们假设：a)不引人注目地收集的智能手机传感器数据将与个人的日常压力水平有关，b)传感器数据将与抑郁、压力和主观孤独感的变化有关。研究共招募了47名年轻人（年龄范围：19-30岁）。个体被招募为一个单独的队列，并参与了为期10周的研究。研究人员为参与者提供了嵌入了一系列传感器和软件的智能手机，这些传感器和软件可以持续跟踪他们的地理空间活动（使用GPS和WiFi）、动觉活动（使用多轴加速度计）、睡眠持续时间（使用设备使用数据、加速度计推断、环境声音特征和环境光线水平建模），以及接近人类语言的时间（即使用麦克风和语音检测算法的语音持续时间）。参与者完成了每日压力评分，以及抑郁（患者健康问卷-9）、压力（感知压力量表）和孤独感（修订的加州大学洛杉矶分校孤独感量表）的前后测量。混合效应线性模型显示，传感器衍生的地理空间活动（p< 0.05）、睡眠时间（p< 0.05）和地理空间活动的变异性（p< 0.05）与日常压力水平相关。惩罚功能回归显示抑郁变化与传感器衍生的语言持续时间（p< 0.05）、地理空间活动（p< 0.05）和睡眠持续时间（p< 0.05）之间存在关联。孤独感的变化与传感器产生的动觉活动相关（p< 0.01）。智能手机可以作为工具，对心理健康的一些行为指标进行不显眼的监测。创造性地利用智能手机传感将为近距离无形的精神病学评估创造新的机会，其规模和效率远远超过目前现有评估技术的可行性。
Optimal mental health care is dependent upon sensitive and early detection of mental health problems. The current study introduces a state-of-the-art method for remote behavioral monitoring that transports assessment out of the clinic and into the environments in which individuals negotiate their daily lives. The objective of this study was examine whether the information captured with multi-modal smartphone sensors can serve as behavioral markers for one’s mental health. We hypothesized that: a) unobtrusively collected smartphone sensor data would be associated with individuals’ daily levels of stress, and b) sensor data would be associated with changes in depression, stress, and subjective loneliness over time. A total of 47 young adults (age range: 19–30 y.o.) were recruited for the study. Individuals were enrolled as a single cohort and participated in the study over a 10-week period. Participants were provided with smartphones embedded with a range of sensors and software that enabled continuous tracking of their geospatial activity (using GPS and WiFi), kinesthetic activity (using multi-axial accelerometers), sleep duration (modeled using device use data, accelerometer inferences, ambient sound features, and ambient light levels), and time spent proximal to human speech (i.e., speech duration using microphone and speech detection algorithms). Participants completed daily ratings of stress, as well as pre/post measures of depression (Patient Health Questionnaire-9), stress (Perceived Stress Scale), and loneliness (Revised UCLA Loneliness Scale). Mixed-effects linear modeling showed that sensor-derived geospatial activity (p<.05), sleep duration (p<.05), and variability in geospatial activity (p<.05), were associated with daily stress levels. Penalized functional regression showed associations between changes in depression and sensor-derived speech duration (p<.05), geospatial activity (p<.05), and sleep duration (p<.05). Changes in loneliness were associated with sensor-derived kinesthetic activity (p<.01). Smartphones can be harnessed as instruments for unobtrusive monitoring of several behavioral indicators of mental health. Creative leveraging of smartphone sensing will create novel opportunities for close-to-invisible psychiatric assessment at a scale and efficiency that far exceed what is currently feasible with existing assessment technologies.