Psychiatr Rehabil J. 2015 Dec;38(4):313. doi: 10.1037/prj0000169.
Reports an error in "Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health" by Dror Ben-Zeev, Emily A. Scherer, Rui Wang, Haiyi Xie and Andrew T. Campbell (Psychiatric Rehabilitation Journal, 2015[Sep], Vol 38[3], 218-226). Model fit statistics in Table 1 are reported as a row for Model 2, but not for Model 1, due to a production error. Model 1 fit statistics should appear as a row with the following information: 2LL 1490.0, AIC 1498.0 & BIC 1505.3. (The following abstract of the original article appeared in record 2015-14736-001.)
Optimal mental health care is dependent upon sensitive and early detection of mental health problems. We have introduced a state-of-the-art method for the current study 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 to examine whether the information captured with multimodal 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 years) 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 the Global Positioning System and wireless fidelity), kinesthetic activity (using multiaxial accelerometers), sleep duration (modeled using device-usage 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- and postmeasures of depression (Patient Health Questionnaire-9; Spitzer, Kroenke, & Williams, 1999), stress (Perceived Stress Scale; Cohen et al., 1983), and loneliness (Revised UCLA Loneliness Scale; Russell, Peplau, & Cutrona, 1980).
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 could provide novel opportunities for close-to-invisible psychiatric assessment at a scale and efficiency that far exceeds what is currently feasible with existing assessment technologies. (PsycINFO Database Record
《下一代精神科评估：使用智能手机传感器监测行为和心理健康》，作者为德罗尔·本 - 泽夫、艾米丽·A·谢勒、王锐、谢海艺和安德鲁·T·坎贝尔（《精神康复期刊》，2015年9月，第38卷第3期，218 - 226页）报告了一处错误。由于制作错误，表1中的模型拟合统计数据在模型2中作为一行列出，但在模型1中未列出。模型1的拟合统计数据应如下列信息作为一行呈现：2LL 1490.0，AIC 1498.0和BIC 1505.3。（原始文章的以下摘要出现在记录2015 - 14736 - 001中。）
最佳的精神卫生保健依赖于对心理健康问题的敏感且早期的检测。我们为当前研究引入了一种先进的远程行为监测方法，将评估从诊所转移到个体日常生活的环境中。本研究的目的是检验通过多模式智能手机传感器捕获的信息是否可作为个体心理健康的行为标志物。我们假设：（a）以不显眼方式收集的智能手机传感器数据将与个体的日常压力水平相关，以及（b）传感器数据将与抑郁、压力和主观孤独感随时间的变化相关。
共招募了47名年轻成年人（年龄范围：19 - 30岁）参与本研究。个体作为一个单一队列入组，并在10周期间参与研究。为参与者提供了嵌入一系列传感器和软件的智能手机，这些传感器和软件能够持续跟踪他们的地理空间活动（使用全球定位系统和无线保真）、动觉活动（使用多轴加速度计）、睡眠时间（使用设备使用数据、加速度计推断、环境声音特征和环境光水平进行建模）以及在人类语音附近花费的时间（即使用麦克风和语音检测算法的语音持续时间）。参与者完成每日压力评分，以及抑郁（患者健康问卷 - 9；斯皮策、克罗恩克和威廉姆斯，1999年）、压力（感知压力量表；科恩等人，1983年）和孤独感（修订的加州大学洛杉矶分校孤独量表；拉塞尔、佩普劳和卡特罗纳，1980年）的前后测量。
混合效应线性模型显示，源自传感器的地理空间活动（p <.05）、睡眠时间（p <.05）以及地理空间活动的变异性（p <.05）与每日压力水平相关。惩罚函数回归显示抑郁变化与源自传感器的语音持续时间（p <.05）、地理空间活动（p <.05）和睡眠时间（p <.05）之间存在关联。孤独感的变化与源自传感器的动觉活动（p <.01）相关。
智能手机可被用作不显眼地监测多种心理健康行为指标的工具。创造性地利用智能手机传感技术可为近乎无形的精神科评估提供新机会，其规模和效率远超现有评估技术目前可行的程度。（《心理学文摘数据库记录》
