Ben-Zeev Dror, Brian Rachel, Wang Rui, Wang Weichen, Campbell Andrew T, Aung Min S H, Merrill Michael, Tseng Vincent W S, Choudhury Tanzeem, Hauser Marta, Kane John M, Scherer Emily A
Geisel School of Medicine, Dartmouth College.
Department of Computer Science, Dartmouth College.
Psychiatr Rehabil J. 2017 Sep;40(3):266-275. doi: 10.1037/prj0000243. Epub 2017 Apr 3.
This purpose of this study was to describe and demonstrate CrossCheck, a multimodal data collection system designed to aid in continuous remote monitoring and identification of subjective and objective indicators of psychotic relapse.
Individuals with schizophrenia-spectrum disorders received a smartphone with the monitoring system installed along with unlimited data plan for 12 months. Participants were instructed to carry the device with them and to complete brief self-reports multiple times a week. Multimodal behavioral sensing (i.e., physical activity, geospatials activity, speech frequency, and duration) and device use data (i.e., call and text activity, app use) were captured automatically. Five individuals who experienced psychiatric hospitalization were selected and described for instructive purposes.
Participants had unique digital indicators of their psychotic relapse. For some, self-reports provided clear and potentially actionable description of symptom exacerbation prior to hospitalization. Others had behavioral sensing data trends (e.g., shifts in geolocation patterns, declines in physical activity) or device use patterns (e.g., increased nighttime app use, discontinuation of all smartphone use) that reflected the changes they experienced more effectively.
Advancements in mobile technology are enabling collection of an abundance of information that until recently was largely inaccessible to clinical research and practice. However, remote monitoring and relapse detection is in its nascence. Development and evaluation of innovative data management, modeling, and signal-detection techniques that can identify changes within an individual over time (i.e., unique relapse signatures) will be essential if we are to capitalize on these data to improve treatment and prevention. (PsycINFO Database Record
本研究旨在描述和展示CrossCheck，这是一种多模态数据收集系统，旨在协助对精神病复发的主观和客观指标进行持续远程监测和识别。
患有精神分裂症谱系障碍的个体收到一部安装了监测系统的智能手机，并获得为期12个月的无限数据套餐。参与者被要求随身携带该设备，并每周多次完成简短的自我报告。自动捕捉多模态行为感知数据（即身体活动、地理空间活动、语音频率和时长）以及设备使用数据（即通话和短信活动、应用程序使用情况）。选取了五名经历过精神病住院治疗的个体进行描述以作指导之用。
参与者有其精神病复发的独特数字指标。对一些人来说，自我报告在住院前对症状加重提供了清晰且可能可采取行动的描述。其他人则有行为感知数据趋势（如地理位置模式的变化、身体活动的减少）或设备使用模式（如夜间应用程序使用增加、停止所有智能手机使用），这些更有效地反映了他们所经历的变化。
移动技术的进步使得能够收集大量信息，而这些信息直到最近在临床研究和实践中大多无法获取。然而，远程监测和复发检测尚处于起步阶段。如果我们要利用这些数据来改善治疗和预防，那么开发和评估能够识别个体随时间变化（即独特的复发特征）的创新数据管理、建模和信号检测技术将至关重要。（PsycINFO数据库记录）
