School of Computer Science, Faculty of Science, University of Auckland, Auckland, New Zealand.
JMIR Mhealth Uhealth. 2024 May 23;12:e40689. doi: 10.2196/40689.
Unaddressed early-stage mental health issues, including stress, anxiety, and mild depression, can become a burden for individuals in the long term. Digital phenotyping involves capturing continuous behavioral data via digital smartphone devices to monitor human behavior and can potentially identify milder symptoms before they become serious.
This systematic literature review aimed to answer the following questions: (1) what is the evidence of the effectiveness of digital phenotyping using smartphones in identifying behavioral patterns related to stress, anxiety, and mild depression? and (2) in particular, which smartphone sensors are found to be effective, and what are the associated challenges?
We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) process to identify 36 papers (reporting on 40 studies) to assess the key smartphone sensors related to stress, anxiety, and mild depression. We excluded studies conducted with nonadult participants (eg, teenagers and children) and clinical populations, as well as personality measurement and phobia studies. As we focused on the effectiveness of digital phenotyping using smartphones, results related to wearable devices were excluded.
We categorized the studies into 3 major groups based on the recruited participants: studies with students enrolled in universities, studies with adults who were unaffiliated to any particular organization, and studies with employees employed in an organization. The study length varied from 10 days to 3 years. A range of passive sensors were used in the studies, including GPS, Bluetooth, accelerometer, microphone, illuminance, gyroscope, and Wi-Fi. These were used to assess locations visited; mobility; speech patterns; phone use, such as screen checking; time spent in bed; physical activity; sleep; and aspects of social interactions, such as the number of interactions and response time. Of the 40 included studies, 31 (78%) used machine learning models for prediction; most others (n=8, 20%) used descriptive statistics. Students and adults who experienced stress, anxiety, or depression visited fewer locations, were more sedentary, had irregular sleep, and accrued increased phone use. In contrast to students and adults, less mobility was seen as positive for employees because less mobility in workplaces was associated with higher performance. Overall, travel, physical activity, sleep, social interaction, and phone use were related to stress, anxiety, and mild depression.
This study focused on understanding whether smartphone sensors can be effectively used to detect behavioral patterns associated with stress, anxiety, and mild depression in nonclinical participants. The reviewed studies provided evidence that smartphone sensors are effective in identifying behavioral patterns associated with stress, anxiety, and mild depression.
未得到解决的早期心理健康问题，包括压力、焦虑和轻度抑郁，从长远来看可能会成为个人的负担。数字表型学通过智能手机等数字设备采集连续的行为数据，以监测人类行为，并有可能在症状变得严重之前发现更轻微的症状。
本系统文献综述旨在回答以下问题：（1）使用智能手机进行数字表型学识别与压力、焦虑和轻度抑郁相关的行为模式的有效性有哪些证据？（2）特别是，哪些智能手机传感器被发现是有效的，以及相关的挑战有哪些？
我们使用 PRISMA（系统评价和荟萃分析的首选报告项目）流程确定了 36 篇论文（报告了 40 项研究），以评估与压力、焦虑和轻度抑郁相关的关键智能手机传感器。我们排除了非成年参与者（例如青少年和儿童）和临床人群的研究，以及人格测量和恐惧症研究。由于我们专注于使用智能手机进行数字表型学的有效性，因此排除了与可穿戴设备相关的结果。
我们根据招募的参与者将研究分为 3 大组：在大学注册的学生、与任何特定组织无关的成年人以及在组织中工作的员工。研究时间从 10 天到 3 年不等。研究中使用了一系列被动传感器，包括 GPS、蓝牙、加速度计、麦克风、照度、陀螺仪和 Wi-Fi。这些传感器用于评估访问的地点、移动性、语音模式、手机使用情况（如屏幕检查）、卧床时间、身体活动、睡眠以及社交互动的方面，例如互动次数和响应时间。在纳入的 40 项研究中，31 项（78%）使用机器学习模型进行预测；其余大多数（n=8，20%）使用描述性统计。经历压力、焦虑或抑郁的学生和成年人访问的地点较少，久坐不动，睡眠不规律，手机使用时间增加。与学生和成年人不同，员工的活动量减少被视为积极的，因为工作场所的活动量减少与更高的绩效相关。总体而言，旅行、身体活动、睡眠、社交互动和手机使用与压力、焦虑和轻度抑郁有关。
本研究重点关注智能手机传感器是否可有效用于检测非临床参与者与压力、焦虑和轻度抑郁相关的行为模式。综述研究提供了证据表明，智能手机传感器可有效识别与压力、焦虑和轻度抑郁相关的行为模式。
