对于压力,焦虑和轻度抑郁症的数字表型:系统文献综述
Adrien Choi1, Aysel Ooi1, Danielle Lottridge1
1School of Computer Science, Faculty of Science, University of Auckland, Auckland, New Zealand.
JMIR mHealth and uHealth
|May 23, 2024
概括
智能手机传感器可以检测与压力,焦虑和轻度抑郁有关的行为变化. 这种数字表型化方法可以在非临床成年人中早期发现心理健康症状.
科学领域:
- 数字健康数字健康
- 心理健康监测 心理健康监测
- 行为科学 行为科学
背景情况:
- 早期的心理健康问题,如压力,焦虑和轻度抑郁,可能会产生长期影响.
- 数字表型利用智能手机数据持续监控行为,潜在地识别早期症状.
研究的目的:
- 审查基于智能手机的数字表型识别对识别与压力,焦虑和轻度抑郁有关的行为模式的有效性的证据.
- 为了确定有效的智能手机传感器和相关的挑战在这个领域.
主要方法:
- 按照PRISMA指南进行的系统文献综述,分析了36篇论文 (40项研究).
- 专注于成年人,非临床人群,并排除了可穿戴设备,儿童和临床人群的研究.
- 评估了各种被动智能手机传感器 (GPS,加速度计,麦克风等). 和它们与行为模式的关系.
主要成果:
- 被动传感器捕获了有关位置,移动性,言语,手机使用,睡眠和社交互动的数据.
- 机器学习模型在78%的研究中用于预测.
- 经历过压力,焦虑或抑郁症的个体减少了参观地点,增加了久坐行为,睡眠不规律,使用手机的频率更高.
结论:
- 智能手机传感器有效地识别与非临床个体的压力,焦虑和轻度抑郁相关的行为模式.
- 数字表型化显示为心理健康状况的早期检测和监测具有前途.
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