智能手机在心理健康障碍中的数字表型:对使用的原始传感器,机器学习处理管道和衍生行为特征的审查
Jake Linardon1, Kelly Chen2, Shruti Gajjar2
1SEED Lifespan Strategic Research Centre, School of Psychology, Faculty of Health, Deakin University, Geelong, Australia.
Psychiatry research
|April 5, 2025
概括
数字表型化使用智能设备数据进行健康洞察,但方法差异很大. 本次审查强调了对标准化传感器使用和数据分析的需求,以减少数字健康研究中的混乱.
科学领域:
- 数字健康数字健康
- 计算精神病学是一种计算精神病学.
- 行为数据科学是一种行为数据科学.
背景情况:
- 数字表型,利用智能设备数据,越来越多地用于健康监测.
- 传感器和分析方法的变化导致了相互矛盾的研究结果.
- 需要在数字表型化数据收集和测量方面进行标准化.
研究的目的:
- 审查数字表型研究中的传感器和方法.
- 确定共同的平台,传感器和分析技术.
- 为了指导未来的研究,减少在该领域的混乱.
主要方法:
- 112个数字表型研究的系统审查.
- 对传感器,功能,统计方法和设备类型的数据提取.
- 将变量整合到一个双编码的代码库中.
主要成果:
- 大多数研究都使用了Android设备 (67) 或混合平台 (38).
- 研究的平均持续时间为14.3周.
- 在研究中,GPS传感器是最常用的传感器.
结论:
- 在数字表型化方法中存在显著的异质性.
- 报告,传感器使用和特征提取的标准化至关重要.
- 标准化的方法将提高数字健康研究的可靠性和可解释性.
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