被动感应数据预测大学生的压力:用于数字表型化的监督机器学习方法
Artur Shvetcov1, Joost Funke Kupper2, Wu-Yi Zheng1
1Black Dog Institute, University of New South Wales, Sydney, NSW, Australia.
Frontiers in psychiatry
|September 10, 2024
概括
现在,大学生可以使用来自心理健康应用程序的被动传感数据来数字化表型化他们的压力水平. 这项研究引入了一个机器学习管道来分析这些数据,为新的压力管理工具铺平了道路.
科学领域:
- 数字化表型化是指数字化表型化.
- 机器学习在心理健康中的应用
- 移动健康 (mHealth) 服务提供商
背景情况:
- 大学学生经历高压力水平,往往超过应对能力.
- 学生越来越多地使用心理健康智能手机应用程序来应对压力.
- 这些应用程序的被动传感数据 (例如GPS,步骤检测) 提供了一个丰富的,但未得到充分利用的数据源.
研究的目的:
- 建立一个机器学习 (ML) 管道,用于处理被动传感数据,用于心理健康应用.
- 研究被动传感数据与大学生压力水平之间的关系.
- 通过被动感知,为数字化表型学生压力提供原则证明.
主要方法:
- 开发基于ML的方法管道,用于被动传感数据处理.
- 从与心理健康相关的被动传感数据中提取特征.
- 管道的应用来分析被动传感数据和大学生中的压力.
主要成果:
- 成功建立了被动传感数据分析的方法管道.
- 在大学生中证明被动感应数据和压力之间的关系.
- 概念验证数据显示被动感应可以数字化表现学生的压力.
结论:
- 被动传感数据对大学生理解和管理压力的潜力很大.
- 开发的ML管道为心理健康指标的数字表型提供了一种新的方法.
- 进一步的研究可以利用这种方法来创建针对学生心理健康的有针对性的干预措施.
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