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使用被动传感器数据对心理健康状况进行基准评估:前性观察研究的协议.

Robyn E Kilshaw1, Abigail Boggins1, Olivia Everett1

  • 1Department of Psychology, University of Utah, Salt Lake City, UT, United States.

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概括

这项研究正在为一般人群的计算精神病学研究创建一个新的,以隐私为重点的数据集,使用智能手机数据来改进心理健康风险评估.

关键词:
音频数据 音频数据计算精神病学是一种计算精神病学.数据仓库数据仓库.数字化表型化是指数字化表型化.机器学习是机器学习.被动传感器数据被动传感器数据

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科学领域:

  • 计算精神病学是一种计算精神病学.
  • 数字化表型化是指数字化表型化.
  • 心理健康研究 心理健康研究

背景情况:

  • 计算精神病学为推进心理健康诊断和治疗提供了潜力.
  • 现有的方法在一般人口研究中的可扩展性有限.
  • 使用智能手机传感器的数字表型化可以将计算精神病学扩展到公众.

研究的目的:

  • 开发第一个用于一般人口心理健康风险评估的计算精神病学数据集.
  • 为了整合多式联运,基于传感器的行为特征.
  • 确保广泛的数据共享与强大的隐私和完整性.

主要方法:

  • 根据情绪调节和生活压力进行分层抽样,招募了400名社区成年人.
  • 在7天内收集了自我报告问卷,每日情绪/事件日志,智能手机传感器数据和音频录音.
  • 在6个月和12个月后进行了后续调查问卷.

主要成果:

  • 数据收集正在进行中 (2022年6月 - 2024年7月);310名参与者同意.
  • 149名参与者完成了初步数据收集;随访正在进行中.
  • 该数据集将以保护隐私的方法提供.

结论:

  • 这一数据集补充了现有研究,旨在提高一般人口心理健康风险评估.
  • 它支持在计算精神病学中转向跨学科合作和开放的数据共享.
  • 其目标是促进协作,整合临床,技术和定量专业知识.