智能手机键盘动态预测影响自杀想法
Loran Knol1,2, Anisha Nagpal3, Imogen E Leaning4,5
1Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands. loran.knol@donders.ru.nl.
NPJ digital medicine
|March 1, 2024
概括
数字表型提供实时心理健康见解,但面临着整合挑战. 本研究提出了一个使用时间独立组件分析 (ICA) 和混合效应模型的管道,以合并多种数据,将打字行为与门诊患者的无意识症联系起来.
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.
- 心理健康评估 心理健康评估
背景情况:
- 数字表型化使得不引人注目的,实时的心理健康监测成为可能.
- 整合不同样本率的不同数据模式带来了重大挑战.
- 现有的方法难以处理高维数据和时间同步.
研究的目的:
- 开发一个整合的管道,用于融合数字表型化数据模式.
- 为应对多式联网数据中高维度和不同采样频率的挑战.
- 调查数字行为数据与心理健康症状之间的关系.
主要方法:
- 开发了一个管道,将数据转换为一个共同的时间单位.
- 将时间独立组件分析 (ICA) 应用于高维自报告数据.
- 利用线性混合效果模型来融合自我报告和键盘动态数据.
主要成果:
- 在自我报告数据上,ICA确定了与幸福感,无情感,易怒和社会功能障碍相关的组件.
- 打字时手机运动减少与无情感增加相关 (β = -0.12,p = 0.00030).
- 该管道成功集成了每日自我报告和BiAffect键盘动态.
结论:
- 拟议的管道有效地整合了多式联运数字表型化数据.
- 这种方法可以通过行为模式更深入地了解心理健康状况.
- 该方法广泛适用于密集的纵向数字表型化数据集.
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