智能手机键盘动态预测影响自杀想法
Loran Knol1,2, Anisha Nagpal3, Imogen E Leaning1,2
1Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
medRxiv : the preprint server for health sciences
|December 11, 2023
概括
数字表型化提供实时心理健康见解. 这项研究整合了多样化的数据流,发现减少手机移动与精神健康患者的无情感增加有关.
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.
- 整合不同的数据模式 (例如,自我报告,传感器数据) 由于高维度和不同的采样速率而带来挑战.
- 准确的心理健康评估需要强大的方法来结合异质数据.
研究的目的:
- 开发一个整合的管道,用于融合数字表型化模式.
- 为应对多式联网数据中高维度和不同采样频率的挑战.
- 将管道应用于心理健康门诊患者的自我报告和键盘动态数据.
主要方法:
- 开发了一个集成的管道,将模式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.
- 使用线性混合效应模型进行纵向分析的融合模式.
主要成果:
- 关于自我报告数据的ICA揭示了与幸福感,无情感,易怒和社会功能障碍相关的组件.
- 线性混合效应模型表明,打字时手机运动减少与无情感增加有关 (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:
- 拟议的综合管道解决了将数字表型化数据用于心理健康评估的关键挑战.
- 这种方法有助于发现数字行为与临床症状之间的有意义关联.
- 该方法广泛适用于各种密集的纵向数字表型化数据集,用于心理健康研究.
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