个性化建模日常变化和情绪状态的个性化建模
Adrien Choi1, Danielle Lottridge2, Jim Warren2
1School of Computer Science, University of Auckland, Auckland, New Zealand. xcui868@aucklanduni.ac.nz.
NPJ digital medicine
|October 6, 2025
概括
智能手机传感器数据揭示了日常生活的变化与心理健康的联系. 分析行为模式可以为自我调节提供个性化的见解,并为焦虑和抑郁症的小组干预提供信息.
科学领域:
- 数字化表型化是指数字化表型化.
- 计算精神病学是一种计算精神病学.
- 行为科学 行为科学
背景情况:
- 多模式智能手机传感器数据为与焦虑和抑郁相关的现实世界行为提供了深入的见解.
- 日常生活中的变化可以作为个性化的数字标记,将行为与情感状态联系起来.
研究的目的:
- 调查来自智能手机传感器数据的日常生活变化是否可以作为心理健康状况的数字标记.
- 探索使用非负矩阵分解 (NMF) 和通用线性模型 (GLM) 来分析常规变化及其与心理健康的关联.
- 利用大型语言模型 (LLM) 来将复杂的建模结果转化为可访问的自我调节见解.
主要方法:
- 使用NMF将移动传感数据分解为个人特定的例行程序及其每周的变化.
- 构建个体特定的GLM,以将感知衍生的例行变化与焦虑或抑郁状态联系起来.
- 使用LLM (GPT-4o) 将NMF和GLM结果翻译成用户可以理解的语言.
主要成果:
- 在人口层面上对GLM进行分组表明,在各组之间的心理健康措施中存在显著差异.
- 通过LLM生成的洞察力显示了发现个性化的行为模式和支持情绪驱动因素的自我理解的潜力.
- 常规相似性被认为是为基于小组的干预提供信息的因素.
结论:
- 智能手机衍生的例行变化是心理健康的有希望的数字标记.
- 法律学士可以加强与数字表型化数据的接触,以获得个性化的心理健康见解和自我调节.
- 这种方法有可能根据共享的行为模式来定制群体干预.
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