通过因果分析量化行为自我调节对幸福感的影响:纵向健康数据的方法框架
Jialou Wang1, Pingfan Wang1, Wai Lok Woo1
1School of Computer Science, Northumbria University, Newcastle upon Tyne, United Kingdom.
Journal of biomedical informatics
|January 11, 2026
概括
通过行为自我调节得分 (BSRS-S) 测量的短期行为一致性,比长期一致性 (BSRS-L) 更多地对福祉产生因果影响. 因果推断揭示了强大的数字健康干预目标.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算健康 计算健康
- 行为科学 行为科学
背景情况:
- 纵向行为数据分析对于理解福祉至关重要.
- 传统方法往往将相关性与因果关系混为一谈,限制了干预洞察力.
研究的目的:
- 应用因果推断来从纵向自我报告数据中解脱幸福驱动因素.
- 引入一种用于量化行为一致性的新型指标.
- 确定数字健康干预的因果信息目标.
主要方法:
- 开发了行为自我调节评分 (BSRS) 以获得特征类和状态类一致性.
- 利用因果图形模型和倾向得分方法来估计因果效应.
- 在分析中控制动机和感知混因素.
主要成果:
- 短期行为一致性 (BSRS-S) 与长期一致性 (BSRS-L) 相比,对幸福感的因果影响更强,尽管相关性较低.
- 因果特征选择在机器学习模型中提高了幸福预测的准确性.
- 确定了影响日常幸福感的独特因果途径.
结论:
- 因果推理为分析纵向自我报告数据提供了一个强大的框架.
- 短期的行为一致性是日常幸福感的强大因果驱动因素.
- 因果知情建模增强了有效的数字健康干预目标的识别.
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