仅使用有限的基线数据预测抑郁症的发作:比较一个特定的人和基于多层模型的指数加权移动平均线方法
Evelien Schat1, Francis Tuerlinckx1, Marieke J Schreuder1
1Research Group of Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, KU Leuven.
Psychological assessment
|June 3, 2024
概括
早期发现抑郁症发作至关重要. 使用历史数据的新多层次建模方法改善了从经验采样方法 (ESM) 数据中检测情绪变化的方法,特别是在有限的个人基线信息中.
科学领域:
- 精神病学是一个精神病学.
- 心理学 心理学 心理学
- 计算神经科学是一种神经科学.
背景情况:
- 抑郁症发作之前,情感经验会发生变化.
- 经验采样方法 (ESM) 的数据可以使用指数加权移动平均 (EWMA) 程序检测这些变化.
- 对于EWMA来说,通常需要足够的个人基线数据,这往往很难获得.
研究的目的:
- 调查健康个体的ESM历史数据是否可以为有限的基线数据的个体建立适当的控制极限.
- 将多层次建模方法与监测情感变化的传统个体特定方法进行比较.
主要方法:
- 利用多层次建模来结合健康个体的历史数据.
- 专注于对被研究个体的最低基线数据 (最多7天) 的场景.
- 使用马修斯相关系数 (MCC) 将多层次方法与个人特定的EWMA方法进行了比较.
主要成果:
- 预测性表现 (MCC) 在多层次和个人特定方法之间是相似的.
- 多层次方法在检测情感体验中平均变化的过程中表现出更高的灵敏度.
- 这表明多层次方法的早期检测能力得到了改进.
结论:
- 多级建模方法为EWMA在个人基线数据稀缺时建立控制极限提供了可行的替代方案.
- 这种方法提高了检测情绪变化的灵敏度,有利于早期干预.
- 这些发现支持使用多层次方法来进行具有成本效益和非有害的心理健康监测.
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