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Forecasting the onset of depression with limited baseline data only: A comparison of a person-specific and a multilevel modeling based exponentially weighted moving average approach.
Psychological Assessment ( IF 3.3 ) Pub Date : 2024-01-01 , DOI: 10.1037/pas0001314
Evelien Schat 1 , Francis Tuerlinckx 1 , Marieke J Schreuder 1 , Bart De Ketelaere 2 , Eva Ceulemans 1
Psychological Assessment ( IF 3.3 ) Pub Date : 2024-01-01 , DOI: 10.1037/pas0001314
Evelien Schat 1 , Francis Tuerlinckx 1 , Marieke J Schreuder 1 , Bart De Ketelaere 2 , Eva Ceulemans 1
Affiliation
The onset of depressive episodes is preceded by changes in mean levels of affective experiences, which can be detected using the exponentially weighted moving average procedure on experience sampling method (ESM) data. Applying the exponentially weighted moving average procedure requires sufficient baseline data from the person under study in healthy times, which is needed to calculate a control limit for monitoring incoming ESM data. It is, however, not trivial to obtain sufficient baseline data from a single person. We therefore investigate whether historical ESM data from healthy individuals can help establish an adequate control limit for the person under study via multilevel modeling. Specifically, we focus on the case in which there is very little baseline data available of the person under study (i.e., up to 7 days). This multilevel approach is compared with the traditional, person-specific approach, where estimates are obtained using the person's available baseline data. Predictive performance in terms of Matthews correlation coefficient did not differ much between the approaches; however, the multilevel approach was more sensitive at detecting mean changes. This implies that for low-cost and nonharmful interventions, the multilevel approach may prove particularly beneficial. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
中文翻译:
仅使用有限的基线数据预测抑郁症的发作:基于指数加权移动平均方法的个人特定模型和多级模型的比较。
抑郁发作之前会出现情感体验平均水平的变化,这可以使用经验采样方法(ESM)数据的指数加权移动平均程序来检测。应用指数加权移动平均程序需要来自被研究人员在健康时期的足够基线数据,这是计算监测传入 ESM 数据的控制限所必需的。然而,从一个人那里获得足够的基线数据并非易事。因此,我们研究来自健康个体的历史 ESM 数据是否可以通过多级建模帮助为研究对象建立适当的控制限度。具体来说,我们重点关注研究对象的可用基线数据非常少(即最多 7 天)的情况。这种多层次方法与传统的、针对特定人员的方法进行了比较,在传统的、针对特定人员的方法中,使用该人的可用基线数据来获得估计值。两种方法之间的马修斯相关系数的预测性能没有太大差异;然而,多级方法在检测平均变化方面更加敏感。这意味着对于低成本和无害的干预措施,多层次方法可能特别有益。 (PsycInfo 数据库记录 (c) 2024 APA,保留所有权利)。
更新日期:2024-01-01
中文翻译:
仅使用有限的基线数据预测抑郁症的发作:基于指数加权移动平均方法的个人特定模型和多级模型的比较。
抑郁发作之前会出现情感体验平均水平的变化,这可以使用经验采样方法(ESM)数据的指数加权移动平均程序来检测。应用指数加权移动平均程序需要来自被研究人员在健康时期的足够基线数据,这是计算监测传入 ESM 数据的控制限所必需的。然而,从一个人那里获得足够的基线数据并非易事。因此,我们研究来自健康个体的历史 ESM 数据是否可以通过多级建模帮助为研究对象建立适当的控制限度。具体来说,我们重点关注研究对象的可用基线数据非常少(即最多 7 天)的情况。这种多层次方法与传统的、针对特定人员的方法进行了比较,在传统的、针对特定人员的方法中,使用该人的可用基线数据来获得估计值。两种方法之间的马修斯相关系数的预测性能没有太大差异;然而,多级方法在检测平均变化方面更加敏感。这意味着对于低成本和无害的干预措施,多层次方法可能特别有益。 (PsycInfo 数据库记录 (c) 2024 APA,保留所有权利)。