OBJECTIVE To date, many prediction studies in psychotherapy research have used cross-sectional data to predict treatment outcome. The present study used intensive longitudinal assessments and continuous time dynamic modeling (CTDM) to investigate the temporal dynamics of affective states and emotion regulation in the early phase of therapy and their ability to predict treatment outcome. METHOD Ninety-one patients undergoing psychological treatment at a university outpatient clinic took part in a 2-week ecological momentary assessment (EMA) period. Participants answered self-report measures on positive affect (PA), negative affect, and emotion regulation (ER) four times a day. Hierarchical Bayesian CTDM was conducted to identify temporal effects within (autoregressive) and between (cross-regressive) PA, negative affect, and ER. The resulting CTDM parameters, simple EMA parameters (e.g., mean), and cross-sectional predictors were entered into a LASSO model to be examined as predictors of treatment outcome at Session 15. RESULTS Two significant predictors were identified: initial impairment and the continuous time cross-effect of PA on ER. The final model explained 40% of variance in treatment outcome, with the cross-effect (PA-ER) accounting for 4% of variance beyond initial impairment. CONCLUSIONS The results demonstrate that temporal patterns of affective EMA data are valuable for the mapping of individual differences and the prediction of treatment outcome. This information can be used to provide therapists with feedback to personalize treatments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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通过情感状态和情绪调节的连续时间动态建模来预测心理治疗的结果。


目的 迄今为止，心理治疗研究中的许多预测研究都使用横截面数据来预测治疗结果。本研究使用强化纵向评估和连续时间动态模型（CTDM）来研究治疗早期阶段情感状态和情绪调节的时间动态及其预测治疗结果的能力。方法 91名在大学门诊接受心理治疗的患者参加了为期两周的生态瞬时评估（EMA）期。参与者每天回答四次关于积极情绪（PA）、消极情绪和情绪调节（ER）的自我报告测量。分层贝叶斯 CTDM 旨在识别 PA、负面情绪和 ER 内部（自回归）和之间（交叉回归）的时间效应。由此产生的 CTDM 参数、简单 EMA 参数（例如平均值）和横截面预测因子被输入 LASSO 模型，作为第 15 节治疗结果的预测因子进行检查。 结果 确定了两个重要的预测因子：初始损伤和持续时间PA 对 ER 的交叉效应。最终模型解释了治疗结果中 40% 的方差，其中交叉效应 (PA-ER) 解释了超出初始损伤的 4% 方差。结论 结果表明，情感 EMA 数据的时间模式对于绘制个体差异和预测治疗结果很有价值。这些信息可用于为治疗师提供反馈，以进行个性化治疗。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。