Outcome prediction in psychological therapy with continuous time dynamic modeling of affective states and emotion regulation.

IF 4.5 1区心理学 Q1 PSYCHOLOGY, CLINICAL Journal of consulting and clinical psychology Pub Date : 2024-08-01 DOI:10.1037/ccp0000862

Miriam I Hehlmann, Danilo Moggia, Brian Schwartz, Charles Driver, Steffen Eberhardt, Wolfgang Lutz

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Abstract

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).

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利用情感状态和情绪调节的连续时间动态模型预测心理治疗的结果。

研究目的迄今为止，心理治疗研究中的许多预测研究都使用横断面数据来预测治疗结果。本研究采用强化纵向评估和连续时间动态模型（CTDM）来研究治疗早期的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：在一所大学门诊部接受心理治疗的 91 名患者参加了为期两周的生态瞬间评估（EMA）。参与者每天四次回答有关积极情绪（PA）、消极情绪和情绪调节（ER）的自我报告测量。我们采用层次贝叶斯 CTDM 方法来识别 PA、负面情绪和 ER 内部（自回归）和之间（交叉回归）的时间效应。由此得出的 CTDM 参数、简单 EMA 参数（如平均值）和横截面预测因子被输入 LASSO 模型，作为疗程 15 治疗结果的预测因子进行检验：结果：发现了两个重要的预测因素：初始损伤和 PA 对 ER 的连续时间交叉效应。最终模型解释了治疗结果中 40% 的变异，交叉效应（PA-ER）占初始损伤之外 4% 的变异：研究结果表明，情感 EMA 数据的时间模式对于绘制个体差异图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，以实现个性化治疗。(PsycInfo Database Record (c) 2024 APA, 版权所有）。

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来源期刊

Journal of consulting and clinical psychology PSYCHOLOGY, CLINICAL-

CiteScore

9.00

自引率

3.40%

发文量

期刊介绍： The Journal of Consulting and Clinical Psychology® (JCCP) publishes original contributions on the following topics: the development, validity, and use of techniques of diagnosis and treatment of disordered behaviorstudies of a variety of populations that have clinical interest, including but not limited to medical patients, ethnic minorities, persons with serious mental illness, and community samplesstudies that have a cross-cultural or demographic focus and are of interest for treating behavior disordersstudies of personality and of its assessment and development where these have a clear bearing on problems of clinical dysfunction and treatmentstudies of gender, ethnicity, or sexual orientation that have a clear bearing on diagnosis, assessment, and treatmentstudies of psychosocial aspects of health behaviors. Studies that focus on populations that fall anywhere within the lifespan are considered. JCCP welcomes submissions on treatment and prevention in all areas of clinical and clinical–health psychology and especially on topics that appeal to a broad clinical–scientist and practitioner audience. JCCP encourages the submission of theory–based interventions, studies that investigate mechanisms of change, and studies of the effectiveness of treatments in real-world settings. JCCP recommends that authors of clinical trials pre-register their studies with an appropriate clinical trial registry (e.g., ClinicalTrials.gov, ClinicalTrialsRegister.eu) though both registered and unregistered trials will continue to be considered at this time.