Wälchli Gwendolyn, Bielinski Laura Luisa, Bur Oliver Thomas, Krieger Tobias, Klein Jan Philipp, Berger Thomas
Department of Clinical Psychology and Psychotherapy, University of Bern, Switzerland.
Department of Psychiatry and Psychotherapy, Luebeck University, Luebeck, Germany.
Internet Interv. 2025 Aug 28;42:100869. doi: 10.1016/j.invent.2025.100869. eCollection 2025 Dec.
Treatment expectations are known to influence therapy outcomes, but their role in internet-based interventions (IBIs) for depression remains unclear. While previous research has primarily focused on expectations as a (PTP), emerging evidence suggests that (EPPs), including evolving expectations during treatment, may provide more relevant insights into therapeutic outcomes.
This secondary analysis of a factorial trial (Bur et al., 2022) investigates the role of treatment expectations as both a and in an internet-based intervention for mild to moderate depression. It also explores the temporal relationship between expectations and depressive symptoms, assessing whether earlier expectations predict later symptom severity and whether depressive symptoms influence subsequent expectations.
Treatment expectancy was measured using the Credibility and Expectancy Questionnaire (CEQ-8; Devilly & Borkovec, 2000; German version: Walach et al. 2008) at baseline (T0), two weeks (T1), and four weeks (T2), while depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9; Kroenke et al., 2001) at the same time points as well as post-treatment (T3). To analyze the relationship between treatment expectations (CEQ-8) and depressive symptoms (PHQ-9 post-treatment), simple regression models were conducted while controlling for baseline PHQ-9 scores. Multiple regression analyses were then used to examine whether CEQ-8 predicted PHQ-9 or vice versa. In addition, as a sensitivity analysis, a cross-lagged panel model (CLPM) was estimated to account for the repeated-measures structure of the data.
Baseline treatment expectations did not significantly predict depressive symptoms at post-treatment. However, expectations measured at two weeks (T1) and four weeks (T2) significantly predicted depressive symptoms at T3. The results of the multiple regression analyses indicate that treatment expectations can predict changes in depressive symptoms, whereas the reverse relationship was not observed. The CLPM yielded results that were consistent with the regression analyses, supporting the robustness of the findings.
Treatment expectations evolve throughout therapy and appear to function as an independent predictor of symptom improvement rather than merely reflecting symptom severity. Monitoring and addressing patient expectations early in treatment may enhance intervention outcomes. These findings support the inclusion of expectation-based strategies in IBIs to optimize engagement and effectiveness.
已知治疗期望会影响治疗效果，但其在基于互联网的抑郁症干预措施（IBIs）中的作用仍不明确。虽然先前的研究主要关注期望作为一种（PTP），但新出现的证据表明，包括治疗期间不断变化的期望在内的（EPPs），可能会为治疗效果提供更相关的见解。
对一项析因试验（Bur等人，2022年）的二次分析，调查治疗期望作为（PTP）和（EPPs）在基于互联网的轻度至中度抑郁症干预措施中的作用。它还探讨了期望与抑郁症状之间的时间关系，评估早期期望是否能预测后期症状严重程度，以及抑郁症状是否会影响后续期望。
在基线（T0）、两周（T1）和四周（T2）时，使用可信度和期望问卷（CEQ - 8；Devilly & Borkovec，2000；德文版：Walach等人，2008）测量治疗期望，同时在相同时间点以及治疗后（T3）使用患者健康问卷 - 9（PHQ - 9；Kroenke等人，2001）评估抑郁症状。为了分析治疗期望（CEQ - 8）与抑郁症状（治疗后PHQ - 9）之间的关系，在控制基线PHQ - 9分数的同时进行简单回归模型分析。然后使用多元回归分析来检验CEQ - 8是否能预测PHQ - 9，反之亦然。此外，作为敏感性分析，估计了一个交叉滞后面板模型（CLPM）以考虑数据的重复测量结构。
基线治疗期望并未显著预测治疗后的抑郁症状。然而，在两周（T1）和四周（T2）时测量的期望显著预测了T3时的抑郁症状。多元回归分析结果表明，治疗期望可以预测抑郁症状的变化，而未观察到相反的关系。CLPM得出的结果与回归分析一致，支持了研究结果的稳健性。
治疗期望在整个治疗过程中会发生变化，并且似乎是症状改善的独立预测因素，而不仅仅是反映症状严重程度。在治疗早期监测并处理患者期望可能会提高干预效果。这些发现支持在IBIs中纳入基于期望的策略，以优化参与度和有效性。
