Gómez Penedo Juan Martín, Babl Anna Margarete, Grosse Holtforth Martin, Hohagen Fritz, Krieger Tobias, Lutz Wolfgang, Meyer Björn, Moritz Steffen, Klein Jan Philipp, Berger Thomas
Department of Clinical Psychology and Psychotherapy, University of Bern, Bern, Switzerland.
Department of Psychosomatic Medicine, Inselspital, University of Bern, Bern, Switzerland.
J Med Internet Res. 2020 Mar 24;22(3):e15824. doi: 10.2196/15824.
Therapeutic alliance has been well established as a robust predictor of face-to-face psychotherapy outcomes. Although initial evidence positioned alliance as a relevant predictor of internet intervention success, some conceptual and methodological concerns were raised regarding the methods and instruments used to measure the alliance in internet interventions and its association with outcomes.
The aim of this study was to explore the alliance-outcome association in a guided internet intervention using a measure of alliance especially developed for and adapted to guided internet interventions, showing evidence of good psychometric properties.
A sample of 223 adult participants with moderate depression received an internet intervention (ie, Deprexis) and email support. They completed the Working Alliance Inventory for Guided Internet Intervention (WAI-I) and a measure of treatment satisfaction at treatment termination and measures of depression severity and well-being at termination and 3- and 9-month follow-ups. For data analysis, we used two-level hierarchical linear modeling that included two subscales of the WAI-I (ie, tasks and goals agreement with the program and bond with the supporting therapist) as predictors of the estimated values of the outcome variables at the end of follow-up and their rate of change during the follow-up period. The same models were also used controlling for the effect of patient satisfaction with treatment.
We found significant effects of the tasks and goals subscale of the WAI-I on the estimated values of residual depressive symptoms (γ=-1.74, standard error [SE]=0.40, 95% CI -2.52 to -0.96, t=-4.37, P<.001) and patient well-being (γ=3.10, SE=1.14, 95% CI 0.87-5.33, t=2.72, P=.007) at the end of follow-up. A greater score in this subscale was related to lower levels of residual depressive symptoms and a higher level of well-being. However, there were no significant effects of the tasks and goals subscale on the rate of change in these variables during follow-up (depressive symptoms, P=.48; patient well-being, P=.26). The effects of the bond subscale were also nonsignificant when predicting the estimated values of depressive symptoms and well-being at the end of follow-up and the rate of change during that period (depressive symptoms, P=.08; patient well-being, P=.68).
The results of this study point out the importance of attuning internet interventions to patients' expectations and preferences in order to enhance their agreement with the tasks and goals of the treatment. Thus, the results support the notion that responsiveness to a patient's individual needs is crucial also in internet interventions. Nevertheless, these findings need to be replicated to establish if they can be generalized to different diagnostic groups, internet interventions, and supporting formats.
治疗联盟已被确认为面对面心理治疗效果的有力预测指标。尽管初步证据表明联盟是网络干预成功的相关预测指标,但对于用于测量网络干预中联盟的方法和工具及其与效果的关联,人们提出了一些概念和方法上的担忧。
本研究旨在使用专门为指导性网络干预开发并适用于该干预的联盟测量方法,探讨在指导性网络干预中联盟与效果的关联,该测量方法具有良好的心理测量学特性证据。
223名患有中度抑郁症的成年参与者样本接受了网络干预(即Deprexis)和电子邮件支持。他们在治疗结束时完成了指导性网络干预工作联盟量表(WAI-I)和治疗满意度测量,并在结束时以及3个月和9个月随访时完成了抑郁严重程度和幸福感测量。对于数据分析,我们使用了二级分层线性模型,该模型将WAI-I的两个子量表(即与项目的任务和目标一致性以及与支持治疗师的联系)作为随访结束时结果变量估计值及其在随访期间变化率的预测指标。同样的模型也用于控制患者对治疗满意度的影响。
我们发现WAI-I的任务和目标子量表对随访结束时残余抑郁症状的估计值(γ=-1.74,标准误[SE]=0.40,95%CI -2.52至-0.96,t=-4.37,P<.001)和患者幸福感(γ=3.10,SE=1.14,95%CI 0.87 - 5.33,t=2.72,P=.007)有显著影响。该子量表得分越高,残余抑郁症状水平越低,幸福感水平越高。然而,该子量表对这些变量在随访期间的变化率没有显著影响(抑郁症状,P=.48;患者幸福感,P=.26)。在预测随访结束时抑郁症状和幸福感的估计值以及该期间的变化率时,联系子量表的影响也不显著(抑郁症状,P=.08;患者幸福感,P=.68)。
本研究结果指出,使网络干预符合患者的期望和偏好对于提高他们对治疗任务和目标的一致性很重要。因此,结果支持这样一种观点,即在网络干预中对患者个体需求的响应能力也至关重要。然而,这些发现需要重复验证,以确定它们是否可以推广到不同的诊断组、网络干预和支持形式。