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谁能从基于互联网的引导式干预中受益?治疗结果预测因素和调节因素的系统评价。

Who benefits from guided internet-based interventions? A systematic review of predictors and moderators of treatment outcome.

作者信息

Haller Katrin, Becker Pauline, Niemeyer Helen, Boettcher Johanna

机构信息

Clinical Psychological Interventions, Freie Universität Berlin, Berlin, Germany.

Clinical Psychology and Psychotherapy, Psychologische Hochschule Berlin, Berlin, Germany.

出版信息

Internet Interv. 2023 Jun 9;33:100635. doi: 10.1016/j.invent.2023.100635. eCollection 2023 Sep.

Abstract

To our knowledge, no systematic review has been conducted on predictors or moderators of treatment outcome across diagnoses in guided internet-based interventions (IBIs) for adults. To identify who benefits from this specific format and therein inform future research on improving patient-treatment fit, we aimed to aggregate results of relevant studies. 2100 articles, identified by searching the databases PsycInfo, Ovid Medline, and Pubmed and through snowballing, were screened in April/May 2021 and October 2022. Risk of bias and intra- and interrater reliability were assessed. Variables were grouped by predictor category, then synthesized using vote counting based on direction of effect.  = 60 articles were included in the review. Grouping resulted in 88 predictors/moderators, of which adherence, baseline symptoms, education, age, and gender were most frequently assessed. Better adherence, treatment credibility, and working alliance emerged as conclusive predictors/moderators for better outcome, whereas higher baseline scores predicted more reliable change but higher post-treatment symptoms. Results of all other predictors/moderators were inconclusive or lacked data. Our review highlights that it is currently difficult to predict, across diagnoses, who will benefit from guided IBIs. Further rigorous research is needed to identify predictors and moderators based on a sufficient number of studies. PROSPERO registration: CRD42021242305.

摘要

据我们所知,尚未对成人指导性互联网干预(IBIs)中跨诊断的治疗结果预测因素或调节因素进行系统评价。为了确定谁能从这种特定形式中获益,并据此为未来改善患者与治疗匹配度的研究提供信息,我们旨在汇总相关研究的结果。通过检索PsycInfo、Ovid Medline和Pubmed数据库以及通过滚雪球法确定的2100篇文章,于2021年4月/5月和2022年10月进行了筛选。评估了偏倚风险以及评分者内和评分者间的信度。变量按预测因素类别分组,然后根据效应方向使用计数表决法进行综合。本综述纳入了60篇文章。分组产生了88个预测因素/调节因素,其中依从性、基线症状、教育程度、年龄和性别是最常评估的因素。更好的依从性、治疗可信度和工作联盟是更好结果的决定性预测因素/调节因素,而更高的基线分数预测变化更可靠,但治疗后症状更高。所有其他预测因素/调节因素的结果尚无定论或缺乏数据。我们的综述强调,目前很难跨诊断预测谁将从指导性IBIs中获益。需要进一步进行严格的研究,以基于足够数量的研究确定预测因素和调节因素。PROSPERO注册编号:CRD42021242305。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/787f/10336165/b494067dccf1/gr1.jpg

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