Abstract:
This systematic review aimed to synthesize evidence on predictors and moderators of treatment outcomes in internet- and mobile-based interventions (IMIs) for depression, informing personalized care. A systematic search across PubMed, PsycInfo, and Cochrane yielded 33,002 results. Two reviewers independently performed screening, data extraction, risk of bias assessment, and methodological quality evaluation. Fifty-eight single studies (m = 466 analyses) focusing on baseline-predictors (59.7 %, m = 278), process-predictors (16.5 %, m = 77), and moderators (21.9 %, m = 102), and six individual patient data meta-analyses (m = 93) were included. Only 24.0 % (m = 112/466) of analyses in single studies and 15.1 % (m = 14/93) in individual patient data meta-analyses were significant. Evidence from single studies was rated as insufficient for all variable categories with only 2 out of 40 categories showing >50 % significant results. Baseline depression severity had the strongest predictive value with higher scores linked to better outcomes followed by variables indicative for the course-of-change. Other frequently analyzed and potentially relevant variables with significant results were adherence, age, educational level, ethnicity, relationship status, treatment history, and behavioral variables. More high quality quantitative studies with sufficient power are essential to validate and expand findings, identifying predictors and moderators specifically relevant in IMIs to explain differential treatment effects.
摘要:
本系统综述旨在综合基于网络和移动的抑郁症干预(IMI)治疗结果的预测因素和调节因素的证据。告知个性化护理。整个PubMed的系统搜索,PsycInfo,Cochrane得出了33,002个结果。两名评审员独立进行筛选,数据提取,偏见风险评估,和方法学质量评价。58项单一研究(m=466项分析)侧重于基线预测因子(59.7%,m=278),过程预测因子(16.5%,m=77),和主持人(21.9%,m=102),纳入6个个体患者数据荟萃分析(m=93).单个研究中只有24.0%(m=112/466)的分析和个体患者数据荟萃分析中的15.1%(m=14/93)是显著的。来自单个研究的证据对于所有变量类别被认为是不充分的,在40个类别中只有2个显示>50%的显著结果。基线抑郁严重程度具有最强的预测价值,较高的分数与更好的结果相关,其次是指示变化过程的变量。其他经常分析和潜在相关的具有显著结果的变量是依从性,年龄,教育水平,种族,关系状态,治疗史,和行为变量。更多具有足够力量的高质量定量研究对于验证和扩展研究结果至关重要,确定与IMI特别相关的预测因子和调节因子,以解释不同的治疗效果。