Sextl-Plötz Theresa, Steinhoff Maria, Baumeister Harald, Cuijpers Pim, Ebert David D, Zarski Anna-Carlotta
Professorship for Psychology & Digital Mental Health Care, Technical University of Munich, Germany.
Department of Clinical Psychology, Division of eHealth in Clinical Psychology, Philipps University of Marburg, Marburg, Germany.
Internet Interv. 2024 Jul 21;37:100760. doi: 10.1016/j.invent.2024.100760. eCollection 2024 Sep.
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.
本系统评价旨在综合基于互联网和移动设备的抑郁症干预措施（IMIs）治疗效果的预测因素和调节因素的证据，为个性化护理提供依据。通过对PubMed、PsycInfo和Cochrane进行系统检索，共获得33,002条结果。两名评审员独立进行筛选、数据提取、偏倚风险评估和方法学质量评估。纳入了58项单项研究（m = 466项分析），重点关注基线预测因素（59.7%，m = 278）、过程预测因素（16.5%，m = 77）和调节因素（21.9%，m = 102），以及6项个体患者数据荟萃分析（m = 93）。单项研究中只有24.0%（m = （112/466）的分析具有显著性，个体患者数据荟萃分析中这一比例为15.1%（m = 14/93）。单项研究的证据对所有变量类别均被评为不足，40个类别中只有2个类别显示超过50%的显著结果。基线抑郁严重程度具有最强的预测价值，得分越高与越好的结果相关，其次是表明变化过程的变量。其他经常分析且结果显著的潜在相关变量包括依从性、年龄、教育水平、种族、关系状况、治疗史和行为变量。需要更多高质量、有足够效力的定量研究来验证和扩展研究结果，确定IMIs中特别相关的预测因素和调节因素，以解释不同的治疗效果。
