Jia Eric, Macon Jushawn, Doering Michelle, Abraham Joanna
Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO, United States.
Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, United States.
J Med Internet Res. 2025 Jun 17;27:e68054. doi: 10.2196/68054.
As digital interventions gain prominence in mental health care, they present opportunities to improve access and scalability. Despite their potential, the overall impact of digital behavioral activation (BA) interventions across different formats and populations is not yet fully understood.
This systematic review examines the characteristics and functions of digital BA interventions and evaluates their effectiveness for mental health and other patient-related outcomes.
A comprehensive search of databases (PubMed, Embase, Web of Science, APA PsycInfo, and ClinicalTrials.gov) was performed in November 2023 to identify randomized controlled trials (RCTs) assessing the effectiveness of digital BA interventions for depression and anxiety. A total of 2 reviewers screened the studies for inclusion. Meta-analysis using a random-effects model assessed intervention impact on outcomes including depression, anxiety, quality of life (QoL), BA scores, functioning and disability, and stress. Statistical heterogeneity was evaluated with the I² statistic and statistical significance was evaluated with P values. Studies that did not meet the meta-analysis criteria underwent narrative synthesis.
A total of 18 articles reporting 17 RCTs were included across three intervention types: (1) internet-based BA (n=12, 71%), delivering digital therapies to foster new behavioral activities for depression management; (2) electronic messaging-based BA (n=2, 12%), involving prompts to support behavior change; and (3) telehealth-based BA (n=3, 17%), providing remote health care services. We identified single-component and multicomponent interventions that combined BA with elements such as problem-solving therapy or cognitive behavioral therapy. A total of 12 RCTs were included in the meta-analysis, while the remaining studies were narratively synthesized. Risk of bias (RoB) was assessed in all included studies. Digital BA interventions significantly reduced depressive symptoms at 2 months (P<.001, I²=0%), 3 months (P=.001, I²=51%), and 6 months (P=.009, I²=29%) post treatment, but not at 12 months (P=.82, I²=89%). Significant improvements in BA scores at 6 months were observed (P<.001, I²=0%). QoL improved significantly at 3 months (P=.002, I²=22%) and 6 months (P=.009, I²=0%). Stress levels were also significantly reduced at 3 months (P<.001, I²=25%). However, no significant changes were identified in anxiety and functioning and disability outcomes at either 3 months (anxiety: P=.08, I²=68%) or 6 months (anxiety: P=.24, I²=44%; functioning and disability: P=.88, I²=90%). Across included studies, RoB was generally low, particularly for random sequence generation and allocation concealment.
Digital BA interventions are effective in reducing depressive symptoms and improving QoL in the short- to midterm. However, these effects tend to diminish over time with no sustained benefits observed at 12 months. Future research should focus on developing and testing interventions with greater long-term efficacy, clarifying the role of BA within multicomponent digital approaches, and identifying the optimal intervention "dose" needed to maintain lasting effects.
随着数字干预在精神卫生保健领域日益突出,它们为改善可及性和扩大规模带来了机遇。尽管具有潜力,但不同形式和人群的数字行为激活(BA)干预的总体影响尚未完全了解。
本系统评价探讨数字BA干预的特征和功能,并评估其对心理健康及其他患者相关结局的有效性。
2023年11月对数据库(PubMed、Embase、Web of Science、美国心理学会心理学文摘数据库和ClinicalTrials.gov)进行全面检索,以识别评估数字BA干预对抑郁和焦虑有效性的随机对照试验(RCT)。共有2名评审员筛选纳入研究。采用随机效应模型进行荟萃分析,评估干预对包括抑郁、焦虑、生活质量(QoL)、BA评分、功能和残疾以及压力等结局的影响。用I²统计量评估统计异质性,用P值评估统计学显著性。不符合荟萃分析标准的研究进行叙述性综合分析。
共纳入18篇报告17项RCT的文章,涉及三种干预类型:(1)基于互联网的BA(n = 【此处英文有误,应为12】,71%),提供数字疗法以促进新的行为活动来管理抑郁;(2)基于电子信息的BA(n = 2,12%),涉及支持行为改变的提示;(3)基于远程医疗的BA(n = 3,17%),提供远程医疗服务。我们识别出将BA与问题解决疗法或认知行为疗法等要素相结合的单组分和多组分干预。荟萃分析纳入了12项RCT,其余研究进行叙述性综合分析。对所有纳入研究评估了偏倚风险(RoB)。数字BA干预在治疗后2个月(P <.001,I² = 0%)、3个月(P =.001,I² = 51%)和6个月(P =.009,I² = 29%)显著降低了抑郁症状,但在12个月时未降低(P =.82,I² = 89%)。在6个月时观察到BA评分有显著改善(P <.001,I² = 0%)。QoL在3个月(P =.002,I² = 22%)和6个月(P =.009,I² = 0%)时显著改善。压力水平在3个月时也显著降低(P <.001,I² = 25%)。然而,在3个月(焦虑:P =.08,I² = 68%)或6个月(焦虑:P =.24,I² = 44%;功能和残疾:P =.88,I² = 90%)时,焦虑以及功能和残疾结局均未发现显著变化。在纳入的研究中,RoB总体较低,特别是在随机序列生成和分配隐藏方面。
数字BA干预在短期至中期可有效减轻抑郁症状并改善QoL。然而,这些效果往往会随着时间减弱,在12个月时未观察到持续益处。未来研究应专注于开发和测试具有更高长期疗效的干预措施,阐明BA在多组分数字方法中的作用,并确定维持持久效果所需的最佳干预“剂量”。