Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Mid-Atlantic Permanente Research Institute, Kaiser Permanente Mid-Atlantic States, Rockville, MD, USA; Mid-Atlantic Permanente Medical Group, Rockville, MD, USA.
J Psychiatr Res. 2023 Aug;164:171-183. doi: 10.1016/j.jpsychires.2023.06.019. Epub 2023 Jun 16.
Anxiety and depressive disorders affect 20% of the population, cause functional impairment, and represent a leading cause of disability. Although evidence-based treatments exist, the shortage of trained clinicians and high demand for mental health services have resulted in limited access to evidence-based care. Digital mental health applications (DMHA) present innovative, scalable, and sustainable solutions to address disparities in mental health care.
The present study used meta-analytic techniques to evaluate the therapeutic effect of DMHAs in randomized controlled trials (RCTs) for individuals experiencing anxiety and/or depressive symptoms. Search terms were selected based on concepts related to digital mental health applications, mental health/wellness, intervention type, trial design, and anxiety and/or depression symptoms/diagnosis outcomes to capture all potentially eligible results. Potential demographic, DMHA, and trial design characteristics were examined as moderators of therapeutic effects.
Random effects meta-analyses found that stand-alone DMHAs produced a modest reduction in anxiety (g = 0.31) and depressive (g = 0.35) symptom severity. Several moderators influenced the therapeutic effects of DMHAs for anxiety and/or depressive symptoms including treatment duration, participant inclusion criteria, and outcome measures.
Minimal information was available on DMHA usability and participant engagement with DMHAs within RCTs.
While DMHAs have the potential to be scalable and sustainable solutions to improve access and availability of evidence-based mental healthcare, moderator analyses highlight the considerations for implementation of DMHAs in practice. Further research is needed to understand factors that influence therapeutic effects of DMHAs and investigate strategies to optimize its implementation and overcome the extant research-to-practice gap.
焦虑和抑郁障碍影响了 20%的人口，导致功能障碍，并成为残疾的主要原因。尽管存在基于证据的治疗方法，但训练有素的临床医生短缺和对心理健康服务的高需求导致获得基于证据的护理的机会有限。数字心理健康应用程序 (DMHA) 为解决心理健康护理方面的差异提供了创新、可扩展和可持续的解决方案。
本研究使用荟萃分析技术评估了针对有焦虑和/或抑郁症状的个体的随机对照试验 (RCT) 中 DMHA 的治疗效果。搜索词是根据与数字心理健康应用程序、心理健康/健康、干预类型、试验设计以及焦虑和/或抑郁症状/诊断结果相关的概念选择的，以捕获所有潜在的合格结果。检查了潜在的人口统计学、DMHA 和试验设计特征作为治疗效果的调节剂。
随机效应荟萃分析发现，独立的 DMHA 可适度降低焦虑（g=0.31）和抑郁（g=0.35）症状的严重程度。一些调节剂影响了 DMHA 对焦虑和/或抑郁症状的治疗效果，包括治疗持续时间、参与者纳入标准和结果测量。
RCT 中关于 DMHA 的可用性和参与者对 DMHA 的参与度的信息很少。
虽然 DMHA 有可能成为改善获得和提供基于证据的心理健康护理的可扩展和可持续的解决方案，但调节剂分析突出了在实践中实施 DMHA 的考虑因素。需要进一步研究以了解影响 DMHA 治疗效果的因素，并研究优化其实施和克服现有研究与实践差距的策略。
