数字心理健康工具在低收入和中等收入国家减轻抑郁和焦虑症状的有效性：系统评价与荟萃分析
Effectiveness of Digital Mental Health Tools to Reduce Depressive and Anxiety Symptoms in Low- and Middle-Income Countries: Systematic Review and Meta-analysis.
作者信息
Kim Jiyeong, Aryee Lois M D, Bang Heejung, Prajogo Steffi, Choi Yong K, Hoch Jeffrey S, Prado Elizabeth L
机构信息
Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, CA, United States.
Department of Nutrition and Food Science, University of Ghana, Accra, Ghana.
出版信息
JMIR Ment Health. 2023 Mar 20;10:e43066. doi: 10.2196/43066.
BACKGROUND
Depression and anxiety contribute to an estimated 74.6 million years of life with disability, and 80% of this burden occurs in low- and middle-income countries (LMICs), where there is a large gap in care.
OBJECTIVE
We aimed to systematically synthesize available evidence and quantify the effectiveness of digital mental health interventions in reducing depression and anxiety in LMICs.
METHODS
In this systematic review and meta-analysis, we searched PubMed, Embase, and Cochrane databases from the inception date to February 2022. We included randomized controlled trials conducted in LMICs that compared groups that received digital health interventions with controls (active control, treatment as usual, or no intervention) on depression or anxiety symptoms. Two reviewers independently extracted summary data reported in the papers and performed study quality assessments. The outcomes were postintervention measures of depression or anxiety symptoms (Hedges g). We calculated the pooled effect size weighted by inverse variance.
RESULTS
Among 11,196 retrieved records, we included 80 studies in the meta-analysis (12,070 participants n=6052, 50.14% in the intervention group and n=6018, 49.85% in the control group) and 96 studies in the systematic review. The pooled effect sizes were -0.61 (95% CI -0.78 to -0.44; n=67 comparisons) for depression and -0.73 (95% CI -0.93 to -0.53; n=65 comparisons) for anxiety, indicating that digital health intervention groups had lower postintervention depression and anxiety symptoms compared with controls. Although heterogeneity was considerable (I=0.94 for depression and 0.95 for anxiety), we found notable sources of variability between the studies, including intervention content, depression or anxiety symptom severity, control type, and age. Grading of Recommendations, Assessments, Development, and Evaluation showed that the evidence quality was overall high.
CONCLUSIONS
Digital mental health tools are moderately to highly effective in reducing depression and anxiety symptoms in LMICs. Thus, they could be effective options to close the gap in depression and anxiety care in LMICs, where the usual mental health care is minimal.
TRIAL REGISTRATION
PROSPERO CRD42021289709; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=289709.
背景
抑郁症和焦虑症导致约7460万年的残疾生活，其中80%的负担发生在低收入和中等收入国家（LMICs），这些国家在医疗护理方面存在巨大差距。
目的
我们旨在系统地综合现有证据，并量化数字心理健康干预措施在减少LMICs中抑郁症和焦虑症方面的有效性。
方法
在这项系统评价和荟萃分析中，我们检索了从起始日期到2022年2月的PubMed、Embase和Cochrane数据库。我们纳入了在LMICs中进行的随机对照试验，这些试验比较了接受数字健康干预的组与对照组（积极对照、常规治疗或无干预）在抑郁或焦虑症状方面的情况。两名评审员独立提取论文中报告的汇总数据，并进行研究质量评估。结局指标是干预后抑郁或焦虑症状的测量值（Hedges g）。我们计算了逆方差加权的合并效应量。
结果
在检索到的11196条记录中，我们在荟萃分析中纳入了80项研究（12070名参与者，干预组n = 6052，占50.14%；对照组n = 6018，占49.85%），在系统评价中纳入了96项研究。抑郁症的合并效应量为-0.61（95%CI -0.78至-0.44；n = 67项比较），焦虑症的合并效应量为-0.73（95%CI -0.93至-0.53；n = 65项比较），这表明与对照组相比，数字健康干预组干预后的抑郁和焦虑症状更低。尽管异质性相当大（抑郁症I² = 0.94，焦虑症I² = 0.95），但我们发现研究之间存在显著的变异来源，包括干预内容、抑郁或焦虑症状严重程度、对照类型和年龄。推荐分级、评估、制定和评价表明证据质量总体较高。
结论
数字心理健康工具在减少LMICs中的抑郁和焦虑症状方面具有中度到高度的有效性。因此，在常规心理健康护理极少的LMICs中，它们可能是缩小抑郁和焦虑护理差距的有效选择。
试验注册
PROSPERO CRD42021289709；https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=289709。
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