数字认知行为治疗失眠的有效性:随机对照试验的元分析
Abouzar Nazari1, Ali Mirzakhani2, Gholamreza Garmaroudi1
1Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Iranian journal of psychiatry
|March 9, 2026
概括
完全自动化的失眠数字认知行为疗法 (dCBT-I) 显著降低了失眠的严重程度. 这种可扩展的干预措施为广泛的失眠治疗提供了传统治疗的可访问的替代方案.
科学领域:
- 行为科学 行为科学
- 睡眠医学 睡眠医学
- 数字健康数字健康
背景情况:
- 失眠是一种普遍的睡眠障碍,影响全球数百万人.
- 对于失眠的传统认知行为疗法 (CBT-I) 是有效的,但面临着可访问性挑战.
- 数字交付平台为广泛实施CBT-I提供了潜在的解决方案.
研究的目的:
- 系统地审查和元分析随机对照试验 (RCT),评估完全自动化的失眠数字认知行为疗法 (dCBT-I).
- 评估dCBT-I在减少失眠严重性的有效性.
- 通过子组分析,探索影响dCBT-I疗效的因素.
主要方法:
- 在主要数据库 (PubMed,PsycINFO,Web of Science,Scopus,Google Scholar) 进行系统的文献搜索.
- 包括使用验证的标准和数字平台评估失眠的成年人RCT.
- 使用随机效应模型的元分析,使用标准化平均差异 (SMD) 和95%置信区间 (CI).
主要成果:
- 包括49个具有20118名参与者的RCT.
- 完全自动化的dCBT-I显著降低了失眠严重程度 (WMD: -3.42;95% CI: -4.35至 -2.48;P < 0.001).
- 在使用严格诊断标准和美国人群的研究中观察到更大的有效性;注意到大量的异质性.
结论:
- 完全自动化的dCBT-I是一种有效的干预措施,可以减少失眠的严重程度.
- 这种方法提供了一个可扩展和可访问的解决方案,以克服传统的CBT-I交付障碍.
- 需要进一步的研究来解决方法的变化,纳入客观的睡眠措施,并适应各种全球背景的干预措施.
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