韩国慢性失眠的数字认知行为疗法:使用决策树和马尔科夫建模的成本效益分析,基于随机临床试验的二次分析
Jung Hyun Kim1, Minjeong Sohn2, Jimin Woo3
1Division of Tourism and Wellness, Hankuk University of Foreign Studies, Yongin, Republic of Korea.
JMIR mHealth and uHealth
|January 19, 2026
概括
通过Somzz应用程序治疗失眠的数字认知行为疗法 (CBT) 在韩国具有成本效益. 从社会角度来看,它提供了改善的健康结果和潜在的成本节约,支持其融入国家卫生战略.
科学领域:
- 数字健康干预措施 数字健康干预措施
- 睡眠的药物 睡眠的药物
- 卫生经济学 卫生经济学
背景情况:
- 失眠是一种常见的睡眠障碍,对健康和经济产生重大影响.
- 认知行为疗法 (CBT) 是一线治疗,但尽管有缺点,药物治疗仍被广泛使用.
- 数字疗法 (DTx) 提供了可扩展的替代方案,用于为失眠提供CBT.
研究的目的:
- 为了评估Somzz的成本效益,一个数字CBT应用程序,与韩国的传统失眠治疗相比.
- 从医疗保健系统和社会角度评估成本和结果.
主要方法:
- 一个决策分析模型 (决策树和马尔科夫模型) 用于在27周的时间范围内进行比较.
- 临床投入来自随机临床试验;来自文学和国家来源的成本数据.
- 衡量质量调整寿命年 (QALYs) 的健康结果;成本包括直接的医疗和间接的社会成本.
主要成果:
- 从医疗保健系统的角度来看,Somzz的成本略高,但改善了QALY (ICER低于WTP值).
- 从社会角度来看,由于减少医疗保健使用和生产力损失,数字CBT节省了成本.
- 敏感性分析证实了数字CBT在各种场景中的成本效益.
结论:
- 对于失眠的数字CBT,以Somzz为例,在韩国证明了有利的临床和经济价值.
- 这些发现支持将数字CBT整合到日常护理,雇主健康计划和国家数字健康政策中.
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