Luik Annemarie I, Kyle Simon D, Espie Colin A
Sleep and Circadian Neuroscience Institute, Nuffield Department of Clinical Neurosciences, University of Oxford, OMPI G, South Parks Road, Oxford, OX1 3RE UK.
Big Health Ltd, London, UK.
Curr Sleep Med Rep. 2017;3(2):48-56. doi: 10.1007/s40675-017-0065-4. Epub 2017 May 8.
Over the past decade, digital solutions have been developed to support the dissemination of Cognitive Behavioral Therapy (CBT). In this paper, we review the evidence for and implications of digital CBT (dCBT) for insomnia.
We propose three categories of dCBT, which differ in the amount of clinician time needed, level of automatization, costs, and scalability: dCBT as support, guided dCBT, and fully automated dCBT. Consistent evidence has been published on the effectiveness of dCBT to address insomnia disorder, in a variety of populations, with effects extending into well-being. Important gaps in the literature are identified around moderators and mediators of dCBT, cost-effectiveness, and the implementation of dCBT.
The evidence base for dCBT is rapidly developing and already suggests that dCBT for insomnia is effective. However, further science and digital innovation is required to realize the full potential of dCBT and address important clinical questions.
在过去十年中，已开发出数字解决方案来支持认知行为疗法（CBT）的传播。在本文中，我们综述了数字认知行为疗法（dCBT）治疗失眠的证据及意义。
我们提出了三类dCBT，它们在所需临床医生时间、自动化程度、成本和可扩展性方面存在差异：作为支持的dCBT、指导性dCBT和全自动dCBT。关于dCBT治疗失眠症的有效性，已有一致的证据发表，适用于各种人群，其效果还延伸至幸福感方面。在dCBT的调节因素和中介因素、成本效益以及dCBT的实施等方面，文献中存在重要空白。
dCBT的证据基础正在迅速发展，并且已经表明dCBT治疗失眠是有效的。然而，需要进一步的科学研究和数字创新来充分发挥dCBT的潜力并解决重要的临床问题。
