Department of Psychiatry, University of California San Diego, 9500 Gilman Dr #0664, La Jolla, CA, 92093-0664, USA.
Digital Health, IBM T.J. Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA.
Sleep Breath. 2024 Jun;28(3):1491-1498. doi: 10.1007/s11325-023-02980-4. Epub 2024 Jan 4.
People with serious mental illnesses (SMIs) have three-fold higher rates of comorbid insomnia than the general population, which has downstream effects on cognitive, mental, and physical health. Cognitive Behavioral Therapy for Insomnia (CBT-i) is a safe and effective first-line treatment for insomnia, though the therapy's effectiveness relies on completing nightly sleep diaries which can be challenging for some people with SMI and comorbid cognitive deficits. Supportive technologies such as mobile applications and sleep sensors may aid with completing sleep diaries. However, commercially available CBT-i apps are not designed for individuals with cognitive deficits. To aid with this challenge, we have developed an integrated mobile application, named "Sleep Catcher," that will automatically incorporate data from a wearable fitness tracker and a bed sensor to track nightly sleep duration, overnight awakenings, bed-times, and wake-times to generate nightly sleep diaries for CBT-i.
The application development process will be described-writing algorithms to generating useful data, creating a clinician web portal to oversee patients and the mobile application, and integrating sleep data from device platforms and user input.
The mobile and web applications were developed using Flutter, IBM Code Engine, and IBM Cloudant database. The mobile application was developed with a user-centered approach and incremental changes informed by a series of beta tests. Special user-interface features were considered to address the challenges of developing a simple and effective mobile application targeting people with SMI.
There is strong potential for synergy between engineering and mental health expertise to develop technologies for specific clinical populations. Digital health technologies allow for the development of multi-disciplinary solutions to existing health disparities in vulnerable populations, particularly in people with SMI.
严重精神疾病患者(SMI)的共患失眠率比一般人群高三倍,这对认知、心理和身体健康都有影响。认知行为疗法(CBT-i)是失眠的安全有效的一线治疗方法,尽管该疗法的有效性依赖于完成每晚的睡眠日记,但这对于一些患有 SMI 和共患认知缺陷的人来说可能具有挑战性。支持性技术,如移动应用程序和睡眠传感器,可以帮助完成睡眠日记。然而,商业上可用的 CBT-i 应用程序并不是为有认知缺陷的人设计的。为了帮助解决这一挑战,我们开发了一个集成的移动应用程序,名为“Sleep Catcher”,它将自动整合来自可穿戴健身追踪器和床传感器的数据,以跟踪每晚的睡眠时间、夜间醒来次数、就寝时间和起床时间,从而为 CBT-i 生成每晚的睡眠日记。
将描述应用程序的开发过程-编写生成有用数据的算法,创建一个用于监督患者和移动应用程序的临床医生网络门户,并整合来自设备平台和用户输入的睡眠数据。
移动应用程序和网络应用程序是使用 Flutter、IBM Code Engine 和 IBM Cloudant 数据库开发的。移动应用程序采用了以用户为中心的方法,并通过一系列 Beta 测试逐步进行了更改。考虑到针对 SMI 患者开发简单有效的移动应用程序的挑战,特别设计了用户界面功能。
工程学和心理健康专业知识之间有很强的协同作用,可以为特定的临床人群开发技术。数字健康技术为解决弱势人群中现有的健康差距,特别是严重精神疾病患者中的健康差距,提供了多学科的解决方案。