SCH: INT: Supporting Healthy Sleep Behaviors through Ubiquitous Computing

睡眠是身体健康的关键因素之一，沿着健康的饮食和规律的锻炼。计算研究人员最近致力于了解系统如何支持营养和运动，但睡眠尽管对健康有重大益处，但相对而言研究较少。适量的优质睡眠可以改善身心健康，并与降低心脏病，糖尿病，抑郁症和肥胖症的风险有关。然而，睡眠障碍通常无法诊断，许多人不知道他们的活动或环境如何影响睡眠。普适计算有机会通过自我监测，意识和识别策略来帮助促进健康的睡眠行为。这个跨学科的研究议程将涉及设计，开发和评估新颖的普适计算方法，以支持良好的睡眠健康和行为。这项研究将结合联合收割机的专业知识，以人为本的设计，计算机科学，睡眠医学和护理。研究人员之前与目标用户和睡眠专家的形成性工作为该领域的技术设计要求提供了信息。工作重点将是通过三项主要活动在这些成果的基础上再接再厉。 首先，他们将应用机器学习来根据一个人的智能手机使用情况来模拟睡眠模式，以不显眼地感知和预测睡眠持续时间和时间。 然后，他们将采用以人为本的设计过程来开发和研究两种新型软件工具的可行性和初步功效，以帮助个人感知，记录和可视化行为（例如，咖啡因使用，食物摄入）和环境因素（例如，噪音环境，光线水平），这可能会扰乱他们的睡眠。 然后，开发并评估一种新的技术和工具的可行性和初步效果，该技术和工具用于评估、建模和可视化睡眠剥夺对用户反应时间、认知功能和情绪的影响，以帮助他们优先考虑睡眠。该研究将使睡眠领域成为以人为中心的计算研究的新领域。睡眠新应用程序的设计和评估将进一步了解如何设计技术以实现长期健康跟踪和行为改变，并且设计和评估超越了目前行业中正在解决的问题。技术贡献是监测睡眠的新方法，以及有关技术如何适应以满足不同用户独特健康需求的知识的扩展。最后，该研究旨在将睡眠研究和计算机研究领域结合起来，以开发更好地理解和治疗睡眠障碍的解决方案。这项工作有可能对美国约4060万患有睡眠障碍或睡眠剥夺的人的生活产生重大影响，这有助于解决一个重大的公共卫生问题。此外，睡眠剥夺的经济成本估计为每年638亿美元。这项研究将通过允许免费获得通过该项目开发的新的行为改变技术产生直接影响。此外，该研究还将通过使用睡眠研究作为吸引妇女和少数民族参与计算研究的手段，并通过学生项目和指导研究小组让学生参与跨学科设计团队，从而影响教育。

Sleep is one of the key aspects of good health, along with a healthy diet and regular exercise. Computing researchers have recently worked to understand how systems can support nutrition and exercise, but sleep has been relatively under-studied despite its significant health benefits. The right amount of quality sleep can improve both physical and mental health and is associated with a lower risk for heart disease, diabetes, depression, and obesity. However, sleep disorders are often undiagnosed, and many people are unaware of how their activities or environments affect sleep. Ubiquitous computing has the opportunity to help through self-monitoring, awareness, and identification of strategies to promote healthy sleep behaviors.This interdisciplinary research agenda will involve the design, development, and evaluation of novel ubiquitous computing approaches to support good sleep health and behaviors. This research will combine expertise in human-centered design, computer science, sleep medicine, and nursing. The researchers' previous formative work with target users and sleep experts has informed design requirements for technologies in this field. The work will focus on building on those results through three main activities. First, they will apply machine learning to model sleep patterns based on a person's smartphone usage to unobtrusively sense and predict sleep duration and timing. Then they will employ a human-centered design process to develop and study the feasibility and initial efficacy of two novel software tools to assist individuals in sensing, recording, and visualizing the behavioral (e.g., caffeine use, food intake) and environmental factors (e.g., noisy environment, light levels) that can disrupt their sleep. And then, they will develop and assess the feasibility and initial efficacy of a new technique and tool for assessing, modeling, and visualizing the impact of sleep deprivation on users' reaction time, cognitive functioning, and mood to help them prioritize sleep.This research will bring into focus the domain of sleep as a new area for human-centered computing research. The design and evaluation of new applications for sleep will further knowledge of how technology can be designed for long-term health tracking and behavior change, and the designs and evaluations move beyond what is currently being addressed in industry. The technical contributions are novel approaches to monitoring sleep and an expansion of knowledge about how technologies can adapt to meet the unique health needs of different users. Finally, the research seeks to unite the fields of sleep research and computing research to develop solutions for better understanding and treating sleep disorders.This work has the potential to significantly affect the lives of the estimated 40.6 million individuals in the U.S. with sleep disorders or sleep deprivation, which helps address a major public health issue. In addition, the economic cost of sleep deprivation has been estimated to be $63.8 billion per year. The research will have immediate impact by allowing free access to new behavior change technologies developed through this project. In addition, the research will also impact education by using sleep research as a means for attracting women and minorities to computing research and engaging students in interdisciplinary design teams through student projects and directed research groups.