CAREER: Advancing Personal Informatics through Semi-Automated and Collaborative Tracking

这项研究探讨了一种称为半自动跟踪的新颖自我跟踪方法，以帮助人们轻松地处理丰富的个人数据，如体重、活动、睡眠模式和药物使用。原则上，了解自我跟踪数据可以帮助人们反思自己的健康状况，了解他们的行为如何影响他们实现目标的进程，从而潜在地改善健康和幸福。然而，在实践中，自我跟踪是困难的。手动跟踪方法（如日记）需要付出很多努力，而自动跟踪方法（如可穿戴传感工具）与手动跟踪相比，显著降低了跟踪者的意识、责任和参与度。为了解决这些问题，本研究提出设计和开发一个半自动化的跟踪平台，将人工和自动数据收集方法相结合。该平台将使人们能够设计和定制自己的跟踪工具，并根据个人不同的跟踪需求捕获多种个人数据。由于定制本身可能很困难，该平台还将通过整合基于专家输入创建的模板来支持协作跟踪。该提案将测试这些想法，同时与两个可以从实践自我跟踪中受益的用户群体合作：(1)生活在退休社区的老年人；(2)在康复计划中的临床医生和手术患者（即在手术前提高患者健康的饮食和锻炼计划）。老年人和康复组之间的个体、动机和跟踪需求的差异将为如何设计半自动和协作跟踪工具提供见解。首席研究员（PI）将在她的人机交互和个人信息学课程中使用最终的案例研究和研究平台，并将课程公开提供给其他教育工作者和研究人员。PI还将与她所在学院的项目密切合作，让本科生、妇女和少数族裔等代表性不足的群体参与研究。研究的第一阶段将集中于通过形成性研究了解人们对使用跟踪技术的关注、需求和挑战。研究团队将使用支持可定制手动跟踪的平台，以及对老年人、临床医生和手术患者的访谈和观察，进行技术探索研究。在此阶段，研究团队将继续开发研究平台，该平台将作为拟议研究和干预措施的技术基础。这些形成性的研究将使我们深入了解这些人群目前是如何进行自我跟踪的：他们在做什么，他们想做什么，是什么让他们感到困难，他们害怕什么。这些见解将为研究平台提供一般设计指南，其中将包括半自动跟踪元素，旨在平衡人们的信息需求和数据捕获负担，同时提高他们的参与度。在第二阶段，研究团队将通过设计迭代的短期部署研究来测试半自动化跟踪方法的可行性。然后，修订后的平台将被纵向部署，以测试其对老年人和外科患者的疗效。在第三阶段，研究团队将专注于协作跟踪方法，旨在帮助患者配置自我跟踪设置并收集对临床医生有用的高质量数据。为了实现这些目标，研究团队将与临床医生一起举办设计研讨会，以生成通用康复方案的模板，这些模板随后将纳入研究平台。该平台的协同跟踪要素将在医院的纵向研究中进行评估。这项研究将有助于个人信息学和健康信息学的知识体系的发展。它将告诉我们老年人和外科病人的自我跟踪实践，以及应该如何设计自我跟踪工具，以支持各种利益攸关方的需求，包括合作伙伴、护理人员和临床医生。为了扩大工作的影响，研究团队将向学术界（如行为科学家、个人信息学研究人员）、医学界（如临床医生和患者）、量化自我社区（即专门的自我跟踪者）和希望从事自我跟踪实践的个人传播半自动跟踪平台。

This research examines a novel self-tracking approach called semi-automated tracking to help people easily engage with a rich set of personal data, such as weight, activities, sleep pattern, and medication use. In principle, being aware of self-tracking data can help people reflect on their health condition and understand how their behavior affects their progress toward goals, potentially improving health and well-being. In practice, however, self-tracking is hard. Manual tracking approaches such as diaries require much effort, while automated tracking approaches such as wearable sensing tools significantly reduce the tracker's awareness, accountability, and involvement compared to manual tracking. To address these problems, this research proposes to design and develop a semi-automated tracking platform, combining both manual and automated data collection methods. The platform will enable people to design and customize their own tracking tools and capture many kinds of personal data depending on individuals' diverse tracking needs. As the customization itself can be hard, the platform will also support collaborative tracking by incorporating templates created based on experts' input. The proposal will test these ideas while working with two user groups who can benefit from practicing self-tracking: (1) older adults living in retirement communities, and (2) clinicians and surgical patients in prehabilitation programs (that is, dietary and exercise plans for enhancing patients' health prior to surgery). The differences in individuals, their motivations, and the demands of tracking between the older adult and prehabilitation groups will provide insight on how to design semi-automated and collaborative tracking tools. The principal investigator (PI) will use both the resulting case studies and research platform in her courses on human-computer interaction and personal informatics, and make the curricula openly available to other educators and researchers. The PI will also work closely with programs at her institution to involve people from under-represented groups in the research, including undergraduates, women, and minorities.The first phase of the research will focus on learning people's concerns, needs, and challenges regarding the use of tracking technologies via formative studies. The research team will conduct a technology probing study using a platform that supports customizable manual tracking, along with interviews and observations of older adults, clinicians, and surgical patients. During this phase, the research team will continue developing the research platform, which will be used as the technical basis for the proposed studies and interventions. These formative studies will generate insights into how these populations currently approach self-tracking: what do they do and what would they like to do, and what makes it hard and what are they afraid of. These insights will provide general design guidelines for the research platform, which will include the semi-automated tracking elements designed to balance people's information needs and data capture burden while enhancing their engagement. In the second phase, the research team will test the feasibility of the semi-automated tracking approach with a short-term deployment study followed by design iterations. Then the revised platform will be deployed longitudinally to test its efficacy with both older adults and surgical patients. In the third phase, the research team will focus on the collaborative tracking approach, aiming to help patients configure self-tracking settings and collect high quality data that are useful for clinicians. To support these goals, the research team will conduct design workshops with clinicians to generate templates for common prehabilitation regimens, which will later be incorporated in the research platform. The collaborative tracking elements of the platform will be evaluated in a longitudinal study in hospitals. This research will contribute to the growing bodies of knowledge in personal informatics and health informatics. It will inform us of elders' and surgical patients' self-tracking practices and ways in which self-tracking tools should be designed to support the needs of various stakeholders, including partners, caretakers, and clinicians. As a way to expand the impact of the work, the research team will disseminate the semi-automated tracking platform to academic communities (e.g., behavioral scientists, personal informatics researchers), medical communities (e.g., clinicians and patients), Quantified Self communities (i.e., dedicated self-trackers), and individuals who wish to engage in self-tracking practices.