CHS: Medium: Collaborative Research: Teachable Activity Trackers for Older Adults

身体活动的自我跟踪可以支持所有年龄段的人了解自己的生活方式行为，做出健康的选择，降低慢性病风险。对于老年人来说，运动行为尤其重要。它们帮助人们保持功能能力并独立生活。智能手表和其他活动跟踪技术已经变得可用，使得自我跟踪比以前更容易，但老年人采用它们的次数较少。一个障碍是，目前的体力活动跟踪器不能有效地识别和跟踪老年人的活动。该项目旨在了解(1)需要从老年人那里获得什么样的数据才能为他们进行活动跟踪；以及(2)如何让老年人收集所需的数据。该项目将开发一种新的方法来个性化老年人的活动跟踪。它将为影响医疗保健、生活质量和隐私的个性化和多模式自我跟踪开辟新的研究途径。该项目预计将对老年人产生更广泛的影响，增强他们参与体育活动的动机，并在培育多元化和包容性文化方面产生社会影响，使老年人和有或没有残疾或健康状况的人的生活受益。该项目使用“可教学界面”来促进老年人身体活动的个性化、自我跟踪，同时考虑到他们在行动能力和不同的身体特征方面的变化。可教界面旨在帮助人们提供个性化的活动标签，这些标签将被用来识别他们独特的动作。它们还将能够自我跟踪有意义的和可修改的运动和非运动活动，支持老年人用体力活动取代不活动，这可以提供显著的健康益处。研究小组将调查：(1)老年人希望改变的运动和非运动活动；(2)新的个性化、多模式活动跟踪器，通过可教学的界面提供自我反思的机会；(3)老年人(如轻度痴呆症患者)亚群的共性和有效性差异，以及需要进行哪些调整来适应他们。该项目结合了人机交互、交互式机器学习、可访问性、老龄化和运动学的专业知识，将采用混合方法研究方法：与老年人共同设计、技术设计和开发，以及在实验室和人类自然环境中进行评估。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Self-tracking of physical activities can support people of all ages in understanding their lifestyle behaviors and making healthy choices, reducing chronic disease risks. For older adults, movement behaviors are especially critical. They help people maintain functional abilities and live independently. Smart watches and other activity tracking technologies have become available, making self-tracking easier than before, but older adults have adopted them less. One barrier is that current physical activity trackers do not effectively identify and track older adults’ activities. This project aims to understand (1) what kind of data are needed from older adults to make activity tracking work for them; and (2) how to engage older adults to collect the needed data. This project will develop a new approach to personalizing older adults’ activity tracking. It will open up new research avenues on personalized and multimodal self-tracking that affect healthcare, quality of life, and privacy. This project is expected to make broader impacts for older adults in enhancing their motivation to engage in physical activities, as well as societal impacts in nurturing a culture of diversity and inclusion that benefits the lives of older adults and people with and without disabilities or health conditions.This project uses “teachable interfaces” to facilitate personalized, self-tracking for older adults’ physical activities, while considering their changes in mobility and diverse physical characteristics. The teachable interfaces are intended to help people provide personalized activity labels, which will be used to recognize their unique movements. They will also enable self-tracking of meaningful and modifiable movement and non-movement activities, supporting older adults to displace inactivity with physical activity, which can provide significant health benefits. The research team will investigate: (1) older adults’ movement and non-movement activities that they wish to change; (2) new personalized, multimodal activity trackers that provide opportunities for self-reflection through teachable interfaces; and (3) commonalities and differences in efficacies for subgroups of older adults (e.g., people with mild dementia) and what adjustments are needed to accommodate them. Combining expertise from human-computer interaction, interactive machine learning, accessibility, aging, and kinesiology, the project will employ a mixed-methods research approach: co-design with older adults, technology design and development, and evaluations both in the lab and in people’s natural environments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.