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

身体活动的自我跟踪可以帮助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，减少慢性病的风险。对于老年人来说，运动行为尤为重要。它们帮助人们维持功能能力和独立生活。智能手表和其他活动跟踪技术已经出现，使自我跟踪比以前更容易，但老年人采用这些技术的人数较少。一个障碍是，目前的身体活动追踪器不能有效地识别和跟踪老年人的活动。本项目旨在了解(1)老年人需要什么样的数据才能使活动跟踪对他们起作用；(2)如何让老年人参与收集所需的数据。该项目将开发一种个性化老年人活动跟踪的新方法。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。预计该项目将对老年人产生更广泛的影响，增强他们参加体育活动的动力，并在培育多样性和包容性文化方面产生社会影响，使老年人和残疾人或健康状况良好的人的生活受益。该项目采用“可教界面”，为老年人的身体活动提供个性化的自我跟踪，同时考虑到他们的流动性变化和不同的身体特征。可教界面旨在帮助人们提供个性化的活动标签，这将用于识别他们独特的动作。它们还将使人们能够自我跟踪有意义和可修改的运动和非运动活动，支持老年人用体育活动取代不运动，这可以提供重大的健康益处。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，通过可教界面提供自我反思的机会；(3)老年人亚群（如轻度痴呆患者）疗效的共性和差异，以及需要进行哪些调整以适应他们。该项目结合了人机交互、交互式机器学习、可访问性、老龄化和运动机能学方面的专业知识，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，以及在实验室和人们的自然环境中进行评估。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.