Much research on older people with memory concerns is focused on tracking and informed by the priorities of others. In this paper, we seek to understand the potential that people with memory concerns see in tracking. We conducted interviews with 29 participants with concerns about their memory and engaged in an affective writing approach. We find a range of potentials that can be traced to how participants are already self-tracking. Emotions associated with these potentials vary: from acceptance to resistance, and positive anticipation to aversion. Participants are emotionally motivated to foreclose possibilities in some instances and keep them open in others. While individual and unique, potential is structured by forces that include individual routines, relationships with others, and macro-level institutions and cultural contexts. We reflect on these findings in the context of research on self-tracking with older adults, designing with ambiguity, and forces that structure the experience of living with memory concerns.

Activity tracking has the potential to promote active lifestyles among older adults. However, current activity tracking technologies may inadvertently perpetuate ageism by focusing on age-related health risks. Advocating for a personalized approach in activity tracking technology, we sought to understand what activities older adults find meaningful to track and the underlying values of those activities. We conducted a reflective interview study following a 7-day activity journaling with 13 participants. We identified various underlying values motivating participants to track activities they deemed meaningful. These values, whether competing or aligned, shape the desirability of activities. Older adults appreciate low-exertion activities, but they are difficult to track. We discuss how these activities can become central in designing activity tracking systems. Our research offers insights for creating value-driven, personalized activity trackers that resonate more fully with the meaningful activities of older adults.

Current activity tracking technologies are largely trained on younger adults’ data, which can lead to solutions that are not well-suited for older adults. To build activity trackers for older adults, it is crucial to collect training data with them. To this end, we examine the feasibility and challenges with older adults in collecting activity labels by leveraging speech. Specifically, we built MyMove, a speech-based smartwatch app to facilitate the in-situ labeling with a low capture burden. We conducted a 7-day deployment study, where 13 older adults collected their activity labels and smartwatch sensor data, while wearing a thigh-worn activity monitor. Participants were highly engaged, capturing 1,224 verbal reports in total. We extracted 1,885 activities with corresponding effort level and timespan, and examined the usefulness of these reports as activity labels. We discuss the implications of our approach and the collected dataset in supporting older adults through personalized activity tracking technologies.