Owing to the multi-factorial nature of fatigue, leveraging context to effectively monitor and intervene with fatigue symptoms presents a significant challenge. This paper aimed to understand how to incorporate context into designing mHealth systems for fatigue management. We conducted a two-week field study with 20 fatigue-vulnerable individuals using an activity-tracking sensor and self-reporting. We conducted data-prompted interviews to explore phenomena about participants’ fatigue experiences. Findings show a heterogeneous relationship between context and fatigue, which can be attributed to the phenomena that: (1) participants were influenced by multiple fatigue-inducing factors for different durations; (2) broad contexts moderated participants’ perceptions and coping strategies in response to local contexts; (3) the predictability and repetition of activities influenced participants’ fatigue perception and coping strategies. We propose a context-informed layer-based framework integrating these phenomena and discuss implications for designing fatigue management tools informed by our framework.

Many people do not get enough physical activity. Establishing routines to incorporate physical activity into people’s daily lives is known to be effective, but many people struggle to establish and maintain routines when facing disruptions. In this paper, we build on prior self-experimentation work to assist people in establishing or improving physical activity routines using a framework we call “reflective iteration.” This framework encourages individuals to articulate, reflect upon, and iterate on high-level “strategies” that inform their day-to-day physical activity plans. We designed and deployed a mobile application, Planneregy, that implements this framework. Sixteen U.S. college students used the Planneregy app for 42 days to reflectively iterate on their weekly physical exercise routines. Based on an analysis of usage data and interviews, we found that the reflective iteration approach has the potential to help people find and maintain effective physical activity routines, even in the face of life changes and temporary disruptions.

Making and executing physical activity plans can help people improve their physical activity levels. However, little is known about how people make physical activity plans in everyday settings and how people can be assisted in creating more successful plans. In this paper, we developed and deployed a mobile app as a probe to investigate the in-the-wild physical activity planning experience for 28 days with 17 participants. Additionally, we explored the impact of presenting successful and unsuccessful planning records on participants’ planning behaviors. Based on interviews before, during, and after the deployment, we offer a description of what factors participants considered to fit their exercise plans into their existing routines, as well as factors leading to plan failures and dissatisfaction with planned physical activity. With access to historical records, participants derived insights to improve their plans, including trends in successes and failures. Based on those findings, we discuss the implications for better supporting people to make and execute physical activity plans, including suggestions for incorporating historical records into planning tools.

In-situ self-reporting on smartwatches has been widely-used to collect data about participants’ experience. Yet it places a burden on participants by requiring immediate response in context. Such user burden can be studied through users’ performance and perceptions. This paper evaluates six interaction techniques to study user burden of in-situ self-reporting from both performance and perception aspects under three simulated scenarios (i.e., gaming, social chatting, and walking) through an in-lab experiment with twenty-four participants. Findings show that user performance was not always aligned with their perceived burden owing to factors such as users’ acceptable interaction technique features for self-reporting, the context of use, and users’ acquired skills over time. This study also sheds lights onto how user burden is experienced across different stages of a self-reporting microinteraction. Reflecting on findings, we discuss the implications for understanding and minimizing user burden related to in-situ self-reporting and as well as other types of microinteractions.