CHS: Small: Experiential Learning Systems for Promoting Wellness in Low-Income Families

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This project will advance research around using health-monitoring tools, such as sensor-based physical activity trackers, to support healthy behaviors for children in low-income families. In comparison to the general population, low-income families face disproportionate barriers to wellness, such as persistent social stressors and limited environmental support for behavior change. As such, a key technical challenge is designing systems that help families learn how to overcome these barriers to physical activity. To identify design opportunities, the team will first study how lower-income families currently use and think about physical activity tracking devices and data. This knowledge will then be used to redesign an existing software application that motivates children to exercise by helping them learn from sensor-collected physical activity data. In this research, the team will work closely with families to target the application more directly at their needs, while adding features to support "experiential learning"--the idea that reflecting on concrete past experiences can help people develop insights that guide their future behavior. The researchers will then conduct long-term studies of the tool in use, evaluating its impact on exercise and health, while learning more about the design issues that arise when supporting family-based activity tracking and learning. The combination of the use of experiential learning approaches, the low-income and family contexts, and the long-term evaluation all promise to extend what is currently known about designing systems that motivate physical activity. This work will have a practical impact on the widespread public health problem of childhood obesity, both by directly helping the families involved in the project and indirectly helping others through releasing the experiential learning tools and new design insights. The work will also provide educational benefits. Apart from the health education aspects of the system itself, the team will develop learning opportunities where college students work closely with local communities both to improve their learning and help those communities. These opportunities will also give lower-income children a way to participate directly in computer science research, helping them see what it would be like to be computer scientists or researchers themselves. In the first stage of work, the project will develop a grounded theory of how well existing physical activity tracking tools do and could support data collection, management, and experiential learning in low-income families. This theory will be developed through a combination of interviews and a trial deployment of activity trackers to both children and their caregivers. This theory will guide the second stage, in which the team will conduct participatory design research with families. Through this work, the team will design, build, and evaluate system prototypes that emphasize elements of experiential learning, particularly abstract conceptualization (identifying meaningful insights from activity data that can be applied to future situations) and active experimentation (testing out these new insights). The second stage will have two main outcomes. The first outcome will be a high-fidelity prototype of a system that tracks and represents physical activity among family members in a way that engages their attention and helps them engage in collaborative learning and experimentation. The second outcome will be a framework for the design of experiential learning systems that builds on the theory from the first stage. The prototype will be the basis of the third stage, a six-month deployment of the system to a number of families. In this stage, the team will gradually deploy features of the full system, and use a combination of survey instruments, interviews, and logged activity and usage data to refine the design framework and to evaluate the system's effect on knowledge, attitudes, and behaviors around physical activity. In total, the work will lead to design recommendations that will help researchers and developers build persuasive systems that encourage active engagement with physical activity data. In addition, this work will produce protocols for evaluating systems that support experiential learning from health data, knowledge about the challenges of designing such systems for families rather than individuals, and the software system itself. Each of these outputs will be valuable to both researchers and designers in this domain. Furthermore, the participatory design workshops will engage lower-income children in the creative process of technology design, which can pique their interest in a computer science career, while also providing the context for an innovative service-learning curriculum for existing college students. Finally, the software system will help to educate families about simple biological principles about the importance of physical activity in a socially and socioeconomically relevant way.