Designing for Human Supported Evidence-Based Planning
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Behavior planning is a technique that can help people bridge goals with the actions that will help them accomplish those goals. Although more than half of all Americans set goals for changing behavior every year, people encounter a range of barriers: knowing what series of behaviors will help accomplish a goal, identifying opportune moments to enact behaviors, identify behaviors relevant to the goal and that are feasible in the moment. Planning can help overcome these barriers, but people either do not know how to use plans on their own, or do not have access to experts who can deliver the techniques. Technology has made it easier to access interventions such as planning. However, technology-supported planning does not provide people with solutions that are tailored to the person’s needs or that incorporate evidence-based interventions that have been shown to help people accomplish intended goals. Social computing has shown that other people can provide informational support to others about incorporating changes in one’s life in contexts such as chronic disease management, yet little is known about how people can help each other for behavior planning. In this research, I investigate how technology can support behavior planning and how people can help each other to create more effective plans using technology. I first evaluate in what ways other people can help create behavior plans to fit with people’s needs. I identify types of preferences and constraints that plans need to satisfy to be tailored to people’s needs. I investigate how people without professional expertise, domain non-experts, can help others with behavior planning for physical activity and healthy eating. I identify strengths and weaknesses that different kinds of domain non-experts (friends, crowd members) have in helping others with tailored planning support. Based on this understanding, I designed, built, and deployed CrowdFit, a fully functioning system for behavior planning that support domain non-experts in creating behavior plans for physical activity that are aligned with exercising evidence-based guidelines. My findings contribute empirical understanding, design guidelines and theoretical implications to the human computer interaction, social computing, and behavior intervention technology researchers. It expands the existing understanding of people’s needs in engaging with behavior plans and of the needs and strengths of domain non-experts in providing behavior planning support. This research also expands understanding of how technology can scaffold behavioral evidence-based interventions to help domain non-experts provide high quality support to others.