Abstract
The authors present an approach for designing self-monitoring technology called which combines both manual and automated data collection methods. Through this approach, they aim to lower the capture burdens, collect data that is typically hard to track automatically, and promote awareness to help people achieve their self-monitoring goals. They first specify three design considerations for semi-automated tracking: data capture feasibility, the purpose of self-monitoring, and the motivation level. They then provide examples of semi-automated tracking applications in the domains of sleep, mood, and food tracking to demonstrate strategies they developed to find the right balance between manual tracking and automated tracking, combining each of their benefits while minimizing their associated limitations.
Highlights
Self-monitoring for health behavior change is an important practice across numerous domains
Studies have shown that self-monitoring can enable greater awareness of behaviors and can create a reactive effect yielding positive, therapeutic behavior changes
To better achieve the benefits of self-monitoring, we argue that designers need to find the right balance between manual tracking and automated tracking, combining each of their benefits while minimizing their associated limitations
Summary
Self-monitoring for health behavior change is an important practice across numerous domains (e.g., diet, physical activity, sleep, stress). One of the goals for these automated systems is to lower the capture burdens such that a person can achieve the benefits of self-monitoring without the time and difficulty of manual data collection. This approach seems intuitive, little evidence shows that automated health activity tracking leads to behavior change [4]. We suspect that this is partially because the complete automation of data collection significantly reduces the awareness, accountability, and involvement achieved when a person actively engages in manual tracking [5]. We conclude with providing further design opportunities in the semi-automated tracking design space
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