Understanding and Supporting Self-Tracking App Selection

People often face barriers to selecting self-tracking tools that support their goals and needs, resulting in tools not meeting their expectations and ultimately abandonment. We therefore examine how people approach selecting self-tracking apps and investigate how technology can better support the process. Drawing on past literature on how people select and perceive the features of commercial and research tracking tools, we surface seven attributes people consider during selection, and design a low-fidelity prototype of an app store that highlights these attributes. We then conduct semi-structured interviews with 18 participants to further investigate what people consider during selection, how people select self-tracking apps, and how surfacing tracking-related attributes could better support selection. We find that people often prioritize features related to self-tracking during selection, such as approaches to collecting and reflecting on data, and trial apps to determine whether they would suit their needs. Our results also show potential for technology surfacing how apps support tracking to reduce barriers to selection. We discuss future opportunities for improving self-tracking app selection, such as ways to enhance existing self-tracking app distribution platforms to enable people to filter and search apps by desirable features.

CCS Concepts: • Human-centered computing → Empirical studies in ubiquitous and mobile computing.