Understanding Self-Tracked Data from Bounded Situational Contexts
Ada Ng, Ashley Marie Walker, Laurie S. Wakschlag, Nabil I. Alshurafa, Madhu C Reddy · Designing Interactive Systems Conference · 2022
As smartphone and wearable tracking devices have grown in popularity, more individuals have begun collecting their own health data. While these data are often perceived as a persistent record of health and used to inform future behaviors, it is inevitable that some data are captured during a period of disruption or non-routine circumstances. If not appropriately contextualized, visualizations of these data can lead to missed opportunities in self-reflection, or worse, misinterpretation. To better understand how self-tracked data captured during non-routine circumstances are reflected upon after the disruption has ended, we interviewed women about how they might reflect on data from a recent pregnancy. We propose the concept of bounded situational context (BSC) to encapsulate how individuals define the boundaries of disruption within their data based on external and internal contexts. We discuss how self-tracking tools can be designed to align data visualizations with individuals’ perceived boundaries to aid in data interpretation.