Designing Reflective Derived Metrics for Fitness Trackers

Abstract

Personal tracking devices are equipped with more and more sensors and offer an ever-increasing level of accuracy. Yet, this comes at the cost of increased complexity. To deal with that problem, fitness trackers use derived metrics---scores calculated based on sensor data, e.g. a stress score. This means that part of the agency in interpreting health data is transferred from the user to the tracker. In this paper, we investigate the consequences of that transition and study how derived metrics can be designed to offer an optimal personal informatics experience. We conducted an online survey and a series of interviews which examined a health score (a hypothetical derived metric) at three levels of abstraction. We found that the medium abstraction level led to the highest level of reflection. Further, we determined that presenting the metric without contextual information led to decreased transparency and meaning. Our work contributes guidelines for designing effective derived metrics.