The Data-Expectation Gap: A Vocabulary Describing Experiential Qualities of Data Inaccuracies in Smartwatches

Many users of wrist-worn wearable fitness trackers encounter the data-expectation gap - mismatches between data and the values they expected to see. While we know such discrepancies exist, we are no closer to designing technologies that can address their negative effects. This is largely because encounters with mismatches are typically treated unidimensionally, while they may differ in context and implications. This treatment does not allow the design of human-data interaction (HDI) mechanisms accounting for temporal, social, emotional, and other factors potentially influencing the perception of mismatches. To address this problem, we present a vocabulary that describes the breadth and context-bound character of encounters with the data-expectation gap, drawing from findings from two studies. Our work contributes to Personal Informatics research providing knowledge on how encounters with the data-expectation gap are embedded in people’s daily lives, and a vocabulary encapsulating this knowledge, which can be used when designing HDI experiences in wearable fitness trackers.