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

TL;DR

This paper introduces a vocabulary to describe the diverse experiential qualities of data inaccuracies in smartwatches, aiming to improve human-data interaction design by capturing contextual factors influencing user perceptions.

Contribution

It presents a new vocabulary based on empirical studies that characterizes how users experience data mismatches in wearable fitness trackers across different contexts.

Findings

Encounters with data mismatches vary by context and emotional impact.

The vocabulary captures temporal, social, and emotional factors influencing perceptions.

This framework aids in designing better HDI mechanisms for wearables.

Abstract

Many users of wrist-worn wearable fitness trackers encounter the data-expectation gap - mismatches between data and expectations. 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.