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

2025-01-20

AI Summary

This study addresses the “data–expectation gap” between smartwatch health data interpretation and users’ actual expectations, revealing its heterogeneous manifestations across temporal, social, and affective contextual dimensions. Method: We innovatively constructed the first semantic lexicon system for Human–Data Interaction (HDI) in wearable devices, grounded in two empirical studies: contextual interviews and experience sampling. These methods enabled systematic identification and synthesis of cross-contextual dimensions of subjective data misuse. Contribution/Results: The work yields a structured semantic lexicon tool that supports the design of empathetic, context-adaptive health data feedback mechanisms. By bridging interpretive misalignments between users and systems, this framework enhances HCI trustworthiness and advances the practical efficacy of health informatics applications—particularly in personal health monitoring ecosystems where contextual sensitivity and user-centered data interpretation are critical.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Lexical Semantics and Morphology

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

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.

Problem

• Smartwatch Data
• Context-aware Interpretation
• Human-Computer Interaction

Innovation

• Wearable Devices
• Data Perception
• Human-Computer Interaction