The quality of health and wellness self‐tracking data: A consumer perspective

Yan Zhang and Ciaran B. Trace

Journal of the Association for Information Science & Technology, 2022, vol. 73, issue 6, 879-891

Abstract: Information quality (IQ) is key to users' satisfaction with information systems. Understanding what IQ means to users can effectively inform system improvement. Existing inquiries into self‐tracking data quality primarily focus on accuracy. Interviewing 20 consumers who had self‐tracked health indicators for at least 6 months, we identified eight dimensions that consumers apply to evaluate self‐tracking data quality: value‐added, accuracy, completeness, accessibility, ease of understanding, trustworthiness, aesthetics, and invasiveness. These dimensions fell into four categories—intrinsic, contextual, representational, and accessibility—suggesting that consumers judge self‐tracking data quality not only based on the data's inherent quality but also considering tasks at hand, the clarity of data representation, and data accessibility. We also found that consumers' self‐tracking data quality judgments are shaped primarily by their goals or motivations, subjective experience with tracked activities, mental models of how systems work, self‐tracking tools' reputation, cost, and design, and domain knowledge and intuition, but less by more objective criteria such as scientific research results, validated devices, or consultation with experts. Future studies should develop and validate a scale for measuring consumers' perceptions of self‐tracking data quality and commit efforts to develop technologies and training materials to enhance consumers' ability to evaluate data quality.

Date: 2022
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