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Data from: Beyond novelty effect: a mixed-methods exploration into the motivation for long-term activity tracker use

Citation

Shin, Grace; Feng, Yuanyuan; Jarrahi, Mohammad Hossein; Gafinowitz, Nicci (2018), Data from: Beyond novelty effect: a mixed-methods exploration into the motivation for long-term activity tracker use, Dryad, Dataset, https://doi.org/10.5061/dryad.f3b04rm

Abstract

Objectives: Activity trackers hold the promise to support people in managing their health through quantified measurements about their daily physical activities. Monitoring personal health with quantified activity tracker-generated data provides patients with an opportunity to self-manage their health. Many activity tracker user studies have been conducted within short time frames, however, which makes it difficult to discover the impact of the activity tracker’s novelty effect or the reasons for the device’s long-term use. This study explores the impact of novelty effect on activity tracker adoption and the motivation for sustained use beyond the novelty period. Materials and Methods: This study uses a mixed-methods approach that combines both quantitative activity tracker log analysis and qualitative one-on-one interviews to develop a deeper behavioral understanding of 23 Fitbit device users who have used their trackers for at least two months (range of use = 69 - 1073 days). Results: Log data from users’ Fitbit devices revealed two stages in their activity tracker use: the novelty period and the long-term use period. The novelty period for Fitbit users in this study was approximately three months, during which they might have discontinued using their devices. Discussion: The qualitative interview data identified various factors that motivate users to continuously use Fitbit devices in different stages. The discussion of these results provides design implications to guide future development of activity tracking technology. Conclusion: This study reveals important dynamics emerging over long-term activity tracker use, contributes new knowledge to consumer health informatics and human-computer interaction, and offers design implications to guide future development of similar health-monitoring technologies that better account for long-term use in support of patient care and health self-management.

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