Identification and Classification of Usage Patterns in Long-Term Activity Tracking
Abstract
Activity trackers are frequently used in health and well-being, but their application in effective interventions is challenging. While research for reasons of use and non-use is ongoing, little is known about the way activity trackers are used in everyday life and over longer periods. We analyzed data of 104 individuals over 14,413 use days, and in total over 2.5 years. We describe general tracker use, periodic changes and overall changes over time, and identify characteristic patterns. While the use of trackers shows large individual heterogeneity, from our findings we could identify and classify general patterns for activity tracker use such as try-and-drop, slow-starter, experimenter, hop-on hop-off, intermittent and power user. Our findings contribute to the body of knowledge towards the successful design of effective health technologies, health interventions, and long-term health applications.
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There is a potential for unmeasured confounding in the analyses because the study was not able to account for an exhaustive list of potential confounders such as job status, environmental factors, and differences in usage patterns between participants over time.
“Further, there is a potential for unmeasured confounding in our analyses because we were not able to account for an exhaustive list of potential confounders such as job status, environmental factors and differences in the usage patterns between participants over time17.”
From Association of step counts over time with the risk of chronic disease in the All of Us Research Program (2022) · cites this paper as [17]
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- Authors
- Meyer, J., Wasmann, M., Heuten, W., El Ali, A., & Boll, S. C. J.
- Affiliations
- Oldenburger Institut für Informatik (DE)
- Carl von Ossietzky University, Oldenburg, Germany
- Carl von Ossietzky Universität Oldenburg (DE)
- Venue
- Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2017-05-02
- Pages
- 667-678
- OpenAlex
- W2611061514
- Cited by
- 72
- References
- 32
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 7 · 2023: 3 · 2022: 10 · 2021: 9 · 2020: 10 · 2019: 15 · 2018: 6 · 2017: 5
- FWCI
- 7.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- Bundesministerium für Bildung und Forschung (01EL1422C)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- wearable activity trackers, usage patterns, long-term tracking, user heterogeneity, health interventions, health technology design, power users
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