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Identification and Classification of Usage Patterns in Long-Term Activity Tracking

Published: 02 May 2017 Publication History

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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References

[1]
Judit Bort-Roig, Nicholas D. Gilson, Anna Puig-Ribera, Ruth S. Contreras, and Stewart G Trost. 2014. Measuring and influencing physical activity with smartphone technology: a systematic review. Sports medicine (Auckland, N.Z.) 44, 5: 671--86. http://doi.org/10.1007/s40279-014-0142-5
[2]
Dena M. Bravata, Crystal Smith-Spangler, Vandana Sundaram, et al. 2007. Using pedometers to increase physical activity and improve health: a systematic review. JAMA Journal of the American Medical Association 298, 19: 2296--304. http://doi.org/10.1001/jama.298.19.2296
[3]
Steindorf, K., Bruhmann, B. A., Schmidt, M. E. 2014. Assessment of physical activity in epidemiological studies: Are questionnaires obsolete in the era of accelerometry? GMS Med Inform Biom Epidemiol 10, 1: 1--12. http://doi.org/10.3205/mibe000155
[4]
James Clawson, Jessica A. Pater, Andrew D. Miller, Elizabeth D. Mynatt, and Lena Mamykina. 2015. No longer wearing. UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing: 647--658. http://doi.org/10.1145/2750858.2807554
[5]
J. Connelly, A. Kirk, J. Masthoff, and S. MacRury. 2013. The use of technology to promote physical activity in Type 2 diabetes management: a systematic review. Diabetic medicine: a journal of the British Diabetic Association 30, 12: 1420--32. http://doi.org/10.1111/dme.12289
[6]
Sunny Consolvo, David W. McDonald, Tammy Toscos, et al. 2008. Activity sensing in the wild: a field trial of ubifit garden. In CHI '08 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1797--1806. http://doi.org/10.1145/1357054.1357335
[7]
Cora L Craig, Alison L Marshall, Michael Sjöström, et al. 2003. International physical activity questionnaire: 12-country reliability and validity. Medicine and science in sports and exercise 35, 8: 1381--95. http://doi.org/10.1249/01.MSS.0000078924.61453.FB
[8]
Endeavour Partners LLC. 2014. Inside Wearables - How the Science of Behavior Change Offers the Secret to Long-Term Engagement.
[9]
Daniel A. Epstein, Monica Caraway, Chuck Johnston, An Ping, James Fogarty, and Sean A. Munson. 2016. Beyond Abandonment to Next Steps: Understanding and Designing for Life after Personal Informatics Tool Use. CHI '16 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. http://doi.org/10.1145/2858036.2858045
[10]
Daniel A. Epstein, Jennifer Kang, Laura R. Pina, James Fogarty, and Sean A. Munson. 2016. Reconsidering the Device in the Drawer: Lapses as a Design Opportunity in Personal Informatics. In UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 829--840. http://doi.org/10.1145/2971648.2971656
[11]
Jordan Etkin. 2016. The Hidden Cost of Personal Quantification. Journal of Consumer Research 42, 6: 967--984.
[12]
Cara Bailey Fausset, Tracy L. Mitzner, Chandler E. Price, Brian D. Jones, Brad W. Fain, and Wendy A. Rogers. 2013. Older Adults? Use of and Attitudes toward Activity Monitoring Technologies. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 57, 1: 1683--1687. http://doi.org/10.1177/1541931213571374
[13]
Ty Ferguson, Alex V. Rowlands, Tim Olds, and Carol Maher. 2015. The validity of consumer-level activity monitors in healthy adults worn in freeliving conditions: a cross-sectional study. The International Journal of Behavioral Nutrition and Physical Activity 12: 42.
[14]
Thomas Fritz, Elaine M. Huang, Gail C. Murphy, and Thomas Zimmermann. 2014. Persuasive technology in the real world: a study of long-term use of activity sensing devices for fitness. CHI '14 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems: 487--496. http://doi.org/10.1145/2556288.2557383
[15]
Rúben Gouveia, Evangelos Karapanos, and Marc Hassenzahl. 2015. How do we engage with activity trackers? A longitudinal study of Habito. In UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 1305--1316. http://doi.org/10.1145/2750858.2804290
[16]
Daniel Harrison, Nadia Berthouze, Paul Marshall, and Jon Bird. 2014. Tracking physical activity: problems related to running longitudinal studies with commercial devices. In UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, 699--702. http://doi.org/10.1145/2638728.2641320
[17]
IDC. 2016. Worldwide Quarterly Wearable Device Tracker.
[18]
Evangelos Karapanos, Rúben Gouveia, Marc Hassenzahl, and Jodi Forlizzi. 2016. Wellbeing in the making: Peoples' experiences with wearable activity trackers. Psychology of Well-Being 2013. http://doi.org/10.1186/s13612-016-0042-6
[19]
Katja Karrer, Charlotte Glaser, Caroline Clemens, and Carmen Bruder. 2009. Technikaffinität erfassen - der Fragebogen TA-EG (Assessing affinity to technology - the TA-EG questionnaire). In Der Mensch im Mittelpunkt technischer Systeme, 196-- 201.
[20]
Amanda Lazar, Christian Koehler, Joshua Tanenbaum, and David H. Nguyen. 2015. Why we use and abandon smart devices. UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing: 635--646. http://doi.org/10.1145/2750858.2804288
[21]
James J. Lin, Lena Mamykina, Silvia Lindtner, Gregory Delajoux, and Henry B. Strub. 2006. Fish 'n' Steps: Encouraging Physical Activity with an Interactive Computer Game. In UbiComp '06 Proceedings of the 8th international conference on Ubiquitous computing, 261--278. http://doi.org/10.1007/11853565_16
[22]
Marion E. T. McMurdo, Jacqui Sugden, Ishbel Argo, et al. 2010. Do pedometers increase physical activity in sedentary older women? A randomized controlled trial. Journal of the American Geriatrics Society 58, 11: 2099--106. http://doi.org/10.1111/j.1532-5415.2010.03127.x
[23]
Jochen Meyer, Jutta Fortmann, Merlin Wasmann, and Wilko Heuten. 2015. Making Lifelogging Usable: Design Guidelines for Activity Trackers. In Multimedia Modeling 2015. Sydney, NSW, Autralia, 323--334. http://doi.org/10.1007/978-3319-14442-9_39
[24]
Jochen Meyer, Anastasia Kazakova, Merlin Büsing, and Susanne Boll. 2016. Visualization of Complex Health Data on Mobile Devices. In MMHealth'16: Multimedia for personal health and health care Proceedings. http://doi.org/10.1145/2985766.2985774
[25]
Saskia Muellmann, Sarah Forberger, Tobias Möllers, et al. 2016. Effectiveness of eHealth interventions for the promotion of physical activity in older adults: a systematic review protocol. Systematic Reviews 5, 1: 47. http://doi.org/10.1186/s13643-016-0223-7
[26]
Franz J. Neyer, Juliane Felber, and Claudia Gebhardt. 2012. Entwicklung und Validierung einer Kurzskala zur Erfassung von Technikbereitschaft (Development and validation of a brief measure of technology commitment). Diagnostica 58, 2: 87-- 99. http://doi.org/10.1026/0012-1924/a000067
[27]
NPD. 2015. Consumers and Wearables Report. Las Vegas.
[28]
Massimo F. Piepoli, Ugo Corrà, François Carré, et al. 2010. Secondary prevention through cardiac rehabilitation: physical activity counselling and exercise training. European Heart Journal 31, 16: 1967--76. http://doi.org/10.1093/eurheartj/ehq236
[29]
Rachael Purta, Stephen Mattingly, Lixing Song, et al. 2016. Experiences measuring sleep and physical activity patterns across a large college cohort with fitbits. In Proceedings of the 2016 ACM International Symposium on Wearable Computers ISWC '16, 28--35. http://doi.org/10.1145/2971763.2971767
[30]
Patrick L. Schneider, Scott E. Crouter, Olivera Lukajic, and David R. Bassett. 2003. Accuracy and reliability of 10 pedometers for measuring steps over a 400-m walk. Medicine and Science in Sports and Exercise 35, 10: 1779--1784. http://doi.org/10.1249/01.MSS.0000089342.96098.C4
[31]
Patrick C. Shih, Kyungsik Han, Erika Shehan Poole, Mary Beth Rosson, and John M. Carroll. 2015. Use and Adoption Challenges of Wearable Activity Trackers. In iConference 2015 Proceedings.
[32]
Torben Wallbaum, Melina Frenken, Jochen Meyer, and Andreas Hein. 2014. Acceptance of sensor networks by dementia patients. In 7. Deutscher AAL-Kongress.
[33]
WHO. 2011. Global status report on noncommunicable diseases 2010.
[34]
H. Wienbergen, T. Backhaus, J. Stehmeier, et al. 2016. Assessment and monitoring of physical activity with pedometers and and online documentation in secondary prevention after myocardiac infarct (Erfassung und Kontrolle körperlicher Aktivität mit Schrittzählern und Online-Dokumentation in der Sekundärprävent. In Tagung der Deutschen Gesellschaft für Kardiologie.
[35]
Sha Zhao, Julian Ramos, Jianrong Tao, et al. 2016. Discovering different kinds of smartphone users through their application usage behaviors. In UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 498--509. http://doi.org/10.1145/2971648.2971696

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    cover image ACM Conferences
    CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
    May 2017
    7138 pages
    ISBN:9781450346559
    DOI:10.1145/3025453
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 02 May 2017

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    Author Tags

    1. activity monitoring
    2. activity tracker
    3. longitudinal use
    4. quantitative analysis
    5. usage patterns

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    • (2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 8-Feb-2024
    • (2024)My Data, My Choice, My Insights: Women's Requirements when Collecting, Interpreting and Sharing their Personal Health DataProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642851(1-18)Online publication date: 11-May-2024
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    • (2023)Narrative-Based Visual Feedback to Encourage Sustained Physical ActivityProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35807867:1(1-36)Online publication date: 28-Mar-2023
    • (2022)Adherence and retention to the self-managed community-based Step Into Health program in Qatar (2012–2019)Frontiers in Public Health10.3389/fpubh.2022.92738610Online publication date: 15-Sep-2022
    • (2022)An Evaluation of a Commercialized mHealth Intervention to Promote Physical Activity in the WorkplaceFrontiers in Public Health10.3389/fpubh.2022.74035010Online publication date: 15-Mar-2022
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