extended-abstract

Assessment of activity trackers: toward an acceptance model

Authors:

Karolina BarasAuthors Info & Claims

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

Pages 570 - 575

https://doi.org/10.1145/2968219.2968323

Published: 12 September 2016 Publication History

Abstract

In this project we seek to understand the factors that influence user acceptance of Activity Trackers, through a model that quantifies how users come to adhere to the use of Activity Trackers. The proposed research model and hypotheses were validated and tested with data collected from a cross-sectional survey conducted using a self-selected convenience sample. Constructs from half dozen of established models were gathered into a suppositional model, based on their hypothetical applicable relevance for the Activity Trackers use. The results were analyzed using a variety of statistical techniques including Structural Equation analysis. The final result can be a first step for researchers aiming to complement their own processes of study, ideation or design of Activity by taking into account factors such as Usefulness, Ease of Use, Health Consciousness, Hedonic Motivation, Image, Habit, etc.

References

[1]

Albert Bandura. 1986. Social foundations of thought and action: A social cognitive theory. Prentice-Hall, Inc.

[2]

Angela Bryan, Leona Aiken, and Stephen West. 1997. Young women's condom use: the influence of acceptance of sexuality, control over the sexual encounter, and perceived susceptibility to common STDs. Health Psychology, 16(5), p.468.

[3]

Mei-Fang Chen. 2011. The joint moderating effect of health consciousness and healthy lifestyle on consumers' willingness to use functional foods in Taiwan. Appetite, 57(1), pp.253--262.

[4]

Deborah R. Compeau, Christopher Higgins. 1995. Computer self-efficacy: Development of a measure and initial test. MIS quarterly, pp.189--211.

Digital Library

[5]

Ekaterina Damer, Phelim Bradley. 2014. Prolific Academics. Ret: Jan 2016 from http://prolific.ac

[6]

Fred D. Davis. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, pp.319--340.

Digital Library

[7]

Fred D. Davis, Richard P. Bagozzi, and Paul R. Warshaw. 1992. Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of applied social psychology, 22(14), pp.1111--1132.

[8]

Martin Fishbein, and Icek Ajzen. 1997. Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric 10 (2):130--132.

[9]

Rúben Gouveia, Evangelos Karapanos, and Marc Hassenzahl. 2015. How do we engage with activity trackers?: a longitudinal study of Habito. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '15). http://dx.doi.org/10.1145/2750858.2804290

Digital Library

[10]

Hans Van der Heijden. 2004. User acceptance of hedonic information systems. MIS quarterly, pp.695--704.

Digital Library

[11]

Jeongeun Kim. 2014. A qualitative analysis of user experiences with a self-tracker for activity, sleep, and diet. Interactive journal of medical research, 3.

[12]

Jeongeun Kim, and Hyeoun-Ae Park. 2012. Development of a health information technology acceptance model using consumers' health behavior intention. Journal of medical Internet research, 14(5), p.e133.

[13]

Dan Ledger, and Daniel McCaffrey. 2014. Inside wearables: How the science of human behavior change offers the secret to long-term engagement. Endeavour Partners.

[14]

Gary C. Moore, and Izak Benbasat. 1991. Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), pp.192--222.

Digital Library

[15]

Stuart Moran. 2011. User perceptions of system attributes in ubiquitous monitoring: toward a model of behavioural intention. Ph.D Dissertation. The University of Reading, UK.

[16]

NPD Group. 2015. Connected Intelligence Consumer and Wearable survey. NPD Group's consumer technology report, September.

[17]

Rama Jayanti, and Alvin Burns. 1998. The antecedents of preventive health care behavior: An empirical study. Journal of the Academy of Marketing Science, 26(1), pp.6--15.

[18]

Everett M. Rogers. 2003. Diffusion of Innovations, 5th edition. New York, Free Press.

[19]

Irwin Rosenstock. 1974. Historical origins of the health belief model. Health Education & Behavior, 2(4), pp.328--335.

[20]

Viswanath Venkatesh, and Fred D. Davis. 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), pp.186--204.

Digital Library

[21]

Viswanath Venkatesh, Michael G. Morris, Gordon B. Davis, and Fred Davis. 2003. User acceptance of information technology: Toward a unified view. MIS quarterly, pp.425--478.

Digital Library

[22]

Viswanath Venkatesh, James YL Thong, and Xin Xu. 2012. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 36(1), pp.157--178

Digital Library

Cited By

Ridgers NDrehlich M(2021)Youth applicationsDigital Health10.1016/B978-0-12-818914-6.00011-9(305-317)Online publication date: 2021
https://doi.org/10.1016/B978-0-12-818914-6.00011-9
Capeleti BFerreira JDominguete GPereira MFreire ASantos CBento Villela MGasparini IUchôa Conte T(2020)Heuristic evaluation and user tests of wearable mobile health monitoring applicationsProceedings of the 19th Brazilian Symposium on Human Factors in Computing Systems10.1145/3424953.3426493(1-10)Online publication date: 26-Oct-2020
https://dl.acm.org/doi/10.1145/3424953.3426493
Rosales AFernández-Ardèvol MFerran-Ferrer N(2018)Long-Term Appropriation of Smartwatches Among a Group of Older PeopleHuman Aspects of IT for the Aged Population. Acceptance, Communication and Participation10.1007/978-3-319-92034-4_11(135-148)Online publication date: 1-Jun-2018
https://doi.org/10.1007/978-3-319-92034-4_11
Show More Cited By

Index Terms

Assessment of activity trackers: toward an acceptance model
1. Human-centered computing

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Published In

cover image ACM Conferences

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

September 2016

1807 pages

ISBN:9781450344623

DOI:10.1145/2968219

General Chairs:
Paul Lukowicz
DFKI and University of Kaiserslautern
,
Antonio Krüger
DFKI and Saarland University
,
Program Chairs:
Andreas Bulling
Max Planck Institute for Informatics
,
Youn-Kyung Lim
KAIST
,
Shwetak N. Patel
University of Washington

Copyright © 2016 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 12 September 2016

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Fundação para a Ciência e a Tecnologia

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UbiComp '16

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UbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 12 - 16, 2016

Germany, Heidelberg

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Cited By

Ridgers NDrehlich M(2021)Youth applicationsDigital Health10.1016/B978-0-12-818914-6.00011-9(305-317)Online publication date: 2021
https://doi.org/10.1016/B978-0-12-818914-6.00011-9
Capeleti BFerreira JDominguete GPereira MFreire ASantos CBento Villela MGasparini IUchôa Conte T(2020)Heuristic evaluation and user tests of wearable mobile health monitoring applicationsProceedings of the 19th Brazilian Symposium on Human Factors in Computing Systems10.1145/3424953.3426493(1-10)Online publication date: 26-Oct-2020
https://dl.acm.org/doi/10.1145/3424953.3426493
Rosales AFernández-Ardèvol MFerran-Ferrer N(2018)Long-Term Appropriation of Smartwatches Among a Group of Older PeopleHuman Aspects of IT for the Aged Population. Acceptance, Communication and Participation10.1007/978-3-319-92034-4_11(135-148)Online publication date: 1-Jun-2018
https://doi.org/10.1007/978-3-319-92034-4_11
McMillan DBrown BLampinen AMcGregor MHoggan EPizza SMark GFussell SLampe Cschraefel mHourcade JAppert CWigdor D(2017)Situating WearablesProceedings of the 2017 CHI Conference on Human Factors in Computing Systems10.1145/3025453.3025993(3582-3594)Online publication date: 2-May-2017
https://dl.acm.org/doi/10.1145/3025453.3025993

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