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      User Acceptance of Wrist-Worn Activity Trackers Among Community-Dwelling Older Adults: Mixed Method Study

      research-article
      , MSc 1 , , MHI 1 , , BEng 1 , 2 , , PhD 3 , , PhD 4 , , PhD 1 ,
      (Reviewer), (Reviewer), (Reviewer), (Reviewer)
      JMIR mHealth and uHealth
      JMIR Publications
      health, mHealth, fitness trackers, older adults

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          Abstract

          Background

          Wearable activity trackers are newly emerging technologies with the anticipation for successfully supporting aging-in-place. Consumer-grade wearable activity trackers are increasingly ubiquitous in the market, but the attitudes toward, as well as acceptance and voluntary use of, these trackers in older population are poorly understood.

          Objective

          The aim of this study was to assess acceptance and usage of wearable activity trackers in Canadian community-dwelling older adults, using the potentially influential factors as identified in literature and technology acceptance model.

          Methods

          A mixed methods design was used. A total of 20 older adults aged 55 years and older were recruited from Southwestern Ontario. Participants used 2 different wearable activity trackers (Xiaomi Mi Band and Microsoft Band) separately for each segment in the crossover design study for 21 days (ie, 42 days total). A questionnaire was developed to capture acceptance and experience at the end of each segment, representing 2 different devices. Semistructured interviews were conducted with 4 participants, and a content analysis was performed.

          Results

          Participants ranged in age from 55 years to 84 years (mean age: 64 years). The Mi Band gained higher levels of acceptance (16/20, 80%) compared with the Microsoft Band (10/20, 50%). The equipment characteristics dimension scored significantly higher for the Mi Band ( P<.05). The amount a participant was willing to pay for the device was highly associated with technology acceptance ( P<.05). Multivariate logistic regression with 3 covariates resulted in an area under the curve of 0.79. Content analysis resulted in the formation of the following main themes: (1) smartphones as facilitators of wearable activity trackers; (2) privacy is less of a concern for wearable activity trackers, (3) value proposition: self-awareness and motivation; (4) subjective norm, social support, and sense of independence; and (5) equipment characteristics matter: display, battery, comfort, and aesthetics.

          Conclusions

          Older adults were mostly accepting of wearable activity trackers, and they had a clear understanding of its value for their lives. Wearable activity trackers were uniquely considered more personal than other types of technologies, thereby the equipment characteristics including comfort, aesthetics, and price had a significant impact on the acceptance. Results indicated that privacy was less of concern for older adults, but it may have stemmed from a lack of understanding of the privacy risks and implications. These findings add to emerging research that investigates acceptance and factors that may influence acceptance of wearable activity trackers among older adults.

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          Most cited references41

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          Factors influencing acceptance of technology for aging in place: a systematic review.

          To provide an overview of factors influencing the acceptance of electronic technologies that support aging in place by community-dwelling older adults. Since technology acceptance factors fluctuate over time, a distinction was made between factors in the pre-implementation stage and factors in the post-implementation stage.
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            Assessing Acceptance of Assistive Social Agent Technology by Older Adults: the Almere Model

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              An empirical study of wearable technology acceptance in healthcare

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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                November 2017
                15 November 2017
                : 5
                : 11
                : e173
                Affiliations
                [1] 1 Health Data Science Lab School of Public Health and Health Systems University of Waterloo Waterloo, ON Canada
                [2] 2 Department of Electrical and Computer Engineering University of Waterloo Waterloo, ON Canada
                [3] 3 School of Public Health and Health Systems University of Waterloo Waterloo, ON Canada
                [4] 4 Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo, ON Canada
                Author notes
                Corresponding Author: Joon Lee joon.lee@ 123456uwaterloo.ca
                Author information
                http://orcid.org/0000-0001-9308-7165
                http://orcid.org/0000-0002-6793-5551
                http://orcid.org/0000-0001-6823-2316
                http://orcid.org/0000-0002-5685-0843
                http://orcid.org/0000-0002-0771-2313
                http://orcid.org/0000-0001-8593-9321
                Article
                v5i11e173
                10.2196/mhealth.8211
                5707431
                29141837
                522ff24f-f8bf-45fa-b163-d0f710c9b37c
                ©Arjun Puri, Ben Kim, Olivier Nguyen, Paul Stolee, James Tung, Joon Lee. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 15.11.2017.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

                History
                : 14 June 2017
                : 25 August 2017
                : 6 September 2017
                : 23 September 2017
                Categories
                Original Paper
                Original Paper

                health,mhealth,fitness trackers,older adults
                health, mhealth, fitness trackers, older adults

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