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

Arjun Puri, Ben Kim, Olivier Nguyen, Paul Stolee, James Tung, Joon Lee, Arjun Puri, Ben Kim, Olivier Nguyen, Paul Stolee, James Tung, Joon Lee

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.

Keywords: fitness trackers; health; mHealth; older adults.

Conflict of interest statement

Conflicts of Interest: None declared.

©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.

Figures

Figure 1
Figure 1
The Xiaomi Mi Band (left) and Microsoft Band (right).

References

    1. Statistics Canada. 2015. Sep 29, Canada's population estimates: age and sex, July 1, 2015 .
    1. Canadian Institute for Health Information . National health expenditure trends, 1975 to 2013. Ottawa, ON: CIHI; 2013. .
    1. Canadian Institute for Health Information . Health care in Canada, 2011: a focus on seniors and aging. Ottawa, ON: CIHI; 2011. .
    1. Wiles JL, Leibing A, Guberman N, Reeve J, Allen RE. The meaning of “aging in place” to older people. Gerontologist. 2012 Jun;52(3):357–66. doi: 10.1093/geront/gnr098.
    1. Bacsu JR, Jeffery B, Johnson S, Martz D, Novik N, Abonyi S. Healthy aging in place: supporting rural seniors' health needs. Online J Rural Nurs Health Care. 2012;12(2):77–87.
    1. Chapin R, Dobbs-Kepper D. Aging in place in assisted living: philosophy versus policy. Gerontologist. 2001 Feb;41(1):43–50.
    1. Hollander MJ, Chappell NL. A comparative analysis of costs to government for home care and long-term residential care services, standardized for client care needs. Can J Aging. 2007;26 Suppl 1:149–61. doi: 10.3138/cja.26.suppl_1.149.
    1. Chappell NL, Dlitt BH, Hollander MJ, Miller JA, McWilliam C. Comparative costs of home care and residential care. Gerontologist. 2004 Jun;44(3):389–400.
    1. Graybill EM, McMeekin P, Wildman J. Can aging in place be cost effective? A systematic review. PLoS One. 2014;9(7):e102705. doi: 10.1371/journal.pone.0102705.
    1. Marek KD, Stetzer F, Adams SJ, Popejoy LL, Rantz M. Aging in place versus nursing home care: comparison of costs to Medicare and Medicaid. Res Gerontol Nurs. 2012 Apr;5(2):123–9. doi: 10.3928/19404921-20110802-01.
    1. Davey J. “Ageing in place”: The views of older homeowners on home maintenance, renovation and adaptation. Soc Policy J New Zealand. 2006;27:128.
    1. Mylively. 2015. [2017-06-07]. Live!y - Personal Emergency Response - Reimagined
    1. Santo K, Richtering SS, Chalmers J, Thiagalingam A, Chow CK, Redfern J. Mobile phone apps to improve medication adherence: a systematic stepwise process to identify high-quality apps. JMIR Mhealth Uhealth. 2016 Dec 02;4(4):e132. doi: 10.2196/mhealth.6742.
    1. Preventicesolutions. 2015. [2017-06-07]. Bodyguardian heart .
    1. Carepredict. 2015. [2017-06-07]. Empowering better senior care .
    1. Demiris G, Rantz M, Aud M, Marek K, Tyrer H, Skubic M, Hussam A. Older adults' attitudes towards and perceptions of “smart home” technologies: a pilot study. Med Inform Internet Med. 2004 Jun;29(2):87–94. doi: 10.1080/14639230410001684387.
    1. Peek ST, Wouters EJ, van Hoof J, Luijkx KG, Boeije HR, Vrijhoef HJ. Factors influencing acceptance of technology for aging in place: a systematic review. Int J Med Inform. 2014 Apr;83(4):235–48. doi: 10.1016/j.ijmedinf.2014.01.004.
    1. Salah H, MacIntosh E, Rajakulendran N. Marsdd. Toronto: MaRS; 2014. [2017-06-07]. Wearable tech: leveraging Canadian innovation to improve health .
    1. Accenture. [2017-03-30]. Insight driven health: silver surfers continue to make waves in ehealth. But are healthcare organizations swimming against the tide? .
    1. Accenture. 2016. [2017-06-07]. Insight driven health: Accenture 2016 consumer survey on patient engagement .
    1. Lyons EJ, Swartz MC, Lewis ZH, Martinez E, Jennings K. Feasibility and acceptability of a wearable technology physical activity intervention with telephone counseling for mid-aged and older adults: a randomized controlled pilot trial. JMIR Mhealth Uhealth. 2017 Mar 06;5(3):e28. doi: 10.2196/mhealth.6967.
    1. Clifton L, Clifton DA, Pimentel MA, Watkinson PJ, Tarassenko L. Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors. IEEE J Biomed Health Inform. 2014 May;18(3):722–30. doi: 10.1109/JBHI.2013.2293059.
    1. Kim BY, Lee J. Smart devices for older adults managing chronic disease: a scoping review. JMIR Mhealth Uhealth. 2017 May 23;5(5):e69. doi: 10.2196/mhealth.7141.
    1. Arnhold M, Quade M, Kirch W. Mobile applications for diabetics: a systematic review and expert-based usability evaluation considering the special requirements of diabetes patients age 50 years or older. J Med Internet Res. 2014 Apr 09;16(4):e104. doi: 10.2196/jmir.2968.
    1. Chen CC, Shih HS. A study of the acceptance of wearable technology for consumers: an analytical network process perspective. Int J Anal Hierarchy Process. 2014:1–5.
    1. Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act. 2015;12(1):159. doi: 10.1186/s12966-015-0314-1.
    1. Ridgers ND, McNarry MA, Mackintosh KA. Feasibility and effectiveness of using wearable activity trackers in youth: a systematic review. JMIR Mhealth Uhealth. 2016 Nov 23;4(4):e129. doi: 10.2196/mhealth.6540.
    1. Vooijs M, Alpay LL, Snoeck-Stroband JB, Beerthuizen T, Siemonsma PC, Abbink JJ, Sont JK, Rövekamp TA. Validity and usability of low-cost accelerometers for internet-based self-monitoring of physical activity in patients with chronic obstructive pulmonary disease. Interact J Med Res. 2014;3(4):e14. doi: 10.2196/ijmr.3056.
    1. McMahon SK, Lewis B, Oakes M, Guan W, Wyman JF, Rothman AJ. Older adults' experiences using a commercially available monitor to self-track their physical activity. JMIR Mhealth Uhealth. 2016;4(2):e35. doi: 10.2196/mhealth.5120.
    1. Mercer K, Giangregorio L, Schneider E, Chilana P, Li M, Grindrod K. Acceptance of commercially available wearable activity trackers among adults aged over 50 and with chronic illness: a mixed-methods evaluation. JMIR Mhealth Uhealth. 2016;4(1):e7. doi: 10.2196/mhealth.4225.
    1. Karahanoğlu A, Erbuğ C. Perceived qualities of smart wearables: determinants of user acceptance. Proceedings of the 2011 Conference on Designing Pleasurable Products and Interfaces - DPPI'11; June 22-25, 2011; Milano, Italy. ACM Press; 2011.
    1. Shih PC, Han K, Poole ES, Rosson MB, Carroll JM. Use and adoption challenges of wearable activity trackers. iConference 2015 Proceedings; 2015; Illinois. 2015. p. 1.
    1. Davis F. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319–340. doi: 10.2307/249008.
    1. Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci. 2000;46(2):186–204. doi: 10.1287/mnsc.46.2.186.11926.
    1. Venkatesh V, Bala H. Technology acceptance model 3 and a research agenda on interventions. Decis Sci. 2008;39(2):273–315. doi: 10.1111/j.1540-5915.2008.00192.x.
    1. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Q. 2003 Sep 01;:425–478.
    1. Heerink M, Kröse B, Evers V, Wielinga B. Assessing acceptance of assistive social agent technology by older adults: the almere model. Int J Soc Robot. 2010 Dec 01;2(4):361–375. doi: 10.1007/s12369-010-0068-5.
    1. Pan S, Jordan-Marsh M. Internet use intention and adoption among Chinese older adults: from the expanded technology acceptance model perspective. Comput Human Behav. 2010 Sep;26(5):1111–1119. doi: 10.1016/j.chb.2010.03.015.
    1. Yang H, Yu J, Zo H, Choi M. User acceptance of wearable devices: an extended perspective of perceived value. Telematics Informatics. 2016 May;33(2):256–269. doi: 10.1016/j.tele.2015.08.007.
    1. Gao Y, Li H, Luo Y. An empirical study of wearable technology acceptance in healthcare. Ind Manag Data Syst. 2015 Oct 19;115(9):1704–1723. doi: 10.1108/IMDS-03-2015-0087.
    1. Mihailidis A, Cockburn A, Longley C, Boger J. The acceptability of home monitoring technology among community-dwelling older adults and baby boomers. Assist Technol. 2008;20(1):1–12. doi: 10.1080/10400435.2008.10131927.
    1. Hsiung PC. . 2010. [2017-06-07]. Lives and legacies: a guide to qualitative interviewing .
    1. Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005 Nov;15(9):1277–88. doi: 10.1177/1049732305276687.
    1. Fensli R, Pedersen PE, Gundersen T, Hejlesen O. Sensor acceptance model - measuring patient acceptance of wearable sensors. Methods Inf Med. 2008;47(1):89–95.
    1. Davis F, Bagozzi R, Warshaw P. User acceptance of computer technology: a comparison of two theoretical models. Manag Sci. 1989 Aug;35(8):982–1003. doi: 10.1287/mnsc.35.8.982.
    1. Thompson RL, Higgins CA, Howell JM. Personal computing: toward a conceptual model of utilization. MIS Q. 1991 Mar;15(1):125–43. doi: 10.2307/249443.
    1. Taylor JW. The role of risk in consumer behavior. J Mark. 1974 Apr;38(2):54–60. doi: 10.2307/1250198.
    1. Creswell JW. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. London: Sage Publications, Inc; 2013. Mar 14,
    1. Cheung C, Bietz MJ, Patrick K, Bloss CS. Privacy attitudes among early adopters of emerging health technologies. PLoS One. 2016;11(11):e0166389. doi: 10.1371/journal.pone.0166389.
    1. Segura Anaya LH, Alsadoon A, Costadopoulos N, Prasad PW. Ethical implications of user perceptions of wearable devices. Sci Eng Ethics. 2017 Feb 02;:1–28. doi: 10.1007/s11948-017-9872-8.
    1. Mynatt ED, Melenhorst AS, Fisk AD, Rogers WA. Aware technologies for aging in place: understanding user needs and attitudes. IEEE Pervasive Comput. 2004 Apr;3(2):36–41. doi: 10.1109/MPRV.2004.1316816.
    1. Courtney KL. Privacy and senior willingness to adopt smart home information technology in residential care facilities. Methods Inf Med. 2008;47(1):76–81.
    1. Paul G, Irvine J. Privacy implications of wearable health devices. Proceedings of the 7th International Conference on Security of Information and Networks; September 9-11, 2014; Glasgow, Scotland. New York: ACM; 2014. Sep, p. 117.

Source: PubMed

Szponzorok és közreműködők

Egészségi állapot

Kábítószer-beavatkozások

3
Iratkozz fel