Older Adults’ Changes in Intent to Adopt Wellness Management Technologies

Abstract

Individuals are becoming more involved in managing their own health. Health self-management technologies have the potential to help older adults remain well by promoting exercise and a good diet. However, older adults may or may not decide to adopt wellness management technologies. Adoption is a process and the intent to adopt may change over time. Sixteen older adults (8 females; M_age=70.06, SD=3.09; range=65-75) used one of two wellness management technologies (the Fitbit One or myfitnesspal.com) over a 28-day period. Initially, all participants were open or neutral to adopting their technologies. After 28 days, 12 participants intended to adopt and 4 participants did not intend to adopt. The diary data revealed that over time, adopters made more positive comments than non-adopters. Both adopters and non-adopters mentioned perceived ease of use praises and complaints, whereas only adopters mentioned praises regarding usefulness. Results are interpreted within the frameworks of the unified theory of acceptance and use of technology (Venkatesh et al., 2003) and the diffusion of innovation (Rogers, 2003). Changes in intent to adopt suggest that experience is important in the adoption decision. Adoption of wellness management technologies by older adults may increase if designers attend to the perceived ease of use and perceived usefulness factors identified in this study.

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References

Albaina

I. M.

(2008). Persuasive technology to motivate elderly individuals to walk: A case study. (Master’s Thesis, Delft University of Technology, The Netherlands).

Barg-Walkow

L. H.

Mitzner

T. L.

Rogers

W. A.

(2014). Technology Experience Profile (HFA-TR-1402). Atlanta, GA: Georgia Institute of Technology, School of Psychology, Human Factors and Aging Laboratory.

Becker

Stuifbergen

H. S.

Hall

(1993). Self-rated abilities for health practices: A health self-efficacy measure. Health Values: The Journal of Health Behavior, Education & Promotion, 17(5), 42-50.

Blanson Henkemans

Rogers

Dumay

(2011). Personal characteristics and the law of attrition in randomized controlled trials of eHealth services for self-care. Gerontechnology, 10(3), 157-168.

Bodenheimer

Lorig

Holman

Grumbach

(2002). Patient self-management of chronic disease in primary care. JAMA: The Journal of the American Medical Association, 288(19), 2469-2475.

Czaja

S. J.

Charness

Dijkstra

Fisk

A. D.

Rogers

W. A.

Sharit

(2006). Center for Research and Education on Aging and Technology Enhancement Demographic and Background Questionnaire (CREATE-TR-2006-02). University of Miami: CREATE.

Colcombe

Kramer

A. F.

(2003). Fitness effects on the cognitive function of older adults A meta-analytic study. Psychological Science, 14(2), 125-130.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

Elward

Larson

(1992). Benefits of exercise for older adults. A review of existing evidence and current recommendations for the general population. Clinics in geriatric medicine, 8(1), 35.

10.

Fausset

C. B.

Mitzner

T. L.

Price

C. E.

Jones

B. D.

Fain

B. W.

Rogers

W. A.

(2013). Older adults’ use of and attitudes toward activity monitoring technologies. Proceedings of the Human Factors and Ergonomics Society 57^th Annual Meeting (pp. 1683-1687). Santa Monica, CA: Human Factors and Ergonomics Society.

11.

Fisk

A. D.

Rogers

W. A.

Charness

Czaja

S. J.

Sharit

(2009). Designing for older adults: Principles and creative human factors approaches (2nd ed.). Boca Raton, FL: CRC Press.

12.

Fox

Duggan

(2013). Tracking for Health. Pew Internet & American Life Project. Retrieved from http://pewinternet.org/Reports/2013/Tracking-for-Health.

13.

Freid

V. M.

Bernstein

A. B.

Bush

M. A.

(2012). Multiple chronic conditions among adults aged 45 and over: trends over the past 10 years. Women, 45, 64.

14.

Holtz

Lauckner

(2012). Diabetes management via mobile phones: a systematic review. Telemedicine and e-Health, 18(3), 175-184.

15.

King

A. C.

Ahn

D. K.

Oliveira

B. M.

Atienza

A. A.

Castro

C. M.

Gardner

C. D.

(2008). Promoting physical activity through hand-held computer technology. American Journal of Preventive Medicine, 34(2), 138-142.

16.

Markland

Ingledew

D.K.

(1997). The measurement of exercise motives: Factorial validity and invariance across gender of a revised Exercise Motivations Inventory

17.

Mitzner

T. L.

McBride

S. E.

Barg-Walkow

L. H.

Rogers

W. A.

(2013). Self-management of wellness and illness in an aging population. In Morrow

D. G.

(Ed.), Reviews of Human Factors and Ergonomics (Vol. 8, pp. 277-333). Santa Monica, CA: Human Factors and Ergonomics Society.

18.

Preusse

K. C.

Mitzner

T. L.

Fausset

C. B.

Rogers

W. A.

(in press). Activity monitoring technologies and older adult users: Heuristic analysis and usability assessment. Proceedings of the 2014 Symposium on Human Factors and Ergonomics in Health Care: Leading the Way. Santa Monica, CA: Human Factors and Ergonomics Society

19.

Rejeski

W. J.

Mihalko

S. L.

(2001). Physical activity and quality of life in older adults. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 56(suppl 2), 23-35.

20.

Rogers

E. M.

(2003). Diffusion of innovations (5th edition). New York, NY: Free Press.

21.

Tudor-Locke

Craig

C. L.

Aoyagi

Bell

R. C.

Croteau

K. A.

De Bourdeaudhuij

. . . Lutes

L. D.

(2011). How many steps/day are enough? For older adults and special populations. International Journal of Behavioral Nutrition Physical Activity, 8(1), 80.

22.

Venkatesh

Morris

M. G.

Davis

G. B.

Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.

23.

Wallston

K. A.

Wallston

B. S.

DeVellis

(1978). Development of the multidimensional health locus of control (MHLC) scales. Health Education & Behavior, 6(1), 160-170.