Have Older Adults Joined the “Age of Technology”?

1. Have Older Adults Joined the “Age of Technology”? Demographic and Attitudinal Predictors of Information and Communication Technology (ICT) Use in Late-Life Loren D. Lovegreen, Ph.D. Simon Fraser University Symposium 2008, Université du Québec à Montréal

2. Acknowledgements • This research is part of the Buffers to Impairment and Disability of the Old-Old, funded by the National Institute on Aging, grant number: RO1 AG010738-12. Eva Kahana, Pd.D., Principal Investigator • I would especially like to thank Dr. Eva Kahana, Director of the Elderly Care Research Center, Department of Sociology, Case Western Reserve University, OH USA for her generosity in granting permission to use this study sample and data.

3. Introduction • Older adults have witnessed tremendous technological advances during there life time • However, they are less likely (compared to those younger) to adopt technological resources (e.g., computers, internet, email, and cell phones) in their daily lives (Fox, 2004).

4. Introduction • Contradictory reasons for why elders have limited adoption of technology: • Some argue: that elders possess a lack of interest or ability (Fox, 2001/2004) • Others argue: limited adoption results from insufficient efforts in training and from a lack of senior-focused technology that addresses the special needs of the aged (Charness & Schaie, 2003)

5. Introduction • Despite the current research our understanding of older adults and technology use is limited • Relatively few studies have fully explored the attitudes held toward technology and the patterns of usage of technology among elders • Even fewer studies have examined technology use patterns and attitudes among those 75+

6. Research Statement • This study explores access to and use of information and communication technologies among 471 community-dwelling urban seniors • Goals • To describe access and usage patterns of ICT • To describe attitudes held toward computers • To determine the impact of demographic characteristics on the likelihood of access to and owning a computer and access to internet

7. Older Adults and ICT • Older adults are less likely to be wired (online) than those younger • 65+ represent only 4% of the internet population • However, the percent of US seniors who go online has increased by 47% between 2000 and 2004 (Pew Internet Project: 2004) • This will increase as the “silver tsunami” (today’s 50-60’s) are unlikely to give up their “wired” ways. • These seniors will transform the non-wired senior stereotype • Source: http.www.pewinternet.org

8. Older Adults and ICT • Most of the growth in internet use is among the early 60’s yrs group • Little evidence that the 75+ group is getting the internet bug (Fox, 2006) • Predictors of internet use include: • Being male, higher education, higher income, non-minority

9. Older Adults and ICT • Online Behavior • Wired seniors are less likely than younger internet users to avail themselves to all online activities • Seniors more likely to engage in “cautious clicking” –one false move on the internet can bring disaster (Chadwick-Dias et al., 2004) • While seniors take fewer chances online, they take less precautions (e.g., less likely to have spyware)

10. Methods: Sample • Subset of a ongoing panel study of community-dwelling elders living in a large metropolitan area in Northeast Ohio, USA • Effective N=471

11. Sample Characteristics (N=471) • Mean Age 77.1 yrs (SD=6.8) (Range = 65 to 99) • % Female 57.1 • % Married 73.7 • % Non-Caucasian 18.3 • Education • % < High school 9.6 • % High school degree 28.2 • % Some college 24.2 • % College degree or > 38.0

12. Methods: Data Analysis • Descriptive • Logistic Regression • Three models • Model 1: Demographic Characteristics • Model 2: Marital Status • Model 3: Heath Status Control

13. Methods: Measures • Demographic Characteristics • Age (years) • Gender (female=1, male=0) • Education (higher than HS=1, HS or less=0) • Race/Ethnicity (Caucasian=1, Non-Caucasian=0) • Marital Status (married=1, not married=0) • Health Status (Control) • Self-rated physical health (3-item, response 1-5, >score, worse health) • Composite created (Items summed, range 3 – 15, α = .81)

14. Methods: Outcome Measures • Descriptive • Access to computers and internet (yes/no) • Usage of computer, internet, email and cell phone • Ownership (yes/no) • Frequency of use (1=never, 5=daily) • Number of minutes at a sitting (computer) • Main activity on computer (open ended) • Attitudes toward computers (acceptance) • 4 items, response (1=SD to 4=SA) • Composite created, range 1 to 16, α = .77 • >score, > acceptance • Note: N answering = 282 (thus, this variable not included in LR or OLS)

15. Methods: Outcome Measures • Logistic Regression • Do you own a computer? (yes=1, no=0) • Do you have access to a computer? (yes=1, no=0) • Do you have access to the internet? (yes=1, no=0)

16. Results - Descriptive • Access and Usage % N____ • Has access to computer 74.9 451 • Has access to internet 66.8 352 • Owns computer 62.0 460 • Owns cell phone 53.4 470 • Frequency % Daily % Never__ • Computer 48.5 24.7 • Internet 37.5 39.1 • Email 39.6 40.0 • Cell phone 30.0 24.7

17. Results: (N=282)Attitudes Held Toward Computers • In general, all predictors (EXCEPT AGE) are associated with greater acceptance (i.e., males, married, Caucasian, higher education). • Age is associated with lower acceptance of computers • “I would feel at ease in a computer class” (p=.014) (neg) • “I would feel comfortable working with a computer” (p=.011) (neg) • “Computers make me feel uneasy and confused” (p=.004) • Thus, less ease in class, less comfort with computers, greater unease is associated with greater age.

18. Results:Final Model*: Outcome=Access to Computer • Significant p value Exp(B) • Age .000 .935 • Gender (f=1, m=0) .045 .597 • Education .003 2.04 • Self-rated Health .028 .887 • Marital Status (marginal) .082 1.60 • Not Significant • Race/Ethnicity .201 1.45 • *Nagelkerke R2 = 18.5 N=451

19. Results: Final Model*: Outcome=Own Computer • Significant p valueExp(B) • Age .042 .986 • Education .009 1.74 • Race/Ethnicity .032 1.73 • Marital Status .015 1.83 • Not Significant p valueExp(B) • Gender .151 .735 • Self-rated Health .148 .932 • *Nagelkerke R2 = 12.6 N=460

20. Results: Final Model*: Outcome=Internet Access • Significant p valueExp(B) • Age .042 .960 • Gender .067 (marginal) .614 • Education .000 2.56 • Marital Status .008 2.19 • Self-rated Health .023 .871 • Not Significant p valueExp(B) • Race/Ethnicity .125 1.64 • *Nagelkerke R2 = 19.9 N=352

21. Results: LR Comparison

22. Discussion • Older age is predictive of not having access to computers and the internet and not owning a computer • Being an older women is predictive of not having access to computers and the internet • Having greater than a high school education and being married is predictive of having access to a computer and the internet and owning a computer • Being a minority is predictive of not owning a computer • Worse health is predictive of not having access to a computer or the internet

23. Discussion • However, in this sample, we do see that older adults have joined the “age of technology” • The use of ICT provide an opportunity for older adults to remain independent and to continue engagement in valued activities even after they encounter health limitations • Use of ICT may be viewed as an indicator of proactive behavior. Such behavior may contribute to increased quality of life • Despite that a majority have access and own a computer, attitudes toward computers is less favorable as age increases

24. Limitations and Future Directions • Use of secondary data • Include other predictors (refine attitude measures; geographic location; peer and family influence) • Examine differences between long-time users versus new-users of ICT

25. Thank you! Detailed results from analyses are available upon request. Please contact loren_lovegreen@sfu.ca