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 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. 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 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). 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: Contradictory reasons for why elders have limited adoption of technology: Some argue: that elders possess a lack of interest or ability (Fox, 2001/2004) 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) 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 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 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+ 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 This study explores access to and use of information and communication technologies among 471 community-dwelling urban seniors Goals Goals To describe access and usage patterns of ICT To describe access and usage patterns of ICT To describe attitudes held toward computers 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 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 Older adults are less likely to be wired (online) than those younger 65+ represent only 4% of the internet population 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) 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. 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 These seniors will transform the non-wired senior stereotype Source: http.www.pewinternet.org 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 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) Little evidence that the 75+ group is getting the internet bug (Fox, 2006) Predictors of internet use include: Predictors of internet use include: Being male, higher education, higher income, non- minority Being male, higher education, higher income, non- minority

9. Older Adults and ICT Online Behavior Online Behavior Wired seniors are less likely than younger internet users to avail themselves to all online activities 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) 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) 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 Subset of a ongoing panel study of community- dwelling elders living in a large metropolitan area in Northeast Ohio, USA Effective N=471 Effective N=471

11. Sample Characteristics (N=471) Mean Age 77.1 yrs (SD=6.8) (Range = 65 to 99) Mean Age 77.1 yrs (SD=6.8) (Range = 65 to 99) % Female57.1 % Female57.1 % Married73.7 % Married73.7 % Non-Caucasian18.3 % Non-Caucasian18.3 Education Education % < High school 9.6 % < High school 9.6 % High school degree28.2 % High school degree28.2 % Some college24.2 % Some college24.2 % College degree or >38.0 % College degree or >38.0

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

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

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

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

16. Results - Descriptive Access and Usage % N____ Access and Usage % N____ Has access to computer74.9451 Has access to computer74.9451 Has access to internet66.8352 Has access to internet66.8352 Owns computer62.0460 Owns computer62.0460 Owns cell phone53.4470 Owns cell phone53.4470 Frequency % Daily % Never__ Frequency % Daily % Never__ Computer48.524.7 Computer48.524.7 Internet 37.5 39.1 Internet 37.5 39.1 Email39.640.0 Email39.640.0 Cell phone30.024.7 Cell phone30.024.7

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). 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 Age is associated with lower acceptance of computers “I would feel at ease in a computer class” (p=.014) (neg) “I would feel at ease in a computer class” (p=.014) (neg) “I would feel comfortable working with a computer” (p=.011) (neg) “I would feel comfortable working with a computer” (p=.011) (neg) “Computers make me feel uneasy and confused” (p=.004) “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. Thus, less ease in class, less comfort with computers, greater unease is associated with greater age.

18. Results: Final Model*: Outcome=Access to Computer Significantp valueExp(B) Significantp valueExp(B) Age.000.935 Age.000.935 Gender (f=1, m=0).045.597 Gender (f=1, m=0).045.597 Education.0032.04 Education.0032.04 Self-rated Health.028.887 Self-rated Health.028.887 Marital Status (marginal).0821.60 Marital Status (marginal).0821.60 Not Significant Not Significant Race/Ethnicity.2011.45 Race/Ethnicity.2011.45 *Nagelkerke R 2 = 18.5N=451 *Nagelkerke R 2 = 18.5N=451

19. Results: Final Model*: Outcome=Own Computer Significant p value Exp(B) Significant p value Exp(B) Age.042.986 Age.042.986 Education.0091.74 Education.0091.74 Race/Ethnicity.0321.73 Race/Ethnicity.0321.73 Marital Status.0151.83 Marital Status.0151.83 Not Significantp value Exp(B) Not Significantp value Exp(B) Gender.151.735 Gender.151.735 Self-rated Health.148.932 Self-rated Health.148.932 *Nagelkerke R 2 = 12.6N=460 *Nagelkerke R 2 = 12.6N=460

20. Results: Final Model*: Outcome=Internet Access Significant p value Exp(B) Significant p value Exp(B) Age.042.960 Age.042.960 Gender.067 (marginal).614 Gender.067 (marginal).614 Education.0002.56 Education.0002.56 Marital Status.0082.19 Marital Status.0082.19 Self-rated Health.023.871 Self-rated Health.023.871 Not Significantp value Exp(B) Not Significantp value Exp(B) Race/Ethnicity.1251.64 Race/Ethnicity.1251.64 *Nagelkerke R 2 = 19.9N=352 *Nagelkerke R 2 = 19.9N=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 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 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 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 Being a minority is predictive of not owning a computer Worse health is predictive of not having access to a computer or the internet 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” 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 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 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 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 Use of secondary data Include other predictors (refine attitude measures; geographic location; peer and family influence) Include other predictors (refine attitude measures; geographic location; peer and family influence) Examine differences between long-time users versus new-users of ICT 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