Older adults: Are they ready to adopt health-related ICT?
Tsipi Heart ∗ , Efrat Kalderon
Department of Industrial Engineering & Management, Ben-Gurion University of the Negev, Israel
Received 4 March 2010 Received in revised form 16 August 2010 Accepted 10 March 2011 Available online xxx Keywords: Older adults Technology and ICT use Healthcare ICT
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Fig. 1 – Average annual health care costs for Medicare enrollees aged 65 and over. Source: http://www.agingstats.gov/Agingstatsdotnet/Main Site/default.aspx. porary situation that will significantly change within a few years as the older generation acquires experience in using ICT. However, technological innovations such as the multi-touch user interface used in the iPhone and other devices, or more recently the motion-activated user interface, are being introduced at a fast pace. These innovations significantly change the way people interact with ICT. Is it likely that future generations of older people will adopt technological innovations more readily than the present generation? If indeed impairments inherent to older age are major barriers to adoption, then it is quite likely that the present difficulties will persist into the future as well. In order to investigate changes in technology use patterns in general and ICT use in particular, the present study replicated the data collection work of Selwyn and collaborators [12] in the UK in 2000. The following questions were addressed: (1) Does age still affect ICT use among older adults? (2) What access do older adults have to ICT? Where can older adults access ICTs? What access do older adults have to ICT support? How does access to ‘new’ ICTs such as computers and the Internet compare with access to other technologies? (3) What factors are associated with older adults’ access to ICT (e.g. gender, age, educational background, health conditions, marital status)? (4) What are older adults using ICT for? (5) What are the reasons behind older adults’ non-use of ICT? Data for this study were collected in 2007–2008 from 63 elderly people in the US and 60 in Israel. Our findings corroborated those of Selwyn et al., although the proportion of older adults using ICT was higher. Similar to the earlier results, most non-users indicated that lack of need for ICT was the primary reason for not using a computer, suggesting that not much had changed in the years that elapsed since the earlier study. Age was still found to significantly and negatively affect use, with health emerging as a moderating factor that augments the effect of age. This evidence is worrying in light of the intensive effort being invested in introducing technologies to improve the quality of life of older people. The implications of these findings will be discussed further on. The rest of the paper is organized as follows: literature survey, description of the methods employed, and results. The
1.
Introduction
With the dramatic increase of people with chronic conditions and an ageing population there is a need to extend care from the hospital to the home. GE Healthcare and Intel are helping to address these pressing issues. The market for telehealth and home health monitoring is predicted to grow from US$ 3 billion in 2009 to an estimated US$ 7.7 billion by 2012 [1]. This quote relates to just one of many ICT-driven endeavors recently initiated all over the developed world to enhance healthcare provided for elderly people. It has been suggested that the use of information and communication technologies (ICT) such as computerized devices, home computers, Internet, and other communication devices could significantly improve the quality of life of elderly people, as well as facilitate cost-effective care by formal and informal care providers [2]. This is particularly desirable in light of the expected growth in the percentage of people 65 and over in the population and in the average annual cost of health care for this cohort (Fig. 1). For such technologies to be efficient and effective, elderly people must be willing and able to use them. Prior research has indicated that age is negatively related to use of ICT, for such reasons as age-related changes and decline of sensory and cognitive abilities [3], as well as difficulty in obtaining technical support [4–9]. It is suggested that these barriers, among others, explain the imparity in adoption of modern technologies [3], for example personal computer (PC) use and Internet access, where senior people are under-represented, as evident in Table 1. Although the data in Table 1 relate to the US adult population [10], similar numbers have been reported in other developed countries [11]. The picture that emerges casts doubt on the prospects for successful adoption of healthcare-related ICT among older people. It is therefore imperative to investigate the patterns of ICT use by this group in order to determine what needs to be done to ensure effective implementation, adoption and use. Although there is a consensus among researchers as to the effect of age on ICT use, some maintain that this is a tem-
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Table 1 – Internet use by age [10]. Years Born
1977–1990 1965–1976 1955–1964 1946–1954 1937–1945 >1936
Ages in 2009
18–32 33–44 45–54 55–63 64–72 >73
% of total adult population
26% 20% 20% 13% 9% 9%
% of Internet using population
30% 23% 22% 13% 7% 4%
Pew Internet & American Life Project December 2008 survey. N = 2253 total adults, and margin of error is ±2%. N = 1650 total Internet users, and margin of error is ±3%.
paper concludes with a discussion and presentation of conclusions.
1.1.
Literature survey
The most rapidly growing segment of the population in developed countries consists of the elderly, particularly the ‘oldest old’ aged 80 plus. For example, the UK Office for National Statistics found that over the last 25 years the percentage of the population aged 65 and over increased from 15% in 1983 to 16% in 2008, an increase of 1.5 million people. Over the same period, the percentage of the population aged 16 and under decreased from 21% to 19%. This trend is projected to continue so that by 2033, 23% of the population will be aged 65 and over versus 18% aged 16 or younger [13]. Similar figures are being reported all over the developed world. Given this demographic forecast, there is a clear and urgent need to provide more effective and efficient healthcare while reducing the number of care providers. Information and communication technologies are seen as the key to achieving these goals [14], yet recent studies showed that acceptance and adoption of ICT by the elderly is problematic [15,16], and particularly so in the healthcare context [2,17–19]. This evidence is troubling in light of the extensive investments by governments and institutions in the development of applications and devices to improve healthcare for older people [2], the success of which largely depends on adoption and use by the elderly consumers. Indeed, as reported in a review by Marschollek et al. [2], most such projects failed for various reasons, prominent among which was consumer reluctance. Studies investigating older ICT users found that many of the restrictions and social isolation problems that elderly people face on a daily basis could be alleviated through the use of computers and online technologies [6,15]. Indeed, older people who used computers thought they enjoyed better social interaction, memory enhancement, and mental stimulation. It is claimed that using the Internet can improve the quality of life of ageing people by inducing them to learn new technologies and thus maintain their cognitive capabilities and self esteem, as well as by opening new avenues to information and services that would otherwise be difficult to access [6].
information transfer, will enable the greatest advances yet in public health. Real health care reform will come only from demand reduction, as individuals learn to take charge of their health. Communication technology can work wonders for us in this vital endeavor. Eventually, personal home telemedicine links could provide every home with access to health information 24 h a day, 7 days a week, encouraging personal wellness and prevention, and leading to better informed decisions about health care” [20, p. 760]. Indeed, fifteen years later, the dominant use of ICT in healthcare is in conveying high-quality health information and educative materials to consumer patients, in order to empower them to make informed decisions and deal more actively with their health [2,21]. Some of the relevant websites focus on specific diseases [22–24], whereas others are general portals such as MedlinePlus [25]. A notable application is NIHSeniorHealth (www.nihseniorhealth.org), which is especially designed for older users. While the number of accesses to these applications has been growing, a study of a German portal revealed that older consumers aged >65 comprised only 2% of users, with younger surfers, possibly relatives and caregivers, constituting the vast majority of users [2]. Telemedicine and pervasive and ubiquitous computerized services have mainly been used for monitoring and communication purposes to facilitate remote care for people at home; all report limited success in terms of adoption and use [14,26–33], with reasons cited for non-use ranging from excessively complex to offering no advantage over more traditional alternative solutions.
1.3.
Theories explaining ICT adoption
1.2.
ICT applications in healthcare
In 1995, former U.S. Surgeon General C. Everett Koop stated: “. . .cutting-edge technology, especially in communication and
One of the fundamental theories that seek to explain particular behaviors is the theory of planned behavior (TPB) [34], which posits that a behavior is the result of an intention to carry it out, which is in turn influenced by attitude (ATT) toward the behavior, perceived behavioral control (PBC), and subjective norm (SN). PBC “reflects perceptions of internal and external constraints on behavior and encompasses self-efficacy, resource facilitating conditions, and technology facilitating conditions” [35, p. 454], while SN is defined as “The person’s perception that most people who are important to him think he should or should not perform the behavior in question” [35, p. 452]. Drawing upon the TPB, the technology acceptance model (TAM) [36] has been developed to explain ICT use. According to TAM, perceived usefulness (PU)
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and perceived ease of use (PEOU) are the primary determinants affecting attitude toward use, which affects intention to use. Additionally, PU, which is impacted by PEOU, was found to directly affect intention to use. Later research developed TAM2 [37], which included antecedents to PU and PEOU, and the unified theory of acceptance and use of technology (UTAUT), which posited that four categories of variables influence information technology acceptance: performance expectancy, effort expectancy, facilitating conditions, and social influence [35]. Several researchers have adapted the above frameworks to describe adoption of ICT by older adults [11,19,27,38–41].
2. Determinants affecting ICT use in healthcare
A large number of determinants have been posited to affect ICT use in the healthcare context. The reader is referred to a systematic review of patient acceptance of consumer health information technology (CHIT) by Or and Karsh [42] which lists 94 determinants, of which 67 are patientrelated factors associated with patient health (37 factors), socio-demographic variables, and variables relating to the individual’s prior experience with computers. The remaining factors relate to human–computer interaction (HCI) and organizational/environmental factors, such as satisfaction with the health package provided. HCI factors include PU, PEOU, Internet dependence, self-efficacy toward computers, computer anxiety or fear of technology, intrinsic motivation, perceived information reliability, and some others. Age was the most frequently used factor, yet did not show a consistent effect. In contrast, other studies showed that, relative to younger adults, older adults reported less comfort, lower efficacy, and less control over computing technologies [16,43], perceptions that are likely to decrease acceptance [8,12,44]. While gender, the second most studied variable in the above review, demonstrated no effect in the majority of studies, higher education was positively associated with acceptance in 68% of the studies, as was prior experience or exposure [42]. The role of patient health status in CHIT acceptance also yielded mixed results, as some researchers showed that people experiencing poorer health conditions were more likely to accept CHIT [45,46], whereas others e.g. [47] showed that better health (less severely ill patients) was associated with increased acceptance. Having school age children at home was also found to drive acceptance, possibly via determinants such as social influence, support availability, and computer anxiety [42]. These findings were further corroborated by studies of specific health IT applications [17,26,41].
affected by attitude than among younger employees, and more affected by opinions of important persons and by perceived control; but it is important to note that the authors studied users at the workplace, where ‘older’ users are not very old. In contrast, the review of 52 articles devoted to CHIT adoption [42] showed mixed results regarding the association between age and technology acceptance. However, sufficient evidence supports the negative association between age and use of PC and Internet [12]. Declining physical and cognitive capabilities may cause seniors to experience greater difficulties in using computers, and these may serve as internal controls or may inhibit conditions that increase effort expectancy associated with IT use [53]. Czaja et al. [16] found that adults over 65 years of age had fewer computer skills and had less computer self-efficacy than younger adults, possibly leading to reduced use or intention to use computer technology; these older users, however, were willing to attempt training and ready to use ICT when sufficient instructions on use of the technology were provided [11,54]. Table 2 summarizes determinants affecting ICT use by elderly people in the healthcare context. While designing easy-to-use systems adapted to specific impairments typical of older age is clearly recommended, researchers found that older users tended to focus on benefits more than on costs [3,12] and were willing to make the effort to acquire skills if they were convinced of the technology’s advantages and adequacy for their specific needs, above and beyond traditional healthcare related means [16,55]. This evidence somewhat contradicts the common assertion that limited use of technology by older adults stems from low self-efficacy, computer anxiety, lack of accessibility, or technophobia—a negative attitude toward modern technology in general [7,8].
3.
Research method
This study attempts to answer the research questions outlined above by replicating Selwyn et al.’s 2003 study [12] and by applying the TPB to a sample of older adults in their natural residence setting (private or nursing home, when nursing homes were the respondents’ permanent residence).
3.1.
Sample
2.1.
ICT use by elderly people
Extensive research has focused on ICT use by elderly people in general [6,7,11,12,18,19,38–40,43,44] and in the healthcare context in particular [2,14,24,27,32,48–52]. Morris and Venkatesh [43], for instance, found that age moderated the effects of attitude, PBC, and subjective norm on ICT use in the workplace, with the result that among older users use intention was less
Due to the difficulty of accessing respondents, no statistical sampling method was applied. Rather, a sample of convenience was used. Although this can be considered a limitation, the distribution of the sample in terms of personal characteristics was sufficiently representative of this population in terms of gender (i.e. a higher percentage of females, as is typical for this age group), education level, marital status, and health status, as can be seen in Table 3. Data were collected from elderly people in Texas, USA, and in Israel. In the USA four nursing homes were visited, and residents who were available in public areas were approached for an interview; 63 residents agreed to participate. In Israel we interviewed 17 persons in two nursing homes and 43 persons living in their private homes. All the interviews were carried out in 2007 and 2008 and lasted 40 min to 1 h each.
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Table 2 – Determinants of older users’ acceptance of ICT. Determinant
Perceived usefulness, perceived impact, relevance Perceived ease of use Issues associated with the technology Personal traits Social issues Facilitating issues
Description
The degree to which the potential user perceives the technology as beneficial, useful, and able to significantly contribute to the purpose it is intended for The degree to which an individual perceives using the technology as free of effort Factors such as the system’s cost, complexity, and safety Factors such as the individual’s age, health condition, self-actualization, self-efficacy, prior experience with computers, computer anxiety Factors related to the social reference groups of the individual, such as subjective norm and image Factors related to the external environment, such as availability of support
Table 3 – Descriptive statistics for the US and Israeli samples. Parameter
Respondents Gender Female Male Age 60–69 70–79 80–89 90+ Total Residence: Private home Nursing home Education Elementary Junior High High School Bachelor Master Ph.D. Total Mean S.D. Living with a partner: No Yes Severe Health Problems: No Yes
U.S.A
63 (51%)
Israel
60 (49%)
Total
123
Mean
S.D.
70% 30%
67% 33%
68% 32%
13% 27% 41% 19% 100%
17% 38% 33% 12% 100%
15% 33% 37% 15% 100%
80.2
8.58
0% 100%
72% 28%
35% 65%
0% 6% 48% 30% 10% 6% 100% 14.93 2.46
25% 17% 47% 10% 2% 0% 100% 10 1.91
15% 11% 47% 20% 6% 3% 100%
12.55
1.99
84% 16%
45% 55%
65% 35%
17% 83%
25% 75%
20% 79%
3.2.
The research instrument
We used Selwyn et al.’s questionnaire, which is composed of five sections1 : 1. Statements related to accessibility to and use of various technologies. 2. Statements related to reasons for not using a computer, for persons stating they have not used computers in the last 12 months. 3. Statements related to use of computers, for persons stating they have used computers in the last 12 months.
4. Statements measuring the TPB model: ATT, PBC, and SN, adapted from Morris and Venkatesh [43] (Appendix A). 5. Personal questions relating to age, education, computer experience, health status, marital status, etc.
1 We refrained from attaching the questionnaire due to its length, but it can be secured from the first author.
The questionnaire included 180 questions, with nominal or ordinal scales used for all but the demographic questions. A nominal scale was used to code values such as places of accessibility, perceptions, or frequency of use. An ordinal 4point Likert scale (1—strongly disagree to 4—strongly agree) was used for statements with ascending scales to simplify the data collection process in view of the relatively advanced age of the responding population. Data were analyzed using SPSS 17.0.
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Table 4 – Access to technologies by location. Own/Access at home (%)
Computers and Peripherals Laptop Palmtop PC 5 years old Printer Scanner Telephone Payphone* (p < 0.001) Videophone Landline* (p < 0.001) Fax* (p < 0.001) Mobile phone (p < 0.015) TV and VCR Cable TV Digital Cable TV* (p < 0.001) DVD Video recorder/player* (p < 0.001) Other entertainment Personal music player CD player Digital radio Analog radio Digital Camera* (p < 0.05) Video camera* (p < 0.035) Handheld games machines Video games machine
∗
No home access but access from family/friends (%)
30 3 32 17 23 17
Access elsewhere (%)
2 0 7 15 14 3
No access (%)
59 97 33 50 34 68
9 0 29 18 29 13
0 1 96 15 56
0 5 4 15 17
24 0 0 31 1
76 94 0 40 26
92 38 40 48
1 7 20 12
0 0 6 2
7 55 35 38
14 48 55 48 22 13 2 0
19 11 11 5 35 29 27 33
0 7 11 5 1 2 0 0
68 33 23 42 42 56 71 68
Statistically significant difference between US and Israel.
4.
Results
The US respondents were all nursing home residents. They were significantly better educated (p < 0.001) than the Israeli respondents and were more likely to live without a partner (p < 0.001). These facts, however, may be mutually dependent, as people who enjoy a higher financial status (known to positively correlate with education), are not very healthy, and live alone may have a greater tendency to move to relatively expensive nursing homes such as those where the data were collected in the US. Therefore, we conducted the various tests with due attention to statistically significant differences between the populations (Table 3).
Accessibility of technology was graded from more accessible (own/at home), to less accessible (family/friends, public places), to no access. The responses listed reflect the highestgraded location indicated by the respondent (Table 4).
4.1.1.
Accessibility of ICT
4.1.
Older adults’ access to technology and support
While this paper focuses primarily on ICT use by older people, use of non-ICT technologies, both traditional and modern, was also examined in order to determine whether older people are generally technophobic (refrain from adopting any type of modern technology) or selectively adopt technologies they perceive as useful for their specific needs. Therefore, respondents were asked about use of and accessibility to both traditional and modern common technologies, such as phones and entertainment equipment, in addition to computers and peripherals.
Twenty nine percent of the respondents had first-grade accessibility (owned or had access at home) to PCs that were less than 5 years old (Table 4). 18% of the respondents had the same level of accessibility to PCs that were older than five years, and 9% owned a laptop or had one at home. Assuming that most respondents had either a laptop, a newer PC or an older PC (but not both), these three figures mean that 56% of respondents had access to a PC at home. There was no significant difference in first- and second-grade accessibility between Israeli and US respondents. Twenty two percent of the respondents indicated they could access a PC elsewhere, mainly Americans who referred to public computer rooms at their nursing homes. Together these two figures indicate that nearly 80% of the respondents had fairly easy access to computers. Regarding other technologies, 56% of the respondents owned or had at home mobile phones, 92% had cable TVs, and 38% had digital cable TV; 55% had digital radios, 40% had DVDs, and 22% had digital cameras. Israeli respondents had more first-grade access to digital cameras ( 2 = 12.05, df = 4, p = 0.017), to fax machines ( 2 = 22.92, df = 5, p < 0.001), to mobile phones ( 2 = 12.36, df = 4, p = 0.015), and to video cam-
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Table 5 – Older adults’ potential access to computers. Site of Access
A relative’s home Home Library A friend’s home A community center School/university Work/place of study Museum/science center A pay-per-use’ site (e.g. Internet café)
Table 6 – Actual and potential sources of support. %
66 48 29 21 15 13 10 10 9
Source of support
Users (n = 51) Actual %
Non-users (n = 72) Potential %
2 37 1 21 2 7 13
Own/Partner Children Household Family Neighbors Friends Others
65 43 8 16 10 16 30
eras ( 2 = 8.63, df = 3, p = 0.035) than the US respondents. For example, 33% of Israelis owned or could access at home a digital camera, whereas this was the case for only 11% of the Americans. Compared to results in Selwyn el al.’s study, slightly more people in our sample had first-grade access to mobile phones (56% vs. 50%), and significantly more to DVD players (40% vs. 10%), perhaps due to transition from video recorders/players to the more modern technology (48% vs. 80%). A similar picture emerged in regard to digital cameras and digital radios. Additionally, first-grade accessibility to PCs nearly doubled overall (from 28% to 56%), but nearly tripled (to 68%) for the Israeli respondents, the majority of whom lived at home and hence were more comparable to the UK sample. This increase in rate of adoption can be attributed to an accelerated pace of ICT adoption by older people, and the difference between the Israeli and American populations possibly results from the technology-savvy character of Israeli culture, although this assumption requires further substantiation. The results indicate that modern technologies are indeed adopted by older adults, supporting the assumption that this population selectively adopts technologies perceived as beneficial to them, albeit perhaps more slowly than the younger population. The results further imply that older people are participating more than ever in the technological era and associated culture. Respondents were then asked to indicate where they could have accessed a PC had they wanted to (Table 5). Interestingly, all respondents indicated they could have done so, with no difference between the two populations. Potential sites for PC access were similar in our study and in Selwyn et al.’s, with a relative’s home, own home, a friend’s home and a library emerging as the four most accessible sites, in descending order. Overall, 63% of the respondents (n = 77) had used computers before, 33% (n = 41) of them at work. Forty percent (n = 25) of the Americans and 43% (n = 26, insignificant difference) of the Israelis (total 41.5%) indicated they had used a PC in the last 12 months (these will henceforth be termed ‘users’, as opposed to the rest – the ‘non-users’). Determinants relating to older adults’ access to and use of ICT will be analyzed next, considering users and non-users separately to gain a clearer picture. Americans and Israelis will be analyzed separately whenever differences are statistically significant.
about actual sources of support and non-users about potential sources of support had they needed any (Table 6). Table 7 presents data regarding available sources as indicated by users and non-users. Non-users and users alike indicated their main sources of support were their children and other family members, yet 65% of the users mentioned that they actually relied on themselves or on a partner; the latter, however, were mentioned as a potential sources by only 2% of non-users. Interestingly, all non-users mentioned they could enlist support from at least one source, yet clearly the level of accessibility was lower than for users.
4.2.
Determinants of technology use
To refine our understanding of the association between accessibility and use, we grouped the respondents according to access to PCs and support. Following Selwyn et al., people were classed into five groups: (1) core access—newer PCs and close support (nearby relatives, neighbors, or friends), (2) peripheral home access—older PCs and some support, (3) remote access—access at family’s or friends’ homes and support, (4) public access and support (close or remote), and (5) no access. The distribution of the populations among the five groups and the level of PC use within each group are displayed in Table 8. Difference in level of use among the five groups was statistically significant (Mann–Whitney U < 0.001) and not unexpectedly showed that accessibility to equipment and support is strongly associated with actual use. A closer look at the frequency of use of various technologies revealed that, as could have been anticipated, most respondents watched TV, listened to the radio, and in Israel also used a mobile phone (Table 9). Thirty-three percent of the respondents indicated they used a PC at home very often, more often
Table 7 – Number of accessible support resources. Number of accessible support sources Users Actual %
1 2 3 4 5 6 7 45 27 16 8 2 2 0
Non-users Potential %
68 24 7 1 0 0 0
4.1.2.
Accessibility of support
As prior research had highlighted lack of access to support as a barrier to ICT use by elderly people [56,57], we asked users
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Table 8 – Level of access to computers by location and support proximity. Level of accessibility
Core Peripheral home Remote Public No Total
N
39 15 38 8 23 123
Number of users
34 12 3 2 0 51
% (within)
87 80 8 25 0 42
than they watched video/DVD. Hence in our study use of a PC was by no means a negligible activity, in contradistinction with the findings of Selwyn et al. Clearly, home was the preferred location for using a PC (100% of the actual users), with the possibility of accessing a PC at the homes of family or friends rated very low. Public places, in contrast, were rated higher by both populations, possibly referring to public computer rooms at nursing homes, yet perhaps lending some support to the call of Selwyn et al. [12] to make PCs available in public places for the use of otherwise excluded populations.
users (i.e. said they had used a PC in the last 12 months). Hence some older adults with prior experience in using a PC, despite having access to both the technology and related support, chose not to. The question is, why? We attempted to arrive at an answer by analyzing the effect of personal and social characteristics on PC use and then examining PC use by our respondents under the TPB. Finally, we analyzed the respondents’ direct answers to this question.
4.3.1.
Personal and social characteristics and PC use
4.2.1.
Activities comprising ICT use by older adults
The computer activity cited by users as most common was sending/receiving e-mails, followed by writing documents and playing computer games (Table 10). While the first two were similarly rated in Selwyn’s study, playing games was quite negligible. Thus we see that older adults do indeed make use of computers for social activities and entertainment more frequently than before, as is the case with younger users. Although the above results generally support Selwyn et al.’s conclusions from data collected in 2000, we see some improvement in the rate of PC use, particularly among ‘younger olds’, as well as in the variety of activities performed.
4.3.
A closer look at computer use by older adults
Although all respondents indicated having some level of access to PCs and support, and although 63% (n = 77) indicated they had used computers before, only 42% (n = 51) were actual
A binary logistic regression allowing assessment of the singular effect of each independent variable on the dependent variable, as well as the effect of interactions among variables, was employed to gain more insight into the role of personal characteristics in determining use. ‘Used a PC last year’ (0: no, 1: yes) was the dependent variable, and country, gender, age group, health status, marital status, and education were the independent variables. All the independent variables were factorial, with age grouped into four categories: 60–69, 70–79, 80–89, and >90. We used the backward stepwise method with Wald as the stop criterion, and ran the regression with the independent variables and the interaction between health and age. The full results of the binary logistic regression are presented in Appendix B. The model fitted the data well, as evidenced by the insignificant 2 in the model summary and the significant 2 for the omnibus tests of the model coefficients. It took five steps to converge, explained between 31% (Cox and Snell) and 42% (Nagelkerke R-square) of the variance in use, and substantially improved model predictive power (from 59% to 77%). Multi-
Table 9 – Frequency of technology use. Very often US
Watch TV Watch video/DVD Listen to music via Hi-Fi Listen to Radio* Play video games Talk on mobile phone* Send/receive SMS Use a PC by location Home Relatives Friends Workplace* Public places
∗
Fairly often Total
96 20 37 67 1 46 8
Rarely Total
2 20 14 12 1 4 1
Never Israel
0 25 12 5 5 7 12
Israel
98 22 35 82 2 60 13
US
3 21 17 19 0 5 2
Israel
2 20 10 5 2 3 0
US
3 14 10 10 5 10 5
Total
2 20 11 7 5 8 8
US
0 48 35 17 95 54 90
Israel
0 33 43 8 92 30 75
Total
0 41 39 13 93 42 83
94 17 38 54 0 32 3
33 0 0 2 8
32 2 3 12 10
33 2 2 7 9
0 3 0 2 11
2
7 5 0 0 10
3 4 0 1 11
6 6 0 0 3
0 2 0 0 2
3 4 0 0 2
60 90 100 97 78
62 88 97 88 78
61 89 98 93 78
Statistically significant difference between US and Israel (
test, p < 0.05).
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Table 10 – Computer-based activities. Activities (n = 51)
Sending/receiving e-mails Writing and editing documents and letters Playing games Online banking/personal financial activities Searching information about jobs/education/business Navigate/surf without a specific purpose Organizing files Learning by a computer software Watching adult entertainment Listening to music on a computer Surfing/navigating to acquire personal knowledge Collecting information about or searching products/services Creating and manipulating images (e.g. photos) Downloading software/music/movies/computer pictures Watching DVD on the computer Participating in chats/forums Purchasing products/services Making movies and computer animations Programming Creating/maintaining a personal website Taking online courses or lessons Making music with a computer
collinearity was negated since the highest variance inflation factor (VIF) value of the independent variables was 1.469, well below even the strict value of 2.5–3 used as multicollinearity cutoff value [58,59]. Having a partner, age, and being better educated had a significant effect on use (Table 11). The odds for PC use were 6.6 times greater (an increase of 660%) for older adults living with a partner than for persons who lived alone. Age negatively affected use, with odds for computer use among respondents aged 70–79, 80–89, and >90 respectively 90%, 85%, and 93% lower than for a person aged 60–69. Older people with higher education were 3.1 times (an increase of 310%) more likely to use a PC as compared with respondents with a lower educational level. Interestingly, health on its own was insignificant, yet had a major effect when interacting with age. Thus, being 70–79 and healthy increased the odds of use 4.4 times, while being a healthy 80–89-year-old increased the odds 14 times as compared with being unhealthy! Being >90 and healthy, however, had no significant effect. It therefore seems that health is actually moderating the effect of age on use. This result might explain the mixed findings of previous studies concerning the effect of health on ICT use and carries significant implications for the potential adoption of ICT by unhealthy older adults.
4.3.2.
TPB and PC use by older adults
TPB explains that the behaviors of individuals are positively influenced by a positive attitude toward the behavior as well as by perceived behavioral control over resources and efforts associated with undertaking the behavior. These individuals are also positively influenced when important and influential people around them think they should undertake the behavior (SN). Morris and Venkatesh [43] supported the conjecture that older employees placed less importance on attitude but more on PBC and SN. Hence, adapting TPB to the present context, it was posited that older people with a more positive attitude toward PC use and a higher PBC as well as a more highly valued SN will have a greater tendency to use a PC.
As stated above, we used the measures employed by Morris and Venkatesh, though in view of the different context and Likert scale, we re-tested their reliability and validity. A principal factor analysis extracted three factors with eigenvalues greater than 1, as expected, and loadings of the items on their respective factors were significant and clean with the exception of one item (‘A computer is not compatible with other technologies I use’), which was culled. Cronbach alpha values were between 0.79 and 0.91, and correlations among the factors were sufficiently small to negate multicollinearity effects, with independent variable multicollinearity already ruled out in the previous section Appendix C. These indicators attest to the convergent and discriminant validity of the factor scales. However, having noticed a salient difference between responses of Americans and Israelis, we analyzed them separately. The full results of the two regressions are displayed in Appendices D and E. In spite of the sufficient goodness-of-fit indicators, results should be interpreted with caution due to the relatively small sample size of the two sub-groups, which was reduced further due to omission of cases with missing values. Intention to use a computer in the forthcoming 12 months (scale: do not intend, perhaps: 0, intend, definitely intend: 1) was the dependent variable, while the values of the three factors as calculated by SPSS during the factor analysis were the independent variables. Age (classed into four groups as above) was taken as an independent variable, as were factors expressing the interaction of age with each of the TPB factors (i.e. Age × ATT, Age × PBC, Age × SN). Both models demonstrated good fit to the data as shown in Table 12 and explained about 50% of the variance in intention to use a computer. PBC emerged as a strong predictor of intention to use a computer in both populations (Table 13). Each increase by one standard deviation in PBC increased the odds of using a computer by 480% in the US population, and by 320% in the Israeli population. Contrary to TPB, the other two factors (ATT and
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Compared to living alone. Compared to high-school or less. Compared to being unhealthy.
Table 12 – Parameters of the binary logistic regressions. US sample (N = 63) Final step (7)
Classification table improvement R-Square (Cox & Snell to Nagelkerke) Hosmer & Lemeshow test Variables in the final equation From 53% to 81% Between 45% and 59% 2 = 8.58, df = 8, p = 0.379 PBC, Age × ATT
Israeli sample (N = 60) Final step (9)
From 56% to 88% Between 47% and 63% 2 = 12.35, df = 8, p = 0.136 PBC, Age × SN
SN) were insignificant determinants of use, as was age on its own. Rather, age moderated the effect of ATT in the American sample and of SN in the Israeli sample. Contrary to the findings of Morris and Venkatesh, age increased the impact of ATT, though only for age group 70–79. While age had no effect on ATT in the Israeli sample, it moderated the effect of SN, though again only for age group 70–79. Thus, age reduced the effect of SN on the intention to use a computer among Israeli respondents, contrary to the findings of Morris and Venkatesh. Although the above results shed light on the barriers to computer use, we sought to achieve a deeper understanding by asking non-users to indicate reasons for their behavior (with multiple reasons allowed; see Table 14). Quite surprisingly, answers do not reflect technophobia or an inherent reluctance to use technology, but rather a
cognitive choice, as 62% of non-users said they did not use computers because there was ‘no need’ or it was ‘not interesting’, whereas only 23% perceived computer use as too difficult or indicated they were too old to learn. ‘No access’ or ‘don’t know how’ was indicated by 21%, whereas 10% mentioned computers were too expensive. Only one respondent indicated ‘don’t like’ as a reason. Similar results were found by Selwyn et al., so evidently there is more to ICT non-use than generally advocated by prior research.
5.
Discussion
ICT has revolutionized our lives in terms of access to information. Yet for several segments in population, particularly elderly people, the ‘digital barrier’ remains. The older people
Table 13 – Variables in the binary logistic regression equations. B S.E. Wald df Sig. Exp(B) 95% C.I. for EXP(B) Lower
US sample Step 7 PBC Age × Att Age(1) × Att Age(2) × Att Age(3) × Att Constant 1.568 2.663 .846 4.134 .382 1.166 −1.823 −1.199 −4.396 .300 .509 1.279 .553 2.514 .382 .461 .840 .810 3.896 .432 9.489 8.992 4.333 2.345 2.703 1.001 6.407 8.269 4.717 2.189 1.273 .481 1 3 1 1 1 1 1 3 1 1 1 1 .002 .029 .037 .126 .100 .317 .011 .041 .030 .139 .259 .488 4.796 14.344 2.331 62.403 1.465 3.208 .161 .301 .012 1.350 1.769 1.169 .789 .452
Upper
13.003 176.087 6.886 8615.233
Israeli sample Step 9 PBC Age × SN Age(1) × SN Age(2) × SN Age(3) × SN Constant
1.301 .031 .062 .000
7.912 .837 1.476 25.522
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Table 14 – Reasons for not using a computer. US (%)
No need Not Interesting Too old Don’t know how Too difficult No access Too expensive Medical problem Don’t like 37 21 18 5 5 13 16 3 3
Israel (%)
38 26 15 15 6 9 3 12 0
Total (%)
38 24 17 10 6 11 10 7 1
are, the less likely they are to use ICT [2]. Is this a temporary situation? Will future older adults more readily adopt ICT in general and technology related to healthcare in particular? Before attempting an answer or referring to the research questions, a review of the limitations of this study is in order.
6.
Limitations
The small sample size was a major limitation because it restricted the statistical tests that could be employed and reduced statistical power. In addition, generalizability is limited due to the employment of a sample of convenience. Likewise, the salient differences between the two populations imply possible cultural effects which merit further study. Future research should aim to collect data from a larger and more representative sample in various countries to extend the internal and external validity of the results.
6.1.
Answers to the research questions
Computers have been part of the industrialized world for more than three decades, and today’s older adults are not as
detached from technology as before. Research supports the importance of prior experience, leading several scholars to maintain that the inclusion of older people in the digital world is just a matter of time. This issue is addressed by the first question formulated in the present study. Our results point to answers to this and to the other research questions, as summarized in Table 15. Age remains a significant negative determinant of use of technology in general and ICT in particular, although there has been an increase in adoption rate as compared with data collected in 2000. Indeed, perceived behavioral control, which decreases with age – possibly due to cognitive and physical impairments – was found to positively affect ICT use. Adoption of ICT, however, lags behind adoption of other types of modern technologies, such as digital cameras, DVDs and cell phones, whose rate of use by older adults is quite high albeit different between the two sample populations. This difference merits further research. The results show that accessibility of ICT is a diminishing issue, as is the accessibility of support, although both still emerged as important determinants of ICT use by the elderly. People clearly prefer to use computers at home rather than at relatives or in public places, as could have been antici-
Table 15 – Summary of evidence answering the research questions. No.
1
Research Question
Age and ICT use Does age still affect ICT use?
Answer
Yes, although there is an increase in the proportion of older adults who adopt modern technology and ICT. We still see that older people are less inclined to use technology, including ICT.
2
Patterns of use of ICT and other technology What access to ICT do older adults have? Where can older adults access ICTs? What access to support when using ICT do older adults have? How does access to ‘new’ ICTs such as computers and the Internet compare with access to other technologies?
Most respondents indicated they could access ICT and support fairly easily. Mostly relative’s homes, at own home, and at a public library. Core accessible to modern technologies as well as to ICT has increased compared with 2000. Modern technologies are adopted albeit selectively. Whereas some modern technologies have been widely adopted, ICT adoption still lags behind.
3
Personal characteristics affecting ICT use What factors are associated with adults’ access to ICT (e.g. gender, age, educational background, health conditions, and marital status)? Use of ICT by older adults What are older adults using ICT for?
Education, marital status, and health, emerged as significant determinants of ICT use in addition to age. Health, however, was found to moderate the effect of age on use.
4
Sending/receiving e-mails, writing and editing documents, playing games.
5
Reasons for non-use What are the reasons behind older adults’ non-use of ICT?
Perceived behavioral control and ‘no need’.
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pated. We therefore conclude that accessibility is no longer a strong barrier to PC use, although we acknowledge the importance of close support. Looking at results obtained about a decade ago, it appears there has been an increase in adoption of all forms of technology. However, many older people are somewhat reluctant to adopt new technologies – whether ICT or other applications – unless they become convinced that these technologies confer significant benefits. This conclusion follows from the reasons cited by respondents in this study and that of Selwyn et al., which suggest that older adults refrain from using technologies they are not interested in or have no need for. While prior research [15] and current practice (http://www.statistics.gov.uk/cci/nugget.asp?ID=949) concur that ICT can improve the quality of life of older adults, many remain unconvinced. It is therefore essential to find ways of bridging the gap between the perceptions of providers and those of older customers regarding potential benefits of ICT. Corroborating prior research, education, marital status, and health emerged as significant determinants of ICT use in addition to age. Health, however, was found to moderate the effect of age on use, possibly explaining the mixed results cited in the literature. It seems that health as such is not so much a barrier as a factor capable of augmenting the effect of age on use. Healthy older people are many times more likely to use PCs. While this is an encouraging finding if we believe that advances in medicine imply healthier older adults, it carries significant implications for providers of healthcare-related technologies targeting unhealthy older people. An additional concern is the positive effect of living with a partner, which suggests that older people living alone, for whom ICT is posited to facilitate social inclusion and better healthcare, may be the very ones less likely to use it. Although a growing number of elderly have adopted newer technologies, their usage level is basic, and it seems that depth of use has yet to be addressed. For example, searching for products or information, online purchasing and online banking scored low on the usage list. This is clearly not a positive sign from the standpoint of penetration of healthcare-related ICT among older adults. Contrary to the theory of planned behavior, attitude and subjective norm did not emerge as affecting ICT use, although both significantly interacted with age to affect use, albeit differently for the two populations. Quite intriguing is the fact that the most prevalent direct reason given for non-use was ‘no need’, supporting the assertion that if older adults are to be induced to adopt ICTs, the benefits have to be clearly demonstrated.
7.
Conclusions
Seen from the perspective of older adults’ use of ICT in the healthcare context, the results presented in this study are variously encouraging and discouraging. On the positive side, we see that the participation rate among the ‘youngest old’ aged 60–69 nears 8% and that there is a significant increase in the rate of ICT use by older people compared with results from data collected a decade ago. Most of our respondents had reasonable access to comput-
ers and to some degree of support, yet the availability of a close source of support is still as important as it was seven years ago; nowadays, however, nearby support seems more readily available. On the other hand, while older people do adopt modern technology, they are quite selective and tend to invest resources only when the expected benefits far exceed those provided by more traditional technologies with similar functions. We maintain that the prospects for older adults using ICT are better than ever since education and health have emerged as significant determinants of PC use, on the one hand, and on the other it is likely that future older adults in the industrialized countries will be healthier and better educated, as well as more proficient in computer use. On the less positive side, the effects of accessibility of support and of health and marital status on ICT use may pose significant barriers to adoption of healthcare-related ICT now and in the future, in spite of encouraging advances. Health status seems to be a strong moderating factor affecting PC use. Thus, unhealthy people may be less inclined to adopt ICT, including applications intended to improve their quality of life, possibly due to lower perceived behavioral control caused by physical or cognitive weakness. Therefore providers of such technologies should ensure that potential consumers have first-grade access to support from nearby family, friends, or neighbors. Potential adopters should clearly comprehend the benefits that can accrue from the new technology. The significance of perceived usefulness as a determinant of ICT use is another conclusion that emerges from reasons given by respondents for non-use. Similarly to results reported by Selwyn et al., the majority of respondents in our study claimed that computers were of no interest to them and that they did not feel a need to use them. The superior capacity of new healthcare related technology to improve their health status and quality of life compared with simpler traditional technologies has to be made obvious and irrefutable. As shown by the results of the TPB analysis, attitude toward use becomes more important as people grow older, at least as regards the US sample. This finding suggests that, above and beyond pressure from close relatives and friends, efforts to influence positive attitude are warranted. The divergences between the American and Israeli respondents, however, may imply cultural differences in this regard. Such differences, if substantiated by further research, should be taken into account, and suggest that diffusion approaches should be adapted to the specific characteristics of potential consumers. Future research should investigate these differences. In common to both populations, PBC emerges as the most important determinant of use among the three TPB factors. Hence every introduction of new healthcare-related ICT should not only offer straightforward and easy-to-use technology, it must also be bundled with a comprehensive training program. Any new technology that differs significantly from technologies in which older adults are already proficient is likely to encounter adoption difficulties now and in the future due to the cognitive and physiological impairments that affect PBC among the elderly. Currently, the results do not fully support a bright future for health-related ICT targeting older people—unless the technol-
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ogy is kept simple, is seen to be useful, and is bundled with first-rate support.
Author contributions
Tsipi Heart initiated, designed, and supervised this research. She has also guided the writing of the dissertation, verified and improved the data analysis, and wrote the submitted manuscript. Efrat Kalderon conducted the research as part of her graduate studies. She collected the data, analyzed the data, and wrote a dissertation based on the results. Both authors substantially contributed to the present work.
Competing interests
We hereby declare that there is no conflict of interests related to this work. The work was self-funded by the authors, without involvement of any institution or external funding resource. The first author is a full-time employee of an academic institute, and the second author is a full-time employee of a firm whose area of interest is unrelated to the topic of this paper.
• Health was found to augment the effect of age on ICT use. • TPB was only partially supported in the present study setting, implying that intention to use a computer by older adults is mainly affected by PBC yet not by ATT and SN. Age was found to moderate the effects of ATT in the American sample, and of SN in the Israeli sample, possibly implying an association between TPB and culture. Additionally, the effect of age on ATT and SN was opposite to findings in prior research. • The results show that older adults are not yet ready to easily adopt health-related ICT. However, in order for this to materialize, the application should render salient benefits above and beyond traditional technologies, be simple to use, and be provided with adequate support.
Appendix A. TPB Items (adapted from Morris and Venkatesh, 2000)
Intention to use: Assuming you had access to a computer, would you use it during the forthcoming 12 months? Do not intend 01
Attitude (ATT):
Summary points “What was already known on the topic” • Health expenses for older adults increase, ICT is believed to assist in providing more cost-effective healthcare to this population. • Prior effort in this direction has marginally succeeded. • Older people use of technology lags behind younger people. • Apart of age, other personal and social factors, such as health, education, and marital status, were found to affect ICT use by older adults. • According to the theory of planned behavior (TPB), ICT use should be determined by attitude toward use ATT), perceived behavioral control (PBC), and subjective norm (SN). Age was found to moderate these factors in a workplace. “What this study added to our knowledge” • There is an increase in use of technology in general, and of ICT use in particular compared with data collected in 2000. • Nonetheless, most findings of the above study were still supported by the present data collected in 2007–2008. Age was still a barrier to ICT use.
Perhaps 02
Intend 03
Definitely intend 04
Using computers is a (bad/good) idea: Very bad 01 Bad 02 Good 03 Very good 04
Using computers is a (foolish/wise) idea: Very foolish 01 Foolish 02 Wise 03 Very wise 04
I (dislike/like) the idea of using computers: Dislike very much 01 Dislike 02 Like 03 Like very much 04
Using computers is (unpleasant/pleasant): Very unpleasant 01 Unpleasant 02 Pleasant 03 Very pleasant 04
Subjective Norms (SN): People who influence my behavior think that I should use a computer: Strongly do not agree 01 Do not agree 02 Agree 03 Strongly agree 04
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People who are important to me think that I should use a computer: Strongly do not agree Do not agree Agree 01 02 03 Perceived Behavioral Control (PBC): I have control over using a computer: Strongly do not agree 01 Do not agree 02 Agree 03 Strongly agree 04 Strongly agree 04
Categorical Variables Codings Frequency Parameter coding (1) Age age 60–69 age 70–79 80–89 age 90+ Country U.S. Israel Partner .00 1.00 Gender Male Female Edu2Cat High school or Lower Higher education Healthy .00 1.00 (2) (3)
I have the resources necessary to use a computer: Strongly do not agree 01 Do not agree 02 Agree 03 Strongly agree 04
I have the knowledge necessary to use a computer: Strongly do not agree 01 Do not agree 02 Agree 03 Strongly agree 04
Given the resources, opportunities and knowledge it takes to use a computer, it would be easy for me to use a computer: Strongly do not agree 01 Do not agree 02 Agree 03 Strongly agree 04
Block 0: Beginning Block Classification Tablea,b
A computer is not compatible with other technologies I use: Strongly do not agree 01 Do not agree 02 Agree 03 Strongly agree 04 Observed Predicted Used a PC last year No Yes Step 0 last year use computer No 72 Yes 51 Overall Percentage Case Processing Summary. Unweighted Casesa N Percent a b
Percentage Correct
Appendix B. Results of the Binary Logistic Regression – Personal Characteristics
0 0 58.5
100.0 .0
Constant is included in the model. The cut value is .500.
Selected Cases Included in Analysis Missing Cases Total Unselected Cases Total a Variables in the Equation 123 0 123 0 123 100.0 0 100.0 0 100.0 Step 0 Constant
B −.345
S.E. .183
Wald 3.550
df 1
Sig. .060
Exp(B) .708
If weight is in effect, see classification table for the total number of cases.
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Block 1: Method: Backward Stepwise (Wald) Omnibus Tests of Model Coefficients Chi-square Step 1 Step Block Model Step Block Model 50.973 50.973 50.973 −2.034 45.368 45.368 df 11 11 11 1 8 8 Sig. .000 .000 .000 .154 .000 .000
Step 5a
a
A negative Chi-squares value indicates that the Chi-squares value has decreased from the previous step.
Model Summary Step −2 Log likelihood Cox & Snell R Square Nagelkerke R Square .457 .415
1 5 a 115.938a 121.543a
.339 .308
Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be found.
Hosmer and Lemeshow Test Step 1 5 Classification Tablea Observed Predicted last year use computer Percentage Correct Chi-square 7.367 4.765 df 8 7 Sig. .498 .689
No Step 1 last year use computer No 61 Yes 14 Overall Percentage last year use computer No 61 Yes 17 Overall Percentage
Yes
11 37 79.7 11 34 77.2
84.7 72.5
Step 5
84.7 66.7
a The
cut value is .500. Variables in the Equation B S.E. Wald df Sig. Exp(B) 95% C.I. for Exp(B) Lower Upper . 19.503 1.367 .770 1.033
Step 1a
Healthy(1) Partner(1) Gender(1) Age Age(1) Age(2)
20.606 1.823 −.710 −1.870 −1.433
19914.111 .586 .522 .820 .748
.000 9.683 1.852 6.425 5.195 3.672
1 1 1 3 1 1
.999 .002 .174 .093 .023 .055
889288066.917 6.188 .492 .154 .238
.000 1.963 .177 .031 .055
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Component (Cronbach Alpha) Attitude (0.84) Using computer bad/good Using computer foolish/wise Using computer like/dislike Using computer pleasant/unpleasant People who influence my behavior think that I should use a computer People who are important to me think that I should use computers .813 .780 .816 .768 .226 .200 SN (0.91) .154 .242 .158 .052 .896 .904 PBC (0.79) .247 −.024 .327 .047 .217 .241
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Component (Cronbach Alpha) Attitude (0.84) I have control over using a computer I have the resources necessary to use a computer I have the knowledge necessary to use a computer Given the resources, opportunities and knowledge it takes to use a computer, it would be easy for me to use a computer: .021 .039 .360 .202 SN (0.91) .094 .275 .253 .070 PBC (0.79) .865 .658 .669 .799
Results of the principal components factor analysis Component Correlation Matrix Component 1 2 3 Att 1.000 −.282 −.386 SN PBC
1.000 .371
1.000
Extraction Method: Principal Component Analysis.Rotation Method: Direct Oblimin with Kaiser Normalization.
Appendix D. Results of the Binary Logistic regression: TPB and Age for the US Sample
Case Processing Summaryb Unweighted Casesa Selected Cases Included in Analysis Missing Cases Total Unselected Cases Total a N
Percent
59 4 63 0 63
93.7 6.3 100.0 .0 100.0
If weight is in effect, see classification table for the total number of cases. b country = USA.
Categorical Variables Codingsa Age Frequency Parameter coding (1) Age 60–69 Age 70–79 80–89 age >90 a (2) .000 .000 1.000 .000
(3) .000 .000 .000 1.000
8 17 25 9
.000 1.000 .000 .000
Country: USA.
Block 0: Beginning Block Classification Tablea,b,c Observed Predicted Intention to use .00 Step 0 Intention Overall Percentage a Percentage Correct
1.00 28 31 52.5 .0 100.0
.00 1.00
0 0
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Variables in the Equationa B Step 0 a S.E. .261
Wald .152
df 1
Sig. .696
Exp(B) 1.107
Constant
.102
Country: USA.
Block 1: Method: Backward Stepwise (Wald) Omnibus Tests of Model Coefficientsb Chi-square Step 1 Step Block Model Step Block Model 49.582 49.582 49.582 −7.698 34.701 34.701 df 15 15 15 3 4 4 Sig. .000 .000 .000 .053 .000 .000
Step 7a
a
A negative Chi-squares value indicates that the Chi-squares value has decreased from the previous step. b country: USA.
Model Summaryc Step 1 7 a b
(2 Log likelihood 32.056a 46.938b
Cox & Snell R Square .568 .445
Nagelkerke R Square .759 .593
Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be found. Estimation terminated at iteration number 6 because parameter estimates changed by less than .001. c Country: USA.
Hosmer and Lemeshow Testa Step 1 7 a Chi-square 4.805 8.581
df 8 8
Sig. .778 .379
Country: USA.
Classification Tablea,b Observed Predicted Intention to use .00 Step 1 Intention Overall Percentage Step 7 Intention Overall Percentage a b
Percentage Correct
1.00 4 26 84.7 6 26 81.4 85.7 83.9 78.6 83.9
.00 1.00 .00 1.00
24 5 22 5
Country: USA. The cut value is .500.
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Variables in the Equationb B S.E. Wald df Sig. Exp(B) 95% C.I. for Exp(B) Lower Step 1a Age Age(1) 9.254 Age(2) 9.834 Age(3) 190.004 Att US 18.117 21.592 PBC US 16.824 SN US Age × Att US Age(1) × Att US (15.587 Age(2) × Att US (17.264 Age(3) × Att US 268.187 Age × PBC US Age(1) × PBC US(20.030 Age(2) × PBC US(19.712 16.417 Age(3) × PBC US Age × SN US Age(1) × SN US (17.087 Age(2) × SN US (16.816 62.932 Age(3) × SN US Constant (9.640 PBC US Age × Att US Age(1) × Att US Age(2) × Att US Age(3) × Att US Constant 1.568 2.663 .846 4.134 .382 .316 .000 .000 .000 .000 .000 .000 1.252 .000 .000 .000 .044 .000 .000 .000 .078 .000 .000 .000 .000 9.489 8.992 4.333 2.345 2.703 1.001 3 1 1 1 1 1 1 3 1 1 1 3 1 1 1 3 1 1 1 1 1 3 1 1 1 1 .957 1.000 1.000 .995 .999 .999 .999 .741 .999 .999 .996 .998 .999 .999 .999 .994 .999 .999 .998 1.000 .002 .029 .037 .126 .100 .317 Upper
Variable(s) entered on step 1: Age, Att US, PBC US, SN US, Age × Att US, Age × PBC US, Age × SN US. b Country: USA.
Appendix E. Results of the Binary Logistic regression: TPB and Age for the Israeli Sample
Case Processing Summaryb Unweighted Casesa Selected Cases Included in Analysis Missing Cases Total 0 60 N 49 11 60 .0 100.0 Percent 81.7 18.3 100.0
Unselected Cases Total a If weight is in effect, see classification table for the total number of cases. b Country: Israel. Please cite this article in press as: T. Heart, E. Kalderon, Older adults: Are they ready to adopt health-related ICT? Int. J. Med. Inform. (2011), doi:10.1016/j.ijmedinf.2011.03.002
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Categorical Variables Codingsa Age Frequency Parameter coding (1) Age 60–69 Age 70–79 80–89 Age >90 a (2) .000 .000 1.000 .000
(3) .000 .000 .000 1.000
8 22 14 5
.000 1.000 .000 .000
Country: Israel.
Block 0: Beginning Block Classification Tablea,b,c Observed Predicted Intention to use .00 Step 0 Intention Overall Percentage a Percentage Correct
1.00 22 27 55.1 .0 100.0
.00 1.00
0 0
Country: Israel. b Constant is included in the model. c The cut value is .500.
Variables in the Equationb B Step 0a a S.E. .287
Wald .508
df 1
Sig. .476
Exp(B) 1.227
Constant
.205
If weight is in effect, see classification table for the total number of cases. b Country: Israel.
Block 1: Method: Backward Stepwise (Wald) Omnibus Tests of Model Coefficientsa Chi-square Step 1 Step Block Model Step Block Model 41.808 41.808 41.808 8.228 30.916 30.916 df 15 15 15 1 4 4 Sig. .000 .000 .000 .004 .000 .000
Step 9
a
Country: Israel.
Model Summaryc Step 1 9 a b
−2 Log likelihood 25.609a 36.501b
Cox & Snell R Square .574 .468
Nagelkerke R Square .768 .626
Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be found. Estimation terminated at iteration number 6 because parameter estimates changed by less than .001. c Country: Israel.
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Hosmer and Lemeshow Testa Step 1 9 a Chi-square 3.622 12.350
df 8 8
Sig. .890 136
Country: Israel.
Classification Tablea,b Observed Predicted Intention to use .00 Step 1 Intention Overall Percentage Step 9 Intention Overall Percentage a Percentage Correct
1.00 3 25 89.8 3 24 87.8 86.4 92.6 86.4 88.9
.00 1.00 .00 1.00
19 2 19 3
Country: Israel. b Constant is included in the model. c The cut value is .500.
Variables in the Equationc B S.E. Wald df Sig. Exp(B) 95% C.I. for Exp(B) Lower Step 1a Age Age(1) Age(2) Age(3) PBC Is Att Is SN Is Age × SN Is Age(1) × SN Is Age(2) × SN Is Age(3) × SN Is Age × Att Is Age(1) × Att Is Age(2) × Att Is Age(3) × Att Is Age × PBC Is Age(1) × PBC Is Age(2) × PBC Is Age(3) × PBC Is Constant PBC Is Age × SN Is Age(1) × SN Is Age(2) × SN Is Age(3) × SN Is Constant .108 .000 .000 .000 .000 .000 .000 .406 .000 .000 .000 1.693 .000 .000 .000 .189 .000 .000 .000 .000 6.407 8.269 4.717 2.189 1.273 .481 3 1 1 1 1 1 1 3 1 1 1 3 1 1 1 3 1 1 1 1 1 3 1 1 1 1 .991 1.000 1.000 1.000 .999 1.000 1.000 .939 1.000 1.000 1.000 .638 1.000 1.000 1.000 .979 .999 .999 1.000 1.000 .011 .041 .030 .139 .259 .488 Upper
Variable(s) entered on step 1: Age, PBC Is, Att Is, SN Is, Age × SN Is, Age × Att Is, Age × PBC Is. Variable(s) entered on step 8: PBC Is. c Country: Israel.
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