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Pro-Nets versus No-Nets: Differences in Urban Older Adults' Predilections for Internet Use

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Title: Pro-Nets versus No-Nets: Differences in Urban Older Adults' Predilections for Internet Use
Language: English
Authors: Cresci, M. Kay, Yarandi, Hossein N., Morrell, Roger W.
Source: Educational Gerontology. 2010 36(6):500-520.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 21
Publication Date: 2010
Document Type: Journal Articles
Reports - Research
Descriptors: Older Adults, Information Technology, Internet, Prediction, Educational Attainment, Income, Retirement, Health, Physical Activity Level, Comparative Analysis, Computer Attitudes, Health Promotion, Intervention, Interviews, Motivation, Trust (Psychology)
Geographic Terms: Michigan
DOI: 10.1080/03601270903212476
ISSN: 0360-1277
Abstract: Enthusiasm for information technology (IT) is growing among older adults. Many older adults enjoy IT and the Internet (Pro-Nets), but others have no desire to use it (No-Nets). This study found that Pro-Nets and No-Nets were different on a number of variables that might predict IT use. No-Nets were older, had less education and income, were retired, in poor health, and less active than Pro-Nets. No-Nets potentially have the most to gain from access to--and training in using--the Internet as a health management tool. Further research with this population could inform the development of health-related web based interventions. (Contains 4 tables.)
Abstractor: As Provided
Number of References: 38
Entry Date: 2010
Accession Number: EJ883601
Database: ERIC
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  Value: <anid>AN0050218790;egr01jun.10;2019Mar28.13:46;v2.2.500</anid> <title id="AN0050218790-1">Pro-Nets Versus No-Nets: Differences in Urban Older Adults' Predilections for Internet Use. </title> <p>Enthusiasm for information technology (IT) is growing among older adults. Many older adults enjoy IT and the Internet (Pro-Nets), but others have no desire to use it (No-Nets). This study found that Pro-Nets and No-Nets were different on a number of variables that might predict IT use. No-Nets were older, had less education and income, were retired, in poor health, and less active than Pro-Nets. No-Nets potentially have the most to gain from access to—and training inusing—the Internet as a health management tool. Further research with this population could inform the development of health-related web based interventions.</p> <p>Currrently, about 41% of adults over the age of 65 use information technology (IT) to some degree (Horrigan, [<reflink idref="bib12" id="ref1">12</reflink>]). A number of surveys show that there are many other older adults who are excited about computers and the Internet and would like to be online (Morrell, Mayhorn, & Bennett, [<reflink idref="bib23" id="ref2">23</reflink>]). However, a percentage of these older adults face a number of barriers that deny their participation at present. In addition to older adults who use or desire to use computers, there remains a distinct segment of the older population that holds steadfastly to their inclination not to ever surf the Internet or even use a computer at all. Pro-Nets are older adults who use or would like to use IT, while No-Nets are those who have no desire to use IT.</p> <p>It has become apparent that the Internet may be a way to implement health-related interventions. Using the Internet to access health information and decision-making tools may aid the delivery of health-related interventions for managing chronic health conditions. It has been noted that 80% of healthcare expenses can be attributed to 20% of older adults and individuals with chronic health conditions (Bard, [<reflink idref="bib1" id="ref3">1</reflink>]). Individuals with chronic health conditions who have access to, and effectively use, health information may be motivated to improve their health by actively participating in their care and engaging in health-promoting behaviors (Kalichman, Weinhardt, Benotsch, & Cherry, [<reflink idref="bib14" id="ref4">14</reflink>]). It has been noted that once consumers engage the Internet for actively seeking health information, even those with less education are likely to find useful information resources (Tu & Cohen, [<reflink idref="bib34" id="ref5">34</reflink>]). Investigators have found that access to health information on the Internet will empower older adults to better manage their health needs and increase their ability to make more informed decisions about their treatment options (McConatha, [<reflink idref="bib19" id="ref6">19</reflink>]; McMellon & Schiffman, [<reflink idref="bib20" id="ref7">20</reflink>]).</p> <p>Perhaps the most important reason for employing an Internet intervention with respect to today's issues in healthcare revolves around the concern of health disparities. A number of national healthcare disparities reports released since 2005 identify older adults as a health disparities subpopulation (National Healthcare Disparities Report, 2005). Older adults experience disparities related to preventive care, treatment of acute conditions, and management of chronic diseases such as cancer, end stage renal disease, hypertension/heart disease, diabetes, HIV/AIDS, obesity, mental health and substance abuse, and respiratory disease (National Healthcare Disparities Report, 2005, 2006). Older adults of racial/ethnic minority groups are particularly at risk for disparate health status and outcomes (National Healthcare Disparities Report, 2005, 2006).</p> <p>Health disparities among older adults are alarming because the percentage of older adults comprising the general population is expected to rise significantly in the near future, which would increase the peril of more health disparities in this group. With this in mind, it has been suggested that the Internet, which has been described as a potentially transforming and empowering health management tool (Eysenbach & Diepgen, [<reflink idref="bib9" id="ref8">9</reflink>]), could be used by older adults to reduce their vulnerability to health disparities (Gibbons, [<reflink idref="bib11" id="ref9">11</reflink>]). It is imperative that we identify factors obstructing Internet use among older adults and then design and test Internet interventions to improve the health of older adults. The purpose of this study was to (a) compare demographic, health, and activity variables among urban elderly IT users and prospective users (Pro-Nets) with urban elders who have no desire to use IT (No-Nets) in order to identify and address potential barriers to IT use in this population; and (b) to explore predictors of IT use.</p> <hd id="AN0050218790-2">INTERNET USE</hd> <p>Persons age 50 and older have been identified as the fastest growing group on the Internet (U.S. Department of Commerce, 2002). Investigators have examined demographic data including age, gender, education, income, and ethnicity related to use of computers and the Internet among older adults. Findings indicate that older adults using the Internet tend to be younger, more educated, and have higher incomes and are more likely to be Caucasian and equally male or female when compared to seniors who do not use the Internet (Fox, [<reflink idref="bib10" id="ref10">10</reflink>]).</p> <p>In 2000, Lenhart, Rainie, Fox, Horrigan, and Spooner found that among 12,751 respondents age 18 years and older, half did not have Internet access, and of those, 57% stated they were not interested in going online. Older Americans were identified as the most likely group not to be online. Reasons given for not being online included concerns about Internet safety and navigation, lack of need to be online, expense, and confusion in how to access the Internet.</p> <p>Data from the Pew Internet and American Life project collected in late 2008 (Horrigan, [<reflink idref="bib12" id="ref11">12</reflink>]) indicate that 74% of baby boomers (born between 1946 and 1964) used the Internet in 2008 compared to 40% in 2000. Of those persons, 62% have broadband access at home compared to less than 5% in the year 2000, and 43% connect to the Internet wirelessly compared to 0% in 2000. The top three activities performed by baby boomers include e-mail (91%), searching for information (90%), and seeking health information (78%), with 52% going online daily and 35% going online several times per day (Rainie, 2009).</p> <p>As baby boomers enter their senior years, use of IT by older Americans will likely increase. Yet barriers to IT use such as broadband Internet will continue to exist as some older adults struggle to obtain the needed resources to access and use the innovative technology of the future. It is, therefore, important to study variables of IT use and nonuse and predictors of use by older Americans in order to plan healthcare interventions for vulnerable populations.</p> <hd id="AN0050218790-3">Internet Nonuse</hd> <p>A number of barriers to Internet use have been identified in the literature. Investigators have reported general barriers to Internet use by urban elders as access issues, training opportunities, and psychological factors such as motivation and trust in information found online (Morrell, Mayhorn, & Bennett, [<reflink idref="bib24" id="ref12">24</reflink>]).</p> <hd id="AN0050218790-4">Access</hd> <p>According to Lenhart et al. ([<reflink idref="bib16" id="ref13">16</reflink>]), 87% of adults age 65 and older did not have Internet access, and 74% of those over 50 years of age did not plan to access the Internet. The Digital Divide (the social, economic, and demographic factors that exist between individuals who use computers and those who do not) has been identified as a major barrier to accessing the Internet by older adults (Morrell, Dailey, Stoltz-Loike, Mayhorn, & Echt, 2005). It has been shown that economically disadvantaged and less educated individuals, characteristic of many urban older adults, are even less likely to be online (Morrell et al., [<reflink idref="bib22" id="ref14">22</reflink>]). Older adults, especially those who live in rural and low income urban areas, constitute a significant segment of the Digital Divide (Brink, [<reflink idref="bib2" id="ref15">2</reflink>]; Kalichman et al., [<reflink idref="bib14" id="ref16">14</reflink>], [<reflink idref="bib13" id="ref17">13</reflink>]; Loges & Jung, [<reflink idref="bib17" id="ref18">17</reflink>]; Morrell et al., [<reflink idref="bib24" id="ref19">24</reflink>]; also see Cline & Haynes, [<reflink idref="bib5" id="ref20">5</reflink>]; Cresci, Morrell, & Echt, [<reflink idref="bib7" id="ref21">7</reflink>]; Rainie, [<reflink idref="bib22" id="ref22">22</reflink>]). Lack of access may also be related to other cultural factors such as literacy level, disability, inability to afford a computer or pay for an Internet service provider subscription, and local infrastructure requirements or an infrastructure that does not include broadband (Lenhart et al., [<reflink idref="bib15" id="ref23">15</reflink>]).</p> <hd id="AN0050218790-5">Training</hd> <p>Increased access to the Internet is needed, but access alone will not result in optimal benefits from online information. Successful Internet users must possess skills for searching, navigating, sorting, filtering, and utilizing Internet information. These are skills that may be lacking in people with less than a college education, which includes many older adults at present and will include a subset of baby boomers in their senior years. Often there is a lack of training opportunities available for teaching older adults how to use the Internet (Morrell et al., [<reflink idref="bib22" id="ref24">22</reflink>]). Older adults are at a disadvantage because they are less likely to have regular contact with the workplace, educational settings, or intergenerational exchanges with young adults who could teach them how to use a computer (Willis, [<reflink idref="bib38" id="ref25">38</reflink>]).</p> <hd id="AN0050218790-6">Motivation and Trust</hd> <p>Morrell and his colleagues (2000) found that older adults did not use the Internet because they did not see any place for it in their daily lives. Seniors reported that they had no use for it when the information and services they required were available through traditional means (e.g., newspapers, the postal service, telephone, etc.). Other investigators have found that prospective older users were not aware of information that can be gleaned from the Internet or of services and products that can be obtained (Melenhorst, Rogers, & Caylor, [<reflink idref="bib21" id="ref26">21</reflink>]). Therefore, they are not motivated to go online. Many people who do not use the Internet have also noted that they have a fear of fraud, credit card theft, or pornography as their major reasons for avoiding its use. The term e-TRUST is generally defined as "trust in information found on the Internet" (Morrell et al., [<reflink idref="bib24" id="ref27">24</reflink>]). Many older adults do not use the Internet because they do not know how to verify that the information they find online is current and reliable or in a general sense, trustworthy. This may be especially true for medical and other health-related information (see Morrell et al., [<reflink idref="bib22" id="ref28">22</reflink>]; Morrell, Mayhorn, & Echt, [<reflink idref="bib25" id="ref29">25</reflink>]; National Institute on Aging, [<reflink idref="bib28" id="ref30">28</reflink>]; SPRY Foundation, 2002). Further research is needed to determine the characteristics of those who are less likely to own and use computers. Therefore, this study was designed to compare characteristics of urban older adults who want to use IT with those who do not.</p> <hd id="AN0050218790-7">METHOD</hd> <p></p> <hd id="AN0050218790-8">Design</hd> <p>A secondary data analysis of the <emph>2001 Detroit city-wide needs assessment of older adults</emph> (Chapleski, [<reflink idref="bib3" id="ref31">3</reflink>]) was conducted using a comparative study design. The <emph>2001 Detroit city-wide needs assessment of older adults</emph> explored environmental conditions and needs of older adults in the areas of housing, health, transportation, computer ownership and use, and services utilization in Detroit, Michigan (Chapleski, [<reflink idref="bib3" id="ref32">3</reflink>]). For the present study, the sample was divided into two groups, No-Nets (do not use a computer) and Pro-Nets (use and wish to use a computer), using self-reported computer use and interest items.</p> <hd id="AN0050218790-9">Setting and Sample</hd> <p>The original study was conducted in the city of Detroit with participants from 10 neighborhood clusters. The neighborhood clusters were created in 1997 by the city of Detroit to identify land development and reinvestment opportunities as part of a community reinvestment strategy. Each cluster had geographic boundaries and identified neighborhood areas.</p> <p>A dual frame probability sample was used combining random digit dialing (RDD) of all households with telephones in Detroit and an area probability sampling of household addresses in 51 census tracts from the 1990 census with low rates of household telephone subscriptions. A detailed discussion of sampling is described elsewhere (Chapleski, [<reflink idref="bib3" id="ref33">3</reflink>]). The sample included 140 residents per cluster. Of the 1400 telephone interviews, 1310 were completed and an additional 100 face to face interviews were conducted for a total of 1410. In order to guarantee that all areas of the city of Detroit were represented in proportion to the total population of eligible residents, poststratified sampling weights were used in all the analyses (Chapleski, [<reflink idref="bib3" id="ref34">3</reflink>]).</p> <p>All participants eligible for the parent study were included in the comparative analysis. The parent study included all noninstitutionalized persons age 60 and older who resided in households within the city of Detroit. Persons residing in nursing homes, group quarters, prisons, or other institutions were excluded from participating in the survey (Chapleski, [<reflink idref="bib3" id="ref35">3</reflink>]).</p> <hd id="AN0050218790-10">Data Collection</hd> <p>Telephone interview data were collected using a computer-assisted telephone interviewing facility. Participants were contacted via the telephone using RDD by the interviewer. As the survey was conducted, participant responses were concurrently entered into a computer workstation by the interviewer. Data were then transferred from the workstation to the data processing unit.</p> <p>Face-to-face interviews were conducted by field interviewers who contacted sample addresses. The household was screened by the interviewer to determine if one or more residents were qualified to complete the survey. The survey was then administered with a selected participant (Chapleski, [<reflink idref="bib3" id="ref36">3</reflink>]). Telephone and face to face interviews lasted 45–60 minutes.</p> <hd id="AN0050218790-11">Measures</hd> <p>Responses to questions from the original investigator-developed survey were used to measure the variables in this study. Data for the study variables included computer use; demographic characteristics (age, gender, education, employment, income, and racial/ethnic groups); health (physical and mental health, senior optimism, frequency of seeking medical care, use of prescribed and over-the-counter medication, number of chronic health conditions); and activity (possession of a driver's license, library use, reading, taking classes, doing volunteer work, and participation in community organizations) (Chapleski, [<reflink idref="bib3" id="ref37">3</reflink>]).</p> <hd id="AN0050218790-12">Computer Use</hd> <p>Computer use or interest in use was measured using two investigator-developed questions. Participants were asked to indicate one of the following responses: "yes," "no," "did not know," or "refuse." The following was the question on computer use: "I'd like to ask if you have used a computer in the past year?" If participants answered "yes," they were identified as Pro-Nets. If participants answered "no," a second question was asked: "Would you like the opportunity to use one?" If participants answered "yes," they were identified as Pro-Nets. If participants answered "no," they were identified as No-Nets. If participant answered "did not know" or "refuse" to either question, the data were coded as missing.</p> <hd id="AN0050218790-13">Demographic Variables</hd> <p>The demographic variables of age, gender, education, employment, income, and racial/ethnic group were collected using an investigator-developed question for each subvariable. Age was measured using the question "What year were you born?" Response was recorded as the year the participant was born. Gender was recorded as female or male.</p> <p>Education was measured with the question "What is the highest level of school you have completed, or highest degree you have received?" Responses included "less than 5th grade," "5th to 8th grade," "9th grade," "10th grade," "11th grade," "12th grade," "no diploma," "high school graduate or GED," "some college but no degree," "associate degree in college (occupational/vocational program)," "associate degree in college (academic program)," "bachelor's degree," "master's degree," "professional school degree," or "doctorate." Employment was measured using the question "Currently, do you work full time, part time (less than 35 hours per week), on a temporary basis, as a contract employee, or are you retired, or are you unemployed?" Responses included "full time," "part time," "unemployed," "temporary employee," "contract employee," "seasonal employee," or "retired." Income was measured with the question "Is your total household income for the past 12 months under or over $20,000?" Responses included "under $20,000," "over $20,000," "not applicable," "don't know," "or refuse to answer."</p> <p>Ethnicity was measured with the question "What is your race or ethnic group?" Responses included "White/Caucasian," "Black/African American," "American Indian/Aleut/Eskimo," "Asian/Pacific Islander," "Arab/Middle Eastern," "Hispanic/Latino," or "other."</p> <hd id="AN0050218790-14">Health Variables</hd> <p>The health variables of frequency of seeking medical care, chronic health conditions, prescribed and over the counter medications, senior optimism, and perceived physical and mental health were measured with questions within the investigator-developed survey.</p> <p>Frequency of seeking medical care was measured with three variables: number of healthcare provider visits, hospital days, and emergency room (ER) visits. The number of healthcare provider visits was measured using the question "During the past 12 months, how many times have you seen a medical doctor?" The number of hospital days was measured using the question "During the past 12 months, how many days were you an inpatient in a hospital?" The number of emergency room visits was measured using the question "During the past 12 months, how many different times were you a patient in the emergency room?" A number was recorded as a response to each question.</p> <p>Chronic health conditions were measured with the question "Within the past year, has your doctor, nurse, or healthcare provider treated you for or told you that you have ..." followed by selection of arthritis, osteoporosis or "brittle bones," chronic bronchitis or emphysema, a heart problem, stroke or effects of stroke, hypertension or high blood pressure, diabetes or "sugar," cancer, high cholesterol, asthma, kidney disease, hepatitis, liver disease, stomach ulcers, thyroid problem, and disease of the mouth, teeth or gums. Responses included "yes," "no," "did not know," or "refuse." A chronic health condition score was determined by summing the yes answers.</p> <p>Medication use was measured with two questions. Prescription medications were measured with the question "How many prescription medicines are you currently taking?" Over-the-counter medications were measured using the question "How many over-the-counter or nonprescription medicines are you currently taking?" A number was recorded as a response to each health question.</p> <p>Senior optimism was measured with the statement "Some people say that being a senior citizen is the best time of your life." Responses included "strongly disagree," "somewhat disagree," "neither agree nor disagree," "somewhat agree," or "strongly agree." Senior optimism was indicated by a high score on this item.</p> <p>Physical and mental health was measured using the SF-12. The SF-12 uses a four-week recall to measure health status according to the participant's point of view. The SF-12 uses a Likert scale and is derived from the Physical Components Summary (PCS) and Mental Component Summary (MCS) of the SF-36 representing the following eight dimensions of health: physical functioning, role functioning physical, bodily pain, general health, vitality, social functioning, role functioning emotional, and mental health. The Likert scale of 1 to 3 (1 = limited a lot, 2 = limited a little, 3 = not limited at all) is used for the physical function items, whereas a Likert scale of 1 to 6 (1 = all the time, 6 = none of the time) is used for the vital and mental health items. Instrument results are expressed in terms of two meta-scores: PCS and MCS. Better health is indicated by a higher score on the instrument. In the original study, test-retest correlations were reported as.89 for PCS and.76 for MCS (Ware, Kosinski, & Keller, [<reflink idref="bib36" id="ref38">36</reflink>]). Ware, Kosinski, and Keller ([<reflink idref="bib37" id="ref39">37</reflink>]) reported SF 12 mean scores as 43.65 (PCS) and 52.10 (MCS) for persons age 65–74 and 38.65 (PCS) and 50.06 (MCS) for those 75 and older.</p> <hd id="AN0050218790-15">Activity Variables</hd> <p>Activity variables included having a driver's license, library use, reading, taking classes, membership in organizations and neighborhood groups, and volunteer work. Investigator-developed questions and two sets of response statements were used to measure these activities. Driver's license was measured with the following question: "Do you currently possess a valid driver's license?" Library use was measured with the question "Have you used the local library?" Community organization was measured with the question "Are you a member of any community organizations or neighborhood groups?" Volunteer work was measured with the question "Do you do volunteer work? By that I mean both formal and informal work at an agency, hospital, or church, or caring for family member, friend, or neighbor?" For each question, participants were asked to indicate one of the following responses: "yes," "no," "did not know," or "refuse."</p> <p>Reading was measured using the question <bold>"</bold>Do you enjoy a lot, a little, or not at all reading books, magazines, or newspapers?" Taking classes was measured using the question "Do you enjoy a lot, a little, or not at all taking classes or learning something new?" For each question, participants were asked to indicate one of the following responses: "a lot," "a little," "not at all."</p> <hd id="AN0050218790-16">Data Analysis</hd> <p>Data were analyzed using SAS. The first phase of the analysis consisted of using descriptive statistics on demographic variables in computing the summary measures (mean, median, standard deviation, and range) for the variables measured on interval or ratio scales. Frequency distributions (absolute frequency and percent) were used for the variables measured on nominal or ordinal scales. The Wilcoxon Rank Sum Test was utilized to compare demographic, health, and activity variables between the two groups. Analysis of frequency (chi-square test) was utilized to test the homogeneity of the categorical response variables between No-Nets and Pro-Nets. Logistic regression analysis was used to explore potential differences in demographic characteristics, health, and activity variables between No-Nets and Pro-Nets. The point and interval estimates of the odd ratios of the categorical predictor variables were reported. The significance level was 0.05.</p> <p>For the purpose of this study, responses to two of the demographic variables (education and employment) were collapsed into fewer categories. Responses to the education and employment questions were collapsed into three categories: "less than high school education," "high school education," "greater than high school education," and "employed," "unemployed," and "retired," respectively. The response to the senior optimism question was recoded as follows: 0 = disagree, 1 = neutral, 2 = agree.</p> <hd id="AN0050218790-17">RESULTS</hd> <p></p> <hd id="AN0050218790-18">Description of Sample</hd> <p>Of the 1410 participants in the original study, 39 did not answer the computer use question. Therefore, the total number of participants in the present study was 1371. The mean age for the current sample was 71.61 years (<emph>SD</emph> = 7.66) and the majority of participants were female. Over half of the participants reported a high school education or greater, an employment status of retired, and an income of over $20,000 per year. The majority of participants in the study were African American followed by Caucasian. (see Table 1).</p> <p>Table 1. Results of chi-square test between Pro-Nets and No-Nets for gender, education, employment, income, and racial/ethnic group</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Variable</td><td>Total (<italic>n</italic> = <bold>1371</bold>)</td><td>Pro-Nets (<italic>n</italic> = <bold>937</bold>) <bold>68.34%</bold></td><td>No-Nets (<italic>n</italic> = <bold>434</bold>) <bold>31.66%</bold></td><td>χ<sup>2</sup></td><td><italic>p</italic><bold>value</bold></td></tr></thead><tbody><tr><td>Gender</td><td /><td /><td /><td char="." charoff="50">0.2332</td><td char="." charoff="50">.6292</td></tr><tr><td> Female</td><td char="lpar" charoff="50">966 (70.46%)</td><td char="lpar" charoff="50">664 (70.86%)</td><td char="lpar" charoff="50">302 (69.59%)</td><td /><td /></tr><tr><td> Male</td><td char="lpar" charoff="50">405 (29.54%)</td><td char="lpar" charoff="50">273 (29.14%)</td><td char="lpar" charoff="50">132 (30.41%)</td><td /><td /></tr><tr><td>Education</td><td /><td /><td /><td char="." charoff="50">99.6530</td><td char="." charoff="50"><.0001</td></tr><tr><td> <High school</td><td char="lpar" charoff="50">545 (40.28%)</td><td char="." charoff="50">304 (32.90%)</td><td char="lpar" charoff="50">241 (56.18%)</td><td /><td /></tr><tr><td> High school</td><td char="lpar" charoff="50">324 (23.95%)</td><td char="." charoff="50">211 (22.84%)</td><td char="lpar" charoff="50">113 (26.34%)</td><td /><td /></tr><tr><td> >High school</td><td char="lpar" charoff="50">484 (35.77%)</td><td char="." charoff="50">409 (44.26%)</td><td char="lpar" charoff="50">75 (17.48%)</td><td /><td /></tr><tr><td>Employment</td><td /><td /><td /><td char="." charoff="50">37.9687</td><td char="." charoff="50"><.0001</td></tr><tr><td> Employed</td><td char="lpar" charoff="50">127 (9.73%)</td><td char="lpar" charoff="50">115 (12.95%)</td><td char="lpar" charoff="50">12 (2.87%)</td><td /><td /></tr><tr><td> Unemployed</td><td char="lpar" charoff="50">253 (19.39%)</td><td char="lpar" charoff="50">181 (20.38%)</td><td char="lpar" charoff="50">72 (17.27%)</td><td /><td /></tr><tr><td> Retired</td><td char="lpar" charoff="50">925 (70.88%)</td><td char="." charoff="50">592 (66.67%)</td><td char="lpar" charoff="50">333 (79.86%)</td><td /><td /></tr><tr><td>Income</td><td /><td /><td /><td char="." charoff="50">43.8544</td><td char="." charoff="50"><.0001</td></tr><tr><td> <$20,000</td><td char="lpar" charoff="50">776 (65.87%)</td><td char="." charoff="50">492 (59.85%)</td><td char="lpar" charoff="50">284 (79.78%)</td><td /><td /></tr><tr><td> >$20,000</td><td char="lpar" charoff="50">402 (34.13%)</td><td char="." charoff="50">330 (40.15%)</td><td char="lpar" charoff="50">72 (20.22%)</td><td /><td /></tr><tr><td>Household Size</td><td /><td /><td /><td char="." charoff="50">14.1595</td><td char="." charoff="50">.0002</td></tr><tr><td> Live alone</td><td char="lpar" charoff="50">575 (41.94%)</td><td char="lpar" charoff="50">361 (38.53%)</td><td char="lpar" charoff="50">214 (49.31%)</td><td /><td /></tr><tr><td> Live with others</td><td char="lpar" charoff="50">796 (58.06%)</td><td char="." charoff="50">576 (61.47%)</td><td char="lpar" charoff="50">220 (50.69%)</td><td /><td /></tr><tr><td>Race/Ethnic Group</td><td /><td /><td /><td char="." charoff="50">25.8579</td><td char="." charoff="50">.0002</td></tr><tr><td> American Indian/Aleut/Eskimo</td><td char="lpar" charoff="50">9 (0.67%)</td><td char="." charoff="50">6 (0.65%)</td><td char="lpar" charoff="50">3 (0.70%)</td><td /><td /></tr><tr><td> Asian/Pacific Islander</td><td char="lpar" charoff="50">3 (0.22%)</td><td char="." charoff="50">3 (0.33%)</td><td char="lpar" charoff="50">0 (0.0%)</td><td /><td /></tr><tr><td> Arab/Middle Eastern</td><td char="lpar" charoff="50">1 (0.07%)</td><td char="." charoff="50">0 (0.00%)</td><td char="lpar" charoff="50">1 (0.23%)</td><td /><td /></tr><tr><td> Black/African American</td><td char="lpar" charoff="50">1097 (81.14%)</td><td char="lpar" charoff="50">778 (84.29%)</td><td char="lpar" charoff="50">319 (74.36%)</td><td /><td /></tr><tr><td> Hispanic/Latino</td><td char="lpar" charoff="50">20 (1.48%)</td><td char="lpar" charoff="50">12 (1.30%)</td><td char="lpar" charoff="50">8 (1.86%)</td><td /><td /></tr><tr><td> White Caucasian</td><td char="lpar" charoff="50">186 (13.76%)</td><td char="lpar" charoff="50">100 (10.83%)</td><td char="lpar" charoff="50">86 (20.05%)</td><td /><td /></tr><tr><td> Other</td><td char="lpar" charoff="50">36 (2.66%)</td><td char="lpar" charoff="50">24 (2.60%)</td><td char="lpar" charoff="50">12 (2.80%)</td><td /><td /></tr></tbody></table> </ephtml> </p> <hd id="AN0050218790-19">Barriers to Computer Use</hd> <p>The barriers to computer use in this study were access, education, and income. In terms of computer access, 368 (26.8%) of participants indicated that they had used a computer in the past year. Of those participants who had not used a computer in the past year, 551 (40.19%) indicated a desire to use computers and 434 (31.66%) indicated they did not wish to use a computer.</p> <hd id="AN0050218790-20">No-Nets Versus Pro-Nets</hd> <p>The sample of 1371 was divided into two groups, individuals who were No-Nets (<emph>n</emph> = 434, 31.66%) and individuals who were Pro-Nets (<emph>n</emph> = 937, 68.34%), using the two questions related to computer use and interest in using a computer. Bivariate analyses revealed a number of differences between No-Nets and Pro-Nets (Table 1).</p> <hd id="AN0050218790-21">Demographic Characteristics and Barriers to Computer Use</hd> <p>Findings indicated that No-Nets (mean age = 74.27, <emph>SD</emph> = 8.10) were older than Pro-Nets (70.30, <emph>SD</emph> = 7.05). Using the Wilcoxon Rank Sum Test, a statistically significant difference was found in mean rank age between No-Nets and Pro-Nets (<emph>Z</emph> = 8.4501, <emph>p</emph> = .0001). Findings in Table 1 indicate that No-Nets were less likely to have greater than a high school education and to be employed when compared to Pro-Nets, and they were more likely to have an income of less than $20,000 per year. Using chi-square test, a statistically significant difference was found between No-Nets and Pro-Nets on education (χ<sups>2</sups> = 99.65, <emph>p</emph> ≤ .0001), employment (χ<sups>2</sups> = 37.96, <emph>p</emph> ≤ .0001), and income (χ<sups>2</sups> = 43.85, <emph>p</emph> ≤ .0001) (Table 1).</p> <hd id="AN0050218790-22">Health</hd> <p>Using the Wilcoxon Rank Sum Test, statistically significant differences were found between No-Nets and Pro-Nets on two health variables: SF-12 PCS (<emph>Z</emph> = −3.07, <emph>p</emph> = .0011) and hospital days (<emph>Z</emph> = 2.92, <emph>p</emph> = .0017). No-Nets had poorer health when compared to Pro-Nets. No-Nets reported a higher number of hospital days and a lower score on the physical component of the SF-12 than Pro-Nets. There was no statistically significant difference between the two groups on the SF-12 MCS, number of healthcare provider visits, number of ER visits, prescribed and over-the-counter medication, and number of chronic diseases.</p> <p>Using a chi-square test, a statistically significant difference was found between No-Nets and Pro-Nets for senior optimism, heart problems, stroke, diabetes, hepatitis, and stomach ulcers (see Table 2). No-Nets were less likely to be optimistic about this time in their life compared to Pro-Nets. No-Nets were more likely to have heart problems, stroke, diabetes, and hepatitis and less likely to have stomach ulcers when compared to Pro-Nets.</p> <p>Table 2. The results of chi-square test between No-Nets and Pro-Nets for senior optimism and the health conditions of heart problems, stroke, diabetes, hepatitis, and stomach ulcers</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Variable</td><td>Total</td><td>Pro-Nets</td><td>No-NetS</td><td>χ<sup>2</sup></td><td><italic>p</italic> value</td></tr></thead><tbody><tr><td>Senior optimism</td><td /><td /><td /><td char="." charoff="50">13.2565</td><td char="." charoff="50">.0013</td></tr><tr><td> Agree</td><td char="lpar" charoff="50">780 (58.43%)</td><td char="lpar" charoff="50">560 (61.54%)</td><td char="lpar" charoff="50">220 (51.76%)</td><td /><td /></tr><tr><td> Neutral</td><td char="lpar" charoff="50">83 (6.22%)</td><td char="lpar" charoff="50">47 (5.16%)</td><td char="lpar" charoff="50">36 (8.47%)</td><td /><td /></tr><tr><td> Disagree</td><td char="lpar" charoff="50">472 (35.35%)</td><td char="lpar" charoff="50">303 (33.30%)</td><td char="lpar" charoff="50">169 (39.76%)</td><td /><td /></tr><tr><td>Heart problems</td><td /><td /><td /><td char="." charoff="50">10.7678</td><td char="." charoff="50">.001</td></tr><tr><td> Yes</td><td char="lpar" charoff="50">369 (27.07%)</td><td char="lpar" charoff="50">227 (24.38%)</td><td char="lpar" charoff="50">142 (32.87%)</td><td /><td /></tr><tr><td> No</td><td char="lpar" charoff="50">994 (72.93%)</td><td char="lpar" charoff="50">704 (75.62%)</td><td char="lpar" charoff="50">290 (67.13%)</td><td /><td /></tr><tr><td>Stroke</td><td /><td /><td /><td char="." charoff="50">4.4821</td><td char="." charoff="50">.03</td></tr><tr><td> Yes</td><td char="lpar" charoff="50">108 (7.91%)</td><td char="lpar" charoff="50">64 (6.86%)</td><td char="lpar" charoff="50">44 (10.19%)</td><td /><td /></tr><tr><td> No</td><td char="lpar" charoff="50">1257 (92.09%)</td><td char="lpar" charoff="50">869 (93.14%)</td><td char="lpar" charoff="50">388 (89.81%)</td><td /><td /></tr><tr><td>Diabetes</td><td /><td /><td /><td char="." charoff="50">12.26</td><td char="." charoff="50">.0005</td></tr><tr><td> Yes</td><td char="lpar" charoff="50">318 (23.28%)</td><td char="lpar" charoff="50">192 (20.56%)</td><td char="lpar" charoff="50">126 (29.17%)</td><td /><td /></tr><tr><td> No</td><td char="lpar" charoff="50">1048 (76.72%)</td><td char="lpar" charoff="50">742 (79.44%)</td><td char="lpar" charoff="50">306 (70.83%)</td><td /><td /></tr><tr><td>Hepatitis</td><td /><td /><td /><td char="." charoff="50">7.8401</td><td char="." charoff="50">.005</td></tr><tr><td> Yes</td><td char="lpar" charoff="50">174 (12.81%)</td><td char="lpar" charoff="50">103 (11.09%)</td><td char="lpar" charoff="50">71 (16.55%)</td><td /><td /></tr><tr><td> No</td><td char="lpar" charoff="50">1184 (87.19%)</td><td char="lpar" charoff="50">826 (88.91%)</td><td char="lpar" charoff="50">358 (83.45%)</td><td /><td /></tr><tr><td>Stomach ulcers</td><td /><td /><td /><td char="." charoff="50">4.3282</td><td char="." charoff="50">.0375</td></tr><tr><td> Yes</td><td char="lpar" charoff="50">106 (7.79%)</td><td char="lpar" charoff="50">82 (8.82%)</td><td char="lpar" charoff="50">24 (5.57%)</td><td /><td /></tr><tr><td> No</td><td char="lpar" charoff="50">1255 (92.21%)</td><td char="lpar" charoff="50">848 (91.18%)</td><td char="lpar" charoff="50">407 (94.43%)</td><td /><td /></tr></tbody></table> </ephtml> </p> <hd id="AN0050218790-23">Activity</hd> <p>Using a chi-square test, a statistically significant difference was found between No-Nets and Pro-Nets on all activities variables (see Table 3). No-Nets were less likely to use the library, read, and enjoy learning new things compared to Pro-Nets. No-Nets also participated less frequently in community organizations and volunteer work than Pro-Nets.</p> <p>Table 3. Results of chi-square test between No-Nets and Pro-Nets for library use, reading, learning new things, community organizations, volunteer work, and driver's license</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Variable</td><td>Total</td><td>Pro-Nets</td><td>No-Nets</td><td>χ<sup>2</sup></td><td><italic>p</italic><bold>value</bold></td></tr></thead><tbody><tr><td>Library use</td><td /><td /><td /><td char="." charoff="50">74.2538</td><td char="." charoff="50">.0001</td></tr><tr><td> Yes</td><td char="lpar" charoff="50">752 (55.33%)</td><td char="lpar" charoff="50">587 (63.25%)</td><td char="lpar" charoff="50">165 (38.28%)</td><td /><td /></tr><tr><td> No</td><td char="lpar" charoff="50">607 (44.67%)</td><td char="lpar" charoff="50">341 (36.75%)</td><td char="lpar" charoff="50">266 (61.72%)</td><td /><td /></tr><tr><td>Reading</td><td /><td /><td /><td char="." charoff="50">69.053</td><td char="." charoff="50">.0001</td></tr><tr><td> A lot</td><td char="lpar" charoff="50">915 (66.79%)</td><td char="lpar" charoff="50">680 (72.57%)</td><td char="lpar" charoff="50">235 (54.27%)</td><td /><td /></tr><tr><td> A little</td><td char="lpar" charoff="50">301 (21.97%)</td><td char="lpar" charoff="50">192 (20.49%)</td><td char="lpar" charoff="50">109 (25.17%)</td><td /><td /></tr><tr><td> Not at all</td><td char="lpar" charoff="50">127 (9.27%)</td><td char="lpar" charoff="50">57 (6.08%)</td><td char="lpar" charoff="50">70 (16.17%)</td><td /><td /></tr><tr><td> Unable to participate</td><td char="lpar" charoff="50">27 (1.97%)</td><td char="lpar" charoff="50">8 (0.86%)</td><td char="lpar" charoff="50">19 (4.39%)</td><td /><td /></tr><tr><td>Learning new things</td><td /><td /><td /><td char="." charoff="50">153.5728</td><td char="." charoff="50">.0001</td></tr><tr><td> A lot</td><td char="lpar" charoff="50">516 (38.19%)</td><td char="lpar" charoff="50">429 (46.43%)</td><td char="lpar" charoff="50">87 (20.37%)</td><td /><td /></tr><tr><td> A little</td><td char="lpar" charoff="50">323 (23.91%)</td><td char="lpar" charoff="50">245 (26.52%)</td><td char="lpar" charoff="50">78 (18.27%)</td><td /><td /></tr><tr><td> Not at all</td><td char="lpar" charoff="50">463 (34.27%)</td><td char="lpar" charoff="50">221 (23.92%)</td><td char="lpar" charoff="50">242 (56.67%)</td><td /><td /></tr><tr><td> Unable to participate</td><td char="lpar" charoff="50">49 (3.63%)</td><td char="lpar" charoff="50">29 (3.13%)</td><td char="lpar" charoff="50">20 (4.69%)</td><td /><td /></tr><tr><td>Community organizations</td><td /><td /><td /><td char="." charoff="50">27.1568</td><td char="." charoff="50">.0001</td></tr><tr><td> Yes</td><td char="lpar" charoff="50">393 (28.71%)</td><td char="lpar" charoff="50">309 (33.05%)</td><td char="lpar" charoff="50">84 (19.35%)</td><td /><td /></tr><tr><td> No</td><td char="lpar" charoff="50">976 (71.29%)</td><td char="lpar" charoff="50">626 (66.95%)</td><td char="lpar" charoff="50">350 (80.65%)</td><td /><td /></tr><tr><td>Volunteer work</td><td /><td /><td /><td char="." charoff="50">39.9397</td><td char="." charoff="50">.0001</td></tr><tr><td> Yes</td><td char="lpar" charoff="50">414 (30.24%)</td><td char="lpar" charoff="50">333 (35.58%)</td><td char="lpar" charoff="50">81 (18.71%)</td><td /><td /></tr><tr><td> No</td><td char="lpar" charoff="50">955 (69.76%)</td><td char="lpar" charoff="50">603 (64.42%)</td><td char="lpar" charoff="50">352 (81.29%)</td><td /><td /></tr><tr><td>Driver's license</td><td /><td /><td /><td char="." charoff="50">61.5498</td><td char="." charoff="50">.0001</td></tr><tr><td> Yes</td><td char="lpar" charoff="50">1004 (73.23%)</td><td char="lpar" charoff="50">746 (79.62%)</td><td char="lpar" charoff="50">258 (59.45%)</td><td /><td /></tr><tr><td> No</td><td char="lpar" charoff="50">367 (26.77%)</td><td char="lpar" charoff="50">191 (20.38%)</td><td char="lpar" charoff="50">176 (40.55%)</td><td /><td /></tr></tbody></table> </ephtml> </p> <hd id="AN0050218790-24">Predictors of IT Use</hd> <p>Binary logistic regression models, permitting the use of both continuous and categorical variables, were constructed to determine whether differences existed between No-Nets and Pro-Nets in terms of demographic characteristics, health, and activity variables. Those variables that were significantly different between the two groups using the Wilcoxon Rank Sum Test and the chi-square tests were included in the logistic regression model. The results of logistic regression indicated that the variables age, library use, having a driver's license, taking classes/enjoy learning new things, education, volunteering, and diabetes mellitus were significantly discriminated between No-Nets and Pro-Nets (Table 4). In addition, the No-Nets were 2.280 times less likely to take classes/like to learn new things, 1.581 times less likely to have membership in community organizations, and 1.807 times less likely to do volunteer work.</p> <p>Table 4. Logistic regression model for predicting Pro-Nets in urban elders</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Variable</td><td>β</td><td><italic>SE</italic></td><td><italic>p-</italic><bold>value</bold></td><td>Odds ratio</td><td>95% C.I.</td></tr></thead><tbody><tr><td>Age</td><td char="." charoff="50">−0.0696</td><td char="." charoff="50">0.00941</td><td char="." charoff="50"><.0001</td><td char="." charoff="50">0.933</td><td char="." charoff="50">0.916–0.950</td></tr><tr><td>Library use</td><td char="." charoff="50">0.44586</td><td char="." charoff="50">0.1463</td><td char="." charoff="50">.0017</td><td char="." charoff="50">1.582</td><td char="." charoff="50">1.188–2.107</td></tr><tr><td>Drivers license</td><td char="." charoff="50">0.4799</td><td char="." charoff="50">0.1554</td><td char="." charoff="50">.0020</td><td char="." charoff="50">1.616</td><td char="." charoff="50">1.192–2.191</td></tr><tr><td>Learning new things</td><td char="." charoff="50">1.0046</td><td char="." charoff="50">0.3304</td><td char="." charoff="50">.0024</td><td char="." charoff="50">2.731</td><td char="." charoff="50">1.429–5.218</td></tr><tr><td>Senior optimism<sup>1</sup></td><td char="." charoff="50">−0.5669</td><td char="." charoff="50">0.3049</td><td char="." charoff="50">.0630</td><td char="." charoff="50">0.567</td><td char="." charoff="50">0.312–1.031</td></tr><tr><td>Senior optimism<sup>2</sup></td><td char="." charoff="50">−0.3269</td><td char="." charoff="50">0.1475</td><td char="." charoff="50">.0267</td><td char="." charoff="50">0.721</td><td char="." charoff="50">0.540–0.963</td></tr><tr><td>Educ<sup>1</sup></td><td char="." charoff="50">0.1671</td><td char="." charoff="50">0.1681</td><td char="." charoff="50">.3201</td><td char="." charoff="50">1.182</td><td char="." charoff="50">0.850–1.643</td></tr><tr><td>Educ<sup>2</sup></td><td char="." charoff="50">0.8971</td><td char="." charoff="50">0.1848</td><td char="." charoff="50"><.0001</td><td char="." charoff="50">2.452</td><td char="." charoff="50">1.707–3.523</td></tr><tr><td>Volunteer</td><td char="." charoff="50">0.4742</td><td char="." charoff="50">0.1700</td><td char="." charoff="50">.0053</td><td char="." charoff="50">1.607</td><td char="." charoff="50">1.151–2.242</td></tr><tr><td>Diabetes</td><td char="." charoff="50">−0.4003</td><td char="." charoff="50">0.1608</td><td char="." charoff="50">.0128</td><td char="." charoff="50">0.670</td><td char="." charoff="50">0.489–0.918</td></tr><tr><td /></tr><tr><td /></tr><tr><td /></tr><tr><td /></tr></tbody></table> </ephtml> </p> <hd id="AN0050218790-25">DISCUSSION</hd> <p>The Internet has been identified as a potential strategy for implementing health related interventions. This study examined demographic, health, and leisure activity factors that could influence use of the Internet by urban older adults. In this sample (<emph>N</emph> = 1371), 31.66% of urban older adults were identified as No-Nets while 68.3% were identified as Pro-Nets. This research also found significant differences in demographic, health, and leisure activities characteristics between these groups. To the best of our knowledge, these characteristics of urban older adults have not been previously reported; therefore, our findings have important implications for the development of Internet based interventions for older adults and also for future research.</p> <hd id="AN0050218790-26">Demographic Characteristics</hd> <p>No-Nets were older, had less than a high school education, were retired, and had an annual income of less than $20,000 per year compared to those who were favorably inclined toward computer use. These findings are similar to those reported in other studies on computer use among older adults (Chen & Perrson, [<reflink idref="bib4" id="ref40">4</reflink>]; Fox, [<reflink idref="bib10" id="ref41">10</reflink>]; Czaja et al., [<reflink idref="bib8" id="ref42">8</reflink>]; Lenhart et al., [<reflink idref="bib15" id="ref43">15</reflink>]; Stark-Wroblewski, Edelbaum, & Ryan, [<reflink idref="bib33" id="ref44">33</reflink>]; Horrigan, [<reflink idref="bib12" id="ref45">12</reflink>]; Cresci, Yarandi, & Morrell, [<reflink idref="bib6" id="ref46">6</reflink>]). Lenhart et al. ([<reflink idref="bib15" id="ref47">15</reflink>]) conducted a survey that found the "truly unconnected" nonusers not only had demographic characteristics similar to the ones found in this study, but the nonusers indicated that they had never tried the Internet, did not know people who had, lacked the social networks that would encourage them to try it, and believed it was not a good use of their time. Our demographic findings, which focus on urban older adults' differences in demographic variables (i.e., age, education, employment, income, and race/ethnic groups), suggest that the lack of interest in using information technology may be related to No-Nets living their lives without computers and believing that they do not need to learn or use computers now.</p> <p>It is our belief that, like older adults in general, No-Nets are a heterogeneous group requiring further research to develop a clear understanding of the urban No-Net perspective on using or not wanting to use the Internet. Further studies can assist investigators in developing interventions that address the urban No-Nets perspective, thereby potentially engaging them in using the Internet as a health management tool.</p> <hd id="AN0050218790-27">Health</hd> <p>Few studies of older adults have examined health-related variables and computer use. No-Nets had lower scores on the SF 12 PCS (physical), had a greater number of hospital days, and were more likely to have heart problems, stroke, diabetes, and hepatitis than Pro-Nets. These results suggest that the health of No-Nets may interfere with the ability to carry out various physical and social activities important to independent living. In particular, the lower SF12 PCS score indicates that No-Nets are limited in their ability to carry out regular activities, including moderate physical activities and climbing stairs. To our knowledge, this is the first study to examine physical and mental health using the SF-12 in a research investigation of urban senior.</p> <hd id="AN0050218790-28">Pro-Nets and No-Nets</hd> <p>Even though there was no statistically significant difference between No-Nets and Pro-Nets on the SF12 MCS (mental), a statistically significant difference was found between No-Nets and Pro-Nets on senior optimism. No-Nets were less optimistic about their life than Pro-Nets. These health findings are important because the Internet could be a valuable tool in assisting No-Nets in managing their health and remaining independent. For example, No-Nets could use the Internet to carry out certain instrumental activities of daily living online such as medication prescription purchase and renewals, shopping, handling finances, making transportation arrangements, and communicating with family, friends and healthcare providers. In addition, various Internet telehealth care delivery applications could be used to improve access to quality health services without the need to leave home.</p> <p>One explanation for the No-Nets' lack of interest in computers and the Internet could be associated with their poorer physical health status. In a study exploring computer use and satisfaction among middle age and older adults with disabilities (Mann, Belchior, Tomita, & Kemp, [<reflink idref="bib18" id="ref48">18</reflink>]), several factors were identified as influencing computer nonuse. These factors included cost; a lack of perceived need, device knowledge and available training; complex nature of learning how to use a computer; issues of privacy and trust; and personal issues related to mobility, visual and hearing impairment, and pain. Therefore, intervention studies that address not only economic and practical factors but also establish the benefits of computer and Internet use as a health management tool may encourage No-Nets to investigate using the Internet to manage their health and maintain their independent living.</p> <hd id="AN0050218790-29">Activities</hd> <p>The relationship between leisure time activities and Internet use is rarely examined in studies on computer use among older adults. In our study, No-Nets were less likely to be engaged in an active lifestyle such as using the library, learning new things, volunteering, and participating in community organizations when compared to Pro-Nets. One explanation for these differences may be related to the impact of health limitations on the daily physical activities of No-Nets described earlier. Stark-Wroblewski et al. ([<reflink idref="bib33" id="ref49">33</reflink>]), in a study on the use of e-mail by older adults, found that noncomputer users were more likely to report health problems that limited daily activities and were less able to do things independently compared to e-mail users.</p> <p>These are important findings because the development of intervention strategies that demonstrate to older adults that the Internet can be a medium for delivering meaningful experiences will allow those who cannot easily leave their place of residence to continue to interact with the community. For example, McMellon and Schiffman ([<reflink idref="bib20" id="ref50">20</reflink>]) found that respondents noted that their lives changed with the use of computers and the Internet. They were able to communicate better and easier with friends, family, and like-minded others; reduce loneliness; feel as if they were out of the house even when they were housebound; and regain aspects of their lives closer to the levels they had prior to experiencing their age related changes. One respondent reported that the computer was a real asset in keeping the mind active through activities that challenged one's thinking on many subjects and having the opportunity to obtain others' points of view.</p> <p>Study findings indicate the number of physical limitations that can occur with aging. McConatha ([<reflink idref="bib19" id="ref51">19</reflink>]) points out that information technology has the potential to significantly alter what were once considered the limitations of aging and sustain a sense of a contribution to society. Intervention strategies that encourage the use of electronic resources available to older adults may not only help them to control their environment with the ability to shop, learn, teach, mentor, and seek health information. Such strategies may also decrease social isolation and increase social support, improve well-being, and increase life satisfaction.</p> <hd id="AN0050218790-30">Predictors of Use</hd> <p>Age, education, and senior optimism were found to be discriminating factors between urban older adult No-Nets and Pro-Nets in this sample. In addition, the active lifestyle factors of library use, having a driver's license, taking classes/learning new things, membership in community organizations, and volunteering were found to discriminate between No-Nets and Pro-Nets. Implications of these findings indicate that No-Nets potentially have the most to gain from access to and training in using the Internet as a health management tool. Thus, further research is not only needed to develop a clear understanding of the No-Nets' perspective related to IT use, but also to integrate that perspective into the development and testing of interventions using the Internet as a health management tool. For example, interventions could be developed using the Spiral Technology Action Research (STAR) Model (Skinner, Maley, & Norman, [<reflink idref="bib31" id="ref52">31</reflink>]), which promotes a user-centered development process that reflects user needs and wants.</p> <hd id="AN0050218790-31">Summary and Implications</hd> <p>In summary, this study found significant differences on several demographic, health, and leisure time activity variables among urban Pro-Nets and No-Nets. Since this analysis was derived from an already existing data set, it was not possible to examine factors related to computer/Internet use in No-Nets. Further research will explore reasons why/why not No-Nets use/will not use IT. This information will not only help us identify unconnected urban older adults who want to be connected, but it also may assist us in identifying user-centered interventions that will promote the Internet as a health management tool.</p> <ref id="AN0050218790-32"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref3" type="bt">1</bibl> <bibtext> Bard, M. (2003). E-engage chronically ill seniors to help manage costly conditions. Managed Healthcare Executive, 14(4), 42.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref15" type="bt">2</bibl> <bibtext> Brink, A. (2001, September). Is there a digital divide between E-life and successful aging? Paper presented at the International Conference on Technology and Aging, Toronto, Canada.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref31" type="bt">3</bibl> <bibtext> Chapleski, E. (2002). Facing the future 2002: City of Detroit needs assessment of older adults. A Report for the City of Detroit Department of Senior Citizens. 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In D.Burdick & S.Kwon (Eds.), Gerontechnology: Research and practice in technology and aging (pp. 71–85). New York: Springer.</bibtext> </blist> <blist> <bibtext> National Healthcare Disparities Report, 2005. (2005). Agency for Healthcare Research and Quality, Rockville, MD. Retrieved from <ulink href="http://www.ahrq.gov/qual/nhdr05/nhdr05.htm">http://www.ahrq.gov/qual/nhdr05/nhdr05.htm</ulink></bibtext> </blist> <blist> <bibtext> National Healthcare Disparities Report, 2006. (2006). Rockville, MD: Agency for Healthcare Research and Quality. Retrieved from <ulink href="http://www.ahrq.gov/qual/nhdr06/nhdr06.htm">http://www.ahrq.gov/qual/nhdr06/nhdr06.htm</ulink></bibtext> </blist> <blist> <bibtext> National Institute on Aging. (2003). Age page: Online health information: Can you trust it?Bethesda, MD: National Institutes on Aging.</bibtext> </blist> <blist> <bibtext> Rainie, L. (2005). Internet: The mainstreaming of online life, trends 2005. 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Educational Gerontology, 33(4), 293–307.</bibtext> </blist> <blist> <bibtext> Tu, H. T., & Cohen, G. R. (2008). Striking jump in consumers seeking health care information. Tracking Report No. 20. Washington, DC: Center for Studying Health System Change. Retrieved from <ulink href="http://www.hschange.org/CONTENT/1006/">http://www.hschange.org/CONTENT/1006/</ulink></bibtext> </blist> <blist> <bibtext> U.S. Department of Commerce, Economics, & Statistics Administration, & National Telecommunications, & Information Administration. (2002). A nation online: How Americans are expanding their use of the Internet. Retrieved from <ulink href="http://www.ntia.doc.gov/ntiahome/dn/anationonline2.pdf">http://www.ntia.doc.gov/ntiahome/dn/anationonline2.pdf</ulink></bibtext> </blist> <blist> <bibtext> Ware, J. E., Kosinski, M., & Keller, S. (1996). A 12-item short-form health survey: Construction of scales and preliminary tests of reliability and validity. Medical Care, 34, 220–233.</bibtext> </blist> <blist> <bibtext> Ware, J. E., Kosinski, M., & Keller, S. D. (1998). SF-12: How to score the SF-12 Physical and Mental Health Summary Scales (), 3rd ed.. Lincoln, RI: Quality Metric Incorporated.</bibtext> </blist> <blist> <bibtext> Willis, S. (2003, January). Technology and learning activities in current and future elder cohorts. Paper presented at the Workshop on Technology for Adaptive Aging, Washington, DC.</bibtext> </blist> </ref> <ref id="AN0050218790-33"> <title> Footnotes </title> <blist> <bibtext> Senior optimism<sups>1</sups> refers to a neutral response and agree response as the 2 categories.</bibtext> </blist> <blist> <bibtext> Senior optimism<sups>2</sups> refers to a disagree response and agree response as the 2 categories.</bibtext> </blist> <blist> <bibtext> Educ<sups>1</sups> refers to using less than high school education and a high school education as the 2 categories.</bibtext> </blist> <blist> <bibtext> Educ<sups>2</sups> refers to using high school education and more than a high school education as the 2 categories.</bibtext> </blist> </ref> <aug> <p>By M.Kay Cresci; HosseinN. Yarandi and RogerW. Morrell</p> <p>Reported by Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib12" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib23" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib14" firstref="ref4"></nolink> <nolink nlid="nl4" bibid="bib34" firstref="ref5"></nolink> <nolink nlid="nl5" bibid="bib19" firstref="ref6"></nolink> <nolink nlid="nl6" bibid="bib20" firstref="ref7"></nolink> <nolink nlid="nl7" bibid="bib11" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib10" firstref="ref10"></nolink> <nolink nlid="nl9" bibid="bib24" firstref="ref12"></nolink> <nolink nlid="nl10" bibid="bib16" firstref="ref13"></nolink> <nolink nlid="nl11" bibid="bib22" firstref="ref14"></nolink> <nolink nlid="nl12" bibid="bib13" firstref="ref17"></nolink> <nolink nlid="nl13" bibid="bib17" firstref="ref18"></nolink> <nolink nlid="nl14" bibid="bib15" firstref="ref23"></nolink> <nolink nlid="nl15" bibid="bib38" firstref="ref25"></nolink> <nolink nlid="nl16" bibid="bib21" firstref="ref26"></nolink> <nolink nlid="nl17" bibid="bib25" firstref="ref29"></nolink> <nolink nlid="nl18" bibid="bib28" firstref="ref30"></nolink> <nolink nlid="nl19" bibid="bib36" firstref="ref38"></nolink> <nolink nlid="nl20" bibid="bib37" firstref="ref39"></nolink> <nolink nlid="nl21" bibid="bib33" firstref="ref44"></nolink> <nolink nlid="nl22" bibid="bib18" firstref="ref48"></nolink> <nolink nlid="nl23" bibid="bib31" firstref="ref52"></nolink>
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  Data: Pro-Nets versus No-Nets: Differences in Urban Older Adults' Predilections for Internet Use
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  Data: <searchLink fieldCode="SO" term="%22Educational+Gerontology%22"><i>Educational Gerontology</i></searchLink>. 2010 36(6):500-520.
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  Data: <searchLink fieldCode="DE" term="%22Older+Adults%22">Older Adults</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Technology%22">Information Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Internet%22">Internet</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Attainment%22">Educational Attainment</searchLink><br /><searchLink fieldCode="DE" term="%22Income%22">Income</searchLink><br /><searchLink fieldCode="DE" term="%22Retirement%22">Retirement</searchLink><br /><searchLink fieldCode="DE" term="%22Health%22">Health</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+Activity+Level%22">Physical Activity Level</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+Analysis%22">Comparative Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Attitudes%22">Computer Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Health+Promotion%22">Health Promotion</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Interviews%22">Interviews</searchLink><br /><searchLink fieldCode="DE" term="%22Motivation%22">Motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Trust+%28Psychology%29%22">Trust (Psychology)</searchLink>
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  Data: Enthusiasm for information technology (IT) is growing among older adults. Many older adults enjoy IT and the Internet (Pro-Nets), but others have no desire to use it (No-Nets). This study found that Pro-Nets and No-Nets were different on a number of variables that might predict IT use. No-Nets were older, had less education and income, were retired, in poor health, and less active than Pro-Nets. No-Nets potentially have the most to gain from access to--and training in using--the Internet as a health management tool. Further research with this population could inform the development of health-related web based interventions. (Contains 4 tables.)
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      Pagination:
        PageCount: 21
        StartPage: 500
    Subjects:
      – SubjectFull: Older Adults
        Type: general
      – SubjectFull: Information Technology
        Type: general
      – SubjectFull: Internet
        Type: general
      – SubjectFull: Prediction
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      – SubjectFull: Educational Attainment
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      – SubjectFull: Income
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      – SubjectFull: Retirement
        Type: general
      – SubjectFull: Health
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      – SubjectFull: Physical Activity Level
        Type: general
      – SubjectFull: Comparative Analysis
        Type: general
      – SubjectFull: Computer Attitudes
        Type: general
      – SubjectFull: Health Promotion
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      – SubjectFull: Intervention
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      – SubjectFull: Interviews
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      – SubjectFull: Motivation
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      – SubjectFull: Trust (Psychology)
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      – SubjectFull: Michigan
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    Titles:
      – TitleFull: Pro-Nets versus No-Nets: Differences in Urban Older Adults' Predilections for Internet Use
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Cresci, M. Kay
      – PersonEntity:
          Name:
            NameFull: Yarandi, Hossein N.
      – PersonEntity:
          Name:
            NameFull: Morrell, Roger W.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2010
          Identifiers:
            – Type: issn-print
              Value: 0360-1277
          Numbering:
            – Type: volume
              Value: 36
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: Educational Gerontology
              Type: main
ResultId 1