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Important gaps remain in our knowledge of how individuals from low socioeconomic position (SEP) use the Internet for resources and in understanding the full range of activities they perform online. Although self-report data indicate that low SEP individuals use the Internet less than high SEP people for health information and for other beneficial capital-enhancing activities, these results may not provide an accurate overall view of online use.
The aim of this study was to determine the ways in which low SEP individuals use the Internet, including for entertainment, social networking, and capital-enhancing functions, and how they are associated with health information seeking.
Detailed Web tracking data were collected from 118 low SEP individuals who participated in the intervention group of a randomized controlled trial that provided Internet access. Websites were grouped by topic, including categories of capital-enhancing websites that provided access to resources and information. Different types of online activities were summed into an Internet use index. Single and multiple negative binomial regression models were fitted with the Internet use index as the predictor and health information seeking as the outcome. Next, models were fitted with low, medium, and high Web usage in capital-enhancing, entertainment, and social network categories to determine their associations with health information seeking.
Participants used the Web for diverse purposes, with 63.6% (75/118) accessing the Internet for all defined types of Internet use. Each additional category of Internet use was associated with 2.12 times the rate of health information seeking (95% CI 1.84-2.44,
These data clearly show that familiarity and skills in using the Internet enhance the capacity to use it for diverse purposes, including health and to increase capital, and that Internet usage for specific activities is not a zero sum game. Using it for one type of topic, such as entertainment, does not detract from using it for other purposes. Findings may inform ways to engage low SEP groups with Internet resources.
The vast quantities of online information have transcended some barriers to information, such as time and geography, to provide people with relevant, timely information that may increase their health and well-being [
Health information seeking may occur most often when need for a specific disease or medical decision making arises [
Explorations of the ways low SEP individuals use the Internet, including the range and breadth of activities performed on the Internet for a variety of functions [
Despite the potential benefits of such capital-enhancing information, research indicates that individuals with lower SEP take fewer opportunities to use the Internet comprehensively beyond amusement and communication, suggesting that the underserved may not fully take part in the new media environment [
However, many studies reporting such a usage gap rely on self-report Internet use information [
Additionally, providing a broad comparison between the Internet usage patterns of different income strata do not account for the differences in how low SEP individuals may engage with and learn from the Web in unique ways from their high SEP counterparts. Beyond the scope of entertainment versus information, scholars have suggested that a broader and more sophisticated use of the Internet, particularly engaging with the Web for diverse purposes, allows an individual a greater opportunity to acquire benefits and opportunities to meet individual and social goals [
Determining the detailed usage patterns of low SEP individuals may highlight the best ways to engage them in online activities that provide them with resources to improve their health or socioeconomic position.
The purpose of this paper is to build on our prior work [
Data for this study were drawn from “Click to Connect: Improving Health Literacy Through Computer Literacy” (C2C), a randomized controlled trial funded through the National Cancer Institute to understand computer- and Internet-related challenges, barriers, and facilitators among a low SEP population. Intervention details may be found elsewhere (see [
Two sources of data were merged for this study: (1) a baseline 45-minute telephone survey that contained detailed measures of demographic information and (2) Internet use throughout the intervention period (9-18 months) tracked directly through participants’ computers using Spector 360, software that logs each URL visited into a secure server on the study premises through a virtual private network (VPN). The use of the Spector 360 process allowed us to capture real-time data of websites visited and number of times visited. Once all tracking data were collected, we submitted deidentified domain information to an online application program interface (API) through the Webroot BrightCloud Content Classification Service [
Internet health information seeking was conceptualized as the purposeful seeking of health information through visiting health websites. Our definition of “health” was broad to include all topics that participants may perceive as health information, including websites for health information of unknown quality, in order to capture health seeking from the participant’s perspective. Due to the broadened definition of health operationalized in this study, websites categorized by BrightCloud underwent a second, detailed coding process by study staff. We first created a list of health-related topics, derived from several sources, including the Healthy People 2020 topic list and Centers for Disease Control and Prevention and World Health Organization website indexes of health topics. We then used these keywords to search for additional URLs visited by the participants to add to the list of BrightCloud categorized health websites. Once a list of all potential health websites was created, two trained independent coders reviewed each URL and related website description and designated them as a health website (yes/no). A test coding block was first conducted with 10% of the sample to answer questions and clarify coding terms. Then, the two coders coded the full list independently and concurrently. The interrater reliability of the coding was strong, with a Cohen kappa of .94. The final list of health websites included sites such as the Cancer Society, , and HealthyPlace.com, among others. For the purposes of this study, each “hit,” or separate visit to a particular health-related website, was considered an instance of information seeking.
We constructed an Internet use index corresponding to a number of different types of activities one may perform on the Web [
Each category of capital-enhancing information seeking was coded as a separate variable and each hit was considered an instance of information seeking. Websites for each type of capital-enhancing seeking were derived from our modified BrightCloud categories. The category descriptions are described subsequently.
Hits for information pertaining to higher education, including college websites, college-finding services, collegiate test preparation, GED courses or materials, and online degree program information.
Hits for sites for information on employment, including human resources departments, job finders, or resume help.
Hits for information on renting, buying, or selling properties or real estate, including apartment listing services, roommate finders, and real estate websites.
Hits for money-related information, including banking services, loans, credit, accounting, stock trading, asset management, and investment accounts.
Hits to websites for government agencies (local to national level), services, and explanation of laws, including political advocacy websites that promote politicians, political discussions, or other social advocacy issues.
Hits to websites for current events, including radio, newspaper and headline news sites, newswire services, personalized news, and weather sites.
Entertainment usage was derived from the modified BrightCloud categories and was also conceptualized as the number of hits to websites for sites discussing television, movies, music, celebrity news/gossip, entertainment reviews, or the performing arts. Sites for music, online gaming, nudity, and pornography were included. Examples of such websites included FreeGamesOnline.com, Access Hollywood, and IMDb.
Social network usage was defined as the number of hits to sites that have user communities where users interact, post messages, pictures, and communicate, such as Myspace and Facebook.
We measured sex, race/ethnicity (white, African American, Latino), employment status (working yes/no), and age (categorized as younger than 35 years, 35-49 years, and 50 years or older) from our baseline telephone survey. Income and education were not included as covariates due to our recruitment of low SEP participants with a restricted income and education range. We also controlled for study wave to adjust for any differences by administration year.
We first analyzed descriptive statistics and frequencies for all variables. We next fitted an unadjusted and adjusted negative binomial regression model with our Internet use index and our outcome, Internet health information seeking. Negative binomial regression was used for these analyses due to the nature of the outcome as a count-based variable that had a strong right skew [
The demographic characteristics of the sample can be found in
Demographic comparisons between Click to Connect (C2C) and selected national surveys.
Demographic characteristic | C2C, n (%) |
US Census 2010 |
HINTS 2014 Cycle 3, n (%) |
Pew Internet Tracking Survey 2013, n (%) |
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Male | 45 (38.2) | 49% | 1197 (37.58) | 2059 (49.28) |
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Female | 73 (61.8) | 51% | 1906 (59.84) | 2119 (50.72) |
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18-34 | 38 (32.2) | 21% (20-34 years) | 426 (13.38) (18-34 years) | 926 (22.16) (<30 years) |
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35-49 | 54 (45.8) | 33% (35-59 years) | 712 (22.35) | 1329 (31.81) (30-49 years) |
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50-64 | 26 (22.0) | 12% (55-64 years) | 1070 (33.59) (50-64 years) | 1155 (27.64) |
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African American | 65 (55.1) | 13% | 421 (13.22) | 527 (12.61) |
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White | 8 (6.87) | 78% | 1584 (49.73) | 3113 (74.51) |
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Hispanic | 23 (19.1) | 16% | 511 (16.04) | 545 (13.04) |
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<10,000 | 39 (33.1) | 8% | 680 (21.35) (<20K) | 370 (8.86) |
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10,000-19,999 | 37 (31.4) | 6% (10K-<15K) |
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479 (11.46) |
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20,000-29,999 | 19 (17.9) | 11% (15K-<25K) | 418 (13.12) (20K-<35K) | 438 (10.48) |
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30,000-39,999 | 9 (8.4) | 10% (25K-<35K) |
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440 (10.53) |
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40,000-49,999 | 3 (2.5) | 14% (35K-<50K) | 394 (12.37) (35K-<50K) | 286 (6.85) |
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50,000-74,999 | 2 (1.7) | 18% | 446 (14.00) | 622 (14.89) |
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≥75,000 | 0 (0) | 32% | 801 (25.15) | 816 (19.53) |
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≤Grade school | 16 (13.6) | 6% | 297 (9.32) (≤high school) | 312 (7.47) (<high school) |
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Some high school | 74 (62.7) | 8% |
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High school graduate/ GED | 3 (2.5) | 50% | 699 (21.94) | 1401 (33.53) |
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Some college | 0 (0) | 21% | 691 (21.70) | 1311 (31.38) |
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≥Bachelor’s degreeb | 9 (7.6) | 28% | 1167 (36.64) | 1143 (27.36) |
a Population estimate (exact numbers not available).
b For C2C: college completed in another country.
The outcome, Internet health information seeking, received a median of 85.5 hits (range 0-3537; mean 214.59, SD 411.65 hits) over the study period (
Descriptive statistics for online seeking for health, capital-enhancing variables, entertainment, and social networks by number of hits.
Type of seeking | Mean (SD) | Median (range) | |
Health | 214.59 (411.65) | 89 (0-3537) | |
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Job | 234.53 (379.68) | 61 (0-1832) |
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Residence | 119.03 (333.37) | 10 (0-2442) |
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Government | 132.88 (204.65) | 62 (0-1583) |
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Education | 175.81 (287.48) | 70 (0-1470) |
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Finances | 505.20 (1052.30) | 110 (0-7833) |
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News | 509.09 (993.48) | 219 (0-8043) |
Entertainment | 4164.70 (6286.96) | 1497 (0-31,023) | |
Social networks | 15,740 (27,989.97) | 4276 (0-169,875) |
Percentage of total hits contributed by each Web category.
Category | % of total hits |
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Health information hits | 0.49% | |
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5.97% | |
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Residence | 0.29% |
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Government | 0.34% |
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Education | 0.41% |
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Job | 0.48% |
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Financial | 1.22% |
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News | 3.23% |
Entertainment hits | 9.74% | |
Social networks | 36.54% | |
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Internet portals | 12.63% |
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Shopping | 6.11% |
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Search engine | 5.64% |
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Streaming media | 5.01% |
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Personal sites and blogs, peer-to-peer, shareware and freeware, personal storage | 2.04% |
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Society | 1.81% |
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Web-based email | 1.74% |
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Training and tools, reference and research, other education, translation | 1.10% |
Other websites visited (computer sites,a malware, hacking, phishing, frauds, spyware, spam, dead sites) | 10.62% |
a Web ads, Web hosting, parked domains, pay to surf, proxy, content and file delivery systems.
All participants participated in at least 6 of 16 Web activities over the course of the study (
Percentage of participants using the Web for diverse purposes (N=118).
Number of website types visited | Participants, n (%) |
7 | 3 (2.5) |
8 | 2 (1.7) |
11 | 1 (0.8) |
12 | 3 (2.5) |
13 | 6 (5.1) |
14 | 6 (5.1) |
15 | 22 (18.6) |
16 | 75 (63.5) |
As shown in
Compared to low entertainment site users (
High social network site users sought health information at 2.1 times the rate of low users in the adjusted model (IRR 2.06, 95% CI 1.08-3.92,
Bivariate and adjusted associations between each type of capital-enhancing seeking, entertainment site usage, social network site usage, and health information seeking (N=118).
Predictor variable | Bivariate associations | Adjusted modelsa | ||||
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IRR (95% CI) |
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IRR (95% CI) |
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Multimodal use | 2.16 (1.87-2.50) | <.001 | 2.12 (1.84-2.44) | <.001 | ||
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Medium | 2.64 (1.42-4.90) | .002 | 1.93 (1.01-3.68) | .047 |
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High | 5.19 (2.81-9.59) | <.001 | 5.13 (2.81-9.34) | <.001 |
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Medium | 2.570 (1.41-4.68) | .002 | 3.04 (1.64-5.54) | <.001 |
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High | 6.75 (3.72-12.23) | <.001 | 6.94 (3.73-12.92) | <.001 |
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Medium | 2.89 (1.51-5.41) | .001 | 2.16 (1.11-4.19) | .02 |
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High | 3.96 (2.10-7.46) | <.001 | 3.91 (2.03-7.53) | <.001 |
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Medium | 3.23 (1.75-5.97) | <.001 | 3.05 (1.65-5.64) | <.001 |
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High | 5.79 (3.13-10.69) | <.001 | 6.17 (3.28-11.62) | <.001 |
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Medium | 4.14 (2.31-7.43) | <.001 | 4.82 (2.64-8.80) | <.001 |
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High | 8.90 (4.98-15.91) | <.001 | 8.90 (4.82-16.42) | <.001 |
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Medium | 4.91 (2.77-8.71) | <.001 | 5.87 (3.32-10.38) | <.001 |
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High | 11.29 (6.38-19.96) | <.001 | 11.36 (6.21-20.79) | <.001 |
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Medium | 4.69 (2.72-8.09) | <.001 | 4.24 (2.43-7.40) | <.001 |
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High | 14.77 (8.59-25.39) | <.001 | 13.01 (7.29-23.20) | <.001 |
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Medium | 3.65 (1.97-6.76) | <.001 | 3.34 (1.82-6.14) | <.001 | |
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High | 4.66 (2.49-8.73) | <.001 | 3.91 (2.07-7.37) | <.001 | |
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Medium | 0.93 (0.47-1.85) | .85 | 1.04 (0.52-2.08) | .92 | |
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High | 2.15 (1.14-4.08) | .02 | 2.06 (1.08-3.92) | .03 |
a Adjusted for race, age, native language, employment status, and wave.
This study represents in-depth research of natural online behaviors of low SEP individuals over a period of several months that draws from directly tracked Internet data. Through this method, we were able to place health information seeking, capital-enhancing information seeking, entertainment use, social network use, and other diverse forms of Internet use within the context of the total Web use experience of low SEP individuals, data that are not often captured in such detail for this group. Participants sought information on a number of domains; 64% visited all the categories of the Internet use index over the study period and each additional category of computer use was associated with double the rate of health information seeking. Higher use of all individual types of capital-enhancing seeking was associated with increased rates of Internet health information seeking, with the highest increases seen in education and governmental website use. When all capital-enhancing categories were combined, the highest users of capital information sought health information at 13 times the rate of low users. Furthermore, both medium and high entertainment users were significantly associated with higher health information seeking compared to those who used entertainment sources to a low extent, and high social network site use was associated with higher health information seeking compared to low use.
Broader use of the Internet may provide individuals with skills to become more active online consumers [
Evidence shows that health outcomes are patterned by access to material resources, education, and occupation [
In all, these associations suggest that higher levels of Internet use for functions such as searching for a job, financial resources, or educational programs correspond with higher levels of searching for health information. Past literature has found that in certain contexts, financial information served as a competing concern to health information for low SEP individuals [
Although participants visited entertainment websites more frequently than health or capital websites reflecting past literature, we also observed entertainment use of the Internet was positively associated with greater health information seeking. This is an important distinction; instead of entertainment use taking precedence over higher-order activities, individuals who spend more time online may do so in varied topic areas as they gain more confidence in using the Web [
Although both health and capital information seeking represented only a small total of all websites visited compared to categories such as social networking, Internet portals, and entertainment, it may be indicative of the nature of the sites’ structure. For example, sites with constantly changing content, such as celebrity gossip, and particularly user-generated content, such as social network sites, may require more frequent interaction to remain up-to-date with activity. However, static sites may only require one visit to gather needed information, such as referencing a health diagnosis. Other sites, such as job sites, may be visited only as a certain need arises. The frequency with which these dynamic sites, such as social network sites, are accessed provide a promising platform for future eHealth content delivery [
Although these data may give us valuable insight into the information-seeking behaviors of lower income adults, this sample may not be generalizable to other low SEP Internet users in the Boston area or in the United States. Although the restricted range of our sample precluded us from gauging differences by income or education, the nature of the sample made it ideal for studying the communication behaviors of a low SEP group. For this study, we recruited participants through presentations at adult education centers, which may have led to selection bias. Our focus on adults aged 25 to 60 years precludes us from understanding Internet usage patterns in younger adults, who are often more frequent Internet users; however, this allowed us to focus on a sample of novice Internet users who may not have as much exposure to the Internet through school or other sources. Due to our IRB mandate, our data were restricted to the household level, so we were unable to determine use from particular individuals in the household. It is possible that there were several users in each household and that different household members used the Internet for different purposes. To address this discrepancy, self-report Internet use data from each participant was crosschecked with website tracking data to determine if the participant’s level of reported usage matched the observed level of Internet use in the tracking data. Furthermore, additional models accounting for other potential household members did not change our regression estimates. Our use of BrightCloud coding to determine our topic categories may have limited us from including certain relevant websites in our analyses; although we conducted a second-level crosscheck to include or exclude inappropriately categorized sites, we may have overlooked certain URLs or we may be unable to determine if health information seeking occurred on a site such as a social network platform or multipurpose webpage. Despite these limitations, the ability to capture real-time, Web-recorded data provides valuable insight into the information-seeking behaviors of the urban poor.
Results indicate that once online, low SEP individuals use the Internet for a broad range of purposes. The growth of health information technologies provides opportunities to incorporate features of interactivity and multimedia to revolutionize health communication. As evidenced by the diverse Web behaviors in this group, they may be familiar with these concepts and well positioned to participate in upcoming Web interventions. This finding may have important implications for interventions and design of policy-based websites because low SEP individuals may take advantage of a number of different well-being and health-related website formats. Given the popularity of social network sites, this platform may be particularly suited for trusted, reliable health information. However, certain safeguards to information structure, accessibility, and content must be considered when designing Web resources for low SEP groups.
Definitions of website categories.
Click to Connect
Institutional Review Board
incident rate ratio
socioeconomic position
This project was supported by Click to Connect: Improving Health Literacy through Internet Literacy, grant number RO1 CA122894 and grant number R25 CA057711 (KV, PI). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. We would like to thank Sara Minsky, the Click to Connect team, and the Health Communication Core at the Viswanath Lab at the Dana-Farber Cancer Institute for their work on this project.
None declared.