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The extensive availability of online health information offers the public opportunities to become independently informed about their care, but what affects the successful retrieval and understanding of accurate and detailed information? We have limited knowledge about the ways individuals use the Internet and the personal characteristics that affect online health literacy.
This study examined the extent to which age and cognitive style predicted success in searching for online health information, controlling for differences in education, daily Internet use, and general health literacy.
The Online Health Study (OHS) was conducted at Johns Hopkins School of Public Health and Stanford University School of Medicine from April 2009 to June 2010. The OHS was designed to explore the factors associated with success in obtaining health information across different age groups. A total of 346 men and women aged 35 years and older of diverse racial and ethnic backgrounds participated in the study. Participants were evaluated for success in searching online for answers to health-related tasks/questions on nutrition, cancer, alternative medicine, vaccinations, medical equipment, and genetic testing.
Cognitive style, in terms of context sensitivity, was associated with less success in obtaining online health information, with tasks involving visual judgment most affected. In addition, better health literacy was positively associated with overall success in online health seeking, specifically for tasks requiring prior health knowledge. The oldest searchers were disadvantaged even after controlling for education, Internet use, general health literacy, and cognitive style, especially when spatial tasks such as mapping were involved.
The increasing availability of online health information provides opportunities to improve patient education and knowledge, but effective use of these resources depends on online health literacy. Greater support for those who are in the oldest cohorts and for design of interfaces that support users with different cognitive styles may be required in an age of shared medical decision making.
Making informed decisions about health and health care is a key part of enhanced patient care in the twenty-first century. Shared decision making between patients and providers is increasingly the preferred model for health care delivery [
Since the Internet’s introduction, a common activity has been seeking health information online. At the time of the first Health Information National Trends Survey (HINTS) in 2005, 58% of Internet users had used the Internet to search for health information for themselves [
The ability of individuals to participate in informed decisions about their health care depends on the degree to which they have the capacity to obtain, process, and understand health information (health literacy) [
Older adults are a particularly vulnerable group, characterized by low health literacy and poor health outcomes [
Age differences in computer use, skills, and Internet use are well established. Older cohorts are far less likely than younger ones to use computers regularly, more rarely rely on the Internet for information, and report less ease in locating information on the “net” [
Basic computer skills—as well as the ability to discriminate among online resources and understand and use information—are paramount for user success obtaining online health information. Age-related declines in sensory abilities and cognition affect visual acuity, especially the ability to discriminate important information in a graphically challenging visual field. Such declines also serve as key factors influencing the usefulness of online resources for older persons [
Percentage of Internet users who looked for health information online. Tabulations are drawn from the Pew Internet and American Life Project spreadsheet “Usage Over Time” [
Cognitive style reflects the different ways individuals solve problems; people vary in how they acquire and process information [
How cognitive style affects success in navigating the Internet deserves increased examination, given the increasing dependency of decision making on online information. Not all websites are the same. Many use increasingly complex interfaces and rely on multimedia content to convey information [
In this study, we examined the extent to which cognitive style (as dichotomized by context independence vs sensitivity) matters for success with online health seeking, controlling for differences in age, education, health literacy, and Internet experience.
The Online Health Study (OHS) was developed to explore age differences in the strategies used by adults to successfully navigate the Internet for health information and to understand how older adults’ online health literacy compares with that of younger adults. The OHS was designed to examine demographic, cognitive, and environmental factors associated with success obtaining online health information.
The study was conducted at the Johns Hopkins School of Public Health and Stanford University School of Medicine. From April 2009 to June 2010, 346 men and women aged 35 years and older of diverse racial-ethnic backgrounds participated. Participants were recruited from the community and screened for eligibility using a Web-based interface constructed by the research team. Such screening allowed appropriate representation in the sample from different demographic groups and ensured that potential participants had suitable levels of Internet skill to work through the study protocols. Participants who completed all study procedures received a US $35 gift card.
After completing consent procedures, participants provided information on their socioeconomic and demographic backgrounds, health status, and experience with computers and other media. Participants also completed a Rapid Estimate of Adult Literacy in Medicine (REALM) to provide a general measure of health literacy and the Witkin Group Embedded Figures Test (GEFT) to assess context sensitivity or independence.
Participants did a practice search task to familiarize themselves with the protocol and warm-up to using the research computers. Then, participants answered 6 health-related questions by performing online searches on the Hopkins or Stanford project computers. Search time was limited to 15 minutes per task and sessions averaged 60-90 minutes overall. After each online search task, participants reported their answer, which was transcribed and later coded by 2 assistants for response accuracy and specificity. Online search tasks reflected typical and realistic tasks. Health topics covered in the search tasks included diet/nutrition guidelines, skin cancer, alternative medicine, vaccine recommendations, assistive health technology, and over-the-counter genetic testing. In addition to varying on subject matter, each of the tasks required different levels of health literacy and computing skills, including reading texts, reviewing charts, locating health resources on maps, performing simple computations, and evaluating diverse health opinions. For example, the nutrition question asked participants to name 2 heart-healthy foods. This required reading recommendations and lists. The question for assistive technology involved online mapping skills, as participants were asked to locate a store near a specific address where grab bars could be purchased.
Example of an online health task and coding scheme for accuracy and specificity.
To examine the relative importance of age, education, health literacy, Internet use, and cognitive style for accuracy and success in online health searches, we analyzed a sample of 323 participants with complete data (93.1% of the 347 participants enrolled in the study) from both sites (128 from Johns Hopkins and 195 from Stanford).
Outcome measures included both overall accuracy (the number of accurate answers on each of the 6 search tasks) with a potential range of 0-6, and success—a scale that combined both accuracy (coded 0-1 for each item) and specificity (coded 0-2 for each item). The potential range for the success scale was 0-18. Models also were estimated for both outcomes with individual search tasks (range of 0-1 for accuracy and 0-2 for success). STATA 11 (StataCorp LP, College Station, TX, USA) was used to estimate generalized linear models (GLM), logistic regression, and ordinal logistic regression models as appropriate to the specific outcome.
Key predictors included an assessment of health literacy using the REALM [
Cognitive style was measured using the GEFT [
Participants’ ages ranged from 35-90 years, with a stratified design that ensured representation across broad age and sex groups (see
Participants in the Online Health Study (N=323).
Variable | Participants | |
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35-59 years | 190 (58.8) |
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60-69 years | 80 (24.8) |
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≥70 years | 53 (16.4) |
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Male | 130 (40.2) |
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Female | 193 (59.8) |
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High school or less | 113 (35.2) |
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College or more | 208 (64.8) |
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285 (88.3) | |
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REALM score | 128.5 (3.6) |
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Context sensitive | 190 (58.8) |
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Context independent | 133 (41.2) |
On average, participants answered 4.1 of 6 search tasks with no errors and 29 of 323 participants (9.0%) gave accurate responses to all 6 questions. Performance on individual tasks varied substantially, with the highest percentage giving error-free answers for the question about heart-healthy foods (95.4%, 308/323) and the lowest on the flu question (32.5%, 105/323) (see
Scores for accuracy and success for the online health task (N=323).
Topic | Search task/question | Accuracy, % | Success, |
Nutrition | Name 2 heart-healthy foods | 95.4 | 1.8 (0.5) |
Cancer | How do you identify skin cancer? | 81.7 | 1.6 (0.6) |
Alternative medicine | Can herbs help memory? | 59.1 | 0.7(0.6) |
Vaccinations | Who should get flu shots? | 32.5 | 1.2 (0.6) |
Assistive technology | Where can you find a store selling grab bars? | 75.9 | 1.6 (0.8) |
Genetic testing | Should genetic tests be sold over the counter? | 70.0 | 1.3 (0.8) |
Overall (all 6 items) |
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9.0 | 8.2 (1.8) |
Coefficients for generalized linear models predicting overall search success.
Variables | Success, coefficient (95% CI) | |||
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Model 1 | Model 2 | Model 3 | |
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35-59 years | Reference | Reference | Reference |
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60-69 years | –0.36 (–0.84, 0.11) | –0.37 (–0.84, 0.10) | –0.32 (–0.79, 0.15) |
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≥70 years | –0.71 (–1.28,–0.14) | –0.86 (–1.43, –0.29) | –0.78 (–1.34, –0.21) |
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Male | Reference | Reference | Reference |
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Female | 0.22 (–0.19, 0.62) | 0.17 (–0.23, 0.57) | 0.23 (–0.17,0.63) |
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High school or less | Reference | Reference | Reference |
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Some college or more | 0.51 (0.07, 0.94) | 0.44 (0.00,0.87) | 0.36 (–0.07, 0.80) |
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0.94 (0.35, 1.54) | 0.80 (0.21, 1.40) | 0.80 (0.02, 0.13) | |
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REALM score |
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0.09 (0.04, 0.15) | 0.08 (0.22,1.39) |
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Context independent |
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Reference |
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Context sensitive |
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–0.58 (–0.99, –0.18) |
Constant | 7.12 (6.47, 7.77) | –4.29 (–11.31, 2.72) | –2.36 (–9.45, 4.72) |
Odds ratios for ordered logistic regression models predicting success with specific online health-seeking tasks.
Variables | Online health-seeking task, OR (90% CI) | ||||||
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Nutrition | Cancer | Alternative medicine | Vaccinations | Mapping | Genetic testing | |
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35-59 years | Reference | Reference | Reference | Reference | Reference | Reference |
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60-69 years | 0.80 (0.47, 1.36) | 0.92 (0.57, 1.50) | 0.71 (0.46, 1.11) | 1.21 (0.76, 1.92) | 0.59 (0.34, 1.00) | 0.85 (0.55, 1.32) |
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≥70 | 0.59 (0.31, 1.10) | 0.76 (0.42, 1.38) | 0.60 (0.35, 1.03) | 0.78 (0.44, 1.40) | 0.44 (0.24, 0.83) | 0.71 (0.42, 1.22) |
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0.80 (0.50, 1.29) | 2.33 (1.54, 3.54) | 0.88 (0.60, 1.29) | 1.08 (0.73, 1.61) | 0.92 (0.58, 1.45) | 1.35 (0.93, 1.96) | |
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High school or less | Reference | Reference | Reference | Reference | Reference | Reference |
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College or more | 0.98 (0.59, 1.62) | 1.09 (0.69, 1.73) | 0.34 (0.22, 0.53) | 1.67 (1.08, 2.59) | 2.00 (1.23, 3.27) | 2.08 (1.40, 3.10) |
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1.77 (0.96, 3.25) | 2.27 (1.28, 4.04) | 3.73 (2.06, 6.75) | 1.32 (0.72, 2.43) | 0.84 (0.43, 1.62) | 1.10 (0.64, 1.90) | |
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1.02 (0.96, 1.09) | 1.04 (0.99, 1.11) | 0.95 (0.90, 1.01) | 1.07 (1.01, 1.13) | 1.04 (0.98, 1.11) | 1.11 (1.06, 1.17) | |
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Context independent | Reference | Reference | Reference | Reference | Reference | Reference |
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Context sensitive | 0.54 (0.33, 0.88) | 0.57 (0.37, 0.88) | 0.73 (0.50, 1.08) | 0.87 (0.58, 1.29) | 0.59 (0.37, 0.96) | 1.00 (0.68, 1.46) |
Health literacy and cognitive style were significant for different outcomes. A higher REALM score was positively associated with success on the search task about the flu vaccine and over-the-counter genetic tests. Context sensitivity was negatively associated with naming heart-healthy foods, identifying cancerous skin growths, and locating a store that sells grab bars. Context-sensitive participants were roughly half as likely to be successful compared to context-independent individuals
The increasing availability of online health resources would suggest improved patient education and knowledge; however, this outcome requires successful online health literacy. Patients need to retrieve and comprehend online health information in order for it to positively impact decision making and health care. Results from this study suggest that the oldest health seekers may be at a disadvantage compared to younger cohorts, even after controlling for technology use, education, health literacy, and cognitive style, especially when spatial tasks such as mapping are involved.
Cognitive style was hypothesized to be particularly important for success in online health seeking. Our results reveal that context sensitivity was associated with less success in obtaining online health information, with specific tasks involving visual judgment and mapping most affected. These results are consistent with the idea that individuals who are context sensitive will indeed be most greatly affected by tasks that involve spatial abilities (such as using mapping tools to find a store or understanding the visual illustrations of skin cancer). They also seem to perform worse in the relatively straightforward identification of heart-healthy foods, in part because they tend to be less specific and thus more vague about the types of food items that are recommended.
In addition, better health literacy seems important both for overall success in searching and specifically for questions requiring prior health knowledge, such as flu vaccine recommendations or the sale of over-the-counter genetic tests. Interestingly, those with greater education were less likely to answer the question about alternative medicine correctly. This is consistent with other research that has shown that complementary and alternative medicine has been taken up at greater rates by those with higher education levels who are more skeptical of the allopathic medical profession [
Study limitations should be acknowledged. Although the GEFT has been used extensively in studies of hypermedia learning especially to examine differences in website design and other graphical interfaces [
The present study raises issues concerning online health communication, suggesting that vulnerable populations may need targeted assistance if this is to be a primary source of information in decision making. Older cohorts as well as those with different cognitive styles had difficulties finding accurate, detailed information. When posed very specific questions, some people can easily find information; others are lost, even when the information seems readily apparent on a particular webpage.
It is difficult to control the online health information to which people will be exposed. Websites change continuously and search results can yield different outcomes over a course of a single day. Health educators and providers can potentially serve as a resource, providing guidance to reliable and credible websites that maintain appropriate standards of content and usability [
Adaptations to health information websites could reduce distracting content and highlight key sections, as this has been shown to help persons with context sensitivity [
However, the rising dominance of Google search engines as the primary interface for searching means that indexes and other website design tools may be receding in importance [
Group Embedded Figures Test
generalized linear model
Health Information National Trends Survey
Online Health Study
Rapid Estimate of Adult Literacy in Medicine
This research has been supported by NIH grant R01AG026430, with additional contributions from the Hopkins Population Center (R24HD042854) and the Hopkins Center for Population Aging and Health (P30AG34460.) The authors would like to thank Michele Trieb and several research assistants for their dedication to preparing data for this analysis.
None declared.