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J Am Med Inform Assoc. 2009 Sep-Oct; 16(5): 714–722.
PMCID: PMC2744722

Profiling Characteristics of Internet Medical Information Users

James B. Weaver, III, PhD, MPH, a , * Darren Mays, MPH, a , b Gregg Lindner, MA, c Doğan Eroğlu, PhD, a Frederick Fridinger, DrPH, a and Jay M. Bernhardt, PhD, MPH a



The Internet's potential to bolster health promotion and disease prevention efforts has attracted considerable attention. Existing research leaves two things unclear, however: the prevalence of online health and medical information seeking and the distinguishing characteristics of individuals who seek that information.


This study seeks to clarify and extend the knowledge base concerning health and medical information use online by profiling adults using Internet medical information (IMI). Secondary analysis of survey data from a large sample (n = 6,119) representative of the Atlanta, GA, area informed this investigation.


Five survey questions were used to assess IMI use and general computer and Internet use during the 30 days before the survey was administered. Five questions were also used to assess respondents' health care system use. Several demographic characteristics were measured.


Contrary to most prior research, this study found relatively low prevalence of IMI-seeking behavior. Specifically, IMI use was reported by 13.2% of all respondents (n = 6,119) and by 21.1% of respondents with Internet access (n = 3,829). Logistic regression models conducted among respondents accessing the Internet in the previous 30 days revealed that, when controlling for several sociodemographic characteristics, home computer ownership, online time per week, and health care system use are all positively linked with IMI-seeking behavior.


The data suggest it may be premature to embrace unilaterally the Internet as an effective asset for health promotion and disease prevention efforts that target the public.


Internet access is a widely diffused technology in the United States. Almost 80% of adults aged 18 years and older access the Internet, 1,2 and the majority (58%) use high-speed broadband connections. 3,4 Searching for and using health information online appears to be a prevalent activity among United States adults, with some estimates at 40–70%, or 90–160 million people. 3,5–7 Indeed, data suggest that from 2001–2007 the number of United States adults who used the Internet to seek health information nearly doubled 8 and that adults are more likely to seek health information online than from interpersonal sources, such as doctors, friends, family, and coworkers. 9 As a result, the Internet is broadly recognized as a potentially important tool for transforming medical care and public health. 10–12 Researchers conclude that the Internet offers tremendous promise as a health communication and education tool, 13,14 is an economical and effective part of chronic disease management, 15,16 and is a key resource in health behavior change interventions and programs. 17,18 In addition, information technologies such as the Internet are receiving even greater attention for their potential impact on public health and health care in health care reform policy discussions. 19

Inconsistencies exist, however, in the research that profiles individuals who engage in health information seeking behaviors (HISB) online, and these inconsistencies obstruct researchers from making accurate projections about the Internet's potential for health promotion and disease prevention. Both in peer-reviewed 10,20–24 and self-published 1,2,8,9 reports, for example, strong variability in HISB prevalence exists: estimates range from 13% to more than 80%. One explanation may be the different time frame referents used in questions about HISB. In some studies, 1,2 respondents were asked if they had “ever” used Internet health information. The prevalence estimates in these studies ranged from 70% to more than 80%. In studies 8–10,23–25 in which respondents had a narrower time frame (e.g., “over the last 12 months”), prevalence diminished substantially, from 40% to 60%.

In studies that asked respondents to consider both broad and narrow time frames, wide discrepancies also emerged. In one such study, 1 prevalence for the “ever” referent was 71%, whereas for the “in the last month” referent, prevalence was 53%, an 18% decrease. Another study 20 reported a 20% decrease in prevalence between “ever” and a time frame that was “every 2–3 months or more frequently within the last year.” While longer time frames are useful for formulating broad prevalence estimates, requiring respondents to recall exact behaviors over an extended period may introduce considerable response bias. Rather, limiting responses to a shorter time frame could help clarify online HISB and better inform time-sensitive, health promotion and disease prevention efforts.

Another inconsistency in past studies is in the way studies define “health information.” Examples abound, including “information or advice about health care” 10,14,20 , “information about a personal health concern” 8 , “health- and wellness-related information” 9 , and “health or medical information” 26 . Operationalizations as broad as these allow respondents to perceive “health information” in vague and ambiguous terms. Indeed, research 9 shows that the “health- and wellness-related topics” that Internet users search for include many areas related to health status, ranging from “symptoms” (~60%), “treatment” and “diseases/conditions” (~55–58%), “wellness” (i.e., nutrition, exercise, weight loss) and “drugs/medications” (~40–46%), and “vitamins/supplements” and “alternative medicines” (~20–30%). The topics also included “health insurance/healthcare providers”, “doctor/medical practices”, “care services” (i.e., hospitals, clinics, managed care), and “pharmaceutical companies” (all <20%; p. 9). Equally revealing, emerging evidence shows that the public distinguishes between general health information (e.g., wellness, vitamins, etc) and medical information (e.g., treatment, symptoms, diseases/conditions, etc) in their online health information seeking behavior. 27 Other evidence shows that the topics sought in online HISB vary as a function of a respondent's age, 28 health status, 21 or chronic conditions. 8,10,22,29 In light of these observations, it can be argued that many operationalizations of “health information” are problematic because they allow respondents tremendous latitude in ascribing meaning to a focal concept and, consequently, invite tremendous sample-specific response variability. 30

At the same time, the body of research that informs our understanding of Internet HISB reflects several major limitations, both in the targeted samples and in the sampling procedures. Baker et al. 10 for example, drew their conclusions from respondents to whom Internet access was provided as a participation incentive. Although the group took care to account for any consequences, the potential for bias cannot be overlooked. Sampling limitations also must shape interpretations of data drawn from the Health Information National Trends Survey 2005 23–26 and the Pew Internet and American Life Project. 31 For both, the solicitation of a nationally representative sample of adults for an extensive telephone interview yielded low response rates, about 21% and 27%, respectively. Lastly, for other reports, 1,8,9 sufficient methodological details are not available to assess accurately the sampling frame and its potential impact on findings.

Despite these limitations, some characteristics of individuals who seek health information online have emerged. 30,32 For instance, females are more likely to seek health information online, 9,33 as are those with higher educational achievement. 8–10,23,24 Specific health reasons (e.g., being diagnosed with a new health problem, ongoing medical conditions) are also linked with online information seeking. 29,32,34 For other sociodemographic characteristics, however, results are less consistent. Age, income, and race/ethnicity, for instance, have yielded mixed results in several studies. 8–10,21,23–25 Surprisingly, the basic measurements of computer and Internet use—factors thought to be strong determinants underlying online HISB 2 —are essentially missing in most peer-reviewed investigations. 10,25

Taken together, these limitations impede our understanding of the Internet as an asset for health promotion and disease prevention. To shed light on the Internet's potential as a channel, this study examined online use of “medical” information—a more specific operationalization than “health” information use—as the focal behavior. Several possible determinants of Internet medical information (IMI) use that have not been previously explored, including sociodemographics, computer and Internet use, and health care use, were examined via secondary analysis of survey data from a large sample representative of the Atlanta, GA, area.


Respondents and Setting

This study uses data collected in 2006 and 2007 from adult men (n = 2,394) and women (n = 3,725) who reside in the Atlanta, GA, designated market area (DMA). The data were drawn from Scarborough Research, 35,36 which conducts local market, Media Rating Council-accredited, consumer marketing surveys in 81 DMAs in the United States to examine new and traditional media use, health care system use, and lifestyle and consumer behaviors. Comparisons of individual and household demographic variables from 2006 to 2007 reveal only one significant trend, a 3.1% decline in home ownership, χ2(1, n = 6,119) = 4.8, p < 0.05. Consequently, the samples were combined for subsequent analyses (2006, n = 3,077; 2007, n = 3,042).

Sampling Procedure

The sampling frame included all households in the Atlanta DMA with a landline telephone and in which at least one adult, aged 18 years or older, resided. A DMA, as defined by Nielsen Media Research, is a measurement area to which a county is exclusively assigned on the basis of market-specific television viewing. The Atlanta DMA includes 52 counties in north Georgia, 2 counties in western Alabama, and 1 county in North Carolina.

Within this DMA, two sampling strata were defined: the Metro Survey Area (MSA) and the non-Metro area. The MSA corresponds closely to the Office of Management and Budget's Metropolitan Area. 37 Population estimates, annually updated by Claritas, 38 were used in sample balancing. Sample balancing variables included geography, age within gender, household size, education, race and ethnicity. Data were weighted and projected to adults aged 18 years or older in the Atlanta DMA.

Scarborough Research used a two-stage, random sampling procedure. In the first stage, households were selected with a systematic random sampling technique. In the second stage, an adult aged 18 years or older with the most recent (last) birthday was chosen as the designated respondent. Up to 16 attempts were then made to reach that individual.

Survey Methodology

Complete details about the survey methodology are available elsewhere. 39 Briefly, an eligible adult from each selected household completed a Computer Assisted Telephone Interview (CATI) that assessed information about demographics, health care usage, and media use including Internet behaviors. For the 2006 and 2007 surveys, the response rates for the CATI interview were 45.2 and 40.5%, respectively.



Demographic variables were assessed by individual and by household. Individual variables included gender, age, race/ethnicity, level of education, employment status, and marital status. [triangle] details the levels assigned within each measure. Household variables included annual household income, number of members, number of adults, number of children (aged 17 yrs or younger), and home ownership. These measures are detailed in [triangle].

Table 1
Table 1 Individual Demographic Determinants Underlying Internet Medical Information Use
Table 2
Table 2 Household Demographic Determinants Underlying Internet Medical Information Use

Internet Medical Information Use

The use of IMI was assessed via a question set asking respondents to “Check ways you used online/Internet services in the past 30 days.” “Medical services/information” was one of almost 3-dozen online/Internet services including “download/listen to music”, “e-mail”, “financial information/services”, and “pay bills.” A single dichotomous IMI use item was retained to indicate IMI use.

Computer and Internet Use

Four aspects of computer and Internet use were measured (see [triangle]). One item was used to assess Internet access: “Do you have access to the Internet? Include computers at work and home, portable electronic devices, etc.” Another assessed where respondents accessed the Internet in the past 30 days. The choices included home, work, school, public library, or other place; respondents checked all that applied. A “did not access Internet” option was also provided. Individuals who lacked Internet access or who had not accessed the Internet in the past 30 days were excluded from the study.

Table 3
Table 3 Internet and Computer Use Determinants Underlying Internet Medical Information Use

Respondents also estimated time spent online in an average week. The choices included none, less than 1 hour 1–4 hours 5–9 hours 10–19 hours, and 20 hours or more. Respondents indicated if they had home broadband connection; yes if their household used a cable modem or DSL or no if dial-up, other connection, or no Internet connection. Computer ownership was confirmed if a respondent or other household member owned a personal or home computer.

Health Care Use

In all, five measures were used to assess health care system use (see [triangle]). Three determined services use. Respondents were asked if they or anyone in their household had used services in the past 3 years at a hospital, at a nonhospital medical facility, and if during the past 12 months they or anyone in their household had visited a health care specialist (respondents selected from a list of specialists). Two measures determined medication use in the past 12 months. Respondents indicated if they had purchased any over-the-counter or prescription drugs (respondents selected from a list of reasons), and if they had purchased medicine or prescriptions on the Internet. For each of the five measures, a dichotomous variable reflecting any or no use was computed.

Table 4
Table 4 Health Care Utilization Determinants Underlying Internet Medical Information Use

Data Analyses

The SAS Version 9.2 (SAS Institute, Inc, Cary, NC) procedures incorporating stratification and weighting to accommodate complex sampling designs (e.g., PROC SURVEYFREQ, PROC SURVEYLOGISTIC) were used for data analyses. 40 Because Scarborough Research stratified sampling based on Metro Survey Area (MSA) and non-MSA residency status, residency within the MSA served as the sample stratification measure to account for this aspect of the survey design. 40 The data were also adjusted based on weights provided by Scarborough Research to compensate for both differential sample selection probabilities and population subgroup response rates. This analytic approach yielded estimates reflecting the adult population within the Atlanta DMA based on the survey sample.

Missing data management was undertaken by Scarborough Research which reported using a two-step approach. First, respondents were required to provide answers to several items (e.g., age, sex) for sample inclusion. Next, missing data due to item nonresponse were imputed using a line-item ascription procedure that apportioned estimates randomly to maintain initial response distributions.

Included in analyses were respondents in the combined 2006 and 2007 dataset who indicated they had accessed the Internet in the previous 30 days (62.6%; n = 3,829). Data analysis was conducted in two steps. First, bivariate analyses were used to compare IMI users with nonusers on the basis of demographics, Internet access, and health care use. Wald χ2 statistics were computed to examine the statistical significance of each comparison, and unadjusted odds ratios were calculated using logistic regression models. 41 Next, four logistic regression models were created to explain the relationships between the dependent variable, IMI use, and the independent variables including individual demographic characteristics (Model 1), household demographics (added in Model 2), Internet and computer use (added in Model 3), and health care use (added in Model 4). Log likelihood ratio tests were used to assess whether including each group of independent variables added significantly to the subsequent model. 42 The Akaike Information Criterion (AIC), a measure of goodness-of-fit of statistical models, was used to compare the amount of information explained across the models, with lower AIC models indicating improved model fit. 41


Internet medical information (IMI) use during the previous 30 days (n = 807) was reported by 13.2% of the entire sample (n = 6,119). When results were limited to those respondents who accessed the Internet in the past 30 days (n = 3,829), 21.1% of respondents reported IMI use.

Comparing Internet Medical Information Users and Non-users

Demographic Determinants

Significant differences in demographic characteristics emerged, including in age, gender, race/ethnicity, and marital status (see [triangle]). On the basis of unadjusted odds, respondents in older age groups were 1.5–2.7 times more likely to report IMI use than those in the referent group aged 18–24 years. Others more likely to report IMI use included females more than males and white respondents more than African Americans. Additionally, respondents who were married or currently unmarried were more likely to report IMI use than those who were never married. Education, income, and employment, by contrast, yielded no significant relationships. Differences in household demographics can be found in [triangle]. The results of t tests revealed that IMI users lived in households with fewer adults and fewer people overall than nonusers ([triangle]).

Internet Determinants

As [triangle] illustrates, all four measures of computer and Internet use yielded significant differences between IMI users and nonusers. For example, IMI users were more likely to own a home computer and have a household broadband connection. They also tended to spend 5 hours or more online per week. In general, the relationship between IMI use and time spent online per week grew stronger the more time respondents spent online ([triangle]). Finally, based on unadjusted odds, respondents who accessed the Internet in places other than work or home were less likely to report IMI use than those who accessed the Internet at work ([triangle]).

Health Care Use

As [triangle] illustrates, respondents who reported IMI use also reported greater health care system use across all five measures. Specifically, IMI users were more likely to have used a hospital or other medical facility services during the past 3 years, were more likely to have visited a specialist during the previous 12 months, and were more likely to have purchased medications both overall and via the Internet.

Explaining Internet Medical Information Use

In Model 1, IMI use was regressed on the individual demographic characteristics that bivariate analyses showed were significantly associated with IMI use: gender, age, race/ethnicity, and marital status. A respondent's level of education, which past research consistently associates with online health information seeking, was also included. When controlling for other variables in the model, results from Model 1 showed that those more likely to report IMI use included females, respondents aged 55–64 years and white respondents ([triangle]).

Table 5
Table 5 Multiple Logistic Regression Models Examining Internet Medical Information Use

In Model 2, household demographics were added to the logistic regression model. Three included the characteristics that bivariate analyses showed were significantly associated with IMI use: home ownership, number of adults, and number of children. The fourth was household income, which past research consistently associates with online health information seeking. When controlling for other variables in the model, results from Model 2 showed no significant associations between these variables and IMI use ([triangle]). Adding household demographics resulted in a minimal change in the AIC (AIC Δ = 0.004%). The significant relationships across individual demographics remained between Models 1 and 2.

Model 3 incorporated Internet and computer use measures into the logistic regression model. The results showed that, when controlling for other variables in the model (individual and household demographics), respondents who owned a computer were more than twice as likely to report IMI use as those who did not (OR = 2.18, 95% CI = 1.36–3.52, p < 0.05). Similarly, respondents who spent more than 4 hours online per week were more likely to report IMI use ([triangle]). Model 3 showed no significant associations between IMI use and either home broadband connection or place of Internet access. The significant relationships evident in Model 1 proved consistent in Model 3. The addition of computer and Internet use variables in Model 3 significantly improved the explanatory power of the model, resulting in an AIC Δ of 2.5%.

Including health care use variables into Model 4 revealed that respondents who used hospital or other medical facility services, or who visited a specialist, were more likely to report IMI use than those who did not ([triangle]). Furthermore, in the fully controlled model, respondents who purchased medications were more likely to report IMI use: those who had purchased any medications were nearly twice as likely (OR = 1.92, 95% CI = 1.16–3.19, p < 0.05) and those who had purchased medication on the Internet were more than twice as likely (OR = 2.23, 95% CI = 1.61–3.09, p < 0.05). Though health care use measures did not change substantially the significant relationships found in Model 1 or Model 3, the addition did significantly improve the explanatory power of the model, resulting in an AIC Δ of 3.4%. Because a small number of respondents indicated purchasing prescription medications online in the past 12 months and did not use IMI in the past 30 days, we also created the regression models with these respondents removed from the analysis. The point estimates and statistical decisions were unchanged, indicating that these respondents did not bias the results of the models reported.


The Internet's potential to bolster health promotion and disease prevention efforts has attracted considerable attention, but existing research leaves two things unclear: the prevalence of online medical information seeking and the distinguishing characteristics of individuals who seek that information. This study profiles adults who use IMI to shed light on the Internet's potential as a tool for health promotion and disease prevention and on some of the determinants associated with IMI use. In contrast to previous research, this study found much lower prevalence of IMI-seeking behavior. Additionally, logistic regression models suggested previously unrecognized relationships between IMI-seeking behavior and sociodemographic characteristics, computer and Internet use, and health care system use.

Discrepant Prevalence Estimates

Previous research, both peer-reviewed and self-published, estimates a strong prevalence of Internet health information use, from 40 8–10,23–25 to more than 80% of adult Internet users. 1,2 This study, consistent with Bundorf et al. 14 suggests that Internet Medical Information use measured in a shorter time frame (i.e., during the past 30 days) is much less prevalent—in our findings, about 13.2% of all respondents (n = 6,119) and 21.1% of respondents with Internet access (n = 3,289).

This discrepancy in prevalence rates raises important concerns. One is the time frame referent used in questions about information seeking. In most past research, respondents were asked to recall periods ranging from 12 months to “ever.” Though a longer time frame does provide details about prevalence, it may also tax a respondent's ability and motivation to provide accurate information and therefore result in strong measurement errors. Specifically, when required to provide information from memory, respondents frequently feel pressure to answer immediately, even if providing accurate information requires longer recall and calculation efforts. Typically, respondents estimate quickly on the basis of general experience (e.g., “I'm on the Internet every day, so I must have used health information some time over the last year.”). However, these estimates are commonly distorted by telescoping, a phenomenon where respondents include more events in a time frame than actually occurred. 43 This may suggest that past estimates of Internet information seeking are exaggerated. In contrast, this study used a much narrower time frame: Respondents were asked to consider their activities in the past 30 days.

Other studies, however, qualify this time frame explanation. One report that used a 30-day time frame found that approximately 53% of adults had “looked online for health information” 1 . This projection is, of course, larger than the one found in both the current study and the work of others using shorter time reference periods. 20 Unfortunately, sample characteristics and data collection techniques do not provide ready explanations for this discrepancy since each study used representative samples and well-established interview methodologies. Instead, an alternative explanation—that prevalence rate variability results from the diverse way in which the “health information” construct has been operationalized across this body of research—appears most plausible. Most past studies have used a combination of the terms “health−”, “wellness−”, and/or “health care−” information seeking and/or use to assess the focal behavior. Research shows, however, that Internet information seekers interpret these terms broadly to include concepts such as symptoms and treatment; nutrition, exercise, and weight loss; vitamins, supplements, and alternative medicines; and as health insurance and health care providers. 9 Unlike prior research, the current study used a very specific operationalization: Respondents were asked if they had used “medical services/information” from the Internet. This explanation is consistent with other studies 21,30,34 which show that specificity helps explain discrepancies in prevalence. Studies that use a “health” and/or “wellness” operationalization 1,9 yield much higher prevalence estimates than studies that use “health care” or “medical” information. 10,26

One conclusion to be drawn is that prevalence estimates in much past research are less compelling than previously thought. This seems particularly relevant as we deliberate what role the Internet might play in public health efforts and as we explore the relatively new “online health information use” phenomena.

Determinants of Internet Medical Information Use

Although IMI use reported in this study appears modest compared with past research, it still shows that many adults use the Internet each month to seek medical information. Building a coherent portrait of this audience is critical: Knowing what motivates segments of the population to seek IMI, including what types of IMI (e.g., wellness, medical), allows health professionals to design programs that target specific audiences with messages tailored to yield particular outcomes.

The results of this investigation shed considerable light on the determinants of IMI use, including previously unrecognized relationships between IMI-seeking behavior and sociodemographic characteristics, computer and Internet use, and health care system use. The strongest relationships involved health care system use. Past studies suggest that individuals with disabilities or chronic conditions and those with lower perceived health status are more likely to seek health information online. 10,21,29,30,32,34 The results of this study show that respondents who use health care services, whether for themselves or for a household member, are more likely to engage in IMI use. The measures used in this study were not indicators of self-reported health conditions; however, the relationships found suggest that health services use predicts IMI use more strongly than sociodemographic factors and computer and Internet use.

This result is important, particularly because past research suggests that many adults access health information online to self-diagnose, to seek information about alternative treatments or medicine, or to engage in health care strategies inconsistent with medical recommendations. 44,45 While only limited evidence shows that IMI use results in harmful health outcomes, 46 research does raise concerns about the accuracy and credibility of Internet health information. 5,46 Furthermore, trends indicate that IMI seekers are increasingly using peer-generated Internet sources, such as blogs and Wikis, that may contain content that is not credible or accurate. 9 Therefore, it becomes even more critical that we gain a strong understanding of how to reach populations whose health care decisions are susceptible to misinformation obtained online.

The sociodemographic and computer and Internet use determinants associated with IMI use reveal prominent differences between this study and past research, especially in variables such as age, race/ethnicity, household income, and education. Despite research that links race/ethnicity with both Internet access 11 and Internet health information-seeking behaviors, 47 many studies do not consider race/ethnicity. 10,25 So while other reports find that race/ethnicity is not significantly associated with seeking health information online, 29 the data in this study reveal the opposite, specifically that African Americans are less likely than their white counterparts to seek IMI. More importantly, this proved consistent across all four models. Even when controlling for possible confounders, such as sociodemographics, computer and Internet use, and health care system use, the results still show that African American respondents were much less likely than white respondents to use IMI. Furthermore, in contrast to previous studies, 10,25 IMI use was not significantly associated with sociodemographic factors, such as education and household income. Although these findings may reflect differences in the sampling and study methodologies, future research to explore these associations is needed.

Past research also suggests that access to a broadband connection is an important factor underlying health information seeking. 2,48 Indeed, some researchers have proposed reconceptualizing the “digital divide” (which focuses on differences between individuals with and without Internet access), arguing that it is more accurate now to speak of a schism in access speed (i.e., dial-up connection v. broadband connection). 48,49 However, after controlling for sociodemographic factors, the data in this study show that respondents who own a home computer and spend more time online are more likely to report IMI use. Home broadband access did not emerge as significant. Assessing computer and Internet use, a measure lacking in most past studies, 10,25 is potentially very important for future studies.


While the findings of this study are meant to stimulate discussion about the role of the Internet in health promotion and disease prevention, there are some limitations. The sample included adults living in a large, geographic area around Atlanta, GA, and consequently the results may have limited generalizability to the broader United States population. Additionally, although the data were produced using a rigorous methodology, they are from cross-sectional, self-report assessments and, as a result, may reflect certain biases. Furthermore, the sampling frame, while randomly generated, was limited to respondents in households with landline telephone service. The exclusion of adults living in cell phone-only households could have produced a bias, especially among younger respondents. 50,51 Finally, the measure that was used to capture IMI use is not without some limitations. For example, this brief measure does not capture specifically what types of medical information respondents were seeking, or if they found the information that was desired. Clearly, these considerations must be acknowledged when interpreting the findings.


Undoubtedly, the Internet is a promising tool for public health and health care 10–12 and a potentially effective platform for health communication and education. 13,14 As our results show, it may be premature, however, to embrace the Internet unilaterally as an effective asset for efforts that target broad segments of the public. Because the sample in this study was limited to adults from one major United States metropolitan area, additional research is needed to explore the relationships examined here among larger samples that are more representative of the United States adult population. While the IMI use measure applied in this study was more specific than the general “health information” measures in prior research, future research is needed to establish reliable, valid assessments of different dimensions of health information seeking behaviors (e.g., medical, wellness, and other types of information). Further research is also needed to explore the factors that motivate specific populations to seek information online. Such research must examine the constructs used to assess behaviors, including an individual's perceptions of these constructs, also how measurement may impact prevalence estimates. Future research should also examine in-depth any determinants that may impact online information seeking. Finally, more research is needed to identify how best to counteract any adverse consequences that could result from health and medical information gained from the Internet.


The authors are indebted to Jennifer Cadoret, Richard E. Dixon, and Marinella Macri for their significant contributions to this project. This research was supported in part by Scarborough Research and by the appointment of the first and second authors to the Research Participation Program at the Centers for Disease Control and Prevention administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the United States Department of Energy and CDC.

The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention or the United States Department of Health and Human Services.


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