This paper is in the following e-collection/theme issue:

Information Retrieval (287) Consumer & Patient Education and Shared-Decision Making (679) Information Seeking, Information Needs (465)

Published on in Vol 17, No 12 (2015): December

Consumer Use of “Dr Google”: A Survey on Health Information-Seeking Behaviors and Navigational Needs

Consumer Use of “Dr Google”: A Survey on Health Information-Seeking Behaviors and Navigational Needs

Consumer Use of “Dr Google”: A Survey on Health Information-Seeking Behaviors and Navigational Needs

Authors of this article:

Kenneth Lee1 Author Orcid Image ;   Kreshnik Hoti1 Author Orcid Image ;   Jeffery David Hughes1 Author Orcid Image ;   Lynne M Emmerton1 Author Orcid Image
Article Authors Cited by (131) Tweetations (76) Metrics

    Original Paper

    Curtin University, School of Pharmacy, Curtin University, Perth, Australia

    Corresponding Author:

    Lynne M Emmerton, BPharm (Hons), PhD

    Curtin University

    School of Pharmacy

    Curtin University

    GPO Box U1987

    Perth, 6845

    Australia

    Phone: 61 892667352

    Fax:61 892662769

    Email: lynne.emmerton@curtin.edu.au


    Background: The Internet provides a platform to access health information and support self-management by consumers with chronic health conditions. Despite recognized barriers to accessing Web-based health information, there is a lack of research quantitatively exploring whether consumers report difficulty finding desired health information on the Internet and whether these consumers would like assistance (ie, navigational needs). Understanding navigational needs can provide a basis for interventions guiding consumers to quality Web-based health resources.

    Objective: We aimed to (1) estimate the proportion of consumers with navigational needs among seekers of Web-based health information with chronic health conditions, (2) describe Web-based health information-seeking behaviors, level of patient activation, and level of eHealth literacy among consumers with navigational needs, and (3) explore variables predicting navigational needs.

    Methods: A questionnaire was developed based on findings from a qualitative study on Web-based health information-seeking behaviors and navigational needs. This questionnaire also incorporated the eHealth Literacy Scale (eHEALS; a measure of self-perceived eHealth literacy) and PAM-13 (a measure of patient activation). The target population was consumers of Web-based health information with chronic health conditions. We surveyed a sample of 400 Australian adults, with recruitment coordinated by Qualtrics. This sample size was required to estimate the proportion of consumers identified with navigational needs with a precision of 4.9% either side of the true population value, with 95% confidence. A subsample was invited to retake the survey after 2 weeks to assess the test-retest reliability of the eHEALS and PAM-13.

    Results: Of 514 individuals who met our eligibility criteria, 400 (77.8%) completed the questionnaire and 43 participants completed the retest. Approximately half (51.3%; 95% CI 46.4-56.2) of the population was identified with navigational needs. Participants with navigational needs appeared to look for more types of health information on the Internet and from a greater variety of information sources compared to participants without navigational needs. However, participants with navigational needs were significantly less likely to have high levels of eHealth literacy (adjusted odds ratio=0.83, 95% CI 0.78-0.89, P<.001). Age was also a significant predictor (P=.02).

    Conclusions: Approximately half of the population of consumers of Web-based health information with chronic health conditions would benefit from support in finding health information on the Internet. Despite the popularity of the Internet as a source of health information, further work is recommended to maximize its potential as a tool to assist self-management in consumers with chronic health conditions.

    J Med Internet Res 2015;17(12):e288

    doi:10.2196/jmir.4345

    Keywords

    online; health information; health literacy; patient activation; information seeking; information needs; Internet; chronic disease; patients; survey 


    The Internet offers a wealth of information on numerous topics. Its pervasiveness in everyday life means it is a common source of information for many consumers [1]. Many consumers use it to obtain health-related information [2-5]. Accordingly, a number of studies have examined the role of the Internet in health care and its influence on the traditional relationship between consumers and their health professionals [6-10]. Traditionally, health professionals have been the primary source of health information, providing information through patient education [11]. Consumers are now afforded greater access to information, have greater potential to be more informed, and are able to play a greater role in caring for their health [11].

    Consumers also play an important role in health care, particularly given a trend towards greater burden of chronic health conditions [12]. Such conditions often require daily self-management. In Australia, annual expenditure on chronic health conditions is estimated at AUD $11.0 billion [13]. Internationally, a number of chronic health conditions have been listed in the top 10 leading causes of mortality [14]. Consequently, initiatives should focus on supporting consumers with chronic health conditions to better manage their conditions.

    The popularity of the Internet for health-related purposes enables its use to support self-management. Indeed, numerous studies have examined the popularity of Internet use as a source of health information [2,3,5,15-17]. In the United States, 80% of Internet users use it for health information [3]. It appears that the use of the Internet for health information is more popular in Internet users with chronic health conditions or disabilities compared to Internet users without chronic health conditions or disabilities [18]. While fewer data are available within the Australian context, a 2010 study [5] suggested that almost 80% of Internet users in Australia access the Internet for health information. Despite the popularity of its use for health information, a number of studies have identified barriers to accessing Web-based health information [19-22]. The volume of health information available on the Internet [19-22], the abundance of poor quality information [19,20,23], and the lack of strict publishing guidelines [19] are some examples. Furthermore, a 2001 review on consumers’ Web-based health-information seeking identified factors contributing to potential misinformation and subsequent potential for harm if consumers were to access and act upon misleading information [19]. Hence, there is a need to better understand consumers’ Web-based health information-seeking behaviors (HISB) to better support consumers in their self-management.

    Numerous studies have explored the characteristics of consumers’ Web-based HISB [3-5,23-44]. However, within the context of consumers with chronic health conditions, the majority of studies appear to focus on specific chronic health conditions [24,26,28-32,34-38], age [33,41], or ethnic groups [40], or they involve general populations that include consumers without chronic health conditions [3-5,23,25,27,39]. The applicability of findings from such studies to other populations may be limited. We believe that exploration of Web-based HISB in a population of health information consumers with a variety of chronic health conditions can facilitate identification of general characteristics or trends of Web-based HISB; such characteristics can then be compared to existing and future studies that focus on specific populations.

    A qualitative study was recently conducted using consumers of Web-based health information who identified as having one or more chronic health conditions [20]. This study explored the Web-based HISB of its participants and identified a number of potentially related characteristics. However, the applicability of these characteristics to a wider population is unknown. While a large-scale quantitative study has explored the characteristics of consumers with chronic health conditions and the proportion of Internet and non-Internet users [42], no large-scale quantitative studies examine the breadth of HISB characteristics reported by consumers in the aforementioned qualitative study [20]. For example, previous studies have examined characteristics such as the frequency of Internet use for health-related purposes [45] and consumers’ experiences with locating Web-based health information [46]. Within the context of health information consumers with a variety of chronic health conditions, characteristics of Web-based HISB such as the types of health information sought on the Internet and reasons for seeking Web-based health information have yet to be quantitatively determined.

    Related to Web-based HISB, as identified by [20], are the concepts of health literacy, eHealth literacy, and patient activation. Numerous studies have identified health literacy [47-51] and eHealth literacy [52,53] as important skills in locating, accessing, and utilizing quality health information for health care management. Patient activation is defined as patients’ belief that they “have important roles to play in self-managing care, collaborating with providers, and maintaining their health. They know how to manage their condition and maintain functioning and prevent health declines; and they have the skills and behavioral repertoire to manage their condition, collaborate with their health providers, maintain their health functioning, and access appropriate and high-quality care” [54]. Some evidence supports a statistically significant relationship between health literacy and patient activation [55,56]. However, to our knowledge, no study to date has examined the relationship between eHealth literacy and patient activation.

    Despite the aforementioned barriers to acquiring desired Web-based health information, health information seeking using the Internet remains a prevalent activity. Thus, beyond understanding consumers’ Web-based HISB, eHealth literacy, and patient activation, researchers have not yet explored whether consumers have difficulty finding, and indicate a desire for support to find, Web-based health information (ie, navigational needs). While findings from a qualitative study suggest a potential need for support interventions among consumers [20], the applicability of this finding to a wider population has yet to be determined. Furthermore, there are no studies examining potential determinants or predictors of navigational needs. Once an understanding of navigational needs and an estimate of the proportion of the population with navigational needs is ascertained, future studies can then explore consumers’ preferences for support interventions within and between various populations, such as populations with specific chronic health conditions, which better support consumers in their self-management.

    Thus, this study aims to address the following objectives: (1) estimate the proportion of consumers with navigational needs among consumers of Web-based health information living with chronic health conditions, (2) describe the following characteristics of consumers with navigational needs: Web-based HISB, patient activation, and eHealth literacy, and (3) explore variables predicting navigational needs of these consumers.


    Overview

    A Web-based questionnaire was developed via the Qualtrics platform to identify the proportion of consumers with navigational needs and to explore their demographics, Web-based HISB, eHealth literacy, and patient activation.

    Ethical approval for this study was granted by the Curtin University Human Research Ethics Committee (HR06/2013).

    Participants and Recruitment

    The target population for this study was adult Web-based health information consumers with chronic health conditions residing in Australia. Participants were included in this study if they consented to the study and indicated they met the following criteria: (1) able to easily read and write in English, (2) aged 18 years or older, (3) use of the Internet to find information about their health, and (4) have at least one chronic health condition.

    Recruitment was conducted by Qualtrics through their partnership with a Web-based survey research company, ResearchNow, which hosts a large diverse pool of participants and has the ability to select representative samples meeting specified eligibility criteria [57].

    Sample Size

    The sample size was determined using conservative parameters for prevalence studies [58]—our focus for prevalence estimation being the proportion of the target population with navigational needs (Objective 1). In the absence of literature reporting this prevalence, we used the following parameters: expected population proportion of 50%, 95% confidence interval, and a level of precision of estimate within 5% either side of the true population proportion. These parameters indicated a required sample size of 385 participants [58]. To account for potential invalid responses, the required sample was increased to 400 participants (a level of precision of 4.9% either side). This sample size was also deemed adequate to conduct descriptive and inferential statistical analyses to address the other objectives. The research company was contracted to meet the quota of 400 submitted questionnaires.

    Questionnaire Development

    Initial Questionnaire Construction

    Questions and response items pertaining to navigational needs and Web-based HISB were predominantly drawn from interview questions and participant responses from a qualitative study [20] of health consumers with chronic health conditions who used the Internet. To ensure that questions asked verbally in the aforementioned qualitative study [20] were suitable for a written questionnaire, the wording of the interview questions was modified by the primary researcher with review from the other researchers on the research team. Similarly, decisions for choosing which interview questions were to be included as survey questions were made by the primary researcher in collaboration with the research team. Further items were added to supplement these questions and facilitate statistical analysis after discussion with all authors. Question types were a mix of 5-point Likert-type scales and multiple-response, multiple-choice options. Where relevant, multiple-choice items facilitated “other” responses to be typed and later manually coded for analysis. To mitigate the potential for selection bias within questions, the order of response items within each multiple-choice question was randomized where appropriate [59]. To reduce the number of questions and therefore respondent fatigue, adaptive questioning was used [59].

    The eHealth Literacy Scale (eHEALS), a measure of perceived eHealth literacy [60], and PAM-13 [61], a measure of patient activation, were used to assess eHealth literacy and patient activation, respectively. Both of these scales had been assessed for validity and reliability [54,60-68] and were incorporated with permission from their respective authors/licensors.

    Pilot Test

    A target of 40 completed responses (10% of the final sample) was used to pilot test the questionnaire. Participants recruited for this stage were to meet the same eligibility criteria as our test sample and were recruited by Qualtrics via ResearchNow. Participants from the pilot sample were excluded from participation in the test sample to mitigate response bias.

    The purposes of pilot testing were to assess comprehension of questions and response items and to examine questions with invalid or poor responses. Participants were encouraged, in space provided after each question, to provide comments regarding the comprehensibility of questions and response items.

    Questionnaire Refinement

    Based on participant feedback in the pilot test, a number of amendments were made to the wording of questions and response items, along with presentation of the questions for completion in Web-based format. First, the questionnaire enabled “attention-filter” questions; thus, response items were added to identify invalid responses (eg, “I am paying attention; please select ‘disagree’ for this line”). Three attention filters were included in this questionnaire: two questions instructed participants to select a certain option, and one response item instructed participants not to select the item. These attention filters were inserted into parts of the questionnaire that required longer attention spans (eg, long questions or questions with numerous response items). Second, wording of questions with lower response rates were revised, and these questions were marked as forced responses where possible to facilitate statistical analysis. To ensure participants were permitted to respond with “not applicable” for forced response questions, an “Other” option was provided wherever possible, with free-text space to explain their situation. Third, the mean survey completion time from the pilot test was relayed to Qualtrics to determine a “duration filter” for the test sample. The time parameter for the duration filter was calculated to be one-third of the mean pilot questionnaire completion time, as recommended by Qualtrics, and excluded participants who completed the questionnaire in a shorter-than-expected time. All questions and response items were examined by the research team to ensure readability and face validity prior to survey administration.

    A Flesch-Kincaid Grade Level test [69] was conducted to test the readability of the questionnaire, including the informed consent and eligibility screening page, to compare to participants’ reported level of education.

    Reliability Testing

    A subset of 48 participants (approximately 10%, allowing extra in the case of delays in acceptance or questionnaire completion) was invited 2 weeks after completion of the questionnaire to retake the questionnaire, to confirm the test-retest reliability of the eHEALS and PAM-13 against reported values.

    Analysis

    Overview

    All statistical analyses were conducted using SPSS version 21. Descriptive statistics were used to address Objective 1. Descriptive statistics, Pearson correlation, and multivariate linear regression were utilized to address Objective 2. Bivariate and multivariate binary logistic regressions were conducted to address Objective 3. Scores for the eHEALS and PAM-13 were calculated as per the authors’ instructions and were used in the regression modeling (Objectives 2 and 3).

    All variables to be tested in the regression analyses were entered via a forced-entry method, as this method is more stable against random variation in the data, compared to other methods such as stepwise methods [70]. Demographic variables of age, sex, and level of education were entered alongside the other test variables, as these variables have been identified as potential contributors to the usage of Web-based health information [40,71]. The demographic variable examining residence in major cities or rural areas was also included in the regression model, as rurality has been identified as a potential barrier to Internet access [5]. Given the categorical nature of our demographic variables, categories with low or zero frequencies were aggregated with other categories, where logical, to allow for valid statistical conclusions. To illustrate, for the “age” variable (see Table 1), less than 1% of participants indicated that they were above the 55-64 years age category; the categories 65-74, 75-84, and 85+ were therefore combined with the 55-64 age category and relabeled as 55+ for inferential statistical analysis. Similarly, for the remoteness of residence variable (see Table 1), few participants indicated that they reside in remote areas; this category was aggregated with rural or regional areas to allow for comparison between major city areas versus rural/regional/remote. Such decisions for aggregating categories were made by the primary researcher in discussion with all other researchers within the research team. The level of significance (alpha) was set at P<.05.

    Table 1. Demographic descriptors of respondents (N=400).
    CategoryNo navigational needs (N=195),
    n (%)    		Navigational needs (N=205),
    n (%)    		Total,
    n (%)
    Sex



    		Male73 (37.4)82 (40.0)155 (38.8)

    		Female122 (62.6)123 (60.0)245 (61.3)
    Age group (years)

    		18-2422 (11.3)22 (10.7)44 (11.0)

    		25-3449 (25.1)71 (34.6)120 (30.0)

    		35-4435 (17.9)36 (17.6)71 (17.8)

    		45-5452 (26.7)30 (14.6)82 (20.5)

    		55-6436 (18.5)44 (21.5)80 (20.0)

    		65-741 (0.5)2 (1.0)3 (0.8)

    		75-840 (0.0)0 (0.0)0 (0.0)

    		≥850 (0.0)0 (0.0)0 (0.0)
    Level of formal education

    		No formal education0 (0.0)0 (0.0)0 (0.0)

    		Primary school2 (1.0)0 (0.0)2 (0.5)

    		Junior high school21 (10.8)13 (6.3)34 (8.5)

    		Senior high school38 (19.5)47 (22.9)85 (21.3)

    		TAFE or technical college53 (27.2)62 (30.2)115 (28.8)

    		University81 (41.5)83 (40.5)164 (41.0)
    Remoteness of residence

    		Major city area122 (62.6)144 (70.2)266 (66.5)

    		Rural or regional area69 (35.4)61 (29.8)130 (32.5)

    		Remote area4 (2.1)0 (0.0)4 (1.0)
    Navigational Needs

    The term “navigational needs” has been used above and refers to individuals who report having difficulty finding, and would like support in locating, desired Web-based health information. As no objective measure of navigational needs is available in the literature, we operationally defined the term as individuals who identified that they at least “sometimes” have difficulty locating desired Web-based health information (Criterion 1) and indicated that they would like help locating desired Web-based health information (Criterion 2).

    These participants were considered a subset of the total respondents for the purposes of data analysis and were descriptively compared (Objectives 1 and 2). For Objective 3, this subset was compared to the remainder of the sample using binary logistic regression to determine predictors of navigational needs.

    Reliability Tests

    Statistical procedures to test the reliability of the eHEALS and PAM-13 [60,62,64-68] were replicated in our study (Objective 2, patient activation and eHealth literacy). These tests included internal consistency (Cronbach alpha) and intraclass correlation coefficient (ICC). ICC was assessed via a two-way mixed effects model [72] using an absolute agreement definition, ICC (3,1). This decision was made given the self-reported nature of our questionnaire and our intent to assess the agreement of participant responses to both PAM-13 and eHEALS between test and retest. Results from each of these tests were considered alongside relevant guidelines to assist interpretation [72,73].


    Summary

    The survey was conducted during May 2014. In order to obtain our target of 400 submitted questionnaires, a total of 1104 individuals were invited by ResearchNow from their diverse participant pool. Of these 1104 individuals, 1027 agreed to participate (93.03% consent). Of the 1027 individuals, 514 individuals (50.05%) met our eligibility criteria, and 400 (77.82%) completed the questionnaire.

    In the retest sample 2 weeks post-completion, 47 of the 48 participants contacted agreed to participate again (98% consent). Of these, 43 completed the questionnaire a second time (91% completion).

    The Flesch-Kincaid Grade Level for our questionnaire, including the informed consent and eligibility screening questions, resulted in a readability score of 8.0.

    Proportion of Consumers With Navigational Needs

    As established above, participants were operationally defined as having navigational needs if they met both Criterions 1 and 2. To assess Criterion 1, participants were asked to rate, on a 5-point Likert-type scale (Never, Rarely, Sometimes, Most of the Time, Always), how often they have difficulty finding desired Web-based health information. A total of 216 participants (54.0%) indicated that they experienced difficulty at least sometimes, thereby meeting Criterion 1.

    To assess Criterion 2, participants indicated whether they would like help finding desired Web-based health information. A total of 365 participants (91.3%) met this criterion.

    A total of 205 participants (51.3%) met both Criteria 1 and 2 for navigational needs. The estimated proportion of consumers with navigational needs among consumers of Web-based health information living with chronic health conditions was thus estimated at 51.3% (95% CI 46.4%-56.2%).

    Demographic Characteristics

    Of the 400 participants, 61.3% were female (245/400), 41.0% reported having a university-level of education (164/400), and 66.5% (266/400) reported being located in major city areas within Australia (see Table 1). Descriptive comparisons of the demographics of participants with and without navigational needs are included in Table 1; significance testing of these comparisons as potential predictors of navigational needs is illustrated later. Overall, demographic characteristics between participants with and without navigational needs appear comparable (see Table 1). Noteworthy exceptions include a higher proportion of participants with navigational needs who were aged 25-34 years old compared to participants without navigational needs (34.6% vs 25.1%), and a lower proportion of participants with navigational needs who were aged 45-54 years old compared to participants without navigational needs (14.6% vs 26.7%). Reported chronic health conditions varied widely, with conditions involving the major organs most prevalent (see Multimedia Appendix 1).

    Web-Based Health Information-Seeking Behaviors

    Descriptive comparisons of the Web-based HISB between participants with and without navigational needs are provided in Tables 2-4 (as well as Multimedia Appendices 2-4). Significance testing was not performed, as multiple-response items did not allow variables to be analyzed independently.

    The categories of health information reportedly sought varied considerably; however, participants with navigational needs appeared to look for more types of health information compared to participants without navigational needs (see Multimedia Appendix 2). Similarly, when comparing participants with and without navigational needs, participants with navigational needs appeared to use more sources of Web-based health information (see Multimedia Appendix 3).

    Most commonly, participants sought information on the Internet to be more informed and engaged in their self-care (see Table 2). In comparing participants with and without navigational needs, we found that participants with navigational needs appear to seek Web-based health information because they are less satisfied with their health professionals, but less interested in wanting to manage their own conditions (see Table 2). However, more participants with navigational needs appeared to act on the acquired health information compared to participants without navigational needs (see Multimedia Appendix 4).

    Table 2. Why Web-based health information is sought (N=400).
    Reason for seeking Web-based health informationNo navigational needs (N=195),
    n (%)a    		Navigational needs (N=205),
    n (%)a    		Total,
    n (%)a
    I want to be more informed.155 (79.5)169 (82.4)324 (81.0)
    I want to help manage my own condition.143 (73.3)127 (62.0)270 (67.5)
    I want to clarify information that has been given to me by a health professional.114 (58.5)109 (53.2)223 (55.8)
    Just out of interest.105 (53.8)107 (52.2)212 (53.0)
    I want to check information that was discussed during a consultation with a health professional.89 (45.6)109 (53.2)198 (49.5)
    I want to look for alternative or additional treatment options.94 (48.2)98 (47.8)192 (48.0)
    I want to have information to read.91 (46.7)93 (45.4)184 (46.0)
    I find there is limited time during a consultation with a health professional.48 (24.6)69 (33.7)117 (29.3)
    I am not provided with enough information during a consultation with a health professional.38 (19.5)61 (29.8)99 (24.8)
    I disagree with certain points made by a health professional.17 (8.7)24 (11.7)41 (10.3)
    Other12 (6.2)11 (5.4)23 (5.8)

    aRespondents could select multiple options; percentages do not total 100%.

    Most of the participants, 94.5% (378/400) reported that they discussed health information sourced on the Internet with health professionals at least some of the time. Reasons for this behavior are suggestive of seeking professional opinion, along with a desire to engage further in self-management (see Table 3). Such reasons for discussing Web-based health information with health professionals appear comparable between participants with and without navigational needs. A notable exception is that a greater proportion of participants with navigational needs have discussions with health professionals to “find out more information” compared to participants without navigational needs.

    Table 3. Reasons why health information obtained on the Internet is discussed with health professionals (N=378).
    Consultation with health professionalsNo navigational needs (N=181),
    n (%)a    		Navigational needs (N=197),
    n (%)a    		Total,
    n (%)a
    I want to get the health professional’s opinion on information that I found on the Internet.123 (68.0)130 (66.0)253 (66.9)
    I want to find out more information.98 (54.1)121 (61.4)219 (57.9)
    I want to be in control of the management of my health condition(s).95 (52.5)101 (51.3)196 (51.9)
    I trust the health professional.81 (44.8)81 (41.1)162 (42.9)
    I want to discuss alternative treatments, tests, or procedures.79 (43.6)74 (37.6)153 (40.5)
    I want to clarify information that was unclear on the website(s) that I visited.67 (37.0)80 (40.6)147 (38.9)
    Other3 (1.7)5 (2.5)8 (2.1)

    aRespondents could select multiple options; percentages do not total 100%.

    Similarly, 98.8% (395/400) of the participants reported that they do not discuss health information sourced from the Internet with health professionals at least some of the time. Common reasons reported for not always discussing Web-based health information with health professionals relate to not wanting to embarrass oneself in front of health professionals and a belief that health professionals do not have the time to discuss health information sought on the Internet (see Table 4).

    Table 4. Reasons why health information obtained from the Internet may not be discussed with health professionals (N=395).
    Reason for not discussing with health professionalsNo navigational needs (N=193),
    n (%)a    		Navigational needs (N=202),
    n (%)a    		Total,
    n (%)a
    I do not want to embarrass myself in front of my health professional.58 (30.1)75 (37.1)133 (33.7)
    I do not think that health professionals have enough time to discuss what I find on the Internet.53 (27.5)71 (35.1)124 (31.4)
    I feel that I have enough information already.64 (33.2)59 (29.2)123 (31.1)
    I do not want to upset my health professional.35 (18.1)52 (25.7)87 (22.0)
    Other37 (19.2)23 (11.4)60 (15.2)

    aRespondents could select multiple options; percentages do not total 100%.

    Patient Activation and eHealth Literacy

    Summary

    Tables 5 and 6 describe the patient activation and eHealth literacy scores based on the PAM-13 and eHEALS, respectively. Compared to participants without navigational needs, participants with navigational needs appear, on the whole, to be less activated (see Table 5) and have a lower level of eHealth literacy (see Table 6).

    Table 5. Summary statistics: PAM-13 scores.
    PAM-13 score (0.0-100.0)No navigational needs (N=195)Navigational needs (N=204)Total (N=399)a
    Mean (SD)63.1 (12.5)58.9 (13.3)61.0 (13.1)
    Median60.658.158.1
    Mode55.663.155.6
    Range24.1-100.035.5-100.024.4-100.0

    aScore could not be calculated for one participant due to invalid responses.

    Table 6. Summary statistics: eHEALS scores.
    eHEALS Score (8.0-40.0)No navigational needs (N=195)Navigational needs (N=205)Total (N=400)
    Mean (SD)31.0 (4.1)28.2 (4.2)29.5 (4.3)
    Median31.028.030.0
    Mode32.032.032.0
    Range16.0-40.015.0-40.015.0-40.0
    Associations

    Correlations between PAM-13 and eHEALS scores revealed a positive, moderate association (r=.50, P<.001) (see Table 7). After inclusion of sex, age group (compared to the “55+” reference group), education (university vs no university level of education), and place of residence (major city vs rural) variables into a multivariate model, the only statistically significant predictor of PAM-13 scores was eHEALS scores (P<.001).

    Table 7. PAM-13 score vs eHEALS score, and demographic variables.

    		BSE Bβ
    Constant18.014.05—
    Age groups

    		18-24 (vs 55+)-1.482.19-.04

    		25-34 (vs 55+)-1.031.69-.04

    		35-44 (vs 55+)-2.891.89-.08

    		45-54 (vs 55+)-1.341.76-.04
    Female-1.801.21-.07
    University education1.451.21.05
    Living in major city-0.851.27-.03
    eHEALS Score1.530.130.51a
    (R2=.27, Adj. R2=.25)


    aP<.001

    Reliability Tests

    Internal consistency for the PAM-13 and eHEALS were assessed via Cronbach alpha for the test sample (n=400) and the retest sample (n=43). Relative test-retest reliability was assessed using ICC (3,1) to assess the overall test-retest reliability of the subset of the test sample (ie, n=43 from n=400) on retest. Results for the reliability tests indicate good-to-excellent internal consistency and excellent test-retest reliability (see Table 8).

    Table 8. Reliability statistics for the PAM-13 and eHEALS.

    		Cronbach αICC (3,1) (95% CI)
    eHEALS

    		Test (n=400).87N/A

    		Test/Retest (n=43).92/.91.79 (0.65-0.88)
    PAM-13

    		Test (n=400).86N/A

    		Test/Retest (n=43).92/.88.86 (0.75-0.92)

    Predictors of Navigational Needs

    After inclusion of age, sex, level of education (university vs no university level education), place of residence (major city vs rural), the PAM-13 score, and the eHEALS score into a multivariate model, only age (P=.02)—specifically, the 45-54 age group (P=.048)—and the eHEALS score (P<.001) were statistically significant predictors of navigational needs (see Table 9).

    Overall, the predictor variables (demographic variables, PAM-13, and eHEALS scores) used in this binary logistic regression analysis explained 18.7% of the variance in having navigational needs, measured using Nagelkerke’s R2[70].

    Table 9. Predictors of navigational needs.
    PredictorsNavigational needs (N=205)aNo navigational needs (N=195)aOR (95% CI)Adjusted OR (95% CI)
    Age group

    		18-2422 (10.7)22 (11.3)0.80 (0.39-1.67)0.94 (0.42-2.11)

    		25-3471 (34.6)48 (24.6)1.17 (0.66-2.05)1.54 (0.81-2.92)

    		35-4436 (17.6)35 (17.9)0.83 (0.44-1.56)0.96 (0.48-1.96)

    		45-5430 (14.6)52 (26.7)0.46 (0.25-0.87)b0.51 (0.26-0.99)c

    		55+ (reference group for “age group” variable)46 (22.4)37 (19.0)——
    Female123 (60.0)122 (62.6)0.90 (0.60-1.34)0.98 (0.62-1.55)
    University education83 (41.1)81 (41.5)0.96 (0.64-1.43)0.95 (0.60-1.50)
    Living in major city145 (40.5)121 (62.1)1.41 (0.93-2.15)1.33 (0.83-2.15)
    eHEALS score, mean (SD)28.2 (4.2)31.0 (4.1)0.84 (0.80-0.89)d0.83 (0.78-0.89)d
    PAM-13 score, mean (SD)58.9 (13.3)63.1 (12.5)0.98 (0.96-0.99)e1.00 (0.98-1.02)

    aValues presented as n (%) unless otherwise noted.

    bP=.02.

    cP=.048.

    dP<.001.

    eP=.002.

    Based on adjusted odds ratios (adjusted OR), participants aged 45-54 years old were 0.51 times as likely to have navigational needs compared to participants aged 55 years and above. In other words, participants aged 45-54 years old were less likely to have navigational needs compared to participants aged 55 years and above. In addition, participants with a lower eHEALS score, that is, lower eHealth literacy, were more likely to have navigational needs.


    Principal Findings

    Approximately half the population (51.3%, 95% CI 46.4-56.2) of consumers seeking Web-based health information and living with chronic health conditions was estimated to have navigational needs. These consumers reported at least some difficulty locating desired health information and indicated preferences for guidance to find desired health information on the Internet. While age and perceived eHealth literacy levels were associated with consumers’ navigational needs (Table 9), our study suggests that the majority of the population (91.3%), including consumers who did not report having difficulty locating desired Web-based health information, would still like some form of guidance. Given that approximately 75% of the total population in Australia reported having at least one chronic health condition [74], close to 75% of the Australian population has Internet access [5], and nearly 80% of Internet users use the Internet for health-related activities [5], it appears that a sizeable proportion of the total Australian population would likely be willing to receive some form of guidance in locating desired Web-based health information. A previous qualitative study [20] suggested health professionals could play a role in helping consumers locate desired Web-based health information. This and other types of assistance will be explored elsewhere using our current data.

    Findings from this study suggest consumers with at least one chronic condition want to be more informed about their health, and consumers seek information as a way to help manage their conditions. These findings support literature on the use of the Internet as a mechanism by health consumers to assist self-management [6,7,11,26]. When comparing participants with and without navigational needs, this study found that participants with navigational needs appear to look for more types of Web-based health information and from a greater variety of sources. Thus, this study adds to existing literature by providing some descriptive characteristics about the Web-based HISB of consumers with navigational needs.

    The majority of participants reported discussing Web-based health information with health professionals; the most common reason was to ascertain the opinions of health professionals on the retrieved health information. Only 10.3% of our participants indicated they use the Internet to find health information when disagreeing with advice from their health professionals. While not underestimating the proportion of these participants, this suggests consumers living with chronic health conditions predominantly use Web-based health information for reasons other than overriding advice given by health professionals. Collectively, these findings appear in line with studies [6-8] examining the role of the Internet in the consumer-health professional relationship, in that the Internet has the potential to better facilitate this relationship. However, when comparing participants with and without navigational needs, this study found that participants with navigational needs were less likely to be satisfied with their health professionals and more likely to not discuss information with their health professionals because they did not want to embarrass themselves in front of their health professionals (see Table 4). Thus, this study provides initial insight into aspects of HISB in consumers with navigational needs and suggests that health professionals may need to have conversations with consumers that greater encourage discussion of health information sought using the Internet.

    In our study, when compared to participants without navigational needs, participants with navigational needs appeared to have lower levels of patient activation and eHealth literacy (Tables 5 and 6). However, as established earlier, participants with navigational needs sought more types of health information from a greater variety of Web-based sources. Furthermore, participants with navigational needs were more likely to report that they discussed information sought using the Internet with their health professionals for the purpose of obtaining more information (Table 3). Such findings suggest that, despite have a seemingly greater desire to obtain information, participants with navigational needs are less able to find such information (lower eHealth literacy) and are less confident in their searching abilities (lower patient activation). This reinforces the need to provide assistance to consumers with navigational needs and provides further justification that more research needs to be conducted to address navigational needs.

    Further to our use of the PAM-13 and eHEALS measures, our data revealed a moderate but statistically significant correlation between the PAM-13 and eHEALS (r=.50, P<.001), supporting a relationship between patient activation and perceived eHealth literacy, as well as confirming other studies [55,56]. These findings extend the literature in that patient activation appears to be a prominent concept in the context of eHealth literacy and suggests this association is present even after accounting for demographic variables of age, sex, level of education, and place of residence (major cities versus rural). While self-perceived eHealth literacy refers to individuals’ self-perceived abilities to obtain and utilize Web-based health information for the purpose of self-management [52], patient activation refers to individuals’ self-belief in their behavioral repertoires, abilities, and knowledge pertaining to self-management [54]. Given the apparent overlap in these two concepts, health information consumers who self-identify as being motivated and having the ability and knowledge to self-manage their conditions could also be assumed to be more adept at utilizing the Internet for self-management purposes.

    The validity and reliability of the eHEALS and PAM-13 have been well established [54,60-68]. Our internal consistency and test-retest reliability analysis confirmed the reliability of both instruments in the current sample. Given that reliability is a prerequisite for validity [75], and the pre-establishment of validity in these two measures, these measures are likely to also be valid in our sample. By using these two measures as proxies for key concepts in predicting navigational needs, we believe our conclusions regarding the predictors of navigational needs are empirically justified.

    Strengths and Limitations

    A key strength of this study lies in our overall approach to developing the questionnaire. Specifically, the use of attention filters and a duration filter helped ensure that our participants provided complete and valid responses. The incorporation of two scales (PAM-13 and eHEALS) with prior evidence of validity and reliability allowed for trustworthy conclusions to be drawn from the data. The use of forced responses minimized potential for missing data; only one participant’s PAM-13 score could not be calculated from having selected several “Not Applicable” options within the scale. The use of questions and response items pertaining to Web-based HISB and navigational needs, based on a qualitative study conducted on a similar target population [20], provided initial empirical validation. This means the characteristics of consumers’ Web-based HISB and navigational needs from our study may more accurately reflect the target population, compared to questionnaires where the items had not been created from the consumer perspective. Furthermore, our use of randomization of response items perceivably mitigated response bias.

    Various steps ensured our questions were easily comprehended by our participants: inviting participants to comment on comprehension in our pilot survey, face validity checks by our research team, and the use of the Flesch-Kincaid Grade level test. The readability score of 8.0 indicates participants who have completed at least the 8th grade of formal education would be able to comprehend the questionnaire [69]. Based on the demographics of our participants, this suggests that 99.5% of our participants (398/400 participants) would have been able to comprehend the questions and response items in our questionnaire.

    While we requested Qualtrics to gather a representative sample of the Australian population, information pertaining to their sampling technique in doing so was not disclosed. As established earlier, there is a lack of data on population demographics in the context of Australian Internet use, limiting our ability to compare this sample with national demographic data. Furthermore, a representative sample does not necessarily translate to a random sample. Given our sample size calculation for prevalence studies assumes random sampling technique, the level of precision for our study cannot be accurately determined, and external validity cannot be assured. However, based on a report [76] from the Australian Institute of Health and Welfare (AIHW), the population prevalence versus the prevalence in our sample is comparable for cardiovascular diseases (22% vs 29.8%) and mental health conditions (20% vs 25.5%)—two of the three most commonly reported conditions in our sample (Table 2). This comparison, however, does not take into consideration variation in the prevalence of such conditions between Internet users and non-users. Furthermore, our sample is of consumers with chronic health conditions, whereas the AIHW report [76] expresses prevalence as a proportion of the entire Australian population. Nevertheless, our sample size was sufficient for the required analyses, and given the moderately large sample size and the diverse demographic characteristics of our participants, it appears our findings can be applied to a wider population.

    A further limitation to our study was the use of multiple-response, multiple-choice questions for our Web-based HISB domain to generate a comprehensive description of this domain. The permutations of options meant the data were only reported descriptively; a much larger sample would be required to facilitate comparisons between cohorts of respondents. Similarly, test-retest reliability of such multiple-response, multiple-choice questions could not be determined.

    This study did not determine the device(s) or platform(s) used to access Web-based health information. The availability of mobile-friendly versions of certain websites should improve access to Web-based health information [77]. In addition, our questionnaire did not explicitly explore social media as a health information source. These responses were elicited via an “Other” option (Table 5), but not to the extent suggested in literature reporting social network sites are becoming popular sources of general information for many users [42]. While such social network sites are reportedly less popular for people with chronic health conditions in the United States [42], their use is reportedly increasing [78].

    Further Research

    Despite the perception of the health professional as the most trusted source of Web-based health information [79], our study suggests that their role in guiding consumers to Web-based health information appears underutilized by consumers. We therefore recommend further investigation into why this role is underutilized and believe that both consumer and health professional perspectives should be explored. Once more in-depth understanding is attained, further research could explore the current roles of various health professions and investigate pragmatic ways that navigational guidance can be provided. While initiatives such as the use of social networking technologies by health professionals to provide guidance [80] and “information prescriptions” [81-85] have been implemented, to our knowledge, these initiatives have not considered consumers’ navigational needs, and this represents a topic for future development.

    While age and perceived eHealth literacy levels were found to be statistically significant predictors of navigational needs, the variables included in our multivariate binary logistic regression model explain only 18.7% of the variance in a consumer being identified with navigational needs. Other variables associated with the navigational needs of consumers remain unexplored, and these may inform individualized approaches to supporting navigational needs. We therefore recommend further investigation into identifying additional predictors of navigational needs.

    One could expect consumers with higher levels of perceived eHealth literacy would be less likely to have navigational needs. Indeed, this was the case in our study. Our study also identified a significantly lower likelihood of navigational needs in participants aged 45-54 years, compared to those aged 55 years and above. Further investigation is recommended to determine characteristics of this middle-aged group, why this specific age group was less likely to have navigational needs compared to those 55 years and above, and suitable interventions to meet their needs.

    A positive correlation was found between patient activation and eHealth literacy, albeit moderate. Until empirical data can better account for the variance in the relationship, future interventions aimed to address either patient activation or eHealth literacy should retain both constructs. Finally, given our universal approach to exploring Web-based HISB of health information consumers with a variety of chronic health conditions, future studies that focus on specific chronic health conditions can compare their findings against this study to determine commonalities and variations between and across chronic conditions.

    Conclusions

    This study highlights the proportion of people with chronic health conditions who use the Internet and who have navigational needs, and reports that a majority of this population would want help locating desired Web-based health information. While we identified a number of associations that help identify individuals who would benefit from guidance in navigating Web-based health information, given that the majority of the population would want assistance, more universal approaches may be valuable to help all consumers locate desired Web-based health information.

    Acknowledgments

    The authors acknowledge the licensor of the PAM-13 (Insignia Health) and the authors of the eHEALS for permission to use both scales in our questionnaire. We also acknowledge Qualtrics staff for coordinating recruitment and providing technical support, and Dr Richard Parsons for statistical advice. KL is supported by an Australian Postgraduate Award.

    Authors' Contributions

    KL conceived and designed the study and the questionnaire with assistance from LE, KH, and JH. KL liaised with Qualtrics to coordinate recruitment. KL was involved with statistical analyses, interpretating findings, and drafting the manuscript. All authors were involved in reviewing and revising the manuscript.

    Conflicts of Interest

    None declared.

    ‎

    Multimedia Appendix 1

    Reported chronic health conditions (N=400).

    PDF File (Adobe PDF File), 28KB
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    Multimedia Appendix 2

    Health information sought (N=400).

    PDF File (Adobe PDF File), 26KB
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    Multimedia Appendix 3

    Where Web-based health information is usually sought (N=400).

    PDF File (Adobe PDF File), 29KB
    ‎

    Multimedia Appendix 4

    Action(s) taken upon finding Web-based health information (N=400).

    PDF File (Adobe PDF File), 25KB
    1. Internet World Stats. 2014. Internet Usage Statistics: The Internet Big Picture - World Internet Users and 2014 Population Stats   URL: http://www.internetworldstats.com/stats.htm [accessed 2014-01-13] [WebCite Cache]
    2. Fox S. Pew Internet & American Life Project. 2006. Online Health Search 2006   URL: http://www.pewinternet.org/files/old-media/Files/Reports/2006/PIP_Online_Health_2006.pdf.pdf [accessed 2014-07-12] [WebCite Cache]
    3. Fox S. Pew Internet & American Life Project. 2011. Health Topics   URL: http://pewinternet.org/Reports/2011/HealthTopics.aspx [accessed 2012-08-07] [WebCite Cache]
    4. Fox S, Jones S. Pew Internet & American Life Project. 2009. The Social Life of Health Information   URL: http://www.pewinternet.org/2009/06/11/the-social-life-of-health-information/ [accessed 2014-07-12] [WebCite Cache]
    5. McDaid D, Park A. Bupa. 2010. Online Health: Untangling the Web   URL: https:/​/www.​bupa.com.au/​staticfiles/​Bupa/​HealthAndWellness/​MediaFiles/​PDF/​LSE_Report_Online_Health.​pdf [accessed 2014-09-23] [WebCite Cache]
    6. Iverson SA, Howard KB, Penney BK. Impact of internet use on health-related behaviors and the patient-physician relationship: a survey-based study and review. J Am Osteopath Assoc 2008 Dec;108(12):699-711. [Medline]
    7. McMullan M. Patients using the Internet to obtain health information: how this affects the patient-health professional relationship. Patient Educ Couns 2006 Oct;63(1-2):24-28. [CrossRef] [Medline]
    8. Murray E, Lo B, Pollack L, Donelan K, Catania J, White M, et al. The impact of health information on the internet on the physician-patient relationship: patient perceptions. Arch Intern Med 2003 Jul 28;163(14):1727-1734. [CrossRef] [Medline]
    9. Hart A, Henwood F, Wyatt S. The role of the Internet in patient-practitioner relationships: findings from a qualitative research study. J Med Internet Res 2004 Sep 30;6(3):e36 [FREE Full text] [CrossRef] [Medline]
    10. Diaz JA, Griffith RA, Ng JJ, Reinert SE, Friedmann PD, Moulton AW. Patients’ use of the internet for medical information. J Gen Intern Med 2002 Mar;17(3):180-185. [CrossRef]
    11. Hoving C, Visser A, Mullen PD, van den Borne B. A history of patient education by health professionals in Europe and North America: from authority to shared decision making education. Patient Educ Couns 2010 Mar;78(3):275-281. [CrossRef] [Medline]
    12. Donald M, Ware RS, Ozolins IZ, Begum N, Crowther R, Bain C. The role of patient activation in frequent attendance at primary care: a population-based study of people with chronic disease. Patient Educ Couns 2011 May;83(2):217-221. [CrossRef] [Medline]
    13. AIHW. Australian Institute of Health and Welfare. Canberra; 2004. Health system expenditure on disease and injury in Australia, 2000-01   URL: http://www.aihw.gov.au/WorkArea/DownloadAsset.aspx?id=6442457385 [accessed 2015-02-12] [WebCite Cache]
    14. World Health Organization. 2012. The top 10 causes of death   URL: http://www.who.int/mediacentre/factsheets/fs310/en/ [accessed 2015-02-12] [WebCite Cache]
    15. Andreassen HK, Bujnowska-Fedak MM, Chronaki CE, Dumitru RC, Pudule I, Santana S, et al. European citizens' use of E-health services: a study of seven countries. BMC Public Health 2007;7:53 [FREE Full text] [CrossRef] [Medline]
    16. Fox S. Pew Internet & American Life Project. 2011. The Social Life of Health Information, 2011   URL: http://www.pewinternet.org/files/old-media/Files/Reports/2011/PIP_Social_Life_of_Health_Info.pdf [accessed 2015-08-31] [WebCite Cache]
    17. Fox S. Pew Internet & American Life Project. 2011. Peer-to-Peer Healthcare   URL: http://pewinternet.org/Reports/2011/P2PHealthcare.aspx [accessed 2012-08-07] [WebCite Cache]
    18. Fox S. Pew Internet & American Life Project. 2007. E-Patients With a Disability or Chronic Disease   URL: http://www.pewinternet.org/2007/10/08/e-patients-with-a-disability-or-chronic-disease/ [accessed 2015-08-31] [WebCite Cache]
    19. Cline RJ, Haynes KM. Consumer health information seeking on the Internet: the state of the art. Health Educ Res 2001 Dec;16(6):671-692 [FREE Full text] [Medline]
    20. Lee K, Hoti K, Hughes JD, Emmerton L. Dr Google and the consumer: a qualitative study exploring the navigational needs and online health information-seeking behaviors of consumers with chronic health conditions. J Med Internet Res 2014;16(12):e262 [FREE Full text] [CrossRef] [Medline]
    21. McGrath I. Information superhighway or information traffic jam for healthcare consumers? Clin Perform Qual Health Care 1997;5(2):90-93. [Medline]
    22. Metzger MJ, Flanagin AJ. Using Web 2.0 technologies to enhance evidence-based medical information. J Health Commun 2011;16 Suppl 1:45-58. [CrossRef] [Medline]
    23. Anderson JG. Consumers of e-Health: Patterns of Use and Barriers. Soc Sci Comput Rev 2004 May 01;22(2):242-248. [CrossRef]
    24. Burkell JA, Wolfe DL, Potter PJ, Jutai JW. Information needs and information sources of individuals living with spinal cord injury. Health Info Libr J 2006 Dec;23(4):257-265 [FREE Full text] [CrossRef] [Medline]
    25. Caiata-Zufferey M, Abraham A, Sommerhalder K, Schulz PJ. Online health information seeking in the context of the medical consultation in Switzerland. Qual Health Res 2010 Aug;20(8):1050-1061. [CrossRef] [Medline]
    26. Chou WS, Liu B, Post S, Hesse B. Health-related Internet use among cancer survivors: data from the Health Information National Trends Survey, 2003-2008. J Cancer Surviv 2011 Sep;5(3):263-270. [CrossRef] [Medline]
    27. Cotten SR, Gupta SS. Characteristics of online and offline health information seekers and factors that discriminate between them. Soc Sci Med 2004 Nov;59(9):1795-1806. [CrossRef] [Medline]
    28. James N, Daniels H, Rahman R, McConkey C, Derry J, Young A. A study of information seeking by cancer patients and their carers. Clin Oncol (R Coll Radiol) 2007 Jun;19(5):356-362. [CrossRef] [Medline]
    29. Kalichman SC, Benotsch EG, Weinhardt L, Austin J, Luke W, Cherry C. Health-related Internet use, coping, social support, and health indicators in people living with HIV/AIDS: preliminary results from a community survey. Health Psychol 2003 Jan;22(1):111-116. [Medline]
    30. Lee SY, Hawkins R. Why do patients seek an alternative channel? The effects of unmet needs on patients' health-related Internet use. J Health Commun 2010 Mar;15(2):152-166. [CrossRef] [Medline]
    31. Matter B, Feinberg M, Schomer K, Harniss M, Brown P, Johnson K. Information needs of people with spinal cord injuries. J Spinal Cord Med 2009;32(5):545-554 [FREE Full text] [Medline]
    32. McCaughan E, McKenna H. Information-seeking behaviour of men newly diagnosed with cancer: a qualitative study. J Clin Nurs 2007 Nov;16(11):2105-2113. [CrossRef] [Medline]
    33. Soederberg Miller LM, Bell RA. Online health information seeking: the influence of age, information trustworthiness, and search challenges. J Aging Health 2012 Apr;24(3):525-541. [CrossRef] [Medline]
    34. Noh H, Lee JM, Yun YH, Park SY, Bae D, Nam J, et al. Cervical cancer patient information-seeking behaviors, information needs, and information sources in South Korea. Support Care Cancer 2009 Oct;17(10):1277-1283. [CrossRef] [Medline]
    35. O'Grady L. Meeting health information needs of people with HIV/AIDS: sources and means of collaboration. Health Info Libr J 2008 Dec;25(4):261-269 [FREE Full text] [CrossRef] [Medline]
    36. Roberts KJ, Lepore SJ, Urken ML. Quality of life after thyroid cancer: an assessment of patient needs and preferences for information and support. J Cancer Educ 2008;23(3):186-191. [CrossRef] [Medline]
    37. Samal L, Saha S, Chander G, Korthuis PT, Sharma RK, Sharp V, et al. Internet health information seeking behavior and antiretroviral adherence in persons living with HIV/AIDS. AIDS Patient Care STDS 2011 Jul;25(7):445-449 [FREE Full text] [CrossRef] [Medline]
    38. Tustin N. The role of patient satisfaction in online health information seeking. J Health Commun 2010 Jan;15(1):3-17. [CrossRef] [Medline]
    39. Yan YY. Online health information seeking behavior in Hong Kong: an exploratory study. J Med Syst 2010 Apr;34(2):147-153. [Medline]
    40. Lee YJ, Boden-Albala B, Larson E, Wilcox A, Bakken S. Online health information seeking behaviors of Hispanics in New York City: a community-based cross-sectional study. J Med Internet Res 2014;16(7):e176 [FREE Full text] [CrossRef] [Medline]
    41. Medlock S, Eslami S, Askari M, Arts DL, Sent D, de Rooij SE, et al. Health information-seeking behavior of seniors who use the Internet: a survey. J Med Internet Res 2015;17(1):e10 [FREE Full text] [CrossRef] [Medline]
    42. Fox S, Purcell K. Pew Internet & American Life Project. 2010. Chronic Disease and the Internet   URL: http://pewinternet.org/Reports/2010/Chronic-Disease.aspx [accessed 2012-08-07] [WebCite Cache]
    43. Rowlands IJ, Loxton D, Dobson A, Mishra GD. Seeking Health Information Online: Association with Young Australian Women's Physical, Mental, and Reproductive Health. J Med Internet Res 2015;17(5):e120 [FREE Full text] [CrossRef] [Medline]
    44. Jamal A, Khan SA, AlHumud A, Al-Duhyyim A, Alrashed M, Bin SF, et al. Association of Online Health Information-Seeking Behavior and Self-Care Activities Among Type 2 Diabetic Patients in Saudi Arabia. J Med Internet Res 2015;17(8):e196 [FREE Full text] [CrossRef] [Medline]
    45. Ayers SL, Kronenfeld JJ. Chronic illness and health-seeking information on the Internet. Health (London) 2007 Jul;11(3):327-347. [CrossRef] [Medline]
    46. Mayoh J, Bond CS, Todres L. An Innovative Mixed Methods Approach to Studying the Online Health Information Seeking Experiences of Adults With Chronic Health Conditions. Journal of Mixed Methods Research 2011 Aug 26;6(1):21-33. [CrossRef]
    47. Baker DW, Wolf MS, Feinglass J, Thompson JA, Gazmararian JA, Huang J. Health literacy and mortality among elderly persons. Arch Intern Med 2007 Jul 23;167(14):1503-1509. [CrossRef] [Medline]
    48. Cutilli CC, Bennett IM. Understanding the health literacy of America: results of the National Assessment of Adult Literacy. Orthop Nurs 2009;28(1):27-32; quiz 33 [FREE Full text] [CrossRef] [Medline]
    49. Manning DL, Dickens C. Health literacy: more choice, but do cancer patients have the skills to decide? Eur J Cancer Care (Engl) 2006 Dec;15(5):448-452. [CrossRef] [Medline]
    50. Nielsen-Bohlman L, Panzer AM, Kindig DA. Health literacy: a prescription to end confusion. Washington, DC: National Academies Press; 2004.
    51. World Health Organization. 2015. Health literacy and health behavior   URL: http://www.who.int/healthpromotion/conferences/7gchp/track2/en/ [accessed 2015-01-02] [WebCite Cache]
    52. Norman CD, Skinner HA. eHealth Literacy: Essential Skills for Consumer Health in a Networked World. J Med Internet Res 2006;8(2):e9 [FREE Full text] [CrossRef] [Medline]
    53. Collins SA, Currie LM, Bakken S, Vawdrey DK, Stone PW. Health literacy screening instruments for eHealth applications: a systematic review. J Biomed Inform 2012 Jun;45(3):598-607 [FREE Full text] [CrossRef] [Medline]
    54. Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res 2004 Aug;39(4 Pt 1):1005-1026 [FREE Full text] [CrossRef] [Medline]
    55. Greene J, Hibbard J, Tusler M. How much do health literacy and patient activation contribute to older adults' ability to manage their health?. Washington, DC: AARP Public Policy Institute; 2005.   URL: https://assets.aarp.org/rgcenter/health/2005_05_literacy.pdf [accessed 2015-02-12] [WebCite Cache]
    56. Smith SG, Curtis LM, Wardle J, von Wagner C, Wolf MS. Skill set or mind set? Associations between health literacy, patient activation and health. PLoS One 2013;8(9):e74373 [FREE Full text] [CrossRef] [Medline]
    57. Research Now. 2015. Panels   URL: http://www.researchnow.com/en-US/Panels.aspx [accessed 2015-02-12] [WebCite Cache]
    58. Arya R, Antonisamy B, Kumar S. Sample size estimation in prevalence studies. Indian J Pediatr 2012 Nov;79(11):1482-1488. [CrossRef] [Medline]
    59. Eysenbach G. Improving the quality of Web surveys: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). J Med Internet Res 2004 Sep 29;6(3):e34 [FREE Full text] [CrossRef] [Medline]
    60. Norman CD, Skinner HA. eHEALS: The eHealth Literacy Scale. J Med Internet Res 2006;8(4):e27 [FREE Full text] [CrossRef] [Medline]
    61. Hibbard JH, Mahoney ER, Stockard J, Tusler M. Development and testing of a short form of the patient activation measure. Health Serv Res 2005 Dec;40(6 Pt 1):1918-1930 [FREE Full text] [CrossRef] [Medline]
    62. Brenk-Franz K, Hibbard JH, Herrmann WJ, Freund T, Szecsenyi J, Djalali S, et al. Validation of the German version of the patient activation measure 13 (PAM13-D) in an international multicentre study of primary care patients. PLoS One 2013;8(9):e74786 [FREE Full text] [CrossRef] [Medline]
    63. Fowles JB, Terry P, Xi M, Hibbard J, Bloom CT, Harvey L. Measuring self-management of patients' and employees' health: further validation of the Patient Activation Measure (PAM) based on its relation to employee characteristics. Patient Educ Couns 2009 Oct;77(1):116-122. [CrossRef] [Medline]
    64. Maindal HT, Sokolowski I, Vedsted P. Translation, adaptation and validation of the American short form Patient Activation Measure (PAM13) in a Danish version. BMC Public Health 2009;9:209 [FREE Full text] [CrossRef] [Medline]
    65. Rademakers J, Nijman J, van der Hoek L, Heijmans M, Rijken M. Measuring patient activation in The Netherlands: translation and validation of the American short form Patient Activation Measure (PAM13). BMC Public Health 2012;12:577 [FREE Full text] [CrossRef] [Medline]
    66. Stepleman L, Rutter M, Hibbard J, Johns L, Wright D, Hughes M. Validation of the patient activation measure in a multiple sclerosis clinic sample and implications for care. Disabil Rehabil 2010;32(19):1558-1567. [CrossRef] [Medline]
    67. Koo M, Norman CD, Chang HM. Psychometric evaluation of a Chinese version of the eHealth Literacy Scale (eHEALS) in school age children. Int Electron J Health Educ 2012 Dec 27;15:29-36 [FREE Full text]
    68. Magnezi R, Glasser S. Psychometric properties of the hebrew translation of the patient activation measure (PAM-13). PLoS One 2014;9(11):e113391 [FREE Full text] [CrossRef] [Medline]
    69. Kincaid J, Fishburne RP, Rogers RL, Chissom BS. National Technical Information Service. Springfield; 1975. Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy Enlisted Personnel   URL: http://www.dtic.mil/dtic/tr/fulltext/u2/a006655.pdf [accessed 2015-02-12] [WebCite Cache]
    70. Field A. Discovering statistics using SPSS. Los Angeles, CA: SAGE; 2009.
    71. Kontos E, Blake KD, Chou WS, Prestin A. Predictors of eHealth usage: insights on the digital divide from the Health Information National Trends Survey 2012. J Med Internet Res 2014;16(7):e172 [FREE Full text] [CrossRef] [Medline]
    72. Portney LG, Watkins MP. Foundations of clinical research: applications to practice. Upper Saddle River, NJ: Pearson/Prentice Hall; 2009.
    73. Fleiss JL. The design and analysis of clinical experiments. New York: Wiley; 1999.
    74. Australian Bureau of Statistics. 2010. National Health Survey: Summary of Results, 2007-08   URL: http://www.abs.gov.au/AUSSTATS/abs@.nsf/mf/4364.0 [accessed 2012-08-07] [WebCite Cache]
    75. Mitchell M, Jolley JM. Research Design Explained. Boston, MA: Wadsworth Publishing; 2013.
    76. Australian Institute of Health and Welfare. Canberra; 2015. Chronic Diseases   URL: http://www.aihw.gov.au/chronic-diseases/ [accessed 2015-02-12] [WebCite Cache]
    77. Shrestha S. Mobile web browsing: usability study. In: MC07. 2007 Presented at: 4th Mobility Conference: International Conference on Mobile Technology, Applications, and Systems; Sept 12-14, 2007; Singapore. [CrossRef]
    78. Sadah SA, Shahbazi M, Wiley MT, Hristidis V. A Study of the Demographics of Web-Based Health-Related Social Media Users. J Med Internet Res 2015;17(8):e194 [FREE Full text] [CrossRef] [Medline]
    79. Dutta-Bergman M. Trusted online sources of health information: differences in demographics, health beliefs, and health-information orientation. J Med Internet Res 2003;5(3):e21 [FREE Full text] [CrossRef] [Medline]
    80. Young SD. Recommendations for Using Online Social Networking Technologies to Reduce Inaccurate Online Health Information. Online J Health Allied Sci 2011 Jul 30;10(2):- [FREE Full text] [Medline]
    81. Hand A, Greenwell K, Corbett S, Walker R, Kirrane H. Developing an information prescription service. Nurs Times 2008;104(39):34-37. [Medline]
    82. McKnight M. Information prescriptions, 1930-2013: an international history and comprehensive review. J Med Libr Assoc 2014 Oct;102(4):271-280 [FREE Full text] [CrossRef] [Medline]
    83. Oliver KB, Lehmann HP, Wolff AC, Davidson LW, Donohue PK, Gilmore MM, et al. Evaluating information prescriptions in two clinical environments. J Med Libr Assoc 2011 Jul;99(3):237-246 [FREE Full text] [CrossRef] [Medline]
    84. Ritterband LM, Borowitz S, Cox DJ, Kovatchev B, Walker LS, Lucas V, et al. Using the internet to provide information prescriptions. Pediatrics 2005 Nov;116(5):e643-e647. [CrossRef] [Medline]
    85. Siegel ER, Logan RA, Harnsberger RL, Cravedi K, Krause JA, Lyon B, et al. Information Rx: Evaluation of a new informatics tool for physicians, patients, and libraries. Inf Serv Use 2006;26(1):1-10 [FREE Full text] [Medline]

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    AIHW: Australian Institute of Health and Welfare
    eHEALS: eHealth Literacy Scale
    HISB: health information-seeking behaviors
    ICC: intraclass correlation coefficient
    PAM-13: 13-item Patient Activation Measure

    Edited by G Eysenbach; submitted 12.02.15; peer-reviewed by S Medlock, J Chiu, M Silver; comments to author 05.08.15; revised version received 15.09.15; accepted 09.11.15; published 29.12.15

    Copyright

    ©Kenneth Lee, Kreshnik Hoti, Jeffery David Hughes, Lynne M Emmerton. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 29.12.2015.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.