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      Web-Based Self-Assessment Health Tools: Who Are the Users and What Is the Impact of Missing Input Information?

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          Abstract

          Background

          Web-based health applications, such as self-assessment tools, can aid in the early detection and prevention of diseases. However, there are concerns as to whether such tools actually reach users with elevated disease risk (where prevention efforts are still viable), and whether inaccurate or missing information on risk factors may lead to incorrect evaluations.

          Objective

          This study aimed to evaluate (1) evaluate whether a Web-based cardiovascular disease (CVD) risk communication tool (Heart Age tool) was reaching users at risk of developing CVD, (2) the impact of awareness of total cholesterol (TC), HDL-cholesterol (HDL-C), and systolic blood pressure (SBP) values on the risk estimates, and (3) the key predictors of awareness and reporting of physiological risk factors.

          Methods

          Heart Age is a tool available via a free open access website. Data from 2,744,091 first-time users aged 21-80 years with no prior heart disease were collected from 13 countries in 2009-2011. Users self-reported demographic and CVD risk factor information. Based on these data, an individual’s 10-year CVD risk was calculated according to Framingham CVD risk models and translated into a Heart Age. This is the age for which the individual’s reported CVD risk would be considered “normal”. Depending on the availability of known TC, HDL-C, and SBP values, different algorithms were applied. The impact of awareness of TC, HDL-C, and SBP values on Heart Age was determined using a subsample that had complete risk factor information.

          Results

          Heart Age users (N=2,744,091) were mostly in their 20s (22.76%) and 40s (23.99%), female (56.03%), had multiple (mean 2.9, SD 1.4) risk factors, and a Heart Age exceeding their chronological age (mean 4.00, SD 6.43 years). The proportion of users unaware of their TC, HDL-C, or SBP values was high (77.47%, 93.03%, and 46.55% respectively). Lacking awareness of physiological risk factor values led to overestimation of Heart Age by an average 2.1-4.5 years depending on the (combination of) unknown risk factors ( P<.001). Overestimation was greater in women than in men, increased with age, and decreased with increasing CVD risk. Awareness of physiological risk factor values was higher among diabetics (OR 1.47, 95% CI 1.46-1.50 and OR 1.74, 95% CI 1.71-1.77), those with family history of CVD (OR 1.22, 95% CI 1.22-1.23 and OR 1.43, 95% CI 1.42-1.44), and increased with age (OR 1.05, 95% CI 1.05-1.05 and OR 1.07, 95% CI 1.07-1.07). It was lower in smokers (OR 0.52, 95% CI 0.52-0.53 and OR 0.71, 95% CI 0.71-0.72) and decreased with increasing Heart Age (OR 0.92, 95% CI 0.92-0.92 and OR 0.97, 95% CI 0.96-0.97) (all P<.001).

          Conclusions

          The Heart Age tool reached users with low-moderate CVD risk, but with multiple elevated CVD risk factors, and a heart age higher than their real age. This highlights that Web-based self-assessment health tools can be a useful means to interact with people who are at risk of developing disease, but where interventions are still viable. Missing information in the self-assessment health tools was shown to result in inaccurate self-health assessments. Subgroups at risk of not knowing their risk factors are identifiable and should be specifically targeted in health awareness programs.

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          Most cited references39

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          General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

          Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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            eHealth Literacy: Extending the Digital Divide to the Realm of Health Information

            Background eHealth literacy is defined as the ability of people to use emerging information and communications technologies to improve or enable health and health care. Objective The goal of this study was to explore whether literacy disparities are diminished or enhanced in the search for health information on the Internet. The study focused on (1) traditional digital divide variables, such as sociodemographic characteristics, digital access, and digital literacy, (2) information search processes, and (3) the outcomes of Internet use for health information purposes. Methods We used a countrywide representative random-digital-dial telephone household survey of the Israeli adult population (18 years and older, N = 4286). We measured eHealth literacy; Internet access; digital literacy; sociodemographic factors; perceived health; presence of chronic diseases; as well as health information sources, content, search strategies, and evaluation criteria used by consumers. Results Respondents who were highly eHealth literate tended to be younger and more educated than their less eHealth-literate counterparts. They were also more active consumers of all types of information on the Internet, used more search strategies, and scrutinized information more carefully than did the less eHealth-literate respondents. Finally, respondents who were highly eHealth literate gained more positive outcomes from the information search in terms of cognitive, instrumental (self-management of health care needs, health behaviors, and better use of health insurance), and interpersonal (interacting with their physician) gains. Conclusions The present study documented differences between respondents high and low in eHealth literacy in terms of background attributes, information consumption, and outcomes of the information search. The association of eHealth literacy with background attributes indicates that the Internet reinforces existing social differences. The more comprehensive and sophisticated use of the Internet and the subsequent increased gains among the high eHealth literate create new inequalities in the domain of digital health information. There is a need to educate at-risk and needy groups (eg, chronically ill) and to design technology in a mode befitting more consumers.
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              Influences, usage, and outcomes of Internet health information searching: multivariate results from the Pew surveys.

              This paper provides results from seven major nationally representative datasets (two in detail) from the Pew Internet and American Life Project to answer two primary questions: (1) what influences people to seek online health information and (2) what influences their perceived outcomes from having access to this information? Cross-tabulations, logistic regressions, and multidimensional scaling are applied to these survey datasets. The strongest and most consistent influences on ever, or more frequently, using the Internet to search for health information were sex (female), employment (not fulltime), engaging in more other Internet activities, more specific health reasons (diagnosed with new health problem, ongoing medical condition, prescribed new medication or treatment), and helping another deal with health issues. Internet health seeking is consistently similar to general Internet activities such as email, news, weather, and sometimes hobbies. A variety of outcomes from or positive assessments of searching for Internet health information are predicted most strongly by sex (female), engaging in other Internet activities, Internet health information seeking including more frequent health seeking, more specific health reasons, belonging to an online support group sharing health interests, and helping another deal with an illness or major health condition.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications Inc. (Toronto, Canada )
                1439-4456
                1438-8871
                September 2014
                26 September 2014
                : 16
                : 9
                : e215
                Affiliations
                [1] 1Nutrition & Health Department Unilever Research & Development VlaardingenNetherlands
                [2] 2New Business Unit Unilever Research & Development LondonUnited Kingdom
                Author notes
                Corresponding Author: Nicole Neufingerl nicole.neufingerl@ 123456unilever.com
                Author information
                http://orcid.org/0000-0002-8060-2321
                http://orcid.org/0000-0003-2511-4197
                http://orcid.org/0000-0001-5603-1875
                Article
                v16i9e215
                10.2196/jmir.3146
                4211033
                25261155
                d124cc0d-116c-4848-b2bd-b5e33a50af5d
                ©Nicole Neufingerl, Mark R Cobain, Rachel S Newson. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 26.09.2014.

                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.

                History
                : 28 November 2013
                : 14 March 2014
                : 24 April 2014
                : 10 July 2014
                Categories
                Original Paper
                Original Paper

                Medicine
                cardiovascular disease,risk assessment,web applications,consumer health information,preventive health services,cholesterol,blood pressure

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