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

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Participatory Medicine E-Patients 

Web-based and Mobile Health Interventions 
Demographics of Users, Social Digital Divide 
Public (e)Health 

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Original Paper

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

Nicole Neufingerl1, MSc;
Mark R Cobain2, PhD;
Rachel S Newson1, PhD

1Nutrition Health Department, Unilever Research Development, Vlaardingen, Netherlands
2New Business Unit, Unilever Research Development, London, United Kingdom

Corresponding Author:
Nicole Neufingerl, MSc

Nutrition Health Department
Unilever Research Development
Olivier van Noortlaan 120
Vlaardingen, 3133 AT
Phone: 31 10460 ext 5155
Fax: 31 104605993

1,2]. Online health information seekers are typically searching for information regarding a specific disease or medical problem, including the potential to diagnose their own health status [3,4]. Health professionals, public health and governmental organizations, and private health providers are therefore using the Internet as a medium to disseminate health information and preventative educational programs. This makes the Internet a valuable instrument for increasing consumer awareness, promotion of healthy behaviors, and disease prevention [5,6]. Web-based health applications, such as self-assessment health tests or behavior change programs, that combine high-quality health information with interactive components can therefore play a role to benefit prevention, early detection, or treatment of non-communicable diseases [7-9].

The impact that Web-based health assessment tools can have on public health depends on the audience they reach, as well as on the quality and reliability of the information they provide. Criticism of Web-based health applications has been raised suggesting that they may not be reaching the people who need these tools the most: those at risk of developing disease, and where prevention efforts are still viable [10,11]. Furthermore, there is concern about the potential harm that can be caused by inaccurate health assessments provided on the Internet that can deliver incorrect diagnoses and/or cause delays in seeking appropriate medical care [12,13]. This can arise due to poorly designed Web applications that are not based on scientific evidence. Alternatively, inaccurate health assessment results may arise from users providing inaccurate information due to inputting the information incorrectly, not understanding questions, or they may not know the answer to the questions [14]. This may be particularly pertinent to tools that require information on physiological risk factors, which are unknown to a large part of the population [5,15].

The potential impact of Web-based health assessment tools on disease prevention is large, but they need to reach users with an elevated disease risk and provide accurate health assessments. Therefore, the aim of the current study was to (1) evaluate whether a Web-based health assessment tool communicating cardiovascular disease (CVD) risk was reaching users at risk of developing CVD, (2) evaluate the impact of awareness of physiological risk factors on the health assessment provided by the CVD risk communication tool, in particular total cholesterol (TC), HDL-cholesterol (HDL-C), and systolic blood pressure (SBP), and (3) evaluate the key predictors of awareness and reporting of physiological risk factors in order to understand who in the general population is more likely to provide physiological measures to Web-based tools.

Study Design

The current study was based on a global database from users of the Heart Age tool. Heart Age was developed to help users better understand their risk of CVD, which when given as a traditional percentage can be a difficult concept to understand (eg, “your risk of a CVD event in the next 10 years is 12%”) [16,17]. Heart Age is a Web-based tool that obtains an individual’s CVD risk factor information through a series of questions, calculates percentage risk of developing CVD within the next 10 years according to the Framingham risk score, and then translates this risk into a “Heart Age” [18]. An individual’s Heart Age corresponds to the age of a person with the same predicted CVD risk but with all other risk factors considered as normal. This means that depending on their CVD risk, a person’s Heart Age could be younger or older than their chronological age. For example, a 55-year-old man with normal risk factors would have a 10% risk of CVD; accordingly, a 40-year-old man with a 10% risk of CVD (due to unhealthy risk factors) would have a Heart Age of 55 years. The Heart Age tool aims to make users aware of their risk of developing a CVD event and which risk factors are contributing to this risk, in order to motivate them to make lifestyle changes. Therefore the target audience is anyone at risk of developing a CVD event, but where prevention efforts are still viable, as these users can benefit the most from awareness of their current status and potential interventions. After completion of the Heart Age test, users can sign up for a free Web-based health program providing personalized diet and lifestyle advice to lower one’s Heart Age. The Heart Age tool is directed at the general population, but in particular it intends to reach users with unhealthy risk factors. Heart Age was launched globally in 2009 as part of a brand marketing campaign by Flora/Becel, a margarine brand, via on-pack messages, advertisements on television, newspapers, in store applications, etc. The campaign focused on raising awareness of heart health; it was not targeted at specific at risk groups. The tool is available via free open access websites available in different languages [19].


Data from 3,374,769 users of the Heart Age tool was collected between July 2009 and December 2011. The tool was launched in 14 countries, including United Kingdom, Germany, the Netherlands, Belgium, Finland, Austria, Poland, Turkey, Ireland, Portugal, Slovenia, Greece, Australia, and Brazil. By using the tool, users consented to the privacy policy, which could be accessed via a link on the website, and which stated that personal information provided could be collected and used in an aggregated way to evaluate the use of the site and services provided. Visitors to the website did not receive any incentives for using the tool. All of the information users entered was stored in a format where individuals could not be identified. The use of Heart Age was restricted to users aged between 20 and 80 years old with no history of heart disease. This group was selected because this was the population on which the original CVD algorithms were validated, and people with existing heart disease have different levels of risk of a future CVD event. A disproportionate number of users of the Heart Age tool reported to be 20 years old (9.59%, 323,547/3,374,769), possibly because this was the default setting. Therefore, to prevent artificial overrepresentation of 20 year olds, analysis was restricted to users aged 21-80 years.

To identify repeat users, prior to starting the self-assessment users were asked to indicate whether they had used the tool before. All return users were excluded from analysis to prevent duplication (n=143,682). Another 32,700 users whose TC, HDL-C, SBP, and body mass index (BMI) values were all missing or beyond defined valid ranges based on clinical judgment (ie, 80 mmHg≤ SBP ≤220 mmHg; 77 mg/dl≤ TC ≤423 mg/dl; 25 mg/dl≤ HDL ≤90 mg/dl; 15≤ BMI ≤45) were excluded from analysis as it was not possible to calculate a Heart Age for these users. As the tool was never promoted in Brazil and the number of users from Brazil was very low (n=102), it is likely that most entries were from a select group of internal users; therefore, all Brazilian users were excluded from the analysis. Finally, 130,647 users were excluded based on identical data of date of using the tool, BMI, TC, HDL, SPB, Heart Age, and 10-year CVD risk, which was mainly due to testing the functionality of the tool during the developmental phase. This resulted in a final dataset of 2,744,091 users. Among those users who were excluded due to reasons other than being 20 years old (n=307,137), the mean age was 40.98 (SD 14.29) years and 50.78% (155,969/307,137) were women.


The Heart Age tool assessed CVD risk factors by self-report. Users completed a Web-based questionnaire that asked for information on demographic data (ie, age, gender, height, and weight; family history of CVD), physiological measures (ie, TC, HDL-C, and SBP values), other CVD risk factors (ie, smoking status and diabetes prevalence), and use of antihypertensive or cholesterol lowering medication. Further information was also obtained relating to CVD disease but was not relevant to the aims of the current study.

Questions were mostly presented as closed questions with locked answer options (eg, “yes/no” or “male/female”). Family history of CVD was assessed by asking “Have either of your parents ever had heart problems (ie, heart attack, stroke, angina, or heart surgery?)”. Smoking status and diabetes prevalence were determined by asking “Do you smoke?” and “Do you have diabetes?” Use of antihypertensive and blood cholesterol lowering medication was assessed with the questions “Are you taking or have you ever taken medication to lower your blood pressure?” and “Are you taking or have you ever taken medication to lower your cholesterol?” Questions on age, height, weight, and physiological measures were asked using an open answer format. Users could choose in which unit they wanted to enter their values for height (cm or inch) and weight (kg or lb); units for cholesterol were pre-determined and differed per country (mmol/l or mg/dl). Physiological measures were assessed by asking “Do you know your cholesterol level?” and “Do you know your blood pressure?” If users indicated “yes”, a sub-dialogue box opened and they could enter a value for TC and HDL-C or for SBP, respectively. The sub-dialogue boxes also contained explanations stating that “HDL cholesterol is sometimes known as ‘good’ cholesterol” and that “Systolic blood pressure is the higher number (eg, where blood pressure is 120/80, 120 is the systolic reading)”. All subject data were automatically captured in a database. See Figure 1 for a screenshot of the Heart Age tool. Screenshots of the complete user journey through the Heart Age tool are provided in Multimedia Appendix 1.

Depending on the availability of the physiological measures per individual, 10-year CVD risk and Heart Age could be calculated using different algorithms. There was one full algorithm when all physiological measures were known, and 5 alternatives for when one or more of the physiological measures were missing. The following alternative algorithms were available: (1) TC HDL, (2) TC, SBP, BMI, (3) TC, BMI, (4) SBP, BMI, and (5) BMI. The CVD algorithms were developed using Framingham cohort data as sex-specific multivariable risk functions based on Cox proportional-hazards regression. The full CVD risk algorithm included data on age, gender, TC, HDL-C, SBP, antihypertensive medication use, smoking status, and diabetes [18]. Detailed information about the development and validation of the algorithms to calculate 10-year CVD risk can be found elsewhere [20,21]. Once calculated, CVD risk was translated into a Heart Age [20]. An individual’s Heart Age corresponds to the age of a person with the same predicted CVD risk but with all other risk factors considered as “normal”. The reference values for “normal” risk factors in the full Heart Age algorithm, were defined as not smoking, not diabetic, SBP=125 mmHg (130 mmHg if 60 or above), TC=180 mg/dl, and HDL-C=45 mg/dl [18]. For the alternative models where total cholesterol was present without HDL-C, the reference value for a person with “normal cholesterol” was increased to 200 mg/dl to increase the sensitivity of these models. To keep Heart Ages within a reasonable range, it was decided that Heart Age should be capped if it was 15 years lower or higher than chronological age, or if it fell below 18 or exceeded 80 years of age, in order to alert people of the need for change and medical advice without creating alarm. An overview of all CVD and Heart Age algorithms is provided Multimedia Appendix 2.

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