I Don't Believe It, But I'd Better Do Something About It: Patient Experiences of Online Heart Age Risk Calculators

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Abstract

Background

Health risk calculators are widely available on the Internet, including cardiovascular disease (CVD) risk calculators that estimate the probability of a heart attack, stroke, or death over a 5- or 10-year period. Some calculators convert this probability to “heart age”, where a heart age older than current age indicates modifiable risk factors. These calculators may impact patient decision making about CVD risk management with or without clinician involvement, but little is known about how patients use them. Previous studies have not investigated patient understanding of heart age compared to 5-year percentage risk, or the best way to present heart age.

Objective

This study aimed to investigate patient experiences and understanding of online heart age calculators that use different verbal, numerical, and graphical formats, based on 5- and 10-year Framingham risk equations used in clinical practice guidelines around the world.

Methods

General practitioners in New South Wales, Australia, recruited 26 patients with CVD/lifestyle risk factors who were not taking cholesterol or blood pressure-lowering medication in 2012. Participants were asked to “think aloud” while using two heart age calculators in random order, with semi-structured interviews before and after. Transcribed audio recordings were coded and a framework analysis method was used.

Results

Risk factor questions were often misinterpreted, reducing the accuracy of the calculators. Participants perceived older heart age as confronting and younger heart age as positive but unrealistic. Unexpected or contradictory results (eg, low percentage risk but older heart age) led participants to question the credibility of the calculators. Reasons to discredit the results included the absence of relevant lifestyle questions and impact of corporate sponsorship. However, the calculators prompted participants to consider lifestyle changes irrespective of whether they received younger, same, or older heart age results.

Conclusions

Online heart age calculators can be misunderstood and disregarded if they produce unexpected or contradictory results, but they may still motivate lifestyle changes. Future research should investigate both the benefits and harms of communicating risk in this way, and how to increase the reliability and credibility of online health risk calculators.

Related collections

Most cited references 30

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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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To evaluate the impact of telling patients their estimated spirometric lung age as an incentive to quit smoking. Randomised controlled trial. Five general practices in Hertfordshire, England. 561 current smokers aged over 35. All participants were offered spirometric assessment of lung function. Participants in intervention group received their results in terms of "lung age" (the age of the average healthy individual who would perform similar to them on spirometry). Those in the control group received a raw figure for forced expiratory volume at one second (FEV1). Both groups were advised to quit and offered referral to local NHS smoking cessation services. The primary outcome measure was verified cessation of smoking by salivary cotinine testing 12 months after recruitment. Secondary outcomes were reported changes in daily consumption of cigarettes and identification of new diagnoses of chronic obstructive lung disease. Follow-up was 89%. Independently verified quit rates at 12 months in the intervention and control groups, respectively, were 13.6% and 6.4% (difference 7.2%, P=0.005, 95% confidence interval 2.2% to 12.1%; number needed to treat 14). People with worse spirometric lung age were no more likely to have quit than those with normal lung age in either group. Cost per successful quitter was estimated at 280 pounds sterling (366 euros, $556). A new diagnosis of obstructive lung disease was made in 17% in the intervention group and 14% in the control group; a total of 16% (89/561) of participants. Telling smokers their lung age significantly improves the likelihood of them quitting smoking, but the mechanism by which this intervention achieves its effect is unclear. National Research Register N0096173751.

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Author and article information

Contributors

Kirsten McCaffery:

Screening and Test Evaluation Program (STEP)Sydney School of Public HealthThe University of SydneyRm 301, Edward Ford Building A27The University of SydneySydney, NSW 2006Australia61 2 9351 722061 2 9351 5049kirsten.mccaffery@sydney.edu.au

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications Inc. (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: May 2014

Publication date (Electronic): 05 May 2014

Volume: 16

Issue: 5

Electronic Location Identifier: e120

Affiliations

[1] ¹Screening and Test Evaluation Program (STEP) Sydney School of Public Health The University of Sydney SydneyAustralia

[2] ²Centre for Medical Psychology and Evidence-Based Decision-Making (CeMPED) The University of Sydney SydneyAustralia

[3] ³School of Psychology University of New South Wales SydneyAustralia

[4] ⁴Faculty of Health Sciences and Medicine Bond University RobinaAustralia

Author notes

Corresponding Author: Kirsten McCaffery kirsten.mccaffery@ 123456sydney.edu.au

Article

Publisher ID: v16i5e120

DOI: 10.2196/jmir.3190

PMC ID: 4026572

PubMed ID: 24797339

SO-VID: 4573d000-6da2-4a80-be1d-84a0e8287122

Copyright © ©Carissa Bonner, Jesse Jansen, Ben R Newell, Les Irwig, Paul Glasziou, Jenny Doust, Haryana Dhillon, Kirsten McCaffery. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 05.05.2014.

License:

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

Date received : 16 December 2013

Date revision requested : 10 March 2014

Date revision received : 19 March 2014

Date accepted : 14 April 2014

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