Abstract
Objective
“Biological age” calculators are widely used as a way of communicating health risk. This study evaluated the behaviour change techniques (BCTs) within such tools, underlying algorithm differences and suitability for people with varying health literacy.
Methods
Two authors entered terms into Google (eg, biological/heart age) and recorded the first 50 results. A standard patient profile was entered into eligible biological age calculators. Evaluation was based on Michie et al's BCT taxonomy and a readability calculator.
Results
From 4000 search results, 20 calculators were identified: 11 for cardiovascular age, 7 for general biological age and 2 for fitness age. The calculators gave variable results for the same 65‐year‐old profile: biological age ranged from younger to older (57‐87 years), while heart age was always older (69‐85+ years). Only 11/20 (55%) provided a reference explaining the underlying algorithm. The average reading level was Grade 10 (range 8.7‐12.4; SD 1.44). The most common BCTs were salience of consequences, information about health consequences and credible source.
Conclusions
Biological age tools have highly variable results, BCTs and readability.
Practice Implications
Developers are advised to use validated models, explain the result at the average Grade 8 reading level, and incorporate a clear call to action using evidence‐based behaviour change techniques.
Keywords: behaviour change, biological age, ehealth, heart age, risk communication
1. INTRODUCTION
Biological age concepts are used in health promotion to convey risk to patients as “older” age relative to their current age, with the aim of motivating behaviour change. 1 This can include many different ways of comparing risk, ability or developmental stage to either an average or an ideal value. Often this involves putting risk factors into a calculator to determine whether risk is higher (older age) or lower (younger age) than the reference standard. Examples include organ‐specific concepts like heart age (also referred to as cardiovascular age or vascular age), lung age and kidney age, or broader concepts such as effective age. 2
Trials of interventions using biological age concepts have found improved risk factor control compared to standard care (eg, lung age and heart age 3 , 4 ). There are currently millions of users of international, United Kingdom and Australian heart age tools 5 , 6 , 7 designed for health promotion. Clinical applications of biological age tools include bone age, 8 fertility age, 9 , 10 lung age, 4 , 11 kidney age 12 to assess functioning or growth based on expected development stage. Use in the media includes broader age‐based risk concepts, 13 such as reality TV shows where participants are provided with their biological age based on multiple risk factors and tests to motivate them to improve their lifestyle. There are also training programs based on fitness age and brain age. 14 , 15 , 16
Why are these concepts appealing? Marketing utilises age anxiety to increase purchasing of anti‐ageing products, 17 so perhaps this is why they are useful for getting attention – people tend to feel younger than they really are, 18 and this phenomena exists across cultures. 19 Experiments testing heart age against absolute percentage risk formats (eg, 10% chance of a heart attack in the next 5 years) have found that it increases emotional impact and recall, 20 , 21 , 22 supporting the idea that it is an effective and salient marketing tool. But additional support may be needed to translate this attention into meaningful behaviour change.
Despite their popularity in a range of fields, a recent rapid review of biological age interventions found increasing publications but mixed evidence for their effect on lifestyle change. 1 One reason for this is it is unclear what the “active ingredients” are in biological age interventions based on the brief descriptions provided in published studies – what we refer to as biological age interventions may actually involve many different behaviour change techniques. 23
The aim of this study was to characterise the content of publicly available age‐based risk calculators that involve entering risk factors including current age, and provide the risk result as an alternative age, in terms of: (i) “biological age” type, (ii) behaviour change techniques, and (iii) readability.
2. METHODS
To access publicly available tools that consumers might find in searches relating to health promotion, an environmental scan methodology using Google instead of academic databases was selected. This method has previously been used across many health topics to investigate consumer facing tools, 24 , 25 , 26 as interventions in the academic literature may be paywalled or not made public at the end of the research study.
In 2018, CB and MF developed the methodology and supervised two Master of Public Health students to test search terms and conduct an initial pilot search, with advisory input from LT. This method was conducted more formally in 2020, but the initial pilot results were included and reconciled to capture any additional calculators that might have been missed due to differing Google search histories and algorithms. We repeated the student search with trained researchers to ensure rigour and identify any new biological age tools that may have been developed.
The author team developed the search terms based on a previous rapid review of biological age, 1 with additional terms relating to organs (eg, kidney) or function (eg, fertility) added in consultation with a GP (LT). Two authors (CAB and MF) independently entered general and organ‐specific age terms into Google (biological age, heart age, vascular age, cardiovascular age, arterial age, bio age, health age, vitality age, effective age, real age, physiological age, fitness age, metabolic age, bone age, mental age, joint age, brain age, lung age, kidney age, liver age), combined with “risk calculator” (eg, “heart age risk calculator”), resulting in 20 final search terms. Both MF and CAB used Google Australia in Mozilla Firefox Private Mode in order to minimise Google search optimisation. Based on pilot searches conducted in previous environmental scans of consumer health tools, 24 , 25 , 26 we determined 50 results to be the threshold where further eligible webpages are unlikely to be identified. The first 50 URLs from each reviewer‐specific research were exported using the SEO Quake plugin for Mozilla Firefox, resulting in 50 × 20 results from each reviewer. Duplicates were removed, and the final list of both 2020 and 2018 search results was screened and assessed for eligibility using inclusion criteria (freely available and no account required to access the calculator, requires entering risk factors into a calculator [including current age], English language, relates to humans, relates to health, result is an age) and exclusion criteria (aimed at health professionals, cannot access results without entering contact details, does not work after trying on two days and on two separate computers, result is life expectancy).
A standard patient profile from a previous study 24 was entered into the final set of biological age calculators in order to compare the “age” results from each (see Table 1). Other data extracted from the calculators were: each risk factor that was asked, the type of “age” that was calculated, whether the calculation was based on a defined algorithm (and if so, which one) and whether it explained who developed the website. These were extracted by CAB, with CB second‐coding a random sample of 15% of the calculators to ensure reliability. Interrater agreement was 100%. Content evaluation was based on Michie et al's behaviour change technique (BCT) taxonomy 23 with two coders (CAB and CB, with discrepancies resolved by discussion), and “Simple Measure of Gobbledygook” (SMOG) readability calculations (CAB using www.online-utility.org when results contained 10+ sentences). The SMOG score based on US grade scores was used because there were no Australian readability calculators available at the time of the study. Initial interrater agreement for evaluating the presence/absence of 93 BCTs was high overall, with full agreement across six of the calculators. Kappa statistics (κ) for the remaining 14 BCTs were calculated. Kappa statistics range between 0 and 1 and provide a quantitative measure of the magnitude of agreement between observers, where low agreement could be considered less than 0.4, moderate agreement between 0.4 and 0.6, substantial agreement between 0.61 and 0.8 and almost perfect agreement 0.81 and 0.99. 27 One calculator's agreement was low (κ = .187, P = .054), however, the rest were all moderate‐to‐substantial (κ = .489‐.883, P < .001). Some definitions were required to apply the BCT taxonomy consistently, as described in Table 2.
TABLE 1.
Patient profile for biological age calculators
| Characteristic | Value |
|---|---|
| Demographics |
Gender: Male Age: 65 years [DOB 01/01/1955] Ethnicity: Caucasian Location: lives in Cambridgeshire in the United Kingdom Marital status: married Employment status: full time job Education: finished high school but no further education |
| Lifestyle risk factors |
Smoker Overweight (BMI of 26); weight 85 kg and height 180 cm, 100 cm waist circumference 7 hours sleep Moderate exercise 60 minutes per week on 2 days, no vigorous exercise Sits for 7 hours a day Eats breakfast, 1 fruit and 2 vegetables every day 7 light beers per week Resting pulse: 80 |
| Clinical risk factors |
No personal or family history of disease (including CVD event and diabetes) No medications Cholesterol: 6 mmol/L total and 1 mmol/L HDL, 3 mmol/L LDL Blood pressure: systolic 130 mm Hg and diastolic 80 mm Hg Blood glucose: 5.6 mmol/L |
TABLE 2.
Application of behaviour change techniques to biological age calculators
| Behaviour change technique | Examples in biological calculators | Example results text |
|---|---|---|
| Salience of consequences |
Biological age result Personalisation of risk Image or graph to convey risk |
“Your heart age is 70” “for 100 people with the same responses, 8 people will have a heart attack or stroke” |
| Social comparison | Comparing to average in text/graph |
“The health of your heart is comparable to the average 62 year old Canadian” |
| Social support | Contacting another person | “Get free quit smoking assistance, including medication and counselling support, by phone and online” |
| Information about health consequences |
Description of health outcome Life expectancy change |
“You have about a 39% chance of having a stroke or heart attack in the next 10 years” “Your life expectancy is 77.6” |
| Goal setting (behaviour) |
Change in lifestyle See a doctor |
“Quit smoking” “suggested minimum: 180 minutes/week of moderate activity” “get a free NHS Health Check” |
| Goal setting (outcome) |
Specific age goal Specific risk factor goal |
“Lose 4 kg” “suggested blood pressure target: 110/70” |
| Instruction on how to perform a behaviour |
Advice on changing lifestyle Advice on seeing doctor |
“Buy foods like fresh fruits and vegetables, whole grains and lean proteins” |
| Comparative imaging of future outcomes |
Tools to see how reducing risk factors affects results Scenario showing how age/risk could reduce |
“Quit smoking and lower your cardiometabolic age to 65” “Increase your physical activity and lower your risk to 38.9%” |
| Credible source |
Academic paper reference Published risk model Institutions Images |
“calculator is developed by physicians and statisticians at McGill University” |
| Pharmacological support | Mentioning medication |
“You may need medication to reduce cholesterol” “Talk with your health care provider about medicine to help quit smoking” |
| Self‐monitoring of outcomes of behaviour | Instructions to keep records of behaviour change | “continue to monitor your progress by bookmarking this page in your browser and … take the test again to monitor any changes” |
3. RESULTS
Figure 1 shows the PRISMA diagram for the pilot search in 2018 and formal search in 2020. From 4000 search results, 189 remained after ineligible and duplicate websites were removed, and 20 eligible calculators were identified. The final sample included 11 relating to heart/cardio/vascular age, 7 relating to general biological/health age and 2 relating to fitness age.
FIGURE 1.
PRISMA diagram for 2018 pilot search and 2020 formal search
Table 1 describes each calculator in terms of validity, risk results, reading level and behaviour change techniques. The calculators asked about a wide range of risk factors, with the most common being gender, whether they smoke or not, height, weight, whether they drink alcohol and whether they have diabetes. They gave variable results for the same 65 year old patient profile even for the same concept, for example, biological age range: 57 (calculator 3) to 87 (calculator 4) years; heart age range: 69 (calculator 9) to 85+ (calculator 16) years. Only 11/20 provided a reference explaining the underlying algorithm (calculators 1, 2, 5, 6, 10, 12, 13, 14, 15, 16, 18). Those that did provide references were developed by both universities and corporations. Of the seven with sufficient text to evaluate readability, the average reading level was Grade 10 (range 8.7‐12.4; SD 1.44; calculators 1, 2, 9, 11, 13, 16, 20).
The biological age tools contained between 1 and 8 BCTs in different combinations. The frequency of different behaviour change techniques (BCTs) is shown in Figure 2. The most common BCTs used by at least half of the tools were: (i) salience of consequences via the biological age concept itself but also other features such as graphs (20/20); (ii) information about health consequences which included conveying the risk of disease and reduced life expectancy (12/20); and (iii) credible source conveyed by reputable institutions, references to published risk models or medical imagery such as a doctor in a white coat (10/20). Various other BCTs were used in the results and many tools included links to other resources that would likely contain additional BCTs, but this was beyond the scope of our evaluation that was focused on the biological age results within the tool itself.
FIGURE 2.
Frequency of behaviour change techniques in biological age results. BCTs coded as present or absent, once per calculator
See Tables 3 and 4 for calculator‐specific details.
TABLE 3.
Description of biological age calculators
| Calculator link | Risk profile result | Developer and model | Readability (US grade) |
|---|---|---|---|
| Cardiometabolic age 70.6 years | Disease simulation model based on 3992 National Nutrition and Examination Survey participants, McGill University, funded by Canadian Institute for Health Research | 8.7 | |
| “Ubble age” 68 years | United Kingdom Biobank model based on 500 000 participants, Karolinska Institutet, Uppsala University; Stanford University | 12.37 | |
| Biological age 57 years | ‐ | ‐ | |
| Biological age 87 years | Nutrilite Optimal Health Center, Buena Park, California | ‐ | |
| Vascular age 70 years | Reference Values for Arterial Measurements Collaboration, Quipu | ‐ | |
| Heart age > 80 years | Framingham heart study | ‐ | |
| Biological age 66.7 years | ‐ | ‐ | |
| Vascular age 78 years | ‐ | ‐ | |
| Cardiovascular age (heart age) 68.8 years | McGill University, funded by Canadian Institute for Health Research | 10.8 | |
| Heart age 83 years | Framingham heart study, CardioSecur | ‐ | |
| Health and fitness age 75 years | Queensland government | 8.7 | |
| Heart age 73 years | Framingham heart study, National Heart Foundation of Australia | ‐ | |
| Heart age 82 years | JBS Heart Age Calculator (QRISK algorithm), National Health Service (United Kingdom) | 9.51 | |
| Heart age 76 years | Cardiovascular Disease Population Risk Tool (CVDPoRT) based on 104 219 Ontario residents from the Canadian Community Health Surveys (2001 to 2007) linked to hospitalisations and deaths, A group of Canadian researchers, clinicians, data scientists and developed | ‐ | |
| Fitness age > 75 years | A Nonexercise Model of Cardiorespiratory Fitness based on a Norweigan cohort of 37 000, The K. G. Jebsen Center of Exercise in Medicine at the Norwegian University of Science and Technology | ‐ | |
| Heart age > 85 years | Framingham heart study, NYC government | 9.49 | |
| Biological age 65 years, Real age 67 years | ‐ | ‐ | |
| Arterial age 64 years | Framingham heart study, The Multi‐Ethnic Study of Atherosclerosis (MESA) researchers | ‐ | |
| Health age 69 years | ‐ | ‐ | |
| Real age 59 years | ‐ | 11.59 |
TABLE 4.
Behaviour change techniques in biological age calculators
| URL | Salience of consequences | Comparative imagining of future outcomes | Social comparison | Goal setting (behaviour) | Goal setting (outcome) | Information about health consequences | Pharmacological support | Social support (unspecified) | Credible source | Instruction on how to perform a behaviour | Self‐monitoring of outcomes of behaviour |
|---|---|---|---|---|---|---|---|---|---|---|---|
| ✓ | ✓ | ‐ | ✓ | ✓ | ‐ | ‐ | ‐ | ✓ | ✓ | ‐ | |
| ✓ | ‐ | ‐ | ‐ | ‐ | ✓ | ‐ | ‐ | ✓ | ‐ | ‐ | |
| ✓ | ‐ | ‐ | ✓ | ‐ | ‐ | ‐ | ‐ | ✓ | ✓ | ‐ | |
| ✓ | ‐ | ‐ | ✓ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | |
| ✓ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | |
| ✓ | ‐ | ‐ | ‐ | ‐ | ✓ | ‐ | ‐ | ‐ | ‐ | ‐ | |
| ✓ | ‐ | ‐ | ‐ | ‐ | ✓ | ‐ | ‐ | ‐ | ‐ | ‐ | |
| ✓ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | |
| ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ‐ | ‐ | ✓ | ✓ | ‐ | |
| ✓ | ✓ | ‐ | ✓ | ‐ | ✓ | ‐ | ‐ | ✓ | ‐ | ‐ | |
| ✓ | ‐ | ‐ | ✓ | ‐ | ✓ | ‐ | ‐ | ✓ | ✓ | ‐ | |
| ✓ | ‐ | ‐ | ✓ | ‐ | ‐ | ‐ | ‐ | ✓ | ✓ | ‐ | |
| ✓ | ✓ | ‐ | ✓ | ✓ | ✓ | ✓ | ‐ | ✓ | ✓ | ‐ | |
| ✓ | ‐ | ✓ | ‐ | ‐ | ✓ | ‐ | ‐ | ✓ | ‐ | ‐ | |
| ✓ | ✓ | ‐ | ‐ | ✓ | ✓ | ‐ | ‐ | ‐ | ‐ | ‐ | |
| ✓ | ‐ | ✓ | ✓ | ‐ | ✓ | ✓ | ✓ | ✓ | ✓ | ‐ | |
| ✓ | ‐ | ‐ | ‐ | ‐ | ✓ | ‐ | ‐ | ‐ | ‐ | ‐ | |
| ✓ | ‐ | ‐ | ‐ | ‐ | ✓ | ‐ | ‐ | ‐ | ‐ | ‐ | |
| ✓ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | |
| ✓ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ✓ |
4. DISCUSSION AND CONCLUSION
4.1. Discussion
This study found a large number of biological age calculators available online, which varied widely in terms of their risk factors, underlying algorithms and results. How would the average consumer make sense of these tools? For most calculators, it is very unclear whether the underlying algorithm is a valid indicator of health risks, and those with sufficient text explaining the result had a readability level that was too high for the average person to understand and act on. 28 The use of credibility cues (eg, a picture of a doctor) did not necessarily correspond to reliable information (eg, having a reference to a validated model). Where a research study was referenced, it was still not clear whether the biological age tool itself had been validated or was suitable for specific populations. For example, the Framingham model was validated in the United States but under‐estimates risk in other populations, and heart age tools based on such models can still label people at low risk as “older.” 29 , 30
The findings are similar to previous environmental scans of online health materials, where readability is generally too high for the average person to understand and act on. 24 , 25 , 26 An earlier review of cardiovascular disease risk calculators included some heart age tools, which similarly provided different age results for the same profile. 29 It is possible to get an older age on one calculator, but a younger age on another, highlighting the variable meaning of biological age and the importance of being clear about the underlying model and assumptions used to calculate the result. 29
In terms of behaviour change techniques, the tools covered a wide range of different strategies, highlighting the need for research studies to very clearly describe the content of their interventions to enable meaningful systematic reviews of this concept in future. 1 We conceptualised the communication of “biological age” itself as a way to increase the salience of consequences, rather than information about health consequences. This is based on our earlier analysis of the heart age concept that shows very variable relationships between have an “older” biological age and your actual risk of a health consequence such as a heart attack – you can have an older age but be at low risk of a health consequence. 30 The concept of increased salience is supported by several studies showing increased emotional responses and recall for heart age compared to the absolute percentage risk of a heart attack or stroke. 20 , 21 , 31 , 32 We have assumed that this would apply to other types of biological age as well, but few comparisons between different types of age‐based risk labels have been conducted. One study has compared fitness age to heart age and found differences in how these are perceived amongst young adults. 33
There were techniques used in the calculators that seemed to be aimed at changing behaviour which were not reflected in the taxonomy. For example, the Healthier Queensland calculator's results pages congratulated the user for any areas that they were currently performing well in (for example, the user eating a good amount of fruit and vegetable leading to “A big gold star for you”). This technique is not in the current BCT taxonomy. Further BCTs that we would have expected to find in the taxonomy include providing tailored results pages based on the information submitted in the calculator and providing background information about the concept reflected in the calculation (such as heart age, fitness age etc). These are also suggested in Agbadjé et al's paper on BCTs within decision aids, 34 further supporting the need for additional BCTs to be added to the taxonomy.
4.2. Strengths and limitations
This study used a novel methodology to identify behaviour change techniques used in biological age tools, following a rigorous process with multiple searchers and coders to improve reliability. However, we cannot guarantee that other researchers would have found the same results as the Google algorithm is not published and may vary in different locations. Although the coders were trained in using a standardised taxonomy of behaviour change technique definitions, there was some subjectivity in the way these were applied as documented in Table 2. Some behaviour change techniques were not coded based on our decision to only include the biological age results section, and apply the training manual definitions (eg, rewards such as positive verbal reinforcement only apply after some effort has been made to change behaviour, so we did not code phrases like “well done” or “gold star”). Others may have chosen to code these tools as more complex interventions including links to other resource, such as reports and programs to support lifestyle change.
4.3. Conclusions
These findings identify several reasons for the variable effects of biological age tools in a recent rapid review, due to high variability in the actual age result, behaviour change techniques and readability. Future developers of biological age tools are advised to use validated models, explain the result at the average Grade 8 reading level, and incorporate a clear call to action using evidence‐based behaviour change techniques.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ETHICS APPROVAL
This study was exempt from ethics approval as no data was collected from participants.
ACKNOWLEDGEMENT
Open access publishing facilitated by The University of Sydney, as part of the Wiley ‐ The University of Sydney agreement via the Council of Australian University Librarians.
Bonner C, Batcup C, Fajardo M, Trevena L. Biological age calculators to motivate lifestyle change: Environmental scan of online tools and evaluation of behaviour change techniques. Health Promot J Austral. 2023;34(1):202–210. 10.1002/hpja.671
Handling editor: Mark Robinson
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
REFERENCES
- 1. Kulendrarajah B, Grey A, Nunan D. How effective are “age” tools at changing patient behaviour? A rapid review. BMJ Evidence‐Based Med. 2020;25:68–72. 10.1136/bmjebm-2019-111244 [DOI] [PubMed] [Google Scholar]
- 2. Spiegelhalter D. How old are you, really? Communicating chronic risk through ‘effective age’ of your body and organs. BMC Med Informatics Decis Mak. 2016;16(1):1–6. 10.1186/S12911-016-0342-Z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Grover SA, Lowensteyn I, Joseph L, Kaouache M, Marchand S, Coupal L, et al. Patient knowledge of coronary risk profile improves the effectiveness of dyslipidemia therapy: the CHECK‐UP study: a randomized controlled trial. Arch Intern Med. 2007;167(21):2296–303. 10.1001/archinte.167.21.2296 [DOI] [PubMed] [Google Scholar]
- 4. Parkes G, Greenhalgh T, Griffin M, Dent R. Effect on smoking quit rate of telling patients their lung age: the Step2quit randomised controlled trial. BMJ. 2008;336:598–600. 10.1136/bmj.39503.582396.25 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Patel RS, Lagord C, Waterall J, Moth M, Knapton M, Deanfield JE. Online self‐assessment of cardiovascular risk using the joint British societies (JBS3)‐derived heart age tool: a descriptive study. BMJ Open. 2016;6:e011511. 10.1136/bmjopen-2016-011511 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Bonner C, Raffoul N, Battaglia T, Mitchell JA, Batcup C, Stavreski B. Experiences of a national web‐based heart age calculator for cardiovascular disease prevention: user characteristics, heart age results, and behavior change survey. J Med Internet Res. 2020;22:e19028. 10.2196/19028 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Neufingerl N, Cobain MR, Newson RS. Web‐based self‐assessment health tools: who are the users and what is the impact of missing input information? J Med Internet Res. 2014;16:e215. 10.2196/jmir.3146 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Cole AJL, Webb L, Cole TJ. Bone age estimation: a comparison of methods. Br J Radiol. 1988;61:683–6. 10.1259/0007-1285-61-728-683 [DOI] [PubMed] [Google Scholar]
- 9. AMH|Anti Mullerian Hormone Test & Ovarian Reserve . IVF Australia. https://www.ivf.com.au/planning‐for‐pregnancy/female‐fertility/ovarian‐reserve‐amh‐test Accessed 30 Nov 2020.
- 10. Laven JSE, Mulders AGMGJ, Visser JA, Themmen AP, de Jong FH, Fauser BCJM. Anti‐Müllerian hormone serum concentrations in normoovulatory and anovulatory women of reproductive age. J Clin Endocrinol Metab. 2004;89:318–23. 10.1210/jc.2003-030932 [DOI] [PubMed] [Google Scholar]
- 11. Quanjer PH, Enright PL. Should we use ‘lung age’? Prim Care Respir J. 2010;19:197–9. 10.4104/pcrj.2010.00045 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Stevens RJ, Evans J, Oke J, Smart B, Hobbs FDR, Holloway E, et al. Kidney age, not kidney disease. CMAJ. 2018;190:E389–93. 10.1503/cmaj.170674 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Bonner C. What is your real “biological age”, and what does this mean for your health? The Conversation. https://theconversation.com/what-is-your-real-biological-age-and-what-does-this-mean-for-your-health-91327. Accessed 6 Nov 2020.
- 14. Free Brain Age Games . https://freebrainagegames.com/. Accessed 6 Nov 2020.
- 15. Nes BM, Janszky I, Vatten LJ, Nilsen TIL, Aspenes ST, Wisløff U. Estimating V·O 2peak from a nonexercise prediction model. Med Sci Sport Exerc. 2011;43:2024–30. 10.1249/MSS.0b013e31821d3f6f [DOI] [PubMed] [Google Scholar]
- 16. Elliott ML, Belsky DW, Knodt AR, Ireland D, Melzer TR, Poulton R, et al. Brain‐age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort. Mol Psychiatry. 2019;26:3829–38. 10.1038/s41380-019-0626-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Muise A, Desmarais S. Women's perceptions and use of “anti‐aging” products. Sex Roles. 2010;63:126–37. 10.1007/s11199-010-9791-5 [DOI] [Google Scholar]
- 18. Montepare JM. Subjective age: toward a guiding lifespan framework. Int J Behav Dev. 2009;33:42–6. 10.1177/0165025408095551 [DOI] [Google Scholar]
- 19. O'Brien EL, Hess TM, Kornadt AE, Rothermund K, Fung H, Voss P. Context influences on the subjective experience of aging: the impact of culture and domains of functioning. Gerontologist. 2017;57:S127–37. 10.1093/geront/gnx015 [DOI] [PubMed] [Google Scholar]
- 20. Bonner C, Jansen J, Newell BR, Irwig L, Teixeira‐Pinto A, Glasziou P, et al. Is the “heart age” concept helpful or harmful compared to absolute cardiovascular disease risk? An experimental study. Med Decis Making. 2015;35:967–78. 10.1177/0272989x15597224 [DOI] [PubMed] [Google Scholar]
- 21. Soureti A, Hurling R, Murray P, Van Mechelen W, Cobain M. Evaluation of a cardiovascular disease risk assessment tool for the promotion of healthier lifestyles. Eur J Prev Cardiol. 2010;17:519–23. 10.1097/HJR.0b013e328337ccd3 [DOI] [PubMed] [Google Scholar]
- 22. Damman OC, Vonk SI, van den Haak MJ, van Hooijdonk CMJ, Timmermans DRM. The effects of infographics and several quantitative versus qualitative formats for cardiovascular disease risk, including heart age, on people's risk understanding. Patient Educ Couns. 2018;101:1410–8. 10.1016/j.pec.2018.03.015 [DOI] [PubMed] [Google Scholar]
- 23. Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013;46:81–95. 10.1007/s12160-013-9486-6 [DOI] [PubMed] [Google Scholar]
- 24. Bonner C, Patel P, Fajardo MA, Zhuang R, Trevena L. Online decision aids for primary cardiovascular disease prevention: systematic search, evaluation of quality and suitability for low health literacy patients. BMJ Open. 2019;9:e025173. 10.1136/bmjopen-2018-025173 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Fajardo MA, Weir KR, Bonner C, Gnjidic D, Jansen J. Availability and readability of patient education materials for deprescribing: an environmental scan. Br J Clin Pharmacol. 2019;85:1396–406. 10.1111/bcp.13912 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Fajardo M, Mph LH, Mph N, Trevena L, Phd M, Bonner C. Systematic review and evaluation of freely available online diabetes medication decision aids. Eur J Pers Cent Healthc. 2019;7:125–32. 10.5750/EJPCH.V7I1.1615 [DOI] [Google Scholar]
- 27. Viera AJ, Garrett JM. Understanding interobserver agreement: the kappa statistic. Fam Med. 2005;37(5):360–3. [PubMed] [Google Scholar]
- 28. Agency for Healthcare Research and Quality RM . The Patient Education Materials Assessment Tool (PEMAT) and User's Guide. https://www.ahrq.gov/ncepcr/tools/self‐mgmt/pemat.html. Accessed 12 Nov 2020.
- 29. Bonner C, Fajardo MA, Hui S, Stubbs R, Trevena L. Clinical validity, understandability, and actionability of online cardiovascular disease risk calculators: systematic review. J Med Internet Res. 2018;20:e29. 10.2196/jmir.8538 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Bonner C, Bell K, Jansen J, Glasziou P, Irwig L, Doust J, et al. Should heart age calculators be used alongside absolute cardiovascular disease risk assessment? (report). BMC Cardiovasc Disord. 2018;18:19. 10.1186/s12872-018-0760-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Witteman HO, Fuhrel‐Forbis A, Wijeysundera HC, Exe N, Dickson M, Holtzman L, et al. Animated randomness, avatars, movement, and personalization in risk graphics. J Med Internet Res. 2014;16:294–313. 10.2196/jmir.2895 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Timmermans D, Ockhuysen‐Vermey C, Henneman L. Presenting health risk information in different formats: the effect on participants' cognitive and emotional evaluation and decisions. Patient Educ Couns. 2008;73:443–7. [DOI] [PubMed] [Google Scholar]
- 33. Van Der Pol‐Harney E, Bonner C, Turner R, McCaffery K. The effects of communicating CVD risk as “fitness age”. Poster presented at: International Congress of Behavioural Medicine (ICBM), Melbourne, Australia; 2016.
- 34. Agbadjé TT, Elidor H, Perin MS, Adekpedjou R, Légaré F. Towards a taxonomy of behavior change techniques for promoting shared decision making. Implement Sci. 2020;15(1):1–13. 10.1186/S13012-020-01015-W [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.