Skip Navigation LinksHome > January 2014 - Volume 56 - Issue 1 > Workers' Knowledge and Beliefs About Cardiometabolic Health...
Journal of Occupational & Environmental Medicine:
doi: 10.1097/JOM.0000000000000041
Original Articles

Workers' Knowledge and Beliefs About Cardiometabolic Health Risk

Damman, Olga C. PhD; van der Beek, Allard J. PhD; Timmermans, Danielle R.M. PhD

Free Access
Article Outline
Collapse Box

Author Information

From the Department of Public and Occupational Health and the EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands.

Address correspondence to: Olga C. Damman, PhD, Department of Public and Occupational Health and the EMGO+ Institute for Health Care and Research, VU University Medical Center, Van der Boechorststraat 7, 1081 BT Amsterdam, the Netherlands (

This study received financial support from the “Live a Healthy and Long Life” program of the Dutch Diabetes Research Foundation, the Dutch Heart Foundation, and the Dutch Kidney Foundation.

The authors declare no conflicts of interest.

Collapse Box


Objective: Investigate workers' knowledge and beliefs about cardiometabolic risk.

Methods: A survey on the risks of diabetes, cardiovascular disease, and chronic kidney disease was disseminated among Dutch construction workers and employees from the general working population.

Results: We had 482 respondents (26.8%) among construction workers and 738 respondents (65.1%) among the general working population. Employees showed reasonable basic knowledge, especially about cardiovascular disease risk factors and risk reduction. Nevertheless, they also had knowledge gaps (eg, specific dietary intake) and showed misconceptions of what elevated risk entails. Employees having lower education, being male, and having lower health literacy demonstrated less adequate knowledge and beliefs.

Conclusion: To improve the potential effect of health risk assessments in the occupational setting, physicians should explain what it means to be at elevated cardiometabolic risk and target their messages to employee subgroups.

The cardiometabolic diseases diabetes, cardiovascular disease (CVD), and chronic kidney disease are a major health problem worldwide.1 These diseases have common lifestyle-related risk factors2,3 and a lot can be done to reduce cardiometabolic risk, the most important strategies being smoking cessation, becoming more physically active, and adopting a healthy diet.4–7 Nevertheless, to accomplish such lifestyle changes, public awareness and knowledge seem required, because accurate risk knowledge and beliefs are a precondition for several cognitions influencing risk-reducing behavior.8–12

In primary care as well as occupational health services, health risk assessments are increasingly applied to determine people's health risks.13,14 Disease risk prediction models are used to calculate people's risk, to educate them about their risk, and to motivate those at elevated risk to modify their lifestyle. Especially in occupational health care, it is feasible to reach large numbers of (relatively healthy) people with different lifestyle and socioeconomic status.15 Other advantages of the occupational setting are that companies are usually able to offer lifestyle interventions to large populations and that they also have an interest in such initiatives (ie, healthy workers and less sick leave). Despite an increase in such strategies, many people in industrialized countries such as the Netherlands continue to smoke, eat unhealthily, and display sedentary behavior.16–18

Effective health education and especially supporting people in acquiring a correct risk understanding has proved to be difficult.19–21 It may be that the epidemiological approach typically endorsed is not the most effective way to get the message across to employees. In this respect, it seems that employees' own perspective could be more explicitly taken into account in risk education.22 Furthermore, for well-targeted messages, it is important to identify differences in knowledge and beliefs between employee subgroups. People from lower socioeconomic status backgrounds will probably have less correct knowledge and beliefs, because of a cycle of scant formal education, inadequate reading and arithmetic skills, and poor conceptual health knowledge and vocabulary. In addition, the general culture toward health promotion might differ between companies and branches of industry (eg, the construction industry vs the financial sector), which could be related to employees' knowledge and beliefs. If branch of industry, sociodemographic, or other individual characteristics indeed have a profound effect on specific knowledge and beliefs, this could provide clinicians with suggestions for well-targeted risk education. Although several studies investigated people's knowledge and beliefs about diabetes and CVD8,23–29 this research has tended to focus on individual diseases, rather than on disease risk as a consequence of unhealthy lifestyle. Because more and more emphasis is placed on the shared risk factors of cardiometabolic diseases as well as the interplay of detrimental physiological processes,30 it is important to assess how workers themselves think about cardiometabolic risk.

To achieve better results from cardiometabolic health risk assessments in the occupational setting, we conducted this study, in which the knowledge and beliefs regarding cardiometabolic risk were examined in different Dutch employee subgroups. In addition, we compared employees from the construction industry with employees from the general working population, to be able to look at differences between branches of industry. By investigating potential knowledge gaps and misconceptions, we looked for focal points for better and well-targeted risk education by occupational physicians.

Back to Top | Article Outline


We used a survey on the risks of diabetes, CVD, and chronic kidney disease; this survey was based on a qualitative study in which concepts important to experts and lay people were identified.31,22 Because a large number of concepts needed to be captured, we divided the survey into two versions.

Back to Top | Article Outline
Participants and Procedure

We approached two samples: 1800 construction workers eligible for their periodic health check and 1133 people from the general working population. We chose these samples to be able to look at differences between branches of industry; employees in the construction industry were selected because of their relatively low educational level and socioeconomic status as well as a high prevalence of overweight and obesity, a group of workers for whom lifestyle changes are likely to be highly effective.32 Construction workers might have less correct knowledge and beliefs than those from the general working population because of inadequate conceptual health vocabulary and relatively low health literacy levels, and because of the relatively negative culture toward health promotion in the construction industry.33 Construction workers aged 16 to 65 years were invited via a letter sent by Arbouw. Arbouw is the Dutch national institute coordinating occupational health care for all workers in the construction industry. Construction workers could choose to fill out the questionnaire either on-line or using pencil and paper. Employees from the general working population were drawn from an on-line access panel (FlyCatcher Internet Research, 20,000 panel members in total, ISO 20252 and ISO 26362 certified); panel members aged 16 to 65 years with paid employment were invited through e-mail. We oversampled (60%) people aged between 40 and 65 years because of typical higher cardiometabolic risks in older people.

Back to Top | Article Outline

Both survey versions consisted of five parts capturing knowledge and beliefs about (1) causes and risk factors, (2) physiological processes, (3) disease symptoms, (4) disease consequences, and (5) risk-reduction strategies. These categories were based on our previously developed expert model of cardiometabolic risk31 and on typical lay representations of diseases.34,35 First, concepts for which it was clear that they were either correct or incorrect were included: expert concepts (eg, genetic predisposition), lay concepts (eg, poverty), and trick questions (eg, infections). Second, we posed questions about concepts whose accuracy was less definite. These predominantly concerned pronounced lay beliefs (eg, having had another disease) and concepts important to both lay people and experts but with experts' notion that little evidence is available so far (eg, the muscular system). All items were formulated for the three diseases in a matrix format with response categories “true,” “false,” and “don't know.” Third, we posed questions about people's representation of the cardiometabolic risk concept, such as multifactorial influence of factors and the diseases' interconnectedness. These items were measured using a scale from 1 (true) to 5 (false). The survey further consisted of items assessing population risk estimates of the diseases (“Of every 100 people in the Netherlands, I think that .... will develop diabetes/CVD/chronic kidney disease during their lifetime,” and “Of every 100 people in the Netherlands, I think that .... will develop diabetes/CVD/chronic kidney disease before the age of 60”), sociodemographic background (age, gender, and educational level), health (self-rated general health status, self-rated general lifestyle, having diabetes, CVD, or kidney disease, and having experienced cardiovascular problems), having a family history of diabetes and CVD, and subjective health literacy.36 The questions were carefully worded to avoid medical language and pretested among three construction workers.

Back to Top | Article Outline
Statistical Analyses

We conducted descriptive analyses to assess the prevalence of knowledge and beliefs. For all items, we calculated average percentages for reported answers. Composite knowledge scores representing knowledge across items were constructed for the five survey parts; only items being either correct or incorrect were included, with “don't know” being considered incorrect. Concerning items assessing people's representation of the cardiometabolic risk concept, mean values were calculated and reverse-coded: a higher score indicated a higher average agreement. We used t tests and multiple linear regression analyses to evaluate which individual characteristics affected knowledge and beliefs. In the regression analyses, the following characteristics were considered: age, gender, educational level, subjective health literacy, having a family history of cardiometabolic diseases, having a cardiometabolic disease, and general health status. Furthermore, we assessed whether adding a branch of industry variable (construction workers vs general working population) had an additional influence.

Back to Top | Article Outline


Participant Characteristics

Among construction workers, version 1 was filled out by 235 workers (26.1%) and version 2 by 247 workers (27.4%), which made 482 respondents (26.8%). Among employees from the general working population, version 1 was filled out by 364 (64.0%) and version 2 by 374 (65.7%), which made 738 respondents (65.1%). In total, we had 1220 respondents, resulting in a response rate of 41.6% (ie, the total number of respondents divided by the total number of people invited for participation). Table 1 displays respondents' characteristics. No differences between the two versions were found for the characteristics listed.

Table 1
Table 1
Image Tools
Back to Top | Article Outline
Employees' Knowledge

The answers to all items are displayed in the Appendix. Table 2 presents the average percentages of correct answers to the knowledge composites as well as differences between employee subgroups in these answers. Employees were most correct regarding concepts related to CVD, followed by diabetes and chronic kidney disease. They were particularly able to identify CVD risk-reduction strategies. Although only few misconceptions were found (eg, sweet food/sugar causes diabetes and drinking too little water causes kidney disease; see the Appendix), employees did show several knowledge gaps. Especially specific risk factors (eg, physical inactivity, ethnicity, and specific dietary intake such as not eating enough grain and fibers), and physiological processes (eg, disturbed metabolism and inflammation processes) were rather unknown to workers (see the Appendix).

Table 2
Table 2
Image Tools

We found a number of significant differences in knowledge between employee subgroups. Higher-educated workers had better knowledge than lower-educated workers (13 of the 15 composites). Furthermore, women consistently seemed to have more knowledge than men (9 of the 15 composites), especially concerning diabetes risk. Employees with low and high health literacy also differed on a substantial number (5) of composites, with workers with higher health literacy reporting better knowledge.

Back to Top | Article Outline
Employees' Representations of Cardiometabolic Risk

Table 3 displays mean values on the items about the cardiometabolic risk concept. Employees were quite familiar with the meaning of high cholesterol and high blood pressure, but high blood glucose levels were poorly comprehended. Employees knew well that there are usually multifactorial causes of cardiometabolic diseases. They were less informed about the diseases' interconnectedness. Although it seemed that employees were slightly aware of some connection, they also thought that the three diseases had different causes. Respondents were able to define an “elevated risk” as a higher-than-average disease risk, but the fact that there may already be physical damage before occurrence of the disease was relatively unknown.

Table 3
Table 3
Image Tools

Subgroups as defined by educational level differed significantly from each other on 9 of the 18 items representing the cardiometabolic risk concept, with higher-educated employees being more correct than lower-educated employees on eight concepts. Notably, for one item (elevated risk means that there is physical damage [true]), lower-educated people were more correct. We also found that women had more correct representations than men on five items. Employees with low and high health literacy differed on four items, with people with higher health literacy having more correct representations.

Back to Top | Article Outline
Employees' Risk Estimates

Highest estimates were given for the population risks of developing CVD (mean lifetime risk = 33.9%, mean risk before the age of 60 years = 24.0%). For diabetes, the mean lifetime risk was rated as 27.6% and the mean risk before the age of 60 years as 19.0%. Concerning kidney disease, the mean lifetime risk was estimated as 17.5% and the mean risk before the age of 60 years as 12.5%. Consistent differences in employee subgroups were found for educational level (with higher-educated people reporting lower risk estimates) and having a family history of diabetes and/or CVD (with people with a family history reporting higher estimates on all three risks).

Back to Top | Article Outline
Multiple Regression Analyses

In the multiple regression analyses, the same pattern of subgroup differences appeared as demonstrated by t tests. Educational level and gender were most predictive of knowledge and beliefs. Overall, a higher education was associated with better knowledge and more correct beliefs, as well as with lower risk estimates (β value ranging from 0.08 to 0.26). The effect that lower-educated employees knew better that there can be already physical damage in the case of an elevated risk continued to exist while adjusting for other variables (β = 0.10; P = 0.01). Female gender was generally related to better knowledge and beliefs (β value ranging from 0.06 to 0.17), except for two concepts (high blood glucose levels means too much of the hormone insulin in your blood (β = 0.08; P = 0.10) and the representation that an elevated risk means there is nothing serious (β = 0.06; P = 0.05). Female gender was also associated with higher risk estimates (β value ranging from 0.08 to 0.14).

Adding the branch of industry did not result overall in more variance explained. We found this variable to be a significant predictor leading to more variance explained for only a few concepts, namely for knowledge about CVD physiological processes (β = −0.08; P = 0.04), representations of abdominal obesity (mainly fat on your belly is a risk: β = −0.12; P = 0.03), elevated risk representations (elevated risk means that you have certain complaints: β = 0.11; P = 0.00), and representations of the diseases' interconnectedness (same type of diseases: β = −0.12; P = 0.00; different causes: β = 0.09; P = 0.02). For all these concepts, people from the general working population showed more correct responses. Adding the variable also resulted in more explained variance of CVD risk estimates (lifetime risk: β = 0.08; P = 0.04; risk before the age of 60 years: β = 0.10; P = 0.01) and kidney disease (lifetime risk: β = 0.14; P = 0.00; risk before the age of 60 years: β = 0.12; P = 0.00), with construction workers reporting higher risk percentages.

Back to Top | Article Outline


In this study, we demonstrated that Dutch employees had reasonable levels of knowledge about the principal concepts related to cardiometabolic risk, especially concerning CVD risk. At the same time, they seemed to have only vague knowledge about the interconnectedness of cardiometabolic diseases, and misconceptions of what elevated risk entails.

An important finding is that, compared with CVD, people had less knowledge about diabetes and chronic kidney disease. It may be that the public has adequately learned about CVD risk through intensive educational messages, while coverage of diabetes and kidney disease risk has been more rare. This pattern would correspond to UK findings that CVD receives more media coverage than diabetes.37 In this respect, it is encouraging that a substantial percentage of employees did identify overweight as a diabetes risk factor, a factor that steadily receives more media attention.38 An important knowledge gap, however, concerned physical inactivity as a risk factor for diabetes. Although these figures have been worse (eg, only 32% of Dutch respondents linked physical inactivity to diabetes in 2007,39 a more prominent focus on physical inactivity in risk education seems warranted (see also Refs. 40 and 41). Health risk assessments at work may be the ideal setting to achieve this; increasing numbers of people have sedentary occupations and several effective workplace interventions have been developed.42

Although the entire survey concentrated on the three diseases and the link between them may have become apparent, employees failed to answer correctly about this relation. This may nevertheless not sound surprising, as the relation between diseases is complex and not broadly reported on.37 Perhaps physicians are reluctant to discuss such details and address it only when people are already affected by one of the diseases. Although such reluctance on the part of physicians seems justifiable, the downside is that people at risk will not focus on the combination of factors that brings about a set of detrimental cardiometabolic processes, and that their knowledge remains fragmentary. Because more than three quarters of respondents also explicitly stated that the three diseases have different causes, we would urge for more effective education about this aspect. In health risk assessments, occupational physicians could explicitly address cardiometabolic risk and explain it in relation to the three diseases, instead of focusing on either diabetes, CVD, or chronic kidney disease. Especially for construction workers, such an approach may be worthwhile, as they showed relatively less correct knowledge of the diseases' interconnectedness.

A final finding to reflect on is that, although employees were able to think of an elevated cardiometabolic risk as a “higher-than-average chance of disease,” this seemed to be an abstract concept without sufficient meaning. Importantly, employees did not know that an elevated risk can mean that there is physiological damage already, and notably higher-educated employees seemed to be even less knowledgeable than the lower-educated employees. Many employees believed that “there is nothing serious” when you have an elevated risk, and especially lower-educated employees and construction workers thought that an elevated risk means that you have certain complaints. Hence, while the lower-educated employees seem to have trouble distinguishing an elevated risk from actually having a disease, the higher-educated employees seem to be insufficiently aware of the physical damage that can be present during early stage diseases. These findings are particularly interesting in light of research indicating that people usually have difficulty in deriving meaning from numerical risk information.43,44 Verbal labels such as “elevated” are often used in combination with numerical risk information to support people in deriving meaning from risk information. An important question is how we should use such verbal labels if people have misconceptions about the label itself.

Back to Top | Article Outline


We had a relatively low response rate among construction workers (26.8%, compared with 65.1% in the general working population). More differences between the branches of industry might have been demonstrated, if we had had a higher response rate among construction workers. It seems plausible that respondents (compared with nonrespondents) among construction workers were more similar to the people from the general working population (because they were people motivated to complete surveys). Another limitation might be that we had only the construction industry as a separate branch of industry to compare to the general working population. Because of the financial crisis, other branches of industry that were initially interested in participation unfortunately declined to participate. Further research could focus on employees' knowledge and beliefs about the risks communicated in risk assessments in different branches of industry. Another limitation might be that we did not test the questionnaire among a large group of people. Nevertheless, pretesting has been done by collecting information from three interviews, where the third interview did not reveal new insights in addition to the two earlier interviews. Nevertheless, it should be mentioned that, in our pretest, we focused on interpretation problems with specific concepts, and we did not evaluate the complete experience of filling out the questionnaire, nor did we ask respondents for suggestions for improvements. A final limitation is that we had only self-reported data on general health and lifestyle, and no information on employees' actual cardiometabolic risk. Future research could focus on the relation between the extent of people's actual risk and their knowledge and beliefs.

Back to Top | Article Outline


To achieve better results from cardiometabolic risk assessments at work, occupational physicians should (1) explain the relation between cardiometabolic diseases and the overlap in risk factors to the employee. Especially physicians working for the construction industry should focus on explaining the role of abdominal obesity and the accompanying physiological processes as key factors for the three diseases, because it seemed that construction workers were particularly ignorant on these issues compared with the general working population; (2) check what an elevated cardiometabolic risk means for the employee (eg, let them explain the risk in their own words) and help produce a meaningful risk understanding. For example, physicians could compare an employee's risk with that of an average employee of their age in their branch of industry, or with an average person their age in the general working population.

It seems that such support should especially be targeted at employee subgroups as defined by educational level, gender, and health literacy, and to some extent also to the employee's branch of industry. Further tests are needed to assess whether such specific support as noted earlier will actually motivate lifestyle changes, and to investigate whether providing the support can be integrated in occupational health guidelines.45

Back to Top | Article Outline

The authors thank Cor van Duijvenbooden (Arbouw) for his help in organizing the data collection in the construction industry.

Back to Top | Article Outline
Survey Items and Average Percentages of People Stating That the Item Is True (n = 1120) Cited Here...
Back to Top | Article Outline


Table. No title avai...
Table. No title avai...
Image Tools
Table. No title avai...
Table. No title avai...
Image Tools
Table. No title avai...
Table. No title avai...
Image Tools
1. World Health Organization, 2011. Available at Accessed October 24, 2013.

2. Dekker JM, Girman C, Rhodes T, et al. Metabolic syndrome and 10-year cardiovascular disease risk in the Hoorn Study. Circulation. 2005;112:666–673.

3. Kurella M, Lo JC, Chertow GM. Metabolic syndrome and the risk for chronic kidney disease among nondiabetic adults. J Am Soc Nephrol. 2005;16:2134–2140.

4. Gaede P, Vedel P, Parving HH, Pedersen O. Intensified multifactorial intervention in patients with type 2 diabetes mellitus and microalbuminuria: the Steno type 2 randomised study. Lancet. 1999;353:617–622.

5. Holme L, Haaheim S, Tonstad I, Hjermann I. Effect of dietary and antismoking advice on the incidence of myocardial infarction: a 16-year follow-up of the Oslo Diet and Antismoking Study after its close. Nutr Metab Cardiovasc Dis. 2006;16:330–338.

6. Lindstrom J, ILanne-Parikka P, Peltonen M, et al. Sustained reduction in the incidence of type 2 diabetes by lifestyle intervention: follow-up of the Finnish Diabetes Prevention Study. Lancet. 2006;368:1673–1679.

7. World Health Organization. The World Health Report 2002. Reducing Risks, Promoting Healthy Life. Geneva, Switzerland: World Health Organization; 2002.

8. Alm-Roijer C, Fridlund B, Stagmo M, Erhardt L. Knowing your risk factors for coronary heart disease improves adherence to advice on lifestyle changes and medication. J Cardiovasc Nurs. 2006;21:E24–E31.

9. Cameron LD, Leventhal H. The Self-regulation of Health and Illness Behaviour. New York, NY: Routledge; 2003.

10. Hall S, Weinman J, Marteau TM. The motivating impact of informing women smokers of a link between smoking and cervical cancer: the role of coherence. Health Psychol. 2004;23:419–424.

11. Rogers RW, Prentice-Dunn S. Protection motivation theory. In: Gochman DS, ed. Handbook of Health Behavior Research: Vol. 1. Personal and Social Determinants. New York, NY: Plenum Press; 1997:113–132.

12. Schmiege SJ, Bryan A, Klein WMP. Distinctions between worry and perceived risk in the context of the Theory of Planned Behavior. J Appl Soc Psychol. 2009;39:95–119.

13. Freedman AN, Seminara D, Gail MH, et al. Cancer risk prediction models: a workshop on development, evaluation, and application. J Natl Cancer Inst. 2005;97:715–723.

14. Nielen MM, Assendelft WJ, Drenthen AJ, van den Hombergh P, van Dis I, Schellevis FG. Primary prevention of cardio-metabolic diseases in general practice: a Dutch survey of attitudes and working methods of general practitioners. Eur J Gen Pract. 2010;16:139–142.

15. Lusk SL, Raymond DM III. Impacting health through the worksite. Nurs Clin North Am. 2002;37:247–256.

16. Ooijendijk WTM, Hildebrandt VH, Hopman-Rock M. Bewegen in Nederland. 2000-2005. Trendrapport Bewegen en Gezondheid 2004/2005. Hoofddorp/Leiden, the Netherlands: TNO; 2007.

17. Ramsey F, Ussery-Hall A, Garcia D, et al. Prevalence of selected risk behaviors and chronic diseases—Behavioral Risk Factor Surveillance System (BRFSS), 39 steps communities, United States, 2005. MMWR Surveill Summ. 2005;57:1–20.

18. Rijksinstituut voor Volksgezondheid en Milieu. Nationaal Kompas Volksgezondheid: Volksgezondheid Toekomstverkenningen. Bilthoven, the Netherlands: Rijksinstituut voor Volksgezondheid en Milieu; 2010.

19. Edwards AG, Evans R, Dundon J, Haigh S, Hood K, Elwyn GJ. Personalised risk communication for informed decision making about taking screening tests. Cochrane Database Syst Rev. 2006;4:CD001865.

20. Hanlon P, McEwen J, Carey L, et al. Health checks and coronary risk: further evidence from a randomized controlled trial. BMJ. 1995;311:1609–1613.

21. Sheridan SL, Viera AJ, Krantz MJ, Ice CL, Steinman LE, Peters KE. The effect of giving global coronary risk information to adults. Arch Intern Med. 2010;170:230–239.

22. Morgan MG, Fischhoff B, Bostrom A, Atman CJ. Risk Communication. A Mental Models Approach. New York, NY: Cambridge University Press; 2002

23. Furze G, Bull P, Lewin R, Thompson DR. Development of the York Angina Beliefs Questionnaire. J Health Psychol. 2003;8:307–316.

24. Furze G, Lewin RJP, Murberg T, Bull P, Thomson DR. Does it matter what patients think? The relationship between changes in patients' beliefs about angina and their psychological and functional status. J Psychosom Res. 2005;59:323–329.

25. Jafary FH, Aslam F, Mahmud H, et al. Cardiovascular health knowledge and behavior in patient attendants at four tertiary care hospitals in Pakistan—a cause for concern. BMC Public Health. 2005;5:124.

26. Kayaniyil S, Ardern C, Winstanley J, et al. Degree and correlates of cardiac knowledge and awareness among cardiac inpatients. Patient Educ Couns. 2009;75:99–107.

27. Momtahan K, Berkman J, Sellick J, Kearns SA, Lauzon N. Patients' understanding of cardiac risk factors: a point-prevalence study. J Cardiovasc Nurs. 2004;19:13–20.

28. Petrie KJ, Weinman J, Sharpe N, Buckley J. Role of patients' view of their illness in predicting return to work and functioning after myocardial infarction: longitudinal study. BMJ. 1996;312:1191–1194.

29. Valerio MA, Kanjirath PP, Klausner CP, Peters MC. A qualitative examination of patient awareness and understanding of type 2 diabetes and oral health care needs. Diabetes Res Clin Pract. 2011;93:159–165.

30. Cardiometabolic Risk Working Group. Cardiometabolic risk in Canada: a detailed analysis and position paper by the Cardiometabolic Risk Working Group. Can J Cardiol. 2011;17:e1–e33.

31. Damman OC, Timmermans DRM. Educating health consumers about cardio-metabolic health risk: what can we learn from lay mental models of risk? Patient Educ Couns. 2012;89:300–308.

32. Groeneveld IF, Proper KI, van der Beek AJ, van Mechelen W. Sustained body weight reduction by an individual-based lifestyle intervention for workers in the construction industry at risk for cardiovascular disease: results of a randomized controlled trial. Prev Med. 2010;51:240–246.

33. Groeneveld IF, Proper KI, van der Beek AJ, Hildebrand VH, van Mechelen W. Factors associated with non-participation and drop-out in a lifestyle intervention study for workers with an elevated risk of cardiovascular disease. Int J Behav Nutr Phys Act. 2009;6:80.

34. Leventhal H, Benyamini Y, Brownlee S, et al. Illness representations: theoretical foundations. In: Petrie KJ, Weinman JA, eds. Perceptions of Health and Illness. Amsterdam, the Netherlands: Harwood Academic Publishers; 1997:19–45.

35. Hagger M, Orbell S. A meta-analytic review of the common-sense model of illness representations. Psychol Health. 2003;18:141–184.

36. Chew LD, Bradley KA, Boyko EJ. Brief questions to identify patients with inadequate health literacy. Fam Med. 2004;36:588–594.

37. Hellyer NE, Haddock-Fraser J. Reporting diet-related health issues through newspapers: portrayal of cardiovascular disease and type 2 diabetes. Health Educ Res. 2010;26:13–25.

38. Gollust SE, Lantz P. Communicating population health: print news media coverage of type 2 diabetes. Soc Sci Med. 2009;69:1091–1098.

39. Dutch Diabetes Research Foundation. Onderzoek Preventie Diabetes. Tussenmeting 2010. Amersfoort, the Netherlands: Diabetes Research Foundation/GfK; 2010.

40. Reiner Z, Sonicki Z, Tedeschi-Reiner E. Public perceptions of cardiovascular risk factors in Croatia: the PERCRO survey. Prev Med. 2010;51:491–496.

41. Khan KM. Prescribing exercise in primary care. BMJ. 2011;343:d4141.

42. Freak-Poli R, Wolfe R, Backholer K, de Courten M, Peeters A. Change in risk factors for cardiovascular disease and diabetes amongst participants in a four-month pedometer-based workplace health program. Prev Med. 2011;53:162–171.

43. Weinstein ND. What does it mean to understand a risk? Evaluating risk comprehension. J Natl Cancer Inst Monogr. 1999;25:15–20.

44. Fagerlin A, Zikmund-Fisher BJ, Ubel PA. Helping patients decide: ten steps to better risk communication. J Natl Cancer Inst. 2011;103:1436–1443.

45. Verweij LM, Proper KI, Weel AN, Hulshof CT, Van Mechelen W. The application of an occupational health guideline reduces sedentary behavior and increases fruit intake at work: results from an RCT. Occup Environ Med. 2012;69:500–507.

Copyright © 2014 by the American College of Occupational and Environmental Medicine


Article Tools