Cardiovascular diseases (CVDs) are the leading cause of death worldwide . Primary care settings afford an opportunity to initiate CVD prevention efforts as more than 90% of patient interactions with the healthcare system commence in these settings [2,3]. Primary care practitioners (PCPs) are ideally suited to identify patients at elevated risk of CVD because of the size of the populations they serve and patient acceptance of their role in preventive care . The roles of PCPs in preventive care are clearly identified among various clinical guideline recommendations [5,6]. The 2016 Canadian Cardiovascular Society Guidelines recommend an initial risk assessment be completed using the Framingham risk score (FRS) to estimate total cardiovascular risk . Despite the potential benefits of calculating and discussing patients’ cardiovascular risk, most healthcare providers do not routinely use these approaches to guide primary prevention practices . Implementation barriers exist at various patient (e.g. motivation and health literacy), practitioner (e.g. beliefs about effectiveness), practice (e.g. time and organizational capacity), and system (e.g. funding and workforce) levels [8,9]. These barriers contribute to low rates of clinical assessment of risk, lifestyle advice, and referral. Programs available to PCPs and their patients, particularly in established cardiovascular settings, can provide distinct assistance by specifically addressing those factors which produce elevated levels of risk.
A variety of health behaviors have been established as direct and modifiable risk factors for CVD; they include poor dietary habits, physical inactivity, and cigarette smoking each of which may contribute to or accentuate existing levels of risk [1,10]. More than 70% of patients seek the advice of their PCP when trying to modify unhealthy behaviors . PCPs are thus in an ideal position to assess and address lifestyle-related behavioral risk factors. Few PCPs provide behavioral counseling especially around nutrition and physical activity . Many physicians may ask about a patient's physical activity level, but few provide referrals or resources . When helping patients modify unhealthy behaviors, physicians typically rely on health information and their professional status to convince patients to change. It has been noted that physicians do not rate highly their effectiveness in supporting patients’ understanding or taking action to prevent or manage heart disease [14,15▪]. When it comes to helping patients modify unhealthy behaviors, many preventive strategies (e.g., lifestyle screening, behavioral counseling, linkages with community resources) fall outside the perceived scope and culture of clinical medicine . Physician-reported barriers to providing counseling or support include lack of time, inadequate training and skills, lack of supportive resources, and concern regarding the effectiveness of providing counseling [15▪,16–21].
HEALTH BEHAVIOUR COACHING AND TELEHEALTH
Health behavior coaching has been used in a variety of settings to improve health behaviors and potential CVD outcomes [22,23]. Evidence suggests that individuals who exhibit five or more ‘healthy’ characteristics (i.e. nonsmoker, healthy diet, regular physical activity, low cholesterol, low-risk BMI, controlled blood pressure, and normal HbA1c) have up to an 88% reduced risk of death because of circulatory causes . A recent systematic review identified that lifestyle counseling programs assisting individuals at risk for CVD generally result in improvements in physical activity and diet and lower levels of cholesterol, blood pressure, glucose, and adiposity . Evidence suggests that cognitive-behavioral strategies are an essential component of interventions targeting behavior change . These strategies focus on changing how an individual thinks about themselves, their behaviors, and surrounding circumstances and how to modify their lifestyle . Unfortunately, it is rare that a provider can effectively personalize and deliver such counseling given the constraints surrounding a typical patient–provider interaction. Therefore, there is a growing demand for interventions (e.g. interactive health portals, risk management programmes, and other clinical colleagues) to deliver effective behavioral counseling of those identified as being at high risk. The U.S. Preventive Services Task Force recently recommended that adults at risk for CVD (e.g. overweight or obese and hypertension, dyslipidemia, impaired fasting glucose, metabolic syndrome) be offered intensive behavioral counseling interventions to improve diet and physical activity levels .
THE CARDIOPREVENT PROGRAM
CardioPrevent is a global CVD risk reduction program created and tested at the University of Ottawa Heart Institute (http://pwc.ottawaheart.ca/care/cardioprevent-program). The program is intended for patients who have not yet suffered a CVD event, but have a predicted 10-year risk of a hard CVD outcome of at least 10%. Trained coaches assist patients to make behavioral changes (e.g. smoking cessation, dietary modification, increase physical activity) and work with the patient's PCP to optimize management of underlying medical risk factors (e.g. hypertension, dyslipidemia, diabetes). The CardioPrevent program is delivered primarily over the phone; patients complete 23 coaching sessions over a 12-month treatment period. Coaching scripts for each session are standardized in a treatment manual. Figure 1 provides an overview of the CardioPrevent program. The original CardioPrevent program was developed and tested in a randomized controlled trial (RCT) . Results of the RCT informed improvements to the program and the current version of the program has been available to PCPs in the Champlain LHIN since 2014. Based upon our experience, we extrapolate that Ontario-wide implementation of the CardioPrevent program could reduce the number of CVD events by 28 000 over a 10-year period.
The objective of the current investigation was to evaluate the effectiveness of CardioPrevent in reducing CVD risk among those who have completed the program. The primary outcome was change in global CVD risk as measured by percentage change in the FRS . The evaluation also assessed the program's effects on health behaviors, psychosocial variables, and clinical risk factors. Health behaviors include: smoking (previous 6 months), fruit and vegetable consumption (5–7 servings/day) , moderate-to-vigorous intensity physical activity (MVPA; metabolic equivalent of task-minutes/week, minutes/week, ≥150 min/week) , sitting time (minutes/day) , and medication adherence . Psychosocial factors include: anxiety , depression , perceived stress , health-related quality of life (HRQoL) , perceived social support , and level of confidence when interacting with physicians . Bio-physiological risk factors include: total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol-to-HDL ratio (TC-HDL), triglycerides, fasting blood glucose, HbA1c, diastolic blood pressure (DBP), systolic blood pressure (SBP), body weight, BMI, and waist circumference. Program participants completed an anonymous patient satisfaction survey composed of 16 questions assessing satisfaction with program contact, frequency, personalization, and the provision of skills necessary to reduce CVD risk.
Program completers (complete 6-month FRS at time of analysis) and noncompleters (loss to follow-up or drop out) were compared using independent sample t-tests (normally distributed continuous), the independent samples Mann–Whitney U-test (not-normally distributed continuous), and the Pearson Chi-square (categorical data). The evaluation compared changes in FRS, health behaviors, and clinical risk factors from baseline to 6 and 12 months. Participants were included in the evaluations if they had adequate information to generate a FRS at 6 and 12 months, respectively. Little's missing completely at random (MCAR) test was used to test whether data were missing at random or if there was a pattern to their ‘missingness’. Data were initially examined for normality using Q-Q plots. Changes between intake and 6 months were examined using paired t tests for parametric data, Wilxocon signed rank tests for nonparametric data, and the McNemar test for dichotomous variables. Changes across intake, 6-month and 12-month follow-ups were examined using repeated measures ANOVA for normally distributed outcomes, the Friedman test for nonparametric outcomes, and Cochran's Q-test for categorical outcomes. The 6-month analysis was repeated after imputing missing data for noncompleters by carrying the baseline value forward.
A total of 478 participants were enrolled in the program at the time of analysis, 308 had completed the 6-month follow-up and 236 had completed the 12-month follow-up (many remained active in the program). Results of the MCAR test identified that data were missing completely at random (X 2 = 17 916.596, P = 0.371). Table 1 describes baseline sociodemographic characteristics and FRS of all participants who completed the 6-month follow-up (N = 308), and a comparison to program ‘noncompleters’ (N = 63). Program completers were on average older and were more likely to be female, whereas noncompleters were more likely to have a lower income ($10 000–$25 000).
Table 2 describes changes in FRS, clinical risk factors, psychosocial factors, and health behaviors over 6 months in 308 program completers. A significant percentage change in the FRS (6-month – baseline/baseline × 100) was observed with an average reduction of 19.5% ± 38.1% (mean ± standard deviation, median = –26.5%). A significant sex difference in percentage change in the FRS was observed; women experienced a significantly greater reduction in risk compared to men (women = −23.1% ± 35.8 vs. men = −11.4% ± 42.0%, t = 2.50, P = 0.013).
Statistically significant improvements were observed for all bio-physiological outcomes except for HDL and fasting glucose. Significant and positive changes were observed for all psychosocial outcomes except for ‘level of confidence when interacting with physicians’ and perceived social support. Between intake and 6 months, participants reported increasing their level of MVPA, fruit and vegetable consumption, and medication adherence, and reported a reduction in time spent sitting. The proportion of smokers did not change significantly. Analyses with baseline data carried forward for noncompleters generated virtually identical results.
Table 3 describes changes in FRS, clinical risk factors, psychosocial factors, and health behaviors among 236 participants with 12-month outcomes. Percentage change in FRS from baseline to 12 months was significant (mean = −16.6% ± 41.1%, median = −26.2%), but did not differ from 6-month scores (mean = +12.1% ± 49.7%, median = 0%). A significant effect of time was observed for all clinical outcomes except for HDL, fasting glucose, and SBP. Post-hoc analysis identified that 12-month FRS, body weight, BMI, total cholesterol, LDL, DBP, and waist circumference were significantly improved over baseline, but not significantly different from 6-month results. Measurements of TC-HDL showed continued improvement between 6 and 12 months. SBP while reduced at 6 months was no longer significantly different at 12 months. Results of the analysis identified a significant effect of time on measures of perceived stress, anxiety, depression, and mental HRQoL; post-hoc analysis, however, demonstrated that 12 months results while significantly improved over baseline, did not significantly differ from 6-month results.
Participants’ MVPA levels, fruit and vegetable consumption, and time spent sitting remained significantly improved in comparison to baseline values, but were not significantly different than 6-month levels. The proportion of smokers did not change significantly throughout the program. At 12 months, medication adherence was not significantly different from baseline or 6 months.
Results of the satisfaction survey indicated that respondents were very satisfied (98% agreed) with the program and would recommend it to others. The majority (97–99.5%) agreed that: they were involved with setting their health goals, were provided with information and support relevant to their health goals, the program contacts were well structured and helped to progress towards health goals, they had a good relationship with their Health Coach, their Health Coach was knowledgeable, they completed the program with a better understanding of how to prevent CVD, and were confident they could continue their heart healthy lifestyle upon completion of the CardioPrevent program.
The present evaluation describes the changes in cardiovascular risk, clinical and psychosocial factors, and health behaviors of participants in the CardioPrevent CVD risk reduction program. This evaluation also examined whether changes observed in these outcomes were maintained 1 year after program commencement. The evaluation revealed several key findings. First, program completers were on average older and were more likely to be female and have a higher income compared to noncompleters. Second, among those who completed the program (6 months), significant and clinically meaningful improvements were observed for global CVD risk as measured by the FRS, most clinical and psychosocial outcomes, and all health behaviors except smoking. Interestingly, women experienced a greater reduction in their FRS compared to men. Third, the majority of improvements observed at 6 months were maintained at 12 months, supporting the intention to focus on health behavior skill development in the first 6 months of the intervention and ‘booster’ contacts in the latter 6 months to maintain positive gains. Finally, participants expressed a high degree of satisfaction with the program.
Findings of this evaluation suggest that CardioPrevent is an effective program for reducing CVD risk. The results are in contrast to previous systematic reviews that have described the results of telehealth and multifactorial risk factor interventions (for CVD risk reduction) as being inconsistent and of moderate quality [37–40]. In their systematic review of telehealth interventions for the primary prevention of CVD, Merriel et al.  showed no clear evidence for a reduction in 10-year FRS (standardized mean difference = 0.37%, 95% confidence interval, −2.08–1.33) in the intervention compared to control groups. There was also insufficient evidence to suggest that telehealth interventions were effective in reducing SBP, total cholesterol, HDL, or smoking . They did however, note that there was a large degree of heterogeneity between trials . Ebrahim et al.  in their systematic review of multiple risk factor interventions in workforces and primary care found evidence that interventions which used counseling and education for behavior change were not effective at reducing total or coronary heart disease-specific mortality or events in general populations. They did however, see significant effects on mortality and events in high-risk hypertensive and diabetic populations, likely owing to the use of drug treatments for lowering blood pressure . They also noted that the quality of the trials was not optimal with problems with reporting and the consistency of intervention delivery (i.e. variations in interventions over time and across sites). Their systematic review did find interventions produced small reductions in risk factors including blood pressure, total cholesterol and smoking .
Changes in clinical outcomes were not only statistically significant, but often clinically meaningful. The percentage change in the FRS was –19.5% ± 38.1% (median = –26.5%). Previous literature has identified a 7% reduction in the FRS as meaningful . Significant changes in LDL (–11%), total cholesterol (–9%), and TC-HDL (–9.5%) were also observed, and equate to an approximate reduction of 19%, 17%, and 12% in the risk of major coronary events, respectively . Participants experienced an average reduction of ∼4 cm in waist circumference which equates to an approximate 8% decrease in risk of CVD events .
Not only did the CardioPrevent program improve biophysiological factors, it also resulted in improvements in lifestyle behaviors including MVPA, diet, sitting behaviors, and medication adherence. Improvements in lifestyle behaviors have been linked to reduced risk of future CVD, events, and mortality [44,45]. In fact, it has been suggested that the development of CVD can be reduced by ∼80% with lifestyle modifications . The landmark case-controlled study; INTERHEART demonstrated that cigarette smoking, abnormal ratio of blood lipids, diabetes, hypertension, abdominal obesity, depression, stress, poor fruit and vegetable consumption, low levels of physical activity, and higher alcohol consumption could predict 90 and 94% of the risk for a myocardial infarction in men and women, respectively . Although no significant differences in the proportion of smokers was observed from baseline to 6 and 12 months, smokers constituted the largest group lost to follow up. Results identified a 16% increase in the number of participants meeting Canadian MVPA guidelines. Consistent evidence shows that meeting these guidelines is associated with reduced CVD risk and any additional increase in physical activity and fitness can lead to further improvements in health . Although 67% of participants reported meeting MVPA guidelines at intake (similar to 62% of Canadians based on self-reported physical activity ), there was an increase in total minutes/week indicating that many who were already meeting guidelines continued to increase their levels of activity. Results also identified a 9% increase in those eating an adequate amount of fruits and vegetables. Adequate fruit and vegetable consumption has also been directly associated with a reduced risk of CVD [45,46]. Our results are particularly impressive given that primary prevention interventions have generally resulted in modest improvements ranging from ∼0.1 to 0.6 serving/day [48,49]. Fruit and vegetable consumption is likely more difficult to influence given that food preferences are dependent on personal tastes, family composition, socioeconomics and culture, and are largely influenced by availability .
The present evaluation is not without limitations. First, this is an evaluation of a preexisting program, and does not include a control group. As a result, the improvements observed cannot necessarily be attributed to the program alone, however, the program is based on results of a previous RCT . Future evaluation would benefit from longer term tracking to assess continued maintenance of improvements. Participants in the CardioPrevent program were largely Caucasian, married and were of higher socioeconomic status, thus limiting the generalizability of findings beyond these populations. Finally, this evaluation relied on self-reported health behaviors. It has been shown that self-reported PA and fruit and vegetable consumption are often overestimated, whereas sedentary time is often underestimated [51–54]. Important however, is that the improvements in MVPA, diet and sitting behaviors were accompanied by improvements in biophysiological measures. Although future evaluations might benefit from the use of real- time and more objective tracking of behaviors to reduce recall and response bias, it is important to consider that outcome measures for ‘real-world’ programs need to be time and cost-efficient.
Results of this evaluation identified that CardioPrevent is an effective CVD risk reduction program with highly satisfied participants. Significant and clinically meaningful changes were observed in biophysiological, psychosocial, and behavioral outcomes, and these improvements were maintained at 1-year post commencement. CardioPrevent improves patient-centeredness by giving patients easy access to expert behavioral support that is tailored to their unique needs. Efficiency is improved because individual PCPs do not have to create behavior change interventions from scratch; instead referral to CardioPrevent counselors is simple and centralized. Effectiveness is enhanced by having counselors work on behavior change issues whereas physicians focus on any necessary medical management of cardiovascular risk factors, thereby improving the efficiency of provider visits. Timeliness for patient access to needed care is enhanced, as the CardioPrevent program adds needed capacity for high-quality behavior change programming. Ultimately, CardioPrevent is scalable and can be disseminated to improve healthcare system efficiency in preventive care.
We would like to thank the participants of the CardioPrevent program. The authors would also like to thank Natalie Martin, Courtney Westcott, Nadine Elias, Deborah Younger-Lewis, Sarah Ives, Jessica Nooyen, and Sue Perron for their contributions to the implementation and evaluation of the program.
Financial support and sponsorship
The CardioPrevent program is funded by the Prevention and Wellness Centre and the Canadian Women's Heart Health Centre at the University of Ottawa Heart Institute.
Conflicts of interest
There are no conflicts of interest.
REFERENCES AND RECOMMENDED READING
Papers of particular interest, published within the annual period of review, have been highlighted as:
- ▪ of special interest
- ▪▪ of outstanding interest
4. McDonnell LA, Pipe AL, Westcott C, et al. Perceived vs actual knowledge and risk of heart disease in women: findings from a Canadian survey on heart health awareness, attitudes, and lifestyle. Can J Cardiol 2014; 30:827–834.
5. Anderson TJ, Grégoire J, Pearson GJ, et al. 2016 Canadian Cardiovascular Society guidelines for the management of dyslipidemia for the prevention
of cardiovascular disease
in the adult. Can J Cardiol 2016; 32:1263–1282.
6. Ray KK, Kastelein JJ, Boekholdt SM, et al. The ACC/AHA 2013 guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular disease
risk in adults: the good the bad and the uncertain: a comparison with ESC/EAS guidelines for the management of dyslipidaemias 2011. Eur Heart J 2014; 35:960–968.
7. Shaneyfelt TM, Centor RM. Reassessment of clinical practice guidelines: go gently into that good night. JAMA 2009; 301:868–869.
8. Yarnall KS, Pollak KI, Ostbye T, et al. Primary care: is there enough time for prevention
? Am J Public Health 2003; 93:635–641.
9. Cabana MD, Rand CS, Powe NR, et al. Why don’t physicians follow clinical practice guidelines? A framework for improvement. JAMA 1999; 282:1458–1465.
10. O’Doherty MG, Cairns K, O’Neill V, et al. Effect of major lifestyle risk factors, independent and jointly, on life expectancy with and without cardiovascular disease
: results from the Consortium on Health and Ageing Network of Cohorts in Europe and the United States (CHANCES). Eur J Epidemiol 2016; 31:455–468.
11. Elder JP, Ayala GX, Harris S. Theories and intervention approaches to health-behavior change in primary care. Am J Prev Med 1999; 17:275–284.
12. Bock C, Diehl K, Schneider S, et al. Behavioral counseling for cardiovascular disease prevention
in primary care settings: a systematic review of practice and associated factors. Med Care Res Rev 2012; 69:495–518.
13. Petrella RJ, Lattanzio CN, Overend TJ. Physical activity counseling and prescription among Canadian primary care physicians. Arch Intern Med 2007; 167:1774–1781.
14. Mosca L, Linfante AH, Benjamin EJ, et al. National study of physician awareness and adherence to cardiovascular disease prevention
guidelines. Circulation 2005; 111:499–510.
15▪. McDonnell LA, Turek M, Coutinho T, et al. Women's heart health: knowledge, beliefs, and practices of Canadian physicians. J Women Health 2017; In press.
This is the first Canadian survey assessing physicians knowledge, beliefs, and practices around women's heart health.
16. Liddy C, Singh J, Hogg W, et al. Quality of cardiovascular disease
care in Ontario, Canada: missed opportunities for prevention
– a cross sectional study. BMC Cardiovasc Disor 2012; 12:74.
17. Whitlock EP, Orleans CT, Pender N, Allan J. Evaluating primary care behavioral counseling interventions: an evidence-based approach. Am J Prev Med 2002; 22:267–284.
18. Kennedy MF, Meeuwisse W. Exercise counselling by family physicians in Canada. Prev Med 2003; 37:226–232.
19. Hebert ET, Caughy MO, Shuval K. Primary care providers’ perceptions of physical activity counselling in a clinical setting: a systematic review. Br J Sports Med 2012; 46:625–631.
20. Holtrop JS, Malouin R, Weismantel D, Wadland WC. Clinician perceptions of factors influencing referrals to a smoking cessation program. BMC Fam Pract 2008; 9:18.
21. Oberg EB, Frank E. Physicians’ health practices strongly influence patient health practices. J Roy Coll Phys Edin 2009; 39:290–291.
22. Kivelä K, Elo S, Kyngäs H, Kääriäinen M. The effects of health coaching
on adult patients with chronic diseases: a systematic review. Patient Educ Couns 2014; 97:147–157.
23. LeFevre ML. Behavioral counseling to promote a healthful diet and physical activity for cardiovascular disease prevention
in adults with cardiovascular risk factors: US Preventive Services Task Force recommendation statement on behavioral counseling in adults with cardiovascular risk factors. Ann Intern Med 2014; 161:587–593.
24. Ford ES, Greenlund KJ, Hong Y. Ideal cardiovascular health and mortality from all causes and diseases of the circulatory system among adults in the United States. Circulation 2012; 125:987–995.
25. Lin JS, O’Connor EA, Evans CV, et al. Behavioral counseling to promote a healthy lifestyle for cardiovascular disease prevention
in persons with cardiovascular risk factors. An updated systematic evidence review for the U.S. Ann Intern Med 2014; 161:568–578.
26. Artinian NT, Fletcher GF, Mozaffarian D, et al. Interventions to promote physical activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart Association. Circulation 2010; 122:406–441.
27. Reid RD, McDonnell LA, Riley DL, et al. Effect of an intervention to improve the cardiovascular health of family members of patients with coronary artery disease: a randomized trial. CMAJ 2014; 186:23–30.
29. Gans KM, Ross E, Barner CW, et al. REAP and WAVE: new tools to rapidly assess/discuss nutrition with patients. J Nutr 2003; 133:s556–s562.
30. Craig CL, Marshall AL, Sjostrom M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003; 35:1381–1395.
31. Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care 1986; 24:67–74.
32. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta psychiatrica Scandinavica 1983; 67:361–370.
33. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav 1983; 24:385–396.
34. Ware J Jr, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care 1996; 34:220–233.
35. Zimet GD, Dahlem NW, Zimet SG, Farley GK. The multidimensional scale of perceived social support. J Pers Assess 1988; 52:30–41.
36. Maly RC, Frank JC, Marshall GN, et al. Perceived efficacy in patient-physician interactions (PEPPI): validation of an instrument in older persons. J Am Geriatr Soc 1998; 46:889–894.
37. Merriel SWD, Andrews V, Salisbury C. Telehealth
interventions for primary prevention
of cardiovascular disease
: a systematic review and meta-analysis. Prev Med 2014; 64:88–95.
38. Wootton R. Twenty years of telemedicine in chronic disease management–an evidence synthesis. J Telemed Telecare 2012; 18:211–220.
39. Willis A, Davies M, Yates T, Khunti K. Primary prevention
of cardiovascular disease
using validated risk scores: a systematic review. J Roy Soc Med 2012; 105:348–356.
40. Ebrahim S, Smith GD. Systematic review of randomised controlled trials of multiple risk factor interventions for preventing coronary heart disease. BMJ 1997; 314:1666–1674.
41. Richardson G, van Woerden HC, Morgan L, et al. Healthy hearts–a community-based primary prevention
programme to reduce coronary heart disease. BMC Cardiovasc Disord 2008; 8:18.
42. Pedersen TR, Olsson AG, Faergeman O, et al. Lipoprotein changes and reduction in the incidence of major coronary heart disease events in the Scandinavian Simvastatin Survival Study (4S). Circulation 1998; 97:1453–1460.
43. de Koning L, Merchant AT, Pogue J, Anand SS. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur Heart J 2007; 28:850–856.
44. Warburton DER, Nicol CW, Bredin SSD. Health benefits of physical activity: the evidence. CMAJ 2006; 174:801–809.
45. Buttar HS, Li T, Ravi N. Prevention
of cardiovascular diseases: role of exercise, dietary interventions, obesity and smoking cessation. Exper Clin Cardiol 2005; 10:229–249.
46. Yusuf S, Hawken S, Ounpuu S, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 2004; 364:937–952.
47. Garriguet D, Colley RC. A comparison of self-reported leisure-time physical activity and measured moderate-to-vigorous physical activity in adolescents and adults. Health Rep 2014; 25:3.
48. Pomerleau J, Lock K, Knai C, McKee M. Interventions designed to increase adult fruit and vegetable intake can be effective: a systematic review of the literature. J Nutr 2005; 135:2486–2495.
49. Ammerman AS, Lindquist CH, Lohr KN, Hersey J. The efficacy of behavioral interventions to modify dietary fat and fruit and vegetable intake: a review of the evidence. Prev Med 2002; 35:25–41.
50. Kamphuis CB, Giskes K, de Bruijn G-J, et al. Environmental determinants of fruit and vegetable consumption among adults: a systematic review. Br J Nutr 2006; 96:620–635.
51. Prince SA, Adamo KB, Hamel ME, et al. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int J Behav Nutr Phys Act 2008; 5:56.
52. Hill RJ, Davies PS. The validity of self-reported energy intake as determined using the doubly labelled water technique. Br J Nutr 2001; 85:415–430.
53. Schoeller DA. How accurate is self-reported dietary energy intake? Nutr Rev 1990; 48:373–379.
54. Gibbs BB, Hergenroeder AL, Katzmarzyk PT, et al. Definition, measurement, and health risks associated with sedentary behavior. Med Sci Sports Exerc 2015; 47:1295–1300.