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Assessing Cardiorespiratory Fitness to Improve Risk Prediction and Get More People Moving: a Late but Welcome Guest to the Party

Disclosure: Dr. Farrell has nothing to disclose.
Pub Date: Monday, Nov. 21, 2016
Author: Stephen W. Farrell, PhD, FACSM
Affiliation: The Cooper Institute, Dallas Texas

Citation

Ross R, Blair SN, Arena R, Church TS, Despres J-P, Franklin BA, Haskell WL, Kaminsky LA, Levine BD, Lavie CJ, Myers J, Niebauer J, Sallis R, Sawada SS, Sui X, Wisloff U; on behalf of the American Heart Association Physical Activity Committee of the Council on Lifestyle and Cardiometabolic Health; Council on Clinical Cardiology; Council on Epidemiology and Prevention; Council on Cardiovascular and Stroke Nursing; Council on Genomic and Precision Medicine; and Stroke Council. Importance of assessing cardiorespiratory fitness in clinical practice: a case for fitness as a clinical vital sign: a scientific statement from the American Heart Association [published online ahead of print November 21, 2016]. Circulation. doi: 10.1161/CIR.0000000000000461.

Article Text

For over a century, cardiovascular disease (CVD) has been the leading cause of death among U.S. men and women. While major risk factors for CVD such as cigarette smoking, hypertension, obesity, and abnormal blood cholesterol level were identified as early as the 1960’s, it has only been in the past quarter-century or so that a low level of cardiorespiratory fitness (CRF) has emerged as being strongly and independently associated with all-cause, CVD, and cancer mortality.1,2,3  It is important to note that this association is present not only in healthy men and women, but also in those with suspected or known CVD, as well as those with comorbid conditions such as obesity, type 2 diabetes, and hypertension. In fact, many papers have shown that low CRF is a more powerful predictor of mortality risk than the aforementioned ‘traditional’ risk factors. Despite these strong associations, CRF remains the only major risk factor that is not routinely assessed in clinical practice. Furthermore, CRF is not included in risk calculators such as the 2013 AHA/ACC guidelines for the assessment of cardiovascular risk.4

In this paper, Ross and colleagues begin with an impressive review of the literature regarding the inverse association between CRF and numerous health outcomes. These outcomes are not limited to mortality risk; morbidity risk is significantly impacted by CRF as well. For example, there is a strong inverse association between CRF and the risk of developing dementia, Alzheimer’s disease, prediabetes, metabolic syndrome, type 2 diabetes mellitus, and disability. The authors emphasize that the literature consistently shows the greatest mortality/morbidity risk reduction is seen when comparing the least fit group (~<5 METS) to the next least fit group (~5-7 METS).5   They go on to make the important point that one need not have the fitness level of an elite athlete in order to reap the benefits of improved CRF.  Accordingly, the authors state that interventions for increasing physical activity levels would appear to be of the greatest health benefit in those who are the least fit

As mentioned previously, CRF is not included in any of the currently used CVD risk prediction models. In order for CRF to be a true risk factor included in future CVD risk prediction models, assessing CRF must significantly improve risk prediction beyond traditional risk factors. The authors identify recent studies indicating that the use of newer statistical tools such as net reclassification improvement (NRI) show that the addition of CRF to traditional risk factors correctly and significantly alters risk classification. Additionally, they point out numerous studies which have shown that the addition of CRF to established risk prediction models such as the Framingham risk score adds to the predictive value of these models.6,7

A commonly held belief among clinicians is that it is simply not feasible to assess CRF in most clinical settings. The authors provide concise descriptions of the many ways CRF can be assessed, some of which are actually very inexpensive and quick. While it is true that a maximal treadmill or cycle ergometer exercise test with ventilatory expired gas analysis is the gold standard for quantifying CRF, there are many other available options. For example, maximal and submaximal exercise tests without ventilatory expired gas analysis can be utilized to obtain a good estimate of CRF. Many clinicians make the mistake of allowing their patients to tightly grip the treadmill handrails during exercise testing. The authors correctly note that doing so will significantly overestimate CRF, but that lightly resting the hands on the handrail may be acceptable. The authors also note that the 6-minute walk test can be used in a clinical setting for patients who are markedly deconditioned.8 For settings where maximal or submaximal testing is not feasible, there are field tests that can be self-administered which provide good estimates of CRF. These include the Cooper 12 minute run test9 and the 1-mile walk test.10 Because of its lower intensity and reduced impact on joints, the latter is more appropriate than the former for older, sedentary and/or obese individuals.

Perhaps the most realistic way for the majority of physicians to assess CRF is via the use of non-exercise prediction equations. While some might be initially skeptical of this approach, the authors discuss several CRF prediction equations that utilize variables such as age, gender, body mass index, physical activity level, smoking status, and resting heart rate.  With regard to estimating CRF, these non-exercise prediction equations have actually been shown to be similar in accuracy to submaximal exercise testing.11 Thus, the barriers of ‘exercise testing is too time consuming,’ and ‘I don’t have the necessary space, staff, or equipment for exercise testing’ is eliminated. Using a non-exercise prediction equation to estimate CRF takes no more time or equipment than assessing resting blood pressure and heart rate.

It is important that clinicians not view CRF simply as a surrogate measure for physical activity. In studies where CRF and self-reported physical activity are assessed and used as predictors of CVD risk, CRF is typically a significantly stronger predictor of the two.12 This may be due to the fact that there is less misclassification when using measured or estimated CRF as opposed to self-reported physical activity. Along these same lines, it is also important to note that CRF is influenced by genetic13 and non-genetic factors other than habitual physical activity. For example, there are situations where a patient might have a relatively high CRF despite a low level of self-reported physical activity, and vice-versa.

It is well-known that sedentary lifestyle is all too common in our society, with ~80% of U.S. adults failing to meet the minimal physical activity guidelines set forth by the Department of Health and Human Services in 2008.14  By comparison, the current prevalence of cigarette smoking among U.S. adults is only 17%.15 While a smoker would very likely be urged to quit during an annual physical examination, individuals with low CRF would typically not be identified. By failing to assess CRF in clinical practice, accurate risk stratification of patients can be significantly compromised. Thus, the population who would benefit the most from regular moderate-intensity physical activity i.e. those who are the least fit, are unlikely to receive the necessary critical recommendations relative to improving CRF.

It is highly unlikely that a patient could undergo a routine physical examination without measures of body mass index, blood lipids, blood glucose, resting blood pressure and resting heart rate. This paper makes a compelling argument to include routine assessment of CRF as a vital sign as well. All clinicians are urged to assess the CRF of their patients using whatever means are available, to carefully consider CRF when stratifying risk, and to provide physical activity recommendations, particularly for patients with low CRF.

References

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  2. Laukkanen JA, Kurl S, Salonen B, Rauramaa R, Solonen JT. The predictive value of cardiorespiratory fitness for cardiovascular events in men with various risk profiles: a prospective population-based cohort study. Eur Heart J. 2004;25:1428-1437.  doi: 10.1016/j.ehj.2004.06.013
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  6. Barlow CE, Defina LF, Radford NB, Berry JD, Cooper KH, Haskell WL, Jones LW, Lakoski SG. Cardiorespiratory fitness and long-term survival in “low-risk” adults. J Am Heart Assoc. 2012;1:e001354. doi: 10.1161/JAHA.112.001354. 
  7. Gander JC, Sui X, Hebert JR, Hazlett LJ, Cai B, Lavie CJ, Blair SN.  Association of cardiorespiratory fitness with coronary heart disease in asymptomatic men. Mayo Clin Proc. 2015;90(10):1372-1379.  doi:10.1016/jmayocp.2015.07.017.
  8. ATS Committee on Proficiency Standards for Clinical Pulmonary Function Laboratories. ATS statement: guidelines for the six-minute walk test. Am J Respir Crit Care Med. 2002;166:111-117. doi: 10.1164/ajrccm.166.1.at1102.
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  10. Kline GM, Porcari JP, Hintermeister R, Freedson PS, Ward A, McCarron RF, Ross J, Rippe JM. Estimation of VO2max from a one-mile track walk, gender, age, and body weight. Med Sci Sports Exerc. 1987;19:253-259.
  11. Jackson AS, Blair SN, Mahar MT, Wier LT, Ross RM, Stuteville JE. Prediction of functional aerobic capacity without exercise testing. Med Sci Sports Exerc. 1990;22:863-870.
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  13. Bouchard C, An P, Rice T, Skinner JS, Wilmore JH, Gagnon J, Perusse L, Leon AS, Rao DC. Familial aggregation of VO2max response to exercise training:results from the HERITAGE family study. J Appl Physiol. 1999;87(3):1003-1008.
  14. Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee Report, 2008. Washington, DC: U.S. Department of Health and Human Services, 2008.
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-- The opinions expressed in this commentary are not necessarily those of the editors or of the American Heart Association --