Estimating V˙O2peak from a Nonexercise Prediction Model

Nes, Bjarne M.; Janszky, Imre; Vatten, Lars J.; Nilsen, Tom Ivar Lund; Aspenes, Stian Thoresen; Wisløff, Ulrik

doi:10.1249/mss.0b013e31821d3f6f

Cited by 179 publications

(244 citation statements)

References 29 publications

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“…Also in a study by Ainsworth et al (1992) [2] age, gender, BMI and frequency of vigorous exercise per week explain 75% of the variations in VO 2 max for men. Recently, Nes et al (2011) [22] have further demonstrated that age, waist circumference, leisure time physical activity and resting heart rate together explained 61% of variance in VO 2 max for men.…”

Section: Discussionmentioning

confidence: 97%

Estimation of aerobic fitness among young men without exercise test

Tanskanen

Kyröläinen

Santtila

et al. 2015

Biomedical Human Kinetics

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SummaryStudy aim: to develop and estimate the validity of non-exercise methods to predict VO 2 max among young male conscripts entering military service in order to divide them into the different physical training groups. Material and methods: fifty males (age 19.7 ± 0.3 years) reported their physical activity before military service by IPAQ and SIVAQ questionnaires. Furthermore, Jackson's non-exercise method was used to estimate VO 2 max. Body mass and height were measured, body mass index calculated and VO 2 max measured directly in a maximal treadmill test. Subjects were randomly divided into two groups. The results of the Group 1 (N = 25) were used to develop a regression equation to estimate VO 2 max. The results of the Group 2 (N = 25) were used to evaluate the validity of the developed non-exercise methods and Jackson's non-exercise methods to estimate VO 2 max by Bland and Altman plot. The validity was further evaluated by comparing the results to 12-minute running test performed by 877 male conscripts (age 19.6 ± 0.2 years). Results: the developed models explained 68-74% of the variation in VO 2 max. Mean difference between directly measured and estimated VO 2 max was not significant, while Jackson's method overestimated VO 2 max (p < 0.001). Both developed models were equally valid to divide conscripts into tertile group of fitness. However, 5% of the conscripts were classified into the highest fitness group based on both methods, but they were actually in the lowest fitness group based on a running test. Conclusion: in practice, these findings suggest that developed methods can be used as a tool to divide conscripts into different fitness groups in the very beginning of their military service.

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Section: Discussionmentioning

confidence: 97%

Estimation of aerobic fitness among young men without exercise test

Tanskanen

Kyröläinen

Santtila

et al. 2015

Biomedical Human Kinetics

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“…One systematic review 171 of 13 nonexercise equations is presented in Table 6. 172,[179][180][181][182][183][184][185][186][187][188][189][190] These equations were developed with cross-sectional data using age, sex, body weight (or body mass index, percentage of body fat, waist circumference), physical activity/exercise/ training (self-reported or measured), smoking, resting HR, or perceived functional ability as predictors of CRF. The R 2 and SEEs ranged from 0.50 to 0.86 and 2.98 to 6.90 mL·kg…”

Section: Submaximal Exercise Testing Without Cpx Measurementsmentioning

confidence: 99%

“…172,174,181,191 A limitation of these equations is that they tend to underestimate and overestimate CRF at the upper and lower ends of the distribution, respectively. 172,179,180,182,185,190 The underestimation is unlikely to affect highly fit individuals, who will still be correctly classified into the higher CRF categories; however, the overestimation for people with low CRF could be a concern because of the associated heightened risk among these men and women. 38,172,182,192 Despite this, most models derived from large studies correctly classify individuals into low-fitness categories.…”

Section: Submaximal Exercise Testing Without Cpx Measurementsmentioning

confidence: 99%

“…90 In this study, the 2005 nonexercise model proposed by Jurca et al 185 derived previously. 190 The sample included 37 112 individuals who were followed up for a mean of 24 years in the HUNT study (Nord-Trøndelag Health Study). After adjustment for potential confounders, each 1-MET higher CRF was associated with 21% lower CVD mortality for both men and women who were <60 years of age at baseline, and the corresponding lower risks for all-cause mortality were 15% for men and 8% for women.…”

Section: Associations Between Nonexercise Estimated Crf and Cvdmentioning

confidence: 99%

“…The 13 nonexercise equations given in Table 6 90,98 and showed a comparable risk reduction to measured CRF. 16 A stepby-step procedure for using the Nes equation 190 to estimate CRF with routine clinical measures can be readily accessed by both the practitioner and the patient. 194 Estimation of CRF provides the practitioner with a platform for counselling the patient regarding the importance of physical activity.…”

Section: Associations Between Nonexercise Estimated Crf and Cvdmentioning

confidence: 99%

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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

Ross¹,

Blair²,

Arena³

et al. 2016

Circulation

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1,681

1,697

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ABSTRACT:Mounting evidence has firmly established that low levels of cardiorespiratory fitness (CRF) are associated with a high risk of cardiovascular disease, all-cause mortality, and mortality rates attributable to various cancers. A growing body of epidemiological and clinical evidence demonstrates not only that CRF is a potentially stronger predictor of mortality than established risk factors such as smoking, hypertension, high cholesterol, and type 2 diabetes mellitus, but that the addition of CRF to traditional risk factors significantly improves the reclassification of risk for adverse outcomes. The purpose of this statement is to review current knowledge related to the association between CRF and health outcomes, increase awareness of the added value of CRF to improve risk prediction, and suggest future directions in research. Although the statement is not intended to be a comprehensive review, critical references that address important advances in the field are highlighted. The underlying premise of this statement is that the addition of CRF for risk classification presents health professionals with unique opportunities to improve patient management and to encourage lifestyle-based strategies designed to reduce cardiovascular risk. These opportunities must be realized to optimize the prevention and treatment of cardiovascular disease and hence meet the American Heart Association's 2020 goals.M ounting evidence over the past 3 decades has firmly established that low levels of cardiorespiratory fitness (CRF) are associated with a high risk of cardiovascular disease (CVD) and all-cause mortality, as well as mortality rates attributable to various cancers, especially of the breast and colon/digestive tract. [1][2][3][4] Importantly, improvements in CRF are associated with reduced mortality risk.5 Although CRF is now recognized as an important marker of cardiovascular health, it is currently the only major risk factor not routinely assessed in clinical practice.In 2013, the American Heart Association and the American College of Cardiology jointly released new guidelines for the prevention and treatment of coronary artery disease.6 Although CRF is the fourth-leading risk factor for CVD and has long been established as a significant prognostic marker, 7 it was excluded from the risk calculator. The authors of the guidelines noted that the evidence that CRF would enhance risk classification was inconclusive, and thus, the added contribution of CRF to determine CVD risk was uncertain. There is, however, a large body of epidemiological and clinical evidence demonstrating not only that CRF is a potentially stronger predictor of mortality than established risk factors such as smoking, hypertension, high cholesterol, and type 2 diabetes mellitus (T2DM), but that the addition of CRF to traditional risk factors significantly improves the reclassification of risk for adverse outcomes.

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Metabolomic correlates of aerobic capacity among elderly adults

Koh

Gao

Tan

et al. 2018

Clinical Cardiology

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Background: Aerobic capacity is a powerful predictor of cardiovascular disease and all-cause mortality and it declines with advancing age. Since physical activity alters body metabolism, metabolism markers will likely differ between subjects with high versus low aerobic capacities. Methods: Community-based participants without physician-diagnosed heart disease, stroke or cancer underwent same-day multi-modal assessment of cardiovascular function (by echocardiography and magnetic resonance feature tracking of left atrium) and aerobic capacity by peak oxygen uptake (VO2) metrics. Associations between VO2 and cardiovascular and metabolomics profiles were studied in adjusted models including standard covariates. Results: We studied 141 participants, of whom 82 (58.2%) had low VO2 while 59 (41.8%) had high VO2. Compared to participantts with high VO2, participants with low VO2 had more adverse cardiovascular parameters, such as lower ratio of peak velocity flow in early diastole to peak velocity flow in late diastole by atrial contraction of >0.8 (76% vs 35%, adjusted OR 4.1 95%CI (1.7–9.5), p=0.001) and lower LA conduit strain (11.3±4.0 vs 15.6±6.1%, adjusted OR 1.1 95%CI 1.002–1.3), p=0.045). High VO2 was associated with lower accumulation of wide-spectrum acylcarnitines (OR 0.6, 95% CI 0.4–0.9, p=0.013), alanine (OR 0.1, 95%CI 0.01–0.9, p=0.044) and glutamine/glutamate (OR 0.1, 95%CI 0.01–0.5, p=0.007), compared to low VO2. Conclusion: Elderly adults with low VO2 have adverse cardiovascular and metabolic parameters compared to their counterparts with high VO2. Combined cardiac and metabolomics phenotyping may be a promising tool to provide insights into physiological states, useful for tracking future interventions related to physical activity among community cohorts.

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Estimating V˙O2peak from a Nonexercise Prediction Model

Cited by 179 publications

References 29 publications

Estimation of aerobic fitness among young men without exercise test

Estimation of aerobic fitness among young men without exercise test

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

Metabolomic correlates of aerobic capacity among elderly adults

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