An Overview of Non-exercise Estimated Cardiorespiratory Fitness: Estimation Equations, Cross-Validation and Application

Abstract

Purpose

To summarize current non-exercise prediction models to estimate cardiorespiratory fitness (CRF), cross-validate these models, and apply them to predict health outcomes.

Methods

PubMed search was up to August 2018 for eligible publications. The current review was comprised of three steps. The first step was to search the literature on non-exercise prediction models. The key words combined non-exercise, CRF and one among prediction, prediction model, equation, prediction equation and measurement. The second step was to search the literature about cross-validation of non-exercise equations. The key words included non-exercise, CRF and one among validation, cross-validation and validity. The last step was to search for application of CRF assessed from non-exercise equations. The key words were non-exercise, CRF, mortality, all-cause mortality, cardiovascular disease (CVD) mortality and cancer mortality.

Results

Sixty non-exercise equations were identified. Age, gender, percent body fat, body mass index, weight, height and physical activity status were commonly used in the equations. Several researchers cross-validated non-exercise equations and proved their validity. In addition, non-exercise estimated CRF was significantly associated with all-cause mortality and fatal and nonfatal CVD.

Conclusions

Measurement of CRF from non-exercise models is practical and viable when exercise testing is not feasible. Despite the limitations of equations, application of CRF from non-exercise methods showed accuracy and predictive ability.