Journal Article

A 45-Second Self-Test for Cardiorespiratory Fitness: Heart Rate-Based Estimation in Healthy Individuals

Merati, Giampiero, Mohammed, Rahil A., La Torre, Antonio, Bonato, Matteo, Kubis, Hans-Peter, Sartor, Francesco, Papini, Gabriele, Moore, Jonathan P., Bonomi, Alberto G., Bosio, Andrea

2016

Overview

Cardio-respiratory fitness (CRF) is a widespread essential indicator in Sports Science as well as in Sports Medicine. This study aimed to develop and validate a prediction model for CRF based on a 45 second self-test, which can be conducted anywhere. Criterion validity, test re-test study was set up to accomplish our objectives. Data from 81 healthy volunteers (age: 29 ± 8 years, BMI: 24.0 ± 2.9), 18 of whom females, were used to validate this test against gold standard. Nineteen volunteers repeated this test twice in order to evaluate its repeatability. CRF estimation models were developed using heart rate (HR) features extracted from the resting, exercise, and the recovery phase. The most predictive HR feature was the intercept of the linear equation fitting the HR values during the recovery phase normalized for the height2 (r2 = 0.30). The Ruffier-Dickson Index (RDI), which was originally developed for this squat test, showed a negative significant correlation with CRF (r = -0.40), but explained only 15% of the variability in CRF. A multivariate model based on RDI and sex, age and height increased the explained variability up to 53% with a cross validation (CV) error of 0.532 L ∙ min-1 and substantial repeatability (ICC = 0.91). The best predictive multivariate model made use of the linear intercept of HR at the beginning of the recovery normalized for height2 and age2; this had an adjusted r2 = 0. 59, a CV error of 0.495 L·min-1 and substantial repeatability (ICC = 0.93). It also had a higher agreement in classifying CRF levels (κ = 0.42) than RDI-based model (κ = 0.29). In conclusion, this simple 45 s self-test can be used to estimate and classify CRF in healthy individuals with moderate accuracy and large repeatability when HR recovery features are included.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Adult / Algorithms / Analysis / Biology and Life Sciences / Body mass / Cardiorespiratory Fitness / Cardiovascular disease / Classification / Engineering and Technology / Exercise / Exercise Test - methods / Feature extraction / Female / Females / Fitness / Fitness equipment / Healthy Volunteers / Heart Rate / Humans / Linear equations / Linear Models / Male / Mathematical models / Medical research / Medicine and Health Sciences / Metabolism / Models, Statistical / Mortality / Multivariate Analysis / Oxygen Consumption / Personal health / Physical fitness / Physical Sciences / Physiology / Prediction models / Recovery / Regression Analysis / Reproducibility / Reproducibility of Results / Research and Analysis Methods / Rugby / Sensitivity and Specificity / Sport science / Sports medicine / Sports Medicine - methods / Sports sciences / Variability / Young Adult