The Dangers of Estimating V˙O2max Using Linear, Nonexercise Prediction Models

Females Display Lower Risk of Myocardial Infarction From Higher Estimated Cardiorespiratory Fitness Than Males: The Tromsø Study 1994-2014

Objective

To examine the dose-response association between estimated cardiorespiratory fitness (eCRF) and risk of myocardial infarction (MI).

Patients and Methods

Adults who attended Tromsø Study surveys 4-6 (Janurary 1,1994-December 20, 2008) with no previous cardiovascular disease were followed up through December 31, 2014 for incident MI. Associations were examined using restricted cubic splines Fine and Gray regressions, adjusted for education, smoking, alcohol, diet, sex, adiposity, physical activity, study survey, and age (timescale) in the total cohort and subsamples with hyperlipidemia (n=2956), hypertension (n=8290), obesity (n=5784), metabolic syndrome (n=1410), smokers (n=3823), and poor diet (n=3463) and in those who were physically inactive (n=6255).

Results

Of 14,285 participants (mean age ± SD, 53.7±11.4 years), 979 (6.9%) experienced MI during follow-up (median, 7.2 years; 25th-75th, 5.3-14.6 years). Females with median eCRF (32 mL/kg/min) had 43% lower MI risk (subdistributed hazard ratio [SHR], 0.57; 95% CI, 0.48-0.68) than those at the 10th percentile (25 mL/kg/min) as reference. The lowest MI risk was observed at 47 mL/kg/min (SHR, 0.02; 95% CI, 0.01-0.11). Males had 26% lower MI risk at median eCRF (40 mL/kg/min; SHR, 0.74; 95% CI, 0.63-0.86) than those at the 10th percentile (32 mL/kg/min), and the lowest risk was 69% (SHR, 0.31; 95% CI, 0.14-0.71) at 60 mL/kg/min. The associations were similar in subsamples with cardiovascular disease risk factors.

Conclusion

Higher eCRF associated with lower MI risk in females and males, but associations were more pronounced among females than those in males. This suggest eCRF as a vital estimate to implement in medical care to identify individuals at high risk of future MI, especially for females.

Enhancing instantaneous oxygen uptake estimation by non-linear model using cardio-pulmonary physiological and motion signals

Oxygen uptake (VO₂) is an important parameter in sports medicine, health assessment and clinical treatment. At present, more and more wearable devices are used in daily life, clinical treatment and health care. The parameters obtained by wearables have great research potential and application prospect. In this paper, an instantaneous VO₂ estimation model based on XGBoost was proposed and verified by using data obtained from a medical-grade wearable device (Beijing SensEcho) at different posture and activity levels. Furthermore, physiological characteristics extracted from single-lead electrocardiogram, thoracic and abdominal respiration signal and tri-axial acceleration signal were studied to optimize the model. There were 29 healthy volunteers recruited for the study to collect data while stationary (lying, sitting, standing), walking, Bruce treadmill test and recuperating with SensEcho and the gas analyzer (Metalyzer 3B). The results show that the VO₂ values estimated by the proposed model are in good agreement with the true values measured by the gas analyzer (R² = 0.94 ± 0.03, n = 72,235), and the mean absolute error (MAE) is 1.83 ± 0.59 ml/kg/min. Compared with the estimation method using a separate heart rate as input, our method reduced MAE by 54.70%. At the same time, other factors affecting the performance of the model were studied, including the influence of different input signals, gender and movement intensity, which provided more enlightenment for the estimation of VO₂. The results show that the proposed model based on cardio-pulmonary physiological signals as inputs can effectively improve the accuracy of instantaneous VO₂ estimation in various scenarios of activities and was robust between different motion modes and state. The VO₂ estimation method proposed in this paper has the potential to be used in daily life covering the scenario of stationary, walking and maximal exercise.

Predicting Cardiorespiratory Fitness Using the 20-m Shuttle Run Test: New Insights Using Nonlinear Allometry

PURPOSE

Recently, doubts have been raised concerning the validity of the 20-m shuttle run test (20mSRT) to predict cardiorespiratory fitness (CRF) in youth. Despite these doubts, authors continue to provide powerful evidence that CRF can be predicted reliably using the 20mSRT albeit using contrasting models. Therefore, we aimed to compare a new linear model with an alternative allometric model to predict CRF (peak oxygen uptake, V˙O2peak) using the 20mSRT.

METHODS

The study included 148 adolescents (43% girls) age 13.37 ± 1.84 yr. Adolescents were randomly assigned to validation (n = 91) and cross-validation (n = 57) groups. V˙O2peak was measured using a gas analyzer in both maximal exercise tests in the laboratory and the 20mSRT. Multiple linear regression methods were applied to develop the linear models using the 20mSRT (laps), body mass index, and body fat percentage. Alternative allometric models were also proposed/fitted using the 20mSRT (laps), height, and body mass.

RESULTS

The criterion validity values of both the linear and the allomeric models were found to be acceptable, with R2 = 82.5% and 82.7% respectively, providing reassuring evidence that the 20mSRT can be used with confidence to predict CRF. However, the allometric model identified a height-to-mass ratio, not dissimilar to the inverse body mass index (known to be a measure of leanness), to be associated with CRF. The allometric model also revealed that the rise in energy cost (V˙O2peak) with increasing laps was exponential. This will more accurately reflect the nonlinear rise in energy demand of shuttle running as the test progresses to exhaustion.

CONCLUSIONS

These observations provided powerful evidence that allometric models are more than satisfactory in terms of both criterion and construct validity when predicting CRF (V˙O2peak) using the 20mSRT.

Comparing individual and population differences in minute ventilation/carbon dioxide production slopes using centile growth curves and log-linear allometry

Identifying vulnerable groups and/or individuals’ cardiorespiratory fitness (CRF) is an important challenge for clinicians/researchers alike. To quantify CRF accurately, the assessment of several variables is now standard practice including maximal oxygen uptake (Vʹ_CO2) and ventilatory efficiency, the latter assessed using the minute ventilation/carbon dioxide production (Vʹ_E/Vʹ_CO2) slope. Recently, reference values (centiles) for Vʹ_E/Vʹ_CO2 slopes for males and females aged 20 to 80 have been published, using cardiopulmonary exercise testing (CPX) data (treadmill protocol) from the Fitness Registry and the Importance of Exercise National Database (FRIEND Registry).

In the current observational study we provide centile curves for the FRIEND Registry Vʹ_E/Vʹ_CO2 slopes, fitted using the generalised additive model for location, scale and shape (GAMLSS), to provide individuals with a more precise estimate of where their Vʹ_E/Vʹ_CO2 slopes fall within the population. We also confirm that by adopting allometric models (incorporating a log transformation), the resulting ANCOVAs provided more normal and homoscedastic residuals, with superior goodness-of-fit using the Akaike information criterion (AIC)=14 671 (compared with traditional ANCOVA's AIC=15 008) that confirms allometric models are vastly superior to traditional ANCOVA models.

In conclusion, providing sex-by-age centile curves rather than referring to reference tables for ventilatory efficiency (Vʹ_E/Vʹ_CO2 slopes) will provide more accurate estimates of where an individual's particular Vʹ_E/Vʹ_CO2 slope falls within the population. Also, by adopting allometric models researchers are more likely to identify real and valid inferences when analysing population/group differences in Vʹ_E/Vʹ_CO2 slopes.

This article provides centile curves of Vʹ_E/Vʹ_CO2slopes and demonstrates that by adopting log-linear models, more trustworthy inferences with group differences will also be foundhttps://bit.ly/3uitACS

Improving reference equations for cardiorespiratory fitness using multiplicative allometric rather than additive linear models: Data from the Fitness Registry and the Importance of Exercise National Database Registry

New improved reference equations for cardiorespiratory fitness have recently been published, using Data from the Fitness Registry and the Importance of Exercise National Database (FRIEND Registry). The new linear equation for VO_2max (ml.kg^-1.min^-1) was additive, derived using multiple-linear regression. An alternative multiplicative allometric model has also been published recently, thought to improve further the quality of fit. The purpose of the current study was to compare the accuracy and quality/goodness-of-fit of the linear, additive model with the multiplicative allometric model using the FRIEND database. The results identified that the allometric model out performs the linear model based on all model-comparison criteria. The allometric model demonstrates; 1) greater explained variance (R² = 0.645; R = 0.803) vs. (R² = 0.62; R = 0.79), 2) residuals that were more normally distributed, 3) residuals that yielded less evidence of curvature, 4) superior goodness-of-fit statistics i.e., greater maximum log-likelihood (MLL) and smaller Akaike Information Criterion (AIC) statistics, 5) less systematic bias together with smaller unexplained standard error of estimates. The Bland and Altman plots also confirmed little or no evidence of curvature with the allometric model, but systematic curvature (lack-of-fit) in the linear model. The multiplicative allometric model to predict VO_2max was; VO_2max (ml.kg^-1.min^-1) = M^-0.854 · H^1.44 · exp. (0.424-0.346 · (sex) -0.011.age), where M = body mass and H = height (R² = 0.645; R = 0.803) and sex is entered as a [0,1] indicator variable (male = 0 and female = 1). Another new insight obtained from the allometric model (providing construct validity) is that the height-to-body-mass ratio is similar to inverse body mass index or the lean body mass index, both associated with leanness when predicting VO_2max. In conclusion adopting allometric models will provide more accurate predictions of VO_2max (ml.kg^-1.min^-1) using more plausible, biologically sound and interpretable models.

Peak oxygen uptake reference values for cycle ergometry for the healthy Dutch population: data from the LowLands Fitness Registry

Peak oxygen uptake (V′_O2peak) is recognised as the best expression of aerobic fitness. Therefore, it is essential that V′_O2peak reference values are accurate for interpreting a cardiopulmonary exercise test (CPET). These values are country specific and influenced by underlying biological ageing processes. They are normally stratified per paediatric and adult population, resulting in a discontinuity at the transition point between prediction equations. There are currently no age-related reference values available for the lifespan of individuals in the Dutch population. The aim of this study is to determine the best-fitting regression model for V′_O2peak in the healthy Dutch paediatric and adult populations in relation to age.

In this retrospective study, CPET cycle ergometry results of 4477 subjects without reported somatic diseases were included (907 females, age 7.9–65.0 years). Generalised additive models were employed to determine the best-fitting regression model. Cross-validation was performed against an independent dataset consisting of 3518 subjects (170 females, age 6.8–59.0 years).

An additive model was the best fitting with the largest predictive accuracy in both the primary (adjusted R²=0.57, standard error of the estimate (see)=556.50 mL·min⁻¹) and cross-validation (adjusted R²=0.57, see=473.15 mL·min⁻¹) dataset.

This study provides a robust additive regression model for V′_O2peak in the Dutch population.

Peak oxygen uptake has a nonlinear dependence on years of age in the paediatric and adult Dutch populationshttp://ow.ly/H3fH30nIjRy

Predicting oxygen uptake responses during cycling at varied intensities using an artificial neural network

Study aim: Oxygen Uptake (VO2) is avaluable metric for the prescription of exercise intensity and the monitoring of training progress. However, VO2 is difficult to assess in anon-laboratory setting. Recently, an artificial neural network (ANN) was used to predict VO2 responses during aset walking protocol on the treadmill [9]. The purpose of the present study was to test the ability of an ANN to predict VO2 responses during cycling at self-selected intensities using Heart Rate (HR), time derivative of HR, power output, cadence, and body mass data.

Material and methods: 12 moderately-active adult males (age: 21.1 ± 2.5 years) performed a50-minute bout of cycling at a variety of exercise intensities. VO2, HR, power output, and cadence were recorded throughout the test. An ANN was trained, validated and tested using the following inputs: HR, time derivative of HR, power output, cadence, and body mass. A twelve-fold hold-out cross validation was conducted to determine the accuracy of the model.

Results: The ANN accurately predicted the experimental VO2 values throughout the test (R2 = 0.91 ± 0.04, SEE = 3.34 ± 1.07 mL/kg/min).

Discussion: This preliminary study demonstrates the potential for using an ANN to predict VO2 responses during cycling at varied intensities using easily accessible inputs. The predictive accuracy is promising, especially considering the large range of intensities and long duration of exercise. Expansion of these methods could allow ageneral algorithm to be developed for a more diverse population, improving the feasibility of oxygen uptake assessment.