Prediction of peak oxygen uptake from age and power output at RPE 15 in obese women

Submaximal, Perceptually Regulated Exercise Testing Predicts Maximal Oxygen Uptake: A Meta-Analysis Study

BACKGROUND

Recently, several authors have proposed the use of a submaximal 'perceptually regulated exercise test' (PRET) to predict maximal oxygen uptake ([Formula: see text]). The PRET involves asking the individual to self-regulate a series of short bouts of exercise corresponding to pre-set ratings of perceived exertion (RPE). The individual linear relationship between RPE and oxygen uptake (RPE:[Formula: see text]) is then extrapolated to the [Formula: see text], which corresponds to the theoretical maximal RPE (RPE20). Studies suggest that prediction accuracy from this method may be better improved during a second PRET. Similarly, some authors have recommended an extrapolation to RPE19 rather than RPE20.

OBJECTIVES

The purpose of the meta-analysis was to examine the validity of the method of predicting [Formula: see text] from the RPE:[Formula: see text] during a PRET, and to determine the level of agreement and accuracy of predicting [Formula: see text] from an initial PRET and retest using RPE19 and RPE20.

DATA SOURCES

From a systematic search of the literature, 512 research articles were identified.

STUDY ELIGIBILITY CRITERIA

The eligible manuscripts were those which used the relationship between the RPE≤15 and [Formula: see text], and used only the Borg's RPE scale.

PARTICIPANTS AND INTERVENTIONS

Ten studies (n = 274 individuals) were included.

STUDY APPRAISAL AND SYNTHESIS METHODS

For each study, actual and predicted [Formula: see text] from four subgroup outcomes (RPE19 in the initial test, RPE19 in the retest, RPE20 in the initial test, RPE20 in the retest) were identified, and then compared. The magnitude of the difference regardless of subgroup outcomes was examined to determine if it is better to predict [Formula: see text] from extrapolation to RPE19 or RPE20. The magnitude of differences was examined for the best PRET (test vs retest).

RESULTS

The results revealed that [Formula: see text] may be predicted from RPE:[Formula: see text] during PRET in different populations and in various PRET modalities, regardless of the subgroup outcomes. To obtain greater accuracy of predictions, extrapolation to RPE20 during a retest may be recommended.

LIMITATIONS

The included studies reported poor selection bias and data collection methods.

CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS

The [Formula: see text] may be predicted from RPE:[Formula: see text] during PRET, especially when [Formula: see text] is extrapolated to RPE20 during a second PRET.

Developing new VO2max prediction models from maximal, submaximal and questionnaire variables using support vector machines combined with feature selection

Maximal oxygen uptake (VO₂max) is an essential part of health and physical fitness, and refers to the highest rate of oxygen consumption an individual can attain during exhaustive exercise. In this study, for the first time in the literature, we combine the triple of maximal, submaximal and questionnaire variables to propose new VO₂max prediction models using Support Vector Machines (SVM's) combined with the Relief-F feature selector to predict and reveal the distinct predictors of VO₂max. For comparison purposes, hybrid models based on double combinations of maximal, submaximal and questionnaire variables have also been developed. By utilizing 10-fold cross-validation, the performance of the models has been calculated using multiple correlation coefficient (R) and root mean square error (RMSE). The results show that the best values of R and RMSE, with 0.94 and 2.92mLkg^-1min^-1 respectively, have been obtained by combining the triple of relevantly identified maximal, submaximal and questionnaire variables. Compared with the results of the rest of hybrid models in this study and the other prediction models in literature, the reported values of R and RMSE have been found to be considerably more accurate. The predictor variables gender, age, maximal heart rate (MX-HR), submaximal ending speed (SM-ES) of the treadmill and Perceived Functional Ability (Q-PFA) questionnaire have been found to be the most relevant variables in predicting VO₂max. The results have also been compared with that of Multilayer Perceptron (MLP) and Tree Boost (TB), and it is seen that SVM significantly outperforms other regression methods for prediction of VO₂max.

A specific prediction equation is necessary to estimate peak oxygen uptake in obese patients with metabolic syndrome

PURPOSE

The aims were to: (1) compare peak oxygen uptake ([Formula: see text]peak) predicted from four standard equations to actual [Formula: see text]peak measured from a cardiopulmonary exercise test (CPET) in obese patients with metabolic syndrome (MetS), and (2) develop a new equation to accurately estimate [Formula: see text]peak in obese women with MetS.

METHODS

Seventy-five obese patients with MetS performed a CPET. Anthropometric data were also collected for each participant. [Formula: see text]peak was predicted from four prediction equations (from Riddle et al., Hansen et al., Wasserman et al. or Gläser et al.) and then compared with the actual [Formula: see text]peak measured during the CPET. The accuracy of the predictions was determined with the Bland-Altman method. When accuracy was low, a new prediction equation including anthropometric variables was proposed.

RESULTS

[Formula: see text]peak predicted from the equation of Wasserman et al. was not significantly different from actual [Formula: see text]peak in women. Moreover, a significant correlation was found between the predicted and actual values (p < 0.001, r = 0.69). In men, no significant difference was noted between actual [Formula: see text]peak and [Formula: see text]peak predicted from the prediction equation of Gläser et al., and these two values were also correlated (p = 0.03, r = 0.44). However, the LoA95% was wide, whatever the prediction equation or gender. Regression analysis suggested a new prediction equation derived from age and height for obese women with MetS.

CONCLUSIONS

The methods of Wasserman et al. and Gläser et al. are valid to predict [Formula: see text]peak in obese women and men with MetS, respectively. However, the accuracy of the predictions was low for both methods. Consequently, a new prediction equation including age and height was developed for obese women with MetS. However, new prediction equation remains to develop in obese men with MetS.

Predicting VO2peak from Submaximal- and Peak Exercise Models: The HUNT 3 Fitness Study, Norway

Purpose

Peak oxygen uptake (VO_2peak) is seldom assessed in health care settings although being inversely linked to cardiovascular risk and all-cause mortality. The aim of this study was to develop VO_2peak prediction models for men and women based on directly measured VO_2peak from a large healthy population

Methods

VO_2peak prediction models based on submaximal- and peak performance treadmill work were derived from multiple regression analysis. 4637 healthy men and women aged 20–90 years were included. Data splitting was used to generate validation and cross-validation samples.

Results

The accuracy for the peak performance models were 10.5% (SEE = 4.63 mL⋅kg^-1⋅min^-1) and 11.5% (SEE = 4.11 mL⋅kg^-1⋅min^-1) for men and women, respectively, with 75% and 72% of the variance explained. For the submaximal performance models accuracy were 14.1% (SEE = 6.24 mL⋅kg^-1⋅min^-1) and 14.4% (SEE = 5.17 mL⋅kg^-1⋅min^-1) for men and women, respectively, with 55% and 56% of the variance explained. The validation and cross-validation samples displayed SEE and variance explained in agreement with the total sample. Cross-classification between measured and predicted VO_2peak accurately classified 91% of the participants within the correct or nearest quintile of measured VO_2peak.

Conclusion

Judicious use of the exercise prediction models presented in this study offers valuable information in providing a fairly accurate assessment of VO_2peak, which may be beneficial for risk stratification in health care settings.

Prediction of one-repetition maximum from submaximal ratings of perceived exertion in older adults pre- and post-training

BACKGROUND

Individual's one-repetition maximum (1-RM) is required to calculate and prescribe intensity for resistance training, while testing protocols enhance the risk of injuries and are time-consuming.

AIMS

The aim of the present study was to assess the accuracy of 1-RM prediction from ratings of perceived exertion (RPE) of resistance exercises performed at submaximal sets (intensity and volume) in older adult males before and after a 12-week rehabilitation program.

METHODS

18 untrained subjects (70.4 ± 4.5 years) first completed a 1-RM direct assessment with a horizontal leg press pre- and post-training. Thereafter, participants performed, in a random order, 2-repetition sets with loads unknown to them (corresponding to 20, 45 and 70 % of 1-RM). The RPE was recorded immediately after the sets. That RPE associated to its corresponding load was subjected to a linear regression analysis to extrapolate the maximal RPE score and its corresponding 1-RM.

RESULTS

RPE and relative intensities of sets appeared related pre- [r (2) = 0.59, standard error of estimate (SEE) = 13.3 %] and post-training (r (2) = 0.83, SEE = 8.1 %). Differences between measured and predicted 1-RM were reduced from the beginning to the end of training but standard deviations remained high (17.4 ± 11.8 vs. 4.2 ± 11.1 kg). Pre-training, 1-RM expressed relatively to body weight was negatively related with the errors of 1-RM predictions (r (2) = 0.39, p = 0.03).

CONCLUSIONS

In older subjects, RPE may be used to predict 1-RM; however, the predicted value deviates considerably from the measured one, necessitating cautious application. Importantly, this method allows to capture training-induced change in 1-RM, thus making possible assessing training's effectiveness and allowing its modification if necessary.

Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances

Maximal oxygen uptake (VO2max) indicates how many milliliters of oxygen the body can consume in a state of intense exercise per minute. VO2max plays an important role in both sport and medical sciences for different purposes, such as indicating the endurance capacity of athletes or serving as a metric in estimating the disease risk of a person. In general, the direct measurement of VO2max provides the most accurate assessment of aerobic power. However, despite a high level of accuracy, practical limitations associated with the direct measurement of VO2max, such as the requirement of expensive and sophisticated laboratory equipment or trained staff, have led to the development of various regression models for predicting VO2max. Consequently, a lot of studies have been conducted in the last years to predict VO2max of various target audiences, ranging from soccer athletes, nonexpert swimmers, cross-country skiers to healthy-fit adults, teenagers, and children. Numerous prediction models have been developed using different sets of predictor variables and a variety of machine learning and statistical methods, including support vector machine, multilayer perceptron, general regression neural network, and multiple linear regression. The purpose of this study is to give a detailed overview about the data-driven modeling studies for the prediction of VO2max conducted in recent years and to compare the performance of various VO2max prediction models reported in related literature in terms of two well-known metrics, namely, multiple correlation coefficient (R) and standard error of estimate. The survey results reveal that with respect to regression methods used to develop prediction models, support vector machine, in general, shows better performance than other methods, whereas multiple linear regression exhibits the worst performance.

Prediction of VO2 peak using OMNI Ratings of Perceived Exertion from a submaximal cycle exercise test

The primary aim of this study was to develop statistical models to predict peak oxygen consumption (VO2 peak) using OMNI Ratings of Perceived Exertion measured during submaximal cycle ergometry. Male (M = 20.9 yr., SE = 0.4) and female (M = 21.6 yr., SE = 0.5) participants (N = 81) completed a load-incremented maximal cycle ergometer exercise test. Simultaneous multiple linear regression was used to develop separate VO2 peak statistical models using submaximal ratings of perceived exertion for the overall body, legs, and chest/breathing as predictor variables. VO2 peak (L·min(-1)) predicted for men and women from ratings of perceived exertion for the overall body (3.02 ± 0.06; 2.03 ± 0.04), legs (3.02 ± 0.06; 2.04 ± 0.04), and chest/breathing (3.02 ± 0.05; 2.03 ± 0.03) were similar to measured VO2 peak (3.02 ± 0.10; 2.03 ± 0.06, ps > .05). Statistical models based on submaximal OMNI Ratings of Perceived Exertion provide an easily administered and accurate method to predict VO2 peak.

A perceptually regulated, graded exercise test predicts peak oxygen uptake during treadmill exercise in active and sedentary participants

The validity of predicting peak oxygen uptake ([Formula: see text]) in sedentary participants from a perceptually regulated exercise test (PRET) is limited to two cycle ergometry studies. We assessed the validity of a treadmill-based PRET. Active (n = 49; 40.7 ± 13.8 years) and sedentary (n = 26; 33.4 ± 13.2 y) participants completed two PRETS (PRET 1 and PRET2), requiring a change in speed or incline corresponding to ratings of perceived exertion (RPE) 9, 11, 13 and 15. Extrapolation of RPE: [Formula: see text] data to RPE 19 and 20 from the RPE 9-13 and 9-15 ranges were used to estimate [Formula: see text], and compared to [Formula: see text] from a graded exercise test (GXT). The [Formula: see text] :heart rate (HR) data (≥RPE 15) from the GXT were also extrapolated to age-predicted maximal HR (HRmax(pred)) to provide further estimation of [Formula: see text]. ANOVA revealed no significant differences between [Formula: see text] predictions from the RPE 9-15 range for PRET 1 and PRET 2 when extrapolated to RPE 19 in both active (54.3 ± 7.4; 52.9 ± 8.1 ml kg(-1) min(-1)) and sedentary participants (34.1 ± 10.2; 34.2 ± 9.6 ml kg(-1) min(-1)) and no difference between the HRmax(pred) method and measured [Formula: see text] from the GXT for active (53.3 ± 10.0; 53.9 ± 7.5 ml kg(-1) min(-1), respectively) and sedentary participants (33.6 ± 8.4, 34.4 ± 7.0 ml kg(-1) min(-1), respectively). A single treadmill-based PRET using RPE 9-15 range extrapolated to RPE 19 is a valid means of predicting [Formula: see text] in young and middle to older-aged individuals of varying activity and fitness levels.

The validity of predicting peak oxygen uptake from a perceptually guided graded exercise test during arm exercise in paraplegic individuals

STUDY DESIGN

Each participant completed two submaximal, perceptually guided arm crank exercise tests and a graded exercise test (GXT) to volitional exhaustion.

OBJECTIVE

To assess the validity of a submaximal, perceptually guided exercise test to predict peak oxygen uptake (VO(2)peak) during arm cranking in paraplegic individuals.

SETTING

University of Jordan, Amman, Jordan.

PARTICIPANTS

Eleven men with paraplegia as a result of poliomyelitis infection or spinal cord injury completed two submaximal perceptually guided exercise tests and an arm crank GXT to volitional exhaustion.

MAIN OUTCOME MEASURES

The prediction of VO(2)peak was calculated by extrapolating the submaximal rating of perceived exertion (RPE) and VO(2) values by linear regression to RPE20.

RESULTS

There were no significant differences between measured and predicted VO(2)peak from the three submaximal ranges of the RPE (that is, 9-13, 9-15 and 9-17) when extrapolated to RPE20 during both perceptually guided exercise tests (all P>0.05). However, the second perceptually guided exercise tests provided a more accurate prediction of VO(2)peak as reflected by narrower 95% limits of agreement and higher intraclass correlation coefficients.

CONCLUSION

This study has shown that VO(2)peak may be predicted with reasonable accuracy from a perceptually guided exercise test, especially after a full familiarization trial.