Predicting Maximal Oxygen Uptake Using the 3-Minute All-Out Test in High-Intensity Functional Training Athletes

Effects of high-intensity functional training on physical fitness and sport-specific performance among the athletes: A systematic review with meta-analysis

OBJECTIVE

This study aims to meta-analyze the impact of high-intensity functional training on athletes' physical fitness and sport-specific performance.

METHODS

A systematic search was conducted in five well-known academic databases (PubMed, Scopus, Web of Science, EBSCOhost, and the Cochrane Library) up to July 1, 2023. The literature screening criteria included: (1) studies involving healthy athletes, (2) a HIFT program, (3) an assessment of outcomes related to athletes' physical fitness or sport-specific performance, and (4) the inclusion of randomized controlled trials. The Physical Therapy Evidence Database (PEDro) scale was used to evaluate the quality of studies included in the meta-analysis.

RESULTS

13 medium- and high-quality studies met the inclusion criteria for the systematic review, involving 478 athletes aged between 10 and 24.5 years. The training showed a small to large effect size (ES = 0.414-3.351; all p < 0.05) in improving upper and lower body muscle strength, power, flexibility, and sport-specific performance.

CONCLUSION

High-intensity functional training effectively improves athletes' muscle strength, power, flexibility, and sport-specific performance but has no significant impact on endurance and agility. Future research is needed to explore the impact of high-intensity functional training on athletes' speed, balance, and technical and tactical performance parameters.

Feasibility of predicting maximal oxygen uptake by using the efficiency factor in healthy men

Conventionally, efficiency is indirectly estimated through a respiratory gas analyser (oxygen, carbon dioxide), which is a complex and rather costly calculation method that is difficult to perform in many situations. Therefore, the present study proposed a modified definition of efficiency, called the efficiency factor (EF) (i.e., the ratio of work to the corresponding exercise intensity), and evaluated the relation between the EF and maximal oxygen uptake ([Formula: see text]), as well as compared the prediction models established based on the EF. The heart rate (maximal heart rate: 186 ± 6 beats min-1), rating of perceived exertion (19 ± 1), and [Formula: see text] (39.0 ± 7.1 mL kg-1 min-1) of 150 healthy men (age: 20 ± 2 years; height: 175.0 ± 6.0 cm; weight: 73.6 ± 10.7 kg; body mass index [BMI]: 24.0 ± 3.0 kg m-2; percent body fat [PBF]: 17.0 ± 5.7%) were measured during the cardiopulmonary exercise test (CPET). Through multiple linear regression analysis, we established the BMI model using age and BMI as parameters. Additionally, we created the PBF modelHRR utilizing weight, PBF, and heart rate reserve (HRR) and developed PBF modelEF6 and PBF modelEF7 by incorporating EF6 from the exercise stage 6 and EF7 from the exercise stage 7 during the CPET, respectively. EF6 (r = 0.32, p = 0.001) and EF7 (r = 0.31, p = 0.002) were significantly related to [Formula: see text]. Among the models, the PBF modelEF6 showed the highest accuracy, which could explain 62.6% of the variance in the [Formula: see text] at with a standard error of estimate (SEE) of 4.39 mL kg-1 min-1 (%SEE = 11.25%, p < 0.001). These results indicated that the EF is a significant predictor of [Formula: see text], and compared to the other models, the PBF modelEF6 is the best model for estimating [Formula: see text].

Predicting Maximum Oxygen Uptake from Non-Exercise and Submaximal Exercise Tests in Paraplegic Men with Spinal Cord Injury

This study aimed to develop prediction equations for maximum oxygen uptake (VO₂max) based on non-exercise (anthropometric) and submaximal exercise (anthropometric and physiological) variables in paraplegic men with a spinal cord injury. All participants were tested on an arm ergometer using a maximal graded exercise test. Anthropometric variables such as age, height, weight, body fat, body mass index, body fat percentage, and arm muscle mass and physiological variables such as VO₂, VCO₂, and heart rate at 3 and 6 min of graded exercise tests were included in the multiple linear regression analysis. The prediction equations revealed the following. Regarding non-exercise variables, VO₂max was correlated with age and weight (equation R = 0.771, R² = 0.595, SEE= 3.187). Regarding submaximal variables, VO₂max was correlated with weight and VO₂ and VCO₂ at 6 min (equation R = 0.892, R² = 0.796, SEE = 2.309). In conclusion, our prediction equations can be used as a cardiopulmonary function evaluation tool to estimate VO₂max simply and conveniently using the anthropometric and physiological characteristics of paraplegic men with spinal cord injuries.

Design of the Physical Fitness Evaluation Information Management System of Sports Athletes Based on Artificial Intelligence

With the rapid development of science and technology in recent years, more and more researchers began to explore the basic disciplines of sports, namely, biomechanics and physiology, and further update and improve the traditional sports training theory. However, the developed countries, i.e., Europe and the United States, have dedicatedly worked on the effective role of physical training particularly in middle-class farmers in sports powers. They have realized that the continuous improvement of the physical training evaluation system is directly related to the reforming in the evaluation and monitoring methods of physical training. At the same time, there is a demand for athletes' physical fitness evaluation, which requires a large amount of data support during the evaluation. These data collection and analysis are difficult and consume a lot of manpower. Therefore, this paper uses artificial intelligence technology to design the sports athletes' physical fitness evaluation information management system to improve the comprehensiveness of system data collection and the accuracy of data analysis. Through the physical attenuation calculation and physical analysis of the system developed in this paper, data accuracy is high. The coach can make a training plan according to the physical consumption of athletes in sports training or competition, which can greatly improve the ability of athletes.

Monitoring of Maximum Oxygen Intake of Breathing and Heart Rate in Exercise Training Based on Regression Equations

In order to provide a new reference method and basis for the physical testing study in healthy adults aged 40∼and 49, this paper proposes a regression equation-based monitoring study of exercise training breathing and heart rate. Sixty four subjects (30 males and 34 females), aged 40∼and 49 years, were selected. First, the subjects were screened by relevant health test and medical questionnaire to exclude the subjects with exercise contraindications and high-intensity exercise; then, the incremental load test was directly measured by gas analysis, and then, the subjects who completed the maximum oxygen intake test were tested twice, the corresponding heart rate value was recorded, and walking time were averaged for calculation. We show that the regression analysis of each index yielded regression equations and cross-validation regression equations show correlation statistics for measured and inferred maximum oxygen intake of RLOOCV = 0.826 and SEELOOCV = 0.378 (L/min). The equation can effectively speculate on the maximum oxygen intake of 40∼in healthy adults aged 49, with the advantages of efficient, low cost, fast, and convenient.

The Determination of Step Frequency in 3-min Incremental Step-in-Place Tests for Predicting Maximal Oxygen Uptake from Heart Rate Response in Taiwanese Adults

The maximal oxygen uptake (VO_2max) prediction models established by step tests are often used for evaluating cardiorespiratory fitness (CRF). However, it is unclear which type of stepping frequency sequence is more suitable for the public to assess the CRF. Therefore, the main purpose of this study was to test the effectiveness of two 3-min incremental step-in-place (3MISP) tests (i.e., 3MISP_30s and 3MISP_60s) with the same total number of steps but different step-frequency sequences in predicting VO_2max. In this cross-sectional study, a total of 200 healthy adults in Taiwan completed 3MISP_30s and 3MISP_60s tests, as well as cardiopulmonary exercise testing. The 3MISP_30s and 3MISP_60s models were established through multiple stepwise regression analysis by gender, age, percent body fat, and 3MISP-heart rate. The statistical analysis included Pearson's correlations, the standard errors of estimate, the predicted residual error sum of squares, and the Bland-Altman plot to compare the measured VO_2max values and those estimated. The results of the study showed that the exercise intensity of the 3MISP_30s test was higher than that of the 3MISP_60s test (% heart rate reserve (HRR) during 3MISP_30s vs. %HRR during 3MISP_60s = 81.00% vs. 76.81%, p < 0.001). Both the 3MISP_30s model and the 3MISP_60s model explained 64.4% of VO_2max, and the standard errors of the estimates were 4.2043 and 4.2090 mL·kg^-1·min^-1, respectively. The cross-validation results also indicated that the measured VO_2max values and those predicted by the 3MISP_30s and 3MISP_60s models were highly correlated (3MISP_30s model: r = 0.804, 3MISP_60s model: r = 0.807, both p < 0.001). There was no significant difference between the measured VO_2max values and those predicted by the 3MISP_30s and 3MISP_60s models in the testing group (p > 0.05). The results of the study showed that when the 3MISP_60s test was used, the exercise intensity was significantly reduced, but the predictive effectiveness of VO_2max did not change. We concluded that the 3MISP_60s test was physiologically less stressful than the 3MISP_30s test, and it could be a better choice for CRF evaluation.

Five-Minute Power-Based Test to Predict Maximal Oxygen Consumption in Road Cycling

PURPOSE

To examine the ability of a multivariate model to predict maximal oxygen consumption (VO2max) using performance data from a 5-minute maximal test (5MT).

METHODS

Forty-six road cyclists (age 38 [9] y, height 177 [9] cm, weight 71.4 [8.6] kg, VO2max 61.13 [9.05] mL/kg/min) completed a graded exercise test to assess VO2max and power output. After a 72-hour rest, they performed a test that included a 5-minute maximal bout. Performance variables in each test were modeled in 2 independent equations, using Bayesian general linear regressions to predict VO2max. Stepwise selection was then used to identify the minimal subset of parameters with the best predictive power for each model.

RESULTS

Five-minute relative power output was the best explanatory variable to predict VO2max in the model from the graded exercise test (R2 95% credibility interval, .81-.88) and when using data from the 5MT (R2 95% credibility interval, .61-.77). Accordingly, VO2max could be predicted with a 5MT using the equation VO2max = 16.6 + (8.87 × 5-min relative power output).

CONCLUSIONS

Road cycling VO2max can be predicted in cyclists through a single-variable equation that includes relative power obtained during a 5MT. Coaches, cyclists, and scientists may benefit from the reduction of laboratory assessments performed on athletes due to this finding.