Contribution of Body Mass Index Stratification for the Prediction of Maximal Oxygen Uptake

Li, Fang; Yang, Cheng-Pang; Wu, Cheng-You; Ho, Chia-An; Yeh, Hung-Chih; Chan, Yuan-Shuo; ChangChien, Wen-Sheng; Ho, Chen Hsun

doi:10.7150/ijms.77818

Cited by 3 publications

(6 citation statements)

References 42 publications

(100 reference statements)

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“…Previous studies have shown significant differences in cardiorespiratory fitness among adults with different BMI levels, with a higher BMI being associated with lower cardiorespiratory fitness 12 , 36 ; overweight or obesity may increase the risk of cardiovascular disease in the general population 37 , 38 ; and BMI and PBF are both important factors for the prediction of , with PBF being a more accurate predictor of than BMI 11 , 16 , 27 , 39 , 40 . All of these findings were confirmed by the results of this study.…”

Section: Discussionmentioning

confidence: 99%

“…At the same time, participants also wore a suitable respirator mask, which was connected to a sampling line and a digital flow sensor to measure oxygen uptake ( ) and the content of produced carbon dioxide ( ). The initial load for the CPET was 25 W, with an increase of 15 W every two minutes (i.e., one stage every two minutes) until the participants could no longer maintain a pedalling frequency of 70 rpm 12 , 27 . At the same time, the Borg Rating of Perceived Exertion (RPE, 6–20) was used to ask participants about their level of fatigue during the CPET.…”

Section: Methodsmentioning

confidence: 99%

“…At the same time, the Borg Rating of Perceived Exertion (RPE, 6–20) was used to ask participants about their level of fatigue during the CPET. In this study, participants who met any three of the following criteria were considered to have reached exhaustion: (1) the load increased and the oxygen uptake did not increase but slightly decreased; (2) the maximum respiratory exchange ratio was ≥ 1.10; (3) the exercise heart rate reached 90% of the age-predicted maximum heart rate (220-age); and (4) RPE score was > 17 11 , 12 .…”

Section: Methodsmentioning

confidence: 99%

“…Many studies have proposed diverse submaximal exercise testing approaches 9 – 12 , with a considerable level of reliability and validity, and demonstrated the application value thereof in terms of cardiorespiratory fitness evaluation. The most common testing methods include the 6-min walk test 13 , the 20-m shuttle run 14 and the step test 15 , 16 .…”

Section: Introductionmentioning

confidence: 99%

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

Li,

Tu,

Yeh

et al. 2023

Sci Rep

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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 ($${\dot{\text{V}}\text{O}}_{2\max }$$ V ˙ O 2 max ), 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 $${\dot{\text{V}}\text{O}}_{2\max }$$ V ˙ O 2 max (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 $${\dot{\text{V}}\text{O}}_{2\max }$$ V ˙ O 2 max . Among the models, the PBF modelEF6 showed the highest accuracy, which could explain 62.6% of the variance in the $${\dot{\text{V}}\text{O}}_{2\max }$$ V ˙ O 2 max 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 $${\dot{\text{V}}\text{O}}_{2\max }$$ V ˙ O 2 max , and compared to the other models, the PBF modelEF6 is the best model for estimating $${\dot{\text{V}}\text{O}}_{2\max }$$ V ˙ O 2 max .

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Li,

Tu,

Yeh

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…The classical method of estimating

is during the incremental exercise test [ 3 , 24 , 25 , 26 , 27 ]. These studies focus on estimating the

during the exercise intervention to investigate the long-term changes and monitor the performance and fitness of the individual.…”

Section: Related Workmentioning

confidence: 99%

Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models

Balakarthikeyan

Jais

Vijayarangan

et al. 2023

Sensors

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Wearable Heart Rate monitors are used in sports to provide physiological insights into athletes’ well-being and performance. Their unobtrusive nature and ability to provide reliable heart rate measurements facilitate the estimation of cardiorespiratory fitness of athletes, as quantified by maximum consumption of oxygen uptake. Previous studies have employed data-driven models which use heart rate information to estimate the cardiorespiratory fitness of athletes. This signifies the physiological relevance of heart rate and heart rate variability for the estimation of maximal oxygen uptake. In this work, the heart rate variability features that were extracted from both exercise and recovery segments were fed to three different Machine Learning models to estimate maximal oxygen uptake of 856 athletes performing Graded Exercise Testing. A total of 101 features from exercise and 30 features from recovery segments were given as input to three feature selection methods to avoid overfitting of the models and to obtain relevant features. This resulted in the increase of model’s accuracy by 5.7% for exercise and 4.3% for recovery. Further, post-modelling analysis was performed to remove the deviant points in two cases, initially in both training and testing and then only in training set, using k-Nearest Neighbour. In the former case, the removal of deviant points led to a reduction of 19.3% and 18.0% in overall estimation error for exercise and recovery, respectively. In the latter case, which mimicked the real-world scenario, the average R value of the models was observed to be 0.72 and 0.70 for exercise and recovery, respectively. From the above experimental approach, the utility of heart rate variability to estimate maximal oxygen uptake of large population of athletes was validated. Additionally, the proposed work contributes to the utility of cardiorespiratory fitness assessment of athletes through wearable heart rate monitors.

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Cardiorespiratory dynamics of type 2 diabetes mellitus: An extensive view of breathing and fitness challenges in a diabetes prevalent population

Abbas,

Shah,

Babar

et al. 2024

PLoS ONE

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Background Diabetes mellitus (DM) is well known for related micro and macrovascular complications. Uncontrolled hyperglycemia in diabetes mellitus leads to endothelial dysfunction, inflammation, microvascular impairment, myocardial dysfunction, and skeletal muscle changes which affect multiple organ systems. This study was designed to take an extensive view of cardiorespiratory dynamics in patients with type 2 DM. Methods One hundred healthy controls (HC) and 100 DM patients were enrolled. We measured and compared the breathing patterns (spirometry), VO2 max levels (heart rate ratio method) and self-reported fitness level (international fitness scale) of individuals with and without diabetes. Data was analyzed in SPSS v.22 and GraphPad Prism v8.0. Results We observed restrictive spirometry patterns (FVC <80%) in 22% of DM as compared to 2% in HC (p = 0.021). There was low mean VO2 max in DM as compared to HC(32.03 ± 5.36 vs 41.91 ± 7.98 ml/kg/min; p value <0.001). When evaluating physical fitness on self-reported IFiS scale, 90% of the HC report average, good, or very good fitness levels. In contrast, only 45% of the DM shared this pattern, with a 53% proportion perceiving their fitness as poor or very poor (p = <0.05). Restrictive respiratory pattern, low VO2 max and fitness level were significantly associated with HbA1c and long-standing DM. Conclusion This study shows decreased pulmonary functions, decreased cardiorespiratory fitness (VO2 max) and IFiS scale variables in diabetic population as compared to healthy controls which are also associated with glycemic levels and long-standing DM. Screening for pulmonary functions can aid optimum management in this population.

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Contribution of Body Mass Index Stratification for the Prediction of Maximal Oxygen Uptake

Cited by 3 publications

References 42 publications

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

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

Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models

Cardiorespiratory dynamics of type 2 diabetes mellitus: An extensive view of breathing and fitness challenges in a diabetes prevalent population

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