Prediction of maximal oxygen uptake based on anthropometric indices in Iranian male adults

Abstract

BACKGROUND:

Assessment of maximal oxygen consumption is important in both general community and occupational settings. Validity and reliability tests are needed to indicate the functionality of the cardiopulmonary system.

OBJECTIVE:

This study aimed to establish a maximal oxygen consumption (VO₂max) prediction model using anthropometric and demographic variables for young adults in Iran.

METHODS:

This cross-sectional study was conducted on 64 healthy young men aged 19–29 years. Oxygen consumption was measured directly and the prediction models to estimate VO₂max were determined by multiple linear regression. The accuracy of the prediction models was considered using regression coefficient (R), coefficient of determination (R²), and standard error of estimate (SEE).

RESULTS:

The average VO₂max was 44.02±4.31 ml/kg/min. Significant correlations were found between the measured VO₂max and the anthropometric and demographic variables (r = 0.16–0.86, P < 0.05). Three significant regression models with acceptable accuracy were developed (R² = 0.67–0.71, SEE = 3.19–3.21).

CONCLUSION:

The predictive models consisted of 3–5 variables as significant predictors of VO₂max and had acceptable accuracy for Iranian young adults. The proposed models are a simple and valid tool that can be used to estimate the VO₂max in the field and in laboratory settings.

Keywords

cardiopulmonary maximal aerobic capacity physical workload

1 Introduction

Maximal aerobic capacity (VO₂max), also known as maximal oxygen consumption, is an important indicator for determining physical work capacity [1, 2]. It also measures the physical performance of individuals until they feel fatigue. Studies have shown that 33% of VO₂max is considered acceptable for the 8-hour workload. Most ergonomists also argue that this is an acceptable level of energy consumption for daily work [3 –5]. The estimation of maximal oxygen uptake is hence one of the criteria for assessing the physical and physiological abilities of employees, which is a standard for measuring the cardiovascular system performance [6 –8].

Researchers believe that VO₂max is a gold standard in evaluating cardiovascular system performance [9, 10]. This index is measured through either respiratory gas analyzers or exercise tests using an ergometer or treadmill [11, 12]. Despite their high validity, accuracy, and sensitivity, such tests are both costly and time-consuming and also require complex equipment. In addition to direct methods, there are also some indirect methods, e.g. regression models for predicting VO₂max [10 –13]. Such models use exercise variables, e.g. heart rate and oxygen consumption, or non-exercise variables, e.g. demographic and anthropometric data [14, 15]. Regression models based on non-exercise variables can be used as a practical and useful method for measuring VO₂max because they do not require expensive equipment and exercise tests. To estimate VO₂max, such models use data on the age, gender, height, and weight of individuals as well as data on hours of work [15 –17]. Jackson et al. reported that regression models based on non-exercise variables are more accurate than submaximal models, and they are suitable for 96% of the young population [18].

In attempts to design and validate different prediction models based on multiple regression, researchers have found that VO₂max is greatly correlated with demographic, anthropometric, and physiological variables [19, 20]. It seems that such models are used for estimating VO₂max in people of different ages in order to measure their physical workload and physical work capacity [21, 22].

The literature review indicates that most models and equations for VO₂max prediction have been validated on American and European populations. The difference in anthropometric characteristics of participants is considered one of the barriers to the widespread application of such models and equations. For example, since Asian adults have lower weight and height compared to European adults, the physiological responses (VO2max, heart rate) may be different between the two populations. Moreover, other parameters affect VO₂max, such as body composition, nutrition, workload, and socioeconomic conditions [23, 24].

As a result, the models used for predicting VO₂max of American or European populations may fail to accurately estimate VO₂max among the Iranian population. This is the first study that attempted to design and validate regression models for estimating VO₂max in young Iranian men aged 19–29 years. The study findings can provide useful and reliable information for the accurate estimation of the physical workload as well as for selecting individuals in physically demanding jobs.

2 Methods

This cross-sectional study was conducted in the Ergonomics Laboratory of Ahvaz University of Medical Sciences. In this study, sixty-four men aged 19–29 years were participated. The sample size was calculated by considering the 95% confidence interval and an accuracy of 0.015. According to a similar study conducted by Daneshmandi et al. and considering the mean VO2max = 2.84 liters per minute with a standard deviation of 0.165 for age group of 20–29 years in Equation 1 (d = 0.015×2.84, z = 1.96), the required sample was 64 people. $n = \frac{z^{2} \times s^{2}}{d^{2}}$ (1)

None of them had a background of cardiopulmonary or musculoskeletal disorders, or metabolic abnormalities, and they were not taking medications that could affect the study variables. Moreover, addicts and smokers were excluded. Anthropometric characteristics of participants, including weight, height, waist circumference, hip circumference were measured and recorded using a tape measure at a standard standing posture [25]. VO₂max was measured with a maximal graded exercise test according to the Bruce protocol with a treadmill(Saturn 300/125; h/p/cosmos) [26]. VO2max was measured via ergo-spirometry (PowerCube; Ganshorn Medizin Electronic GmbH). Heart rate (HR) of participants was monitored during the GXT by telemetry. To confirm that participants achieved VO₂max, at least 2 of the 3 following criteria were considered: (1) respiratory exchange ratio > 1.10, (2) maximal HR was no less 95% of the predicted maximum heart rate (220-age) and (3) oxygen uptake leveling-off despite the increasing workload.

2.1 Statistical analysis

The statistical analysis was performed using SPSS software version 21. The Pearson correlation coefficients were employed to test the correlation between objectively measured VO₂max and independent variables.

Multiple linear regression was used to create a VO₂max regression model using independent variables for predicting VO₂max. Precision of the regression equations were evaluated via regression coefficient (R), coefficient of determination (R²), and standard error of estimate (SEE).

3 Results

The mean age of participants was 22.37±2.91 years (19 to 29 years). The mean BMI of participants was 24.01±3.91. According to the classification proposed by the World Health Organization, the participants had a normal BMI (ranging between 18.25 and 25 kg/m²) [27]. Their mean VO₂max was 44.02±4.31 ml/min/kg and ranged from 34.4 to 52.6 ml/min/kg. (Table 1).

Table 1
Characteristics of the participants (n = 64)

Min Max Mean±SD

Age (years) 19 29 22.37±2.91

Height (cm) 163 193 176.18±5.42

weight (kg) 52.5 105.2 74.71±13.56

BMI (kg/m²) 16.86 33.4 24.01±3.91

Waist Circumference (cm) 66 107 83.07±10.84

Waist– Hip Ratio 0.65 0.93 0.81±0.067

VO₂ max (ml/min/kg) 34.40 52.6 44.02±4.31

VO₂ max (l/min) 2.4 3.82 3.2±0.30

	Min	Max	Mean±SD
Age (years)	19	29	22.37±2.91
Height (cm)	163	193	176.18±5.42
weight (kg)	52.5	105.2	74.71±13.56
BMI (kg/m²)	16.86	33.4	24.01±3.91
Waist Circumference (cm)	66	107	83.07±10.84
Waist– Hip Ratio	0.65	0.93	0.81±0.067
VO₂ max (ml/min/kg)	34.40	52.6	44.02±4.31
VO₂ max (l/min)	2.4	3.82	3.2±0.30

The results regarding VO₂max correlation with demographic and anthropometric variables are shown in Table 2. The results indicated that VO₂max exhibited a significant correlation with all research variables. However, there was a negative correlation between VO₂max and age. The greatest correlation of VO₂max was observed with BMI, weight and WC respectively, whereas the smallest correlation was found between VO₂max and height.

Table 2

Correlation between VO2max and anthropometric characteristics and demographics

		1	2	3	4	5	6	7
1	VO 2max	1
2	Weight	–0.82^a	1
3	Height	0.16^b	0.45^a	1
4	BMI	–0.86^a	0.84^a	0.13	1
5	Age	–0.37^b	–0.30	–0.22	–0.28	1
6	Waist circumference (WC)	–0.79^a	0.82^a	0.16^b	0.82^a	–0.16	1
7	Waist– Hip Ratio (WHR)	–0.76^a	0.72^a	0.05	0.81^a	–0.13	0.79^a	1

^aCorrelation coefficient is significant (p < 0.001). ^bCorrelation coefficient is significant (p < 0.05).

3.1 VO 2 max estimation models:

Table 3 presents the models developed based on multiple linear regression for estimating VO₂max. About 71.5% of the variance of VO₂max was explained by age, weight, BMI, WC and WHR in the first model. Moreover, age, weight, WC and WHR explained 69.1% of the variance of VO₂max in the second model. The results also demonstrated that 67.4% of the variance of VO₂max was explained by age, weight and WC in the third model. Based on the results, the first model was more accurate and valid than the second and third ones in estimating VO₂max.

Table 3
Linear regression models for VO2max (n = 64)

Model 1 Model 2 Model 3

Intercept 30.08^a 28.52^a 28.93^a

Age (years) –0.303^a –0.390^a –0.393^a

Weight (kg) –0.323^a –0.363^a –0.372^a

Waist circumference (WC) –0.244^a –0.245^a –0.262^a

Waist to hip ratio (WHR) –5.029 –4.883 –

BMI (Kg/m²) –0.035 – –

R 0.84 0.83 0.82

R² 0.715 0.691 0.674

R² change –0.02 –0.02

SEE 3.19 3.20 3.21

	Model 1	Model 2	Model 3
Intercept	30.08^a	28.52^a	28.93^a
Age (years)	–0.303^a	–0.390^a	–0.393^a
Weight (kg)	–0.323^a	–0.363^a	–0.372^a
Waist circumference (WC)	–0.244^a	–0.245^a	–0.262^a
Waist to hip ratio (WHR)	–5.029	–4.883	–
BMI (Kg/m²)	–0.035	–	–
R	0.84	0.83	0.82
R²	0.715	0.691	0.674
R² change		–0.02	–0.02
SEE	3.19	3.20	3.21

a: Statistically significant value (p < 0.000). b: Statistically significant value (p < 0.05).

4 Discussion

The study aimed to develop a practical and accurate model for estimating VO2max based on anthropometric data and demographic variables in the young Iranian male population. To our knowledge, this is the first study on the prediction of VO₂max using direct measurements in young Iranian males. Direct measurements, which are highly accurate and valid in estimating VO₂max, were used for validating the models developed in this study.

The mean VO₂max of participants in this study was estimated at 3.20±0.3 lit/min (equivalent to 44.02±4.31 ml/kg/min). Based on the VO₂max classification for men, it can be stated that the participants had a good status in this regard [28]. In a study conducted by Matlabi et al. for estimating the VO₂max of 105 Iranian male workers aged 20–29 years, the mean VO₂max was reported to be about 3.4 lit/min [29], which is almost similar to the results of this study. The findings of this study are also consistent with the results of Mondal et al. who reported a VO₂max of 43.25 ml/kg/min for Indian men [30]. Nevertheless, the VO₂max estimated was lower than that obtained from American men (4.451 lit/min) [31]. This is somewhat due to the difference in anthropometric characteristics, and other factors, such as physical activity and diet may also have some effect on the VO₂max [17 –32]. It can be hence concluded that the models and equations developed based on anthropometric and physiological information of American or European populations for measuring VO₂max probably leading to overestimated results when employed for other populations, including Iranian individuals.

The study results showed a negative significant relationship between VO₂max and BMI and weight, which is consistent with the findings of some previous studies [33 –35].

Based on the findings of this study, we found a significant and inverse relationship between the VO₂max measured and WHR and WC. Many studies have shown that WC is a more accurate measure of body fat distribution and is strongly correlated with VO₂max. Also, epidemiological studies have indicated that WHR is a more accurate measure of obesity and cardiovascular system performance, and thereby, VO₂max [5 –37]. In fact, Individuals with lower BMI and WC can have more physical activity and their cardiovascular system performs better[38, 39].

In this study, three non-exercise regression models were developed for estimating VO₂max. The study results showed that age, weight, BMI, WHR and WC managed to explain 67–71% of the variance of VO₂max in Iranian men aged 19–29 years (SEE = 3.19–3.21 mL/kg/mil). Many studies have shown that anthropometric variables and age are the most important factors affecting VO₂max (11, 34, 36, 40). Scientific studies also indicate that body composition indices (i.e. weight and BMI) are among the major variables in the linear and nonlinear regression equations for VO₂max estimation; these variables may be either positively or negatively correlated with VO₂max [17 –42].

The findings of this study showed that the addition of multiple variables to prediction models increases the correlation between estimated and measured VO₂max (Model 1, Table 3). This model also made fewer errors than linear models 1 and 2 in estimating VO₂max.

According to previous studies, VO 2 max estimation models from exercise tests have reported R and SEE values ranging from 0.42 to 0.91 and 2.05 to 4.57, respectively [41 –43]. In our study, R² and SEE values of regression models were ranged from 0.67 to 0.71 and 3.19 to 3.20, respectively. Differences in the ethnicity of the populations studied, sample size, age, and variables may explain the wide range of R² and SEE values reported in previous models.

4.1 Limitations

Several limitations of present study need to be considered when using VO2max prediction model. First, because VO2max changes with gender and increasing age, we limited our study to men who were aged 19–29 years, thereby eliminating these confounding variables. Second, due to the small sample size the VO2max prediction model may have limited generalizability. Thus, future studies to develop VO2max prediction model in different age and gender groups with a larger sample need to be conducted. Third, since the participants of this study consisted of healthy individuals without cardiopulmonary diseases, the VO2max regression models cannot be employed for estimating the VO2max of those with such diseases.

5 Conclusion

This study aimed to develop VO₂max regression models based on anthropometric and demographic variables among Iranian men aged 19–29 years. An advantage of this study over similar ones was the validation of developed VO₂max prediction models through direct measurements of aerobic capacity. According to the results of the regression analysis, age, weight and WC were the most effective variables for development of VO2max prediction model. In this study, three simple and accurate models were proposed to use in the field and laboratory settings. Further research will be required to develop an accurate prediction model of VO2max in different age and gender groups with a larger sample among Iranian population.

Ethics statement

The study was approved by the local ethics committee (approval no. IR.AJUMS.REC.1397.279). The participants provided written informed consent to participate in this study.

Funding

The study was financially supported by Ahvaz Jundishapur University of Medical Sciences (grant number: U-97065).

Conflict of interest

The authors declare no conflicts of interest.

Footnotes

Acknowledgments

The authors would like to thank the participants for their kind contributions.

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