Introduction

Oxygen consumption has a close relationship with aerobic-type exercise. In the face of a continued effort at a moderate and constant intensity, first, the anaerobic reserves are activated resynthesize ATP in a phase of oxygen debt; then, when the cardiopulmonary system finishes starting up, the contribution of O₂ is preponderant in the energy production, achieving a stable plateau, until the effort gives up (López-Chicharro & Vicente-Campos, 2017). A similar dynamic occurs at the beginning of the exercise in a staggered, progressive effort until exhaustion. Still, this time, as the exercise progresses, the O₂ consumption has a concomitant increase with the intensity (speed, watts, and treadmill inclination, among others) until reaching a point where VO₂ cannot continue to increase despite the increase in intensity itself, achieving a plateau characteristic of having reached V̇O₂max (Poole & Jones, 2017). In an intermittent dynamic exercise, the behavior of VO₂ is variable. On the one hand, a good cardiopulmonary capacity serves to withstand the duration of the effort (interval methods, HIIT, Split, etc.) and, on the other hand, to recover between high-intensity efforts that occur relatively frequently (Béres et al., 2021).

The most influential variables on V̇O₂max development are essentially those non-modifiable, such as gender or chronological age (Reyes & Soto, 2021), and those modifiable associated with lifestyle, such as physical activity practice, body composition, and smoking (Vásquez-Gómez et al., 2020). Thus, cardiorespiratory fitness has been considered a strong predictor of health and functionality (Mänttäri et al., 2018), and oxygen consumption is listed as the best indicator of this capacity (Costa et al., 2017). The evaluation can be done in laboratory ergometers, but this requires equipment of high economic cost and trained personnel to use and interpret the information. Another way to perform it is by using field tests, one of which is the SMWT (Costa et al., 2017), in which the distance traveled has been used to assess functional capacity and even morbidity and mortality (Britto et al., 2013). The applied test has been useful in healthy (Mänttäri et al., 2018) people and those with pathologies (Chetta et al., 2006).

To evaluate cardiorespiratory fitness in the SMWT was used the distance an individual can cover.. The distance covered was associated with variables such as age, gender, BMI, height, and heart rate (HR) (Britto et al., 2013; Chetta et al., 2006; Mänttäri et al., 2018). These variables correspond to the characteristics of the evaluated group and, therefore, in a specific context. Hence, the importance of generating instances of evaluations that are transferable from the laboratory to the practice field and are an alternative tool for professionals of physical activity and sports. Given this, the aim was to relate and compare the objective measurement of V̇O₂max as the dependent variable, with two prediction formulas of the distance traveled in SMWT, as the independent variables and, at the same time, to relate the distance (dependent variable) with basic demographic and anthropometric variables (independent) in university students of both genders.

Methodology

Participants

This was a correlational, descriptive, and quantitative study with a non-experimental cross-sectional design. The study was disseminated in the university community (library, virtual social networks, face-to-face courses) to attract students from various academic programs and all University semesters of day shift (not including Physical Education students). A non-probabilistic by convenience sample was used, and participants had to meet the following inclusion criteria: not having injuries, not performing a strenuous physical exercise the day before, not consuming substances that alter the HR and the body, and not consuming heavy meals prior to the evaluation. The final sample was of 110 students (51% women) means 21.9 ± 2.1 years old and BMI of 24.3 ± 3.1 kg/m², after giving their written consent (Scientific Ethics Committee, Universidad Católica del Maule, Talca, Chile, n° 17/2018).

Instruments and Procedures

The data of interest were collected in the same session. Blood pressure, HR (OMRON model BP760 series 7, Japan), and oxygen saturation at rest (Carewell model F1 oximeter, China) were recorded. Body weight and height were also recorded (DETECTO model 3P7044 capacity 140 kg, USA). Subsequently, the Bruce graduated exercise test (GXT) was applied, in which the intensity increased every three minutes based on the speed and inclination. The starting speed was 2.7 km/h to increase to 4, 5.4, 6.7, 8, 8.8 y 9.6 km/h and with a slope of 10, 12, 14, 16, 18, 20 y 22° on a treadmill, at each stage of 3 minutes, respectively. This test was applied on a motorized treadmill (3.3 kW) model h/p/cosmos mercury® (Germany). To do this, the participant had to wear comfortable sports clothing (sneakers, shorts, and a t-shirt, generally), a heart-rate monitor attached to the chest was installed, and a gas exchange analyzer was mounted. The analyzer had a mask covering the mouth and nose; it also had cells sensitive to O₂ and CO₂, a thermostat sensitive to temperature, and a turbine sensitive to air flows. The mask was fixed to the head with adjustable straps. The CORTEX ergospirometer model MetaLyzer® 3B (Germany) reported the data in real time, breath by breath, and was used to determine the V̇O₂max, under the following criteria: respiratory exchange ratio (RER) ≥1.11 (Hamlin, Draper, Blackwell, Shearman & Kimber, 2012; Web, Vehrs, George & Hager, 2014), an HR ≤ 10 beats·min^-1 or ≤ 5% in respects to the maximum estimated (220-age) (Poole & Jones, 2017). The RPE was also recorded at the end (Borg & Kaijser, 2006).

Then, aerobic endurance was estimated through the distance covered in SMWT with the equation by Britto et al. (2013) and Chetta et al. (2006). During the test, an individual has to cover distances as far as possible in six minutes. These equations were chosen due to the choice of the sample and its statistical analysis. The Britto et al. equation (2013) included healthy men and women. The independent variables of age, gender, BMI, and Δ HR were significantly related to the distance performed in the SMWT (r = 0.4 to 0.55; p<0.0001). A quadratic regression model showed that these variables, except Δ HR, explained 46% of the variability of the distance covered (R² = 0.46; p<0.0001). The Chetta et al. equation (2006) also considered healthy men and women. The independent variables of age and height were significantly related to the distance covered in the SMWT (r = - 0.42 and 0.46; p<0.001) so that the regression model explained 42% of the variability of the distance performed (R² = 0.42; p≤0.05). Therefore, the formulas used to predict the theoretical distance were the following:

SMWT = 890.46 - (6.11 × age) + (0.0345 × age²) + (48.87 × gender) - (4.87 × BMI) where male gender = 1, and female gender = 0 (Britto et al., 2013).

SMWT = 518.853 + (1.25 × height) - (2.816 × age) - (39.07 × gender) where height was in cm, and male gender = 0, and female gender = 1 (Chetta et al., 2006).

Statistical Analysis

The data were analyzed on average values and standard deviation. Normality was checked with the Kolmogorov-Smirnov test. The data between men and women were compared with the Student-T test or Mann-Whitney U test. The Pearson and Spearman test was used to evaluate the relationship between the different variables. On the one hand, the V̇O₂max was a dependent variable, and SMWT's predicted distance was independent; on the other hand, SMWT’s predicted distance was a dependent variable, and age, BMI, height, and weight were independents ones. The repeated ANOVA measurement tests were used to compare the V̇O₂max and the distances in SMWT, followed by the Tukey post hoc test. The SPSS v.22 program was used, and statistical significance was assumed when p<0.05.

Results

There were differences between men and women in V̇O₂max and in the distance calculated in SMWT; those were higher in men (Table 1).

The correlation of V̇O₂max of GXT, with the distance estimated in SMWT of both equations (Britto et al., 2013; Chetta et al., 2006) were significant in women (r = 0.535 [95% CI: 0.33; 0.68], p<0.001 and r = 0.268 [95% CI: 0.03; 0.46], p=0.046) and in men (r = 0.491 [95% CI: 0.29; 0.67], p<0.001 and r = 0.424 [95% CI: 0.21; 0.61], p=0.001), respectively. The estimated distance (Britto et al., 2013) was related to age, BMI, and height (r = -0.524 [95% CI: -0.07; -0.29]; -0.921 [95% CI: -0.95; -0.87]; -0.799 [95% CI: -0.88; -0.66], all p<0.001) in women, and age, BMI, and weight (r = -0.769 [95% CI: -0.88; -0.61]; -0.867 [95% CI: -0.91; -0.81]; -0.761 [95% CI: -0.85; -0.63], all p<0.001) in men. While in the other equation (Chetta et al., 2006) age, height and weight (r = -0.557 [95% CI: -0.74; -0.34]; 0.85 [95% CI: 0.76; 0.92], both p<0.001; r = 0.326 [95% CI: 0.15; 0.51], p=0.014) in women, and in men, age, BMI, and height (-0.646 [95% CI: -0.78; -0.43]; -0.386 [95% CI: -0.58; -0.18]; 0.722 [95% CI: 0.55; 0.84], all p<0.001) were correlated with the distance. Apparently, the distance (Chetta et al., 2006) underestimates and overestimates V̇O₂max, while BMI (Britto et al., 2013), weight, and height (Chetta et al., 2006) do so with distance (Figure 1) in women and men, respectively.

There were significant differences between the three methods [F (2, 327) = 22561.1; p<0.001)], when comparing the V̇O₂max with the estimated distance in both equations (Britto et al., 2013; Chetta et al., 2006), and also between these two methods (p<0.001). Similarly, it occurred in men [F (2, 159) = 36995.1; p<0.001)] and in women [F (2, 165) = 43679.4; p<0.001)].

Discussion

When it comes to comparing different methods of evaluating aerobic resistance, some studies propose equations to evaluate the performance. The SMWT generally measured the distance traveled (Fleg et al., 2000; Harmsen et al., 2016), which has shown to correlate with the V̇O₂max (Harmsen et al., 2016) and, in some cases, this relationship (V̇O₂ - distance) has been studied in healthy individuals (Andersson, Lundahl, Wecke, Lindblom & Nilsson, 2011; Andersson & Nilsson, 2011; Hill et al., 2011; Limsuwan, Wongwandee & Khowsathit, 2010; Ma, Lin & Qin, 2009). These findings are in line with the research conducted by the authors of this paper and reinforce their postulates.

The authors found an association between performance in the SMWT and the prediction of V̇O₂max in healthy people, in which distance traveled along with age, BMI, height, and HR accounted for 82% of the variation of V̇O₂max in men, and 79% in women, including weight, age, and distance for them (Mänttäri et al., 2018). Thus, the authors elaborated prediction equations of V̇O₂max for the SMWT. It has even been studied in people with heart disease, where gender, age, BMI, functionality status, and distance traveled in the SMWT were significantly related to the peak V̇O₂ estimated on a treadmill, giving way to an equation to predict this variable of interest, and there was no significant difference between the V̇O₂ observed and that estimated through the formula (Costa et al., 2017). Also, the SMWT has been used to relate to cardiopulmonary capacity in people with cerebrovascular (Pang, Eng & Dawson, 2005) and pulmonary pathologies (Oudiz et al., 2006; Tueller et al., 2010; Zapico et al., 2016).

During the last decade, some studies have associated the distance covered in the SMWT with demographic variables and anthropometric characteristics. Osses et al. (2010) found significant correlations between distance and age, weight, and height in young adult subjects. Also, in young subjects, significant relationships have been found with body weight, height, and age, where the latter two explained the variability of the distance performed by 25% (Alameri, Al-Majed & Al-Howaikan, 2009). Finally, Dourado (2011) reports a series of studies in which the SMWT distance has various significant and non-significant correlations with age, height, weight, and BMI. These data do nothing more than confirm and support the associations found between the variables of interest (dependent and independent). Therefore, the measurements of the demographic variables and anthropometric characteristics must be evaluated and taken into account to develop predictive models.

The relevant element of the cited research is that it has shown correlations between the dependent variables (V̇O₂ and distance) and the corporal, demographic, and physiological characteristics, and health state, which makes the SMWT applicable in a real context of each group of people. In this research, there were significant correlations between the V̇O₂max and the distance predicted in the SMWT, which agrees with the comparative evidence; the correlations also indicated that this variable could be an indicator of cardiopulmonary capacity.

The main finding of this study is that the V̇O₂max was significantly different from the aerobic resistance expressed in the distance calculated in SMWT. As already mentioned in this discussion, some studies have shown that V̇O₂max has had a relationship with the distance performed in the SMWT; in turn, the distance covered has been associated with variables such as age, height, body weight, and BMI. Given these interactions, it can be inferred that the three methods used in this study (direct measurement of V̇O₂max and the two equations to estimate the theoretical distance in SMWT) can have a statistical association since, in practice, cardiorespiratory fitness activates the capacities functions of the cardiopulmonary and vascular system and involves large muscle groups (Gomes et al., 2018), characteristic aspects of the tests we investigate. However, there are contradictory findings regarding these results since, on the one hand, some reports in the literature state that there have been no significant differences between the objective measurement of V̇O₂max and prediction equations derived from the submaximal test (Song et al., 2019), and, between V̇O₂max and its prediction from a walk test (Lima et al., 2019). On the other hand, there have been differences between the measurement and estimation of V̇O₂max with the direct and indirect method, respectively (Bandyopadhyay & Pal, 2015).

The main strength of this research was that V̇O₂max was measured through an objective, direct, reliable, and valid method, using a gold standard such as ergospirometry. Another strength was the number of participants evaluated, considering other investigations that have worked with samples of 75 (Mänttäri et al., 2018), 81 (Costa et al., 2017), and 617 (Britto et al., 2013) people. At the same time, as a projection of this research, we intend to carry out the SMWT validation process as a predictor of V̇O₂max, as suggested by some studies (Britto et al., 2013; Mänttäri et al., 2018). On the other hand, a potential limitation was to use an indirect and theoretical method to predict the distance traveled in the SMWT. It was a potential limitation since this way of evaluating was raised within the objective of the research.

Conclusion

The GXT method of measuring V̇O₂max had low and moderate but significant correlations, with the equations of distance prediction in SMWT, for the total sample and separated by gender. In some cases, he distance predicted with both methods was significantly related to age, weight, height, and BMI. On the other hand, the V̇O₂max was significantly different from the two methods of distance prediction, both in the total sample and for each gender.

Future Recommendations and Practical Scopes

It is pertinent to elaborate a predictive model of either the V̇O₂max or the distance performed in the SMWT, both in the women and university men evaluated, or for the total sample that includes the gender variable in the said model. Because of that, an easy-to-apply and less-effort test would better represent the performance of students' daily life activities.

References

Publication Dates

History