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Raso V, Matsudo SMM, Santana MG, Boscolo RA, Viana VAR, Grassmann V, et al. Exercise and non-exercise aerobic power prediction models using six-minute walk test. MEDICALEXPRESS 2016;3(5):M160502



Exercise and non-exercise aerobic power prediction models using six-minute walk test

Vagner Raso1; Sandra Marcela Mahecha Matsudo2,3; Marcos Gonçalves de Santana4,5; Rita Aurélia Boscolo4; Valter Antônio Rocha Viana4; Viviane Grassmann4; Sergio Tufik4,6; Marco Túlio de Mello4,6

1. Universidade de São Paulo, Faculdade de Medicina, Instituto de Ortopedia e Traumatología Laboratório de Estudos do Movimento (LEM-IOT-HCFMUSP), São Paulo, Brazil
2. Centro de Estudos do Laboratório de Aptidão Física de São Caetano do Sul (CELAFISCS), São Caetano do Sul, Brazil
3. Universidad Mayor, Facultad de Ciencias, Escuela de Post Grado, Santiago, Chile
4. Universidade Federal de São Paulo, Centro de Estudos em Psicobiologia e Exercício (CEPE-UNIFESP), São Paulo, Brazil
5. Universidade Federal de Goiás (UFG), Programa de Ciências Aplicadas a Saúde, Goiânia, Brazil
6. Universidade Federal de São Paulo (UNIFESP), Departamento de Psicobiologia, São Paulo, Brazil


Received in June 6 2016.
First Review in July 13 2016.
Accepted in September 8 2016.


BACKGROUND AND OBJECTIVE: A simple, low-cost approach commonly used to objectively analyze the cardiorespiratory fitness of individuals with different health conditions is the six-minute walk test (6-MWT). Our objective was to develop peak aerobic power prediction using the six-minute walk test in healthy older men.
METHODS: We measured body composition (body mass [BM], body mass index [BMI], fat percentage [FAT]) and peak aerobic power breath-by-breath during cardiopulmonary exercise testing (CPET [velocity, heart rate [HR] and VO2 at the anaerobic threshold and peak]) and a 6-MWT (distance [D], weight by distance [WxD], HR and oxygen consumption [VO2] at peak) in 76 healthy older men aged 65 to 80 years (69.1 ± 0.3 yrs-old).
RESULTS: We observed significant correlations for VO2peak during the 6-MWT as a function of WxD (R = 0.75, P < 0.0005), BM (R = 0.56, P < 0.0005), D (R = 0.43, P = 0.0004) and maximum HR (R = 0.37, P = 0.001). Distance correlated significantly with FAT (R = -0.43, P = 0.005), BMI (R = -0.36, P = 0.021) and age (R = -0.31, P < 0.045), whereas WxD correlated with BM (R = 0.86, P<0.005).The inclusion of WxD increased the R2 from 0.65 to 0.74 and decreased the estimative error while yielding the following equation (R = 0.86, standard error of the estimate (SEE) = 182.1 mL·min-1, P < 0.0005) to predict VO2peak: VO2peak = 962.2 + (0.037 x WxD) + (8.565 x maximum HR). A non-exercise model was obtained by univariate regressions but not multiple regressions. The FAT (R = 0.43, SEE = 702.2 m, P < 0.005) yielded the best model for predicting distance, i.e., distance = 702.2 - (3.067 x FAT).
CONCLUSION: Our prediction model seems to accurately estimate VO2peak in healthy older men primarily when WxD is considered.

Keywords: Aging, Cardiorespiratory Fitness, Male, Maximal Oxygen Uptake.


OBJETIVO: O teste de caminhada de seis minutos (TC6M) é uma estrategia simples e de baixo custo operacional que objetivamente mede a aptidão cardiorrespiratória de indivíduos com diferentes condições de saúde. Nosso objetivo foi desenvolver equações de predição da potência aeróbica em homens idosos saudáveis usando o TC6M.
MÉTODOS: Foram medidos a composição corporal (massa corporal [MC], índice de massa corporal [BMI], porcentagem de gordura [%G]) e potência aeróbica de pico através de teste cardiopulmonar de exercício máximo (velocidade, frequência cardíaca [FC] e consumo de oxigênio [VO2] no limiar anaeróbico e no pico]) e através do TC6M (distancia [D], produto massa corporal x distância, FC e VO2de pico) em 76 idosos saudáveis com idade entre 65 e 80 anos (69.1 ± 0.3 anos).
RESULTADOS: Foram observadas correlações significativas para VO2pico no TC6M em função do produto MCxD (R = 0,75, P < 0,0005), MC (R = 0,56, P < 0,0005), D (R = 0,43, P = 0,0004) e FC máxima (R = 0,37, P = 0,001). A correlacionou-se significativamente com %G (R = -0,43, P = 0,005), IMC (R = -0,36, P = 0,021) e idade (R = -0,31, P < 0,045), enquanto que a MCxD correlacionou-se com MC (R = 0,86, P < 0,005). A inclusão da MCxD aumentou o R2 de 0,65 para 0,74 e diminuiu o erro padrão da estimativa (EPE) gerando a equação (R = 0,86, EPE = 182,1 mL·min-1, P < 0,0005) para a predição do VO2peak: VO2peak = 962,2 + (0,037 x MCxD) + (8,565 x FC máxima). Foi também gerado um modelo preditivo não dependente de exercício através de regressão univariada, mas não múltipla. A %G (R=0,43, EPE = 702,2 m, P < 0,005) gerou o melhor modelo para a predição da distancia, i.e., distancia = 702,2 - (3,067 x %G).
CONCLUSÃO: Nossas modelos de predição parecem estimar precisamente o VO2pico de idosos saudáveis, especialmente quando a MCxD é considerada.

Palavras-chave: Aptidão cardiorrespiratória, consumo máximo de oxigênio, envelhecimento, homem.



The assessment of aerobic power during the aging process has been widely studied for decades not only because of the decline of this important health variable with increasing chronological age but also because regardless of the physical activity level, sedentary subjects, active subjects and athletes exhibit decreased aerobic power as they age. Cardiorespiratory fitness has been extensively analyzed because it represents an important predictor of functional independence in elderly individuals1,2 and of all causes of mortality and cardiovascular events.3

A simple, low-cost approach commonly used to objectively analyze the cardiorespiratory fitness of individuals with different health conditions is the six-minute walk test (6-MWT).4 This procedure has been used as a standard reference criterion because the procedure captures a significant portion of the variability in the maximum oxygen consumption (VO2max: 80%) and the maximum heart rate (MHR: 86%) obtained in the cardiopulmonary exercise testing (CPET).5 Thus, the 6-MWT can be used for individuals who are unable to satisfactorily complete a cardiopulmonary CPET in a standard stress test.6,7 In addition, the 6-MWT reflects the physiological demands required for daily activities.4,8-10 Moreover, in recent years, several studies have analyzed the possible clinical uses of the 6-MWT to measure the functional status and the cardiorespiratory fitness of healthy elderly individuals and of elderly individuals with cardiovascular and pulmonary disease and as a form of aerobic exercise for the elderly.11-14 The 6-MWT test has been used to detect changes in the cardiorespiratory fitness of healthy elderly individuals after training15 and to predict cardiovascular events in patients with stable coronary heart disease.16

Thus, there has been an effort to establish standard reference norms that apply to a wide range of individuals.17 Most studies have sought to develop equations that predict the peak O2 consumption (VO2peak) or the distance walked during the 6-MWT as a function of variables that are intrinsic to the protocol per se, or are associated with clinical and sociodemographic characteristics.18,19 These equations seem to explain approximately one- to two-thirds of the variability.19,20 Clearly, the selection of the variable to be included in the model is an important factor,21,22 because men seem to experience twice the VO2max decline over a ten-year period as compared to women (14% versus 7%)23, and exhibit a higher mortality risk.24 However, when adjusted to factors known to be involved, such as body mass, the walking distance per se may represent a more accurate measure of the cardiorespiratory capacity than the walking distance in elderly men25 and in patients with chronic diseases.26 More recently, a generalized equation has been proposed to predict peak VO2 from the 6-MWT using data collected from over 1,000 patients with cardiopulmonary disorders.27 No similar proposals for healthy elderly individuals are available. Therefore, the purpose of this study is to develop VO2peak and distance prediction models using body mass (BM), the body mass index (BMI), body fat (FAT), distance (D), the body weight-walking distance product (WxD) and the maximum heart rate (MHR) in healthy elderly men.




Elderly male volunteers were recruited from the community. In total, 204 volunteers were contacted by telephone and submitted to a preliminary screening, which consisted of questions regarding past and current health, drug and cigarette use and physical activity. Afterward, to determine the health status of the volunteers more precisely before they were admitted to the study, the volunteers were invited to the hospital for a detailed clinical evaluation and a standard clinical and physical examination. The evaluation and examination included (i) a detailed assessment of current and past health status, (ii) a 12-lead electrocardiogram, (iii) a check for depression symptoms, (iv) an ascertainment of the ability to selfreport and perform basic and instrumental activities of daily living, (v) an evaluation of body composition and (vi) laboratory parameters. The volunteers who were under treatment for acute or chronic cardiovascular, pulmonary or metabolic disease or using medication that influences the cardiovascular and pulmonary system were excluded. The following were additional reasons to exclude a volunteer were: (i) central or peripheral nervous system disorders, (ii) malnourishment or obesity, (iii) smoking, (iv) current or past cancer treatment, (v) surgery of any type during the previous three months, (vi) prescribed bed rest during the previous three months or (vii) any orthopedic disturbances would limit performance during a CPET.

Seventy-six elderly volunteers aged 65 to 80 years were included. The volunteers were cleared for participation and expressed their willingness to submit to the conditions of the experiment. They were informed regarding the procedures and risks before reading and providing written consent. This study was approved by the research ethics committee of our university (#1592/07).

Body composition

We measured height using an electronic scale (Bod Pod®, Life Measurement Inc., USA). The body mass index (BMI) was calculated by dividing the total body mass by the squared height (kg/m2). The body fat percentage (FAT%) and the fat-free mass were obtained by plethysmography (Bod Pod®, Life Measurement Inc., USA). The measurement procedures followed the criteria described by equipment manuals and literature.28

Cardiopulmonary exercise testing

The peak oxygen consumption (VO2peak) and the anaerobic threshold (AT) were determined using a CPET performed on a treadmill (Life Fitness®, 9700 HR, USA). The treadmill speed was initially set at 1.6 km.h-1; this was followed by increases of 0.8 km.h-1increments in speed and 1% increments in slope every two minutes until voluntary exhaustion.5 The expired gas samples were analyzed breath-by-breath at 20-second intervals, using a metabolic portable system (K4b2, Cosmed®, Italy). Before each test, the equipment was calibrated according to the manufacturer’s recommendations. The heart rate was recorded simultaneously using a portable system (Polar®, Electro OU, Finland). VO2peak was achieved when the following criteria were satisfied: (i) respiratory exchange ratio (RER) > 1.10, (ii) the age-predicted maximum heart rate (MHR) was reached, (iii) volitional fatigue and (iv) signs of exhaustion toward the test’s end (unsteady gait, hyperpnea, sweating, facial flushing and grimacing).29 The anaerobic threshold (AT) was evaluated through the V-slope method, using a computerized regression analysis of the slopes of the CO2 uptake (VCO2) versus the O2 uptake (VO2) plot, which detects the beginning of the excess CO2 output generated by the buffering of [H+]. The respiratory compensation point (RCP) was detected by examining the minute ventilation per VCO2 plot.30

Six-minute walk test

The volunteers were instructed to walk at regular speed as far as possible carrying a portable metabolic system (K4b2, Cosmed, Italy) for six minutes. Three tests were performed to control for the learning effect and the data reliability.5 The first two tests were performed on the same day separated by a one-hour interval. The third test was performed after 24 hours. The 6-MWT was performed in a 20 m corridor that was clear of obstacles. The corridor length was marked every meter, and the volunteers were informed every 60 seconds regarding the remaining test time.4,31 The participants were instructed to walk at a self-selected regular pace to cover as much distance as they could during the allotted time. If necessary, slowing and stopping to rest were allowed. At the end of each minute, the participants were provided feedback on the elapsed time and standardized encouragement in the form of statements such as "you are doing well, keep it up" and "do your best". The VO2peak was established as described in the previous section. The total walked distance was recorded in meters, and the longest distance of the two tests was used. The body weight-walking distance product was calculated by multiplying the body weight by the distance (W x D, kg·m).25

Statistical analysis

The Kolmogorov-Smirnov test was used to analyze the data normality. A one-way analysis of variance with Bonferroni post-hoc compared subjects grouped into quartiles according to the 6-MWT VO2peak (i.e., P0-P25, P25-P75 and P75-P100). The Pearson product-moment correlation coefficient was used to determine the association of VO2peak with BM, BMI, body fat, D, WxD andMHR. Univariate regression analysis was used to investigate the associations of age with all of the other variables and the body weight-walking distance product as a function of VO2 at the anaerobic threshold and peak. In addition, hierarchical multiple regression analyses were performed to develop exercise (VO2peak) and non-exercise (D and WxD) prediction equation models as a function of BM, BMI, FAT, D, WxD and MHR. All of the analyses were performed using the Predictive Analytics Software17.0 version for Windows package (SPSS, Inc., USA). The data are presented as the mean ± standard error. Significance was set at p < 0.05.



General characteristics

Homogeneity of the population was observed throughout the variables of age and body composition (Table 1). However, VO2 at the anaerobic threshold and peak (CPET and 6-MWT) were significantly higher in individuals at highest quartile (P75-P100). Similar results were observed for distance and the MHR during the 6-MWT (Table 2).





Age effects

We observed an age effect on the anaerobic threshold (velocity: β = -0.39, P = 0.010], heart rate ([β = -0.88, P = 0.0005]), MHR during the CPET ([β = -0.38, P = 0.012]) and D during the 6-MWT ([β = -0.31, P = 0.045]) (data not shown).

Body weight-walking distance product

We analyzed the product of body weight and distance (WxD) as a parameter that could reflect the work rate during the 6-MWT. A significant association was observed between WxD vs. the anaerobic threshold (r = 0.63, β = 0.018 ± 0.004 and P < 0.0005) and VO2peak (r = 0.73, β = 0.031 ± 0.005, P < 0.0005) regardless of age (data not shown).

Univariate correlations

Peak oxygen uptake during the CPET correlated significantly with the corresponding peak oxygen uptake during the 6-MWT, as shown in Figure 1.


Figure 1 - The high level of correlation between VO2peak during the six-minute walk test and cardiopulmonary exercise test shows that the 6MWT is a reliable predictor of aerobic power.


Figure 2 exhibits the correlation of various parameters of the 6MWT with the respective VO2peak: the best observed correlation is between distance x weight and peak oxygen consumption. However, distance, maximum heart rate and body mass also correlated significantly.


Figure 2 - The four parameters relating to the six-minute walk test correlate significantly with peak O2 consumption; the best correlation occurred for the distance x weight product.


Figure 3 illustrates the expected inverse correlations between body fat, body mass and age with travelled distance.


Figure 3 - The percentage of body fat, body mass index and age correlate inversely with travelled distance.


Multiple regression analysis

We developed exercise and non-exercise models. The first strategy was based on a prediction equation to estimate VO2peak predominantly as a function of the 6-MWT variables. Subsequently, we tested the hypothesis of a non-exercise prediction equation to estimate either distance (D) or the WxD product (Table 3). All of the variables of the respective models independently explained VO2peak, D or WxD. When estimating VO2peak, the inclusion of WxD increased the R2 from 0.65 to 0.74, which represents a significant 9% increase in the explained variance of VO2peak. Moreover, there was an important decrease in estimative error, which yielded the following equation (R = 0.86, SEE = 182.1 mL·min-1 and P < 0.0005) for predicting VO2peak: VO2peak = 962.2 + (0.037 x WxD) + (8.565 x MHR), where WxD is the body weight-walking distance product in kg·m and MHR is the maximum heart rate in bpm. A non-exercise model was obtained by univariate regressions but not by multiple regressions. Body fat percentage (R = 0.43, SEE = 702.2m, P < 0.005) yielded the best model for predicting distance, i.e., D= 702.2 - (3.067 x FAT), where FAT represents the percentage of body fat.




An obstacle to measuring the aerobic power of elderly individuals (either in groups or clinical care) has been the lack of a practical, safe, low-cost and scientifically valid method to assess functional status, cardiovascular fitness and the impact of comorbidities on such individuals and capable of providing a parameter for the prescription of exercise. The present study proposes a novel model for predicting aerobic power in healthy elderly men. Our results provide normative data related to cardiopulmonary parameters for maximal CPET and for the 6-MWT in clinically healthy and physically inactive elderly men. The data resulting from this study can serve as an important reference standard for this age range because the strict eligibility criteria employed in the present study excluded participants with cardiopulmonary, metabolic or musculoskeletal disorders. In addition, these data support the hypothesis that WxD can be used as an indicator of the submaximal or maximal workload developed during the 6-MWT. Additionally, our data support the hypothesis that the VO2peak achieved during the 6-MWT is significantly associated with the VO2peak recorded in a maximal CPET performance, which facilitated the development of a VO2peak prediction model with satisfactory accuracy. It was also possible to develop other models to estimate distance (based on a non-exercise pattern) but with low accuracy.

The absolute aerobic power (i.e., L·min-1) was considered as the main outcome variable in our models. We tried to avoid potential multicollinearity between the BM and the WxD product in the models which considered the relative aerobic power (i.e., mL·kg-1·min-1). Therefore, the effect of the body mass in our models is not being overestimated by redundancy. Our analyses demonstrate that each of the predictor variables used in this study was independently related to the distance walked in the 6-MWT and the VO2peak. However, the best models were extracted when VO2peak was the main outcome variable.

There are numerous indirect methods to estimate VO2peak in elderly subjects that result in good accuracy with correlations reaching up to 0.97.21,22,32,33 These results are similar to the findings of our study for both of the generated exercise models, particularly compared with studies on the 6-MWT.33 However, some differences in the magnitude of correlations may be partly explained by the type and the number of predictor variables included in the models.21,22 Moreover, there seems to be a direct relationship between the severity of the clinical condition and the strength of the correlation in the analysis between the walked distance and the VO2peak.4,34,35 A linear mixed-model analysis was used to determine the SEE of the 6-MWT to estimate the peak oxygen uptake; the formula can be useful to compare the peak aerobic capacity without the CPET and can be applied to monitor the course of several cardiovascular and pulmonary diseases and the efficacy of potential treatments.27

Although the VO2peak prediction model developed in the present study has satisfactory accuracy, the extrapolation of the results could be limited because the elderly represent a select group of individuals. Therefore, future studies are required to determine the stability of the proposed model's VO2peak prediction for individuals whose characteristics are substantially different from our sample amplitude (e.g., physical activity level and fitness, health condition and disease severity).

The present study is the first to demonstrate that WxD is a more important VO2peak predictor in elderly individuals than weight and distance separately. In addition to characterizing the work rate performed during the 6-MWT25,26, this strategy improved the regression model's ability to predict VO2peak. The WxD parameter more accurately reflects the workload of walking than D and therefore should be used as an evaluation criterion of cardiorespiratory fitness in the absence of gas exchange analysis.25 However, there may be other sociodemographic (e.g., gender) or physiological (e.g., resting heart rate) variables that would facilitate the development of a non-exercise prediction model to satisfactorily predict WxD. For instance, chronological age seems to represent an important discriminatory parameter of cardiopulmonary capacity, especially among elderly individuals.36 Moreover, a significant proportion of the variability of the cardiorespiratory capacity estimate may be explained by anthropometric and sociodemographic variables.26

Therefore, we may conclude that our prediction model seems to be an accurate strategy to estimate VO2peak in healthy older men primarily when WxD is considered. The new exercise prediction model derived proposed in this study can be used to estimate VO2peak in older men. However, further studies should develop non-exercise models, particularly models based on the WxD parameter.



The authors declare that they have no conflict of interest with respect to this project.



Study concept and design: Raso V. Acquisition of data: Santana MG, Boscolo RA, Viana VAR and Grassmann V. Analysis and interpretation of data: Raso V. Drafting of the manuscript: Raso V. Critical revision of the manuscript for important intellectual content: Raso V. Statistical analysis: Raso V. Administrative, technical, or material support: Matsudo SMM, Santana MG, Boscolo RA, Viana VAR, Grassmann V, Tufik S and Mello MT. Study supervision: Matsudo SMM, Tufik S and Mello MT.



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