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Prediction Equations of Energy Expenditure in Chinese Youth Based on Step Frequency During Walking and Running Bo Sun
a b
a
, Yu Liu , Jing Xian Li
a
Shanghai University of Sport
b
Liaocheng University
a c
a
, Haipeng Li & Peijie Chen
a
c
University of Ottawa Published online: 04 Dec 2013.
To cite this article: Bo Sun , Yu Liu , Jing Xian Li , Haipeng Li & Peijie Chen (2013) Prediction Equations of Energy Expenditure in Chinese Youth Based on Step Frequency During Walking and Running, Research Quarterly for Exercise and Sport, 84:sup2, S64-S71, DOI: 10.1080/02701367.2013.851155 To link to this article: http://dx.doi.org/10.1080/02701367.2013.851155
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Research Quarterly for Exercise and Sport, 84, S64–S71, 2013 Copyright q AAHPERD ISSN 0270-1367 print/ISSN 2168-3824 online DOI: 10.1080/02701367.2013.851155
Prediction Equations of Energy Expenditure in Chinese Youth Based on Step Frequency During Walking and Running
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Bo Sun Shanghai University of Sport Liaocheng University
Yu Liu Shanghai University of Sport
Jing Xian Li Shanghai University of Sport University of Ottawa
Haipeng Li and Peijie Chen Shanghai University of Sport
Purpose: This study set out to examine the relationship between step frequency and velocity to develop a step frequency-based equation to predict Chinese youth’s energy expenditure (EE) during walking and running. Method: A total of 173 boys and girls aged 11 to 18 years old participated in this study. The participants walked and ran on a treadmill at speeds of 3 km/hr, 4 km/hr, 5 km/hr, 6 km/hr, 7 km/hr, and 8 km/hr. EE was measured using indirect calorimetry of open circuit spirometry (Cosmed K4b2 metabolic analyzer). Using multiple regression analysis, the relationship between step frequency and velocity was first examined, and the prediction equation of EE based on step frequency, age, and gender was derived. Results: The hypothesized relationship between step frequency and velocity was confirmed and an accurate (R 2 ¼ .78) EE prediction equation was derived: Net EE ¼ 213.7744 þ 1.8004 (step frequency) – 5.5715 (age) – 11.5244 (gender). Conclusion: A step frequency-, age-, and gender-based equation was derived to predict the EE of youth during walking and running. The equation can be used to develop a simple device to estimate EE during walking and running in this population. Keywords: energy consumption, physical activity, regression equations, velocity
Estimating the energy expenditure (EE) in walking is necessary for health, rehabilitation, and kinesiology studies, and this is especially true for children and youth considering
Correspondence should be addressed to Yu Liu, Key Laboratory of Exercise and Health Sciences, Ministry of Education, Shanghai University of Sport, 650 Qing Yuan Huan Road, Shanghai, 200438 China. E-mail: [email protected]
the fast-spreading childhood obesity epidemic and chronic health conditions worldwide (Riner & Sellhorst, 2013). The estimation is also an important basis for diagnosing diseases in clinical settings (Pols, Peeters, Kemper, & Grobbee, 1998). However, challenges remain in estimating EE of individuals by means of a system that has a low cost, does not interrupt an individual’s daily life, and is able to measure real-time EE and total EE using simple devices. The most accurate methods to measure and estimate EE of
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EE EQUATIONS BASED ON STEP FREQUENCY
free-living activities are the doubly labeled water (DLW) method (Ainslie, Reilly, & Westerterp, 2003) and respiratory metabolism analysis (McLaughlin, King, Howley, Bassett, & Ainsworth, 2001; Pinnington, Wong, Tay, Green, & Dawson, 2001). DLW, in fact, is considered the “gold standard” in measuring total EE. The limitations of both methods are that they can only be used in clinical and laboratory settings and they are not viable measurement tools during physical activity. In recent years, some simple and convenient devices that measure EE during walking or running, such as pedometers, have been developed and used (Crouter, Schneider, Karabulut, & Bassett, 2003; Foster et al., 2005; Schneider, Crouter, & Bassett, 2004). The design and price of pedometers vary greatly. The pedometer has three basic types of designs, including the spring-suspended lever arm with metal-on-metal contact, a magnetic reed proximity switch, and an accelerometer type (Crouter et al., 2003; Schneider, Crouter, Lukajic, & Bassett, 2003). Nevertheless, walking and running—two of the most common forms of physical activity—can be readily measured by pedometers. Pedometers have become increasingly popular as a measurement tool for physical activity. The accuracy and scope of application, however, are not known completely because of the commercial confidential restraints on computational algorithms used in pedometers. Developing and validating open computational algorithms that can predict EE is thus greatly needed. One of the ideas to develop such an algorithm is to employ the “walk ratio” concept. According to Sekiya and Nagasaki (1998), Walk Ratio ¼ Step Length / Step Rate or Step Frequency. The walk ratio is a speed-independent index of walking patterns, which remains constant within a broad speed range (Sekiya & Nagasaki, 1998). The gait velocity (or simply velocity)/speed of human walking is determined by the product of step length and step rate or step frequency. Thus, in theory, a stable functional relationship between step frequency and velocity exists. Stoquart, Detrembleur, and Lejeune (2008) examined the relationship between step frequency, walking velocity, and EE by analyzing kinematics, kinetics, and muscle activity, as well as energy cost on 12 healthy young men. The participants walked on a treadmill at six different speed settings (1 – 6 km/hr) and on the ground, respectively. The
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study results showed an apparent correlation between step frequency and velocity (R 2 ¼ .98; see also Tesio, Rota, Chessa, & Perucca, 2010). Step frequency is known to be influenced by body height and leg length, and step frequency is lower when legs are longer at the same velocity (Grieve & Gear, 1966; Sutherland, Olshen, Biden, & Wyatt, 1988; Wheelwright, Minns, Law, & Elton, 1993). Moreover, EE is influenced by age at natural customary and fast walking speeds (Waters, Hislop, Perry, Thomas, & Campbell, 1983; Waters, Hislop, Thomas, & Campbell, 1983; Waters, Lunsford, Perry, & Byrd, 1988). For children without movement issues, the rate of EE requirement for walking per kilogram of body weight decreases as the child ages (Waters et al., 1983). Therefore, to predict EE of children and youth using the walking ratio information, influencing factors, such as age and height, should be considered. Although some studies on predicting children’s EE at resting or on issues related to obese children have been done (Derumeaux-Burel, Meyer, Morin, & Boirie, 2004; McDuffie et al., 2004), EE prediction using the walking ratio has not been established for children and youth. The purposes of this study were twofold: (a) to verify the expected relationship between step frequency and velocity based on the concept of walking ratio, and (b) to develop a prediction equation based on step frequency and other possible predictors (e.g., age and gender).
METHOD Participants As part of the Chinese City Children and Youth Physical Activity Study, a total of 173 Chinese participants aged 11 to 18 years old were recruited from local schools in Shanghai, China. Before study commencement, participants assented to participate and all participants’ parents or guardians signed an informed consent form approved by the Ethics Advisory Committee of the Shanghai University of Sport. All participants were recreationally active, had no musculoskeletal disorders or injuries, were disease-free, took no medications that affect metabolism, and were nonsmokers (see Table 1).
TABLE 1 General Information of Participants (M ^ SD) Age (years)
Gender
n
Height (cm)
Weight (kg)
BMI (kg·m22)
11– 12
Male Female Male Female Male Female Male Female
10 15 24 21 36 35 15 17
159.00 ^ 5.58 155.79 ^ 6.38 169.08 ^ 5.86 160.07 ^ 4.43 172.15 ^ 5.43 160.43 ^ 4.93 174.19 ^ 5.07 160.40 ^ 3.50
52.74 ^ 5.85 46.77 ^ 7.77 59.83 ^ 9.70 51.89 ^ 6.32 61.50 ^ 10.39 53.17 ^ 9.34 68.53 ^ 7.80 55.73 ^ 7.39
20.98 ^ 2.61 19.18 ^ 2.21 20.98 ^ 3.29 20.25 ^ 2.32 20.68 ^ 2.77 20.62 ^ 3.28 22.64 ^ 2.88 21.72 ^ 3.25
13– 14 15– 16 17– 18
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Instrumentation A Gaitway motorized treadmill (H/P Cosmos Gaitway II S, Germany), with an appropriate conveyor belt (1,500 mm long £ 500 mm wide) and sufficient motor power (2.2 kW) was used in this study. The treadmill was calibrated before use to ensure that proper speed was maintained. EE was measured by indirect calorimetry utilizing open circuit spirometry of the portable metabolic analyzer (weight ¼ 475 g; Cosmed K4b2, COSMED, Rome, Italy). The K4b2 analyzer has been validated in previous studies (McLaughlin et al., 2001; Pinnington et al., 2001). The Cosmed system was calibrated using standard gas for the gas analyzers. A calibration syringe (3 L) was used to calibrate the turbine, which is flow rateindependent. The values were automatically converted by the software (Cosmed Quark b2 win, Version 5.1a) to standardize temperature, pressure, and dry oxygen uptake (VO2). The respiratory quotient, computed as the ratio between carbon dioxide production and VO2, is always less than 1. POLAR heart rate monitors (T34, Finland) were worn on the participants’ thorax to monitor each participant’s heart rate during the tests. A manual counter was used to count the steps in the last 2 min at each speed to calculate the average step frequency after cadence was stabilized during walking or running. An Omron body composition monitor “body scan” (HBF-375, Japan) was used to measure body fat rate and body weight.
were averaged, and the mean value was used for further analyses. Verification that the participants had reached a steady state was obtained in real time by observing the means of heart rate acquisition and graphic visualization (Bernardi et al., 1999). Data Processing and Analysis The physiological data from K4b2 and the step frequency data of the last 2 min of each speed were taken for EE analysis and for calculation of the average EE (cal·kg21·min21), respectively. The Statistical Package for the Social Sciences Version 13.0 was used to conduct the statistical analysis. The net EE during walking and running was calculated using the following equation (Stoquart et al., 2008): Net EE ¼ ðEgross 2 Erest Þ=Mass;
ð1Þ
where Net EE is net energy expenditure (cal·kg21·min21), Egross is gross energy expenditure, Erest is rest energy expenditure (cal·min21), and Mass is body mass (kg). EE of the participants at rest (Erest) was assumed to be equal to the basal energy expenditure (BEE). Erest was calculated via the empirical equations of Harris-Benedict for BEE, which was modified by Frankenfield, Muth, and Rowe (1998). The modified equation increased the multiple correlation coefficients for women. Harris-Benedict equations have been applied across a wide range of body weight and ages. The equations are as follows:
Protocols When the participants arrived at the lab, the researcher explained the details of the test to them. The participants filled out an information sheet. Researchers measured each participant’s body height, weight, leg length, and resting heart rate. Before the tests, the participants were allowed to walk or run on the treadmill for about 6 min for practice (Matsas, Taylor, & McBurney, 2000). A 5-min rest break was given to allow the participants’ heart rates to recover to a level ^ 5% of their resting heart rates. Formal data collection was then conducted. The participants were asked to walk on the treadmill at speeds of 3 km/hr, 4 km/hr, 5 km/hr, and 6 km/hr, and they were asked to run at 7 km/hr and 8 km/hr. The participants were asked to look straight ahead at a point about 158 downward placed about 2 m away during the treadmill walking and running tests. Erect head posture was required because head stabilization in space during gait has been shown to be important for postural control (Vallis, Patla, & Adkin, 2001). The speed was increased from the lowest to the highest level during walking and running tests. Walking and running at each of the test speeds lasted for at least 5 min to enable metabolism to reach and maintain a steady state condition, and there were no rests between test conditions. Step frequency, heart rate, and EE obtained during the last 2 min at each speed
Male : BEE ðkcald21 Þ ¼ 65 þ 13:40 ðwÞ þ 4:96 ðhÞ 2 5:82 ðaÞ ðR 2 ¼ :76; F ¼ 207:7; p , :001Þ ðFrankenfield et al:; 1998Þ Female :
BEE ðkcal·d21 Þ ¼ 447 þ 9:25 ðwÞ þ 3:10 ðhÞ 2 4:33 ðaÞ; ðR 2 ¼ :68; F ¼ 119:2; p , :001Þ ðFrankenfield et al:; 1998Þ
ð2Þ
ð3Þ
where w represents body mass (kg), h is height (cm), and a is age (years). Besides applying linear regression to examine the relationships, analysis of covariance (ANCOVA) was also employed when the influence of leg length on step frequency and the influence of age and gender on EE were examined. The alpha level of the statistical analysis was set at .05. Given the influence of leg length on step frequency, step frequency decreased as leg length increased at the same speed. Additional analysis is needed to eliminate the influence of leg length on step frequency (Mark, 1996). Based on body height, the participants were grouped into three groups: Group 1 had a body height range of 147 cm to 160 cm, Group 2 had a body height range of 160.1 cm to 173 cm, and Group 3 had a body height range of 173.1 cm to 186 cm. The velocity–step
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frequency relationship was examined using multiple analyses. ANCOVA was also conducted to examine the impact of height. Specifically, the height impact was examined in regression analyses, in which velocity was the dependent variable, step frequency was the independent variable, and height grouping was the covariate. The regression equation was further developed to include age as a covariate because the age of the participants ranged from 11 to 18 years old. The participants were grouped into four groups based on their age: Group 1 included 11- to 12-year-olds, Group 2 included 13- to 14-year-olds, Group 3 included 15- to 16-year-olds, and Group 4 included 17- to 18-year-olds. The slopes of regression equation between step frequency and net EE in movement were also examined for each of the groups. In the ANCOVA, net EE was the dependent variable, age was the covariate, and step frequency was the independent variable or predictor.
RESULTS
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Velocity – Step Frequency Relationship by Height Groups The ANCOVA revealed that there was a statistically significant interaction between step frequency and height ( p , .001), which was an indication of the impact of height (see Figure 2). This means that height influenced the estimation of EE by step frequency. Variance can be better explained if height is also brought into the prediction, which was confirmed by the results of the following regression analysis: Velocity ¼ 27:6993 þ 0:06511 ðstep frequencyÞ þ 0:0285 ðheightÞ;
ð5Þ
where Velocity is recorded in km/hr, step frequency in steps/ min, and height in cm. In the regression Equation 5 (F ¼ 3,177.37, p , .001), R 2 was .87, improving slightly from Equation 4. Accordingly, prediction accuracy improved from a standard error of estimate (SEE) of 0.669 in Equation 4 to an SEE of 0.629 in Equation 5.
Relationship Between Step Frequency and Velocity Figure 1 shows a linear relationship between step frequency and velocity: Velocity ¼ 0:064 ðStep FrequencyÞ 2 2:838;
ð4Þ
where velocity is recorded in kilometers per hour and step frequency is recorded in steps per minute. The multiple correlation coefficients (R 2) were .8. (F ¼ 5,507.93, p , .001), indicating a high linear relationship between velocity and step frequency. The 95% confidence intervals for the regression equation (Number 4) were 23.062 and 22.614 for the constant .062 and .066 for step frequency, respectively.
FIGURE 1 Velocity – step frequency relationship. The solid line represents the regression curve of step frequency and velocity and the dotted lines refer to the lower prediction limit (LPL) and upper prediction limit (UPL) of the 95% prediction/confidence interval.
Relationship Between Step Frequency and Net Energy Expenditure Figure 3 illustrates that as step frequency increased, EE also increased. The result showed a linear relationship between step frequency and EE. The linear regression equation showed as follows: Net EE ¼ 1:7958 ðstep frequencyÞ 2 113:6869
ð6Þ
In the regression Equation 6 (F ¼ 2,754.50, p , .001), the multiple correlation coefficient (R 2) was .74 and the SEE of the estimate was 26.50, showing a high linear relationship.
FIGURE 2 Velocity–step frequency relationship in different groups of body height.
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Relationship Between Step Frequency and Net Energy Expenditure With Consideration of Age and Gender
There has been a long interest in the ability to measure and predict EE during walking or running (Saibene & Minetti, 2003; Weyand, Smith, Puyau, & Butte, 2010), and a variety of EE equations have been developed (see, e.g., Brooks, Gunn, Withers, Gore, & Plummer, 2005; Hall, Figueroa, Fernhall, & Kanaley, 2004; Kramer & Sylvester, 2011; Kuffel, Crouter, Haas, Frongillo, & Bassett, 2011), including ones specifically targeting children and youth (Eston, Rowlands, & Ingledew, 1998; Trost, Way, & Okely, 2006; Walker, Murray, Jackson,
Morrow, & Michaud, 1999), gender (Heden, LeCheminant, & Smith, 2012), or special populations (Franceschini et al., 2013). Many different predictors (e.g., velocity, body mass, ground reaction force, acceleration, motion of ankle, knee, and hip joints, etc.; Kramer & Sylvester, 2011) have been employed, and among them, velocity has been the most popular (Weyand et al., 2013). With the development of a simple device, like a pedometer, to measure Chinese children and youth, measuring velocity in practice is not easy and current pedometers cannot predict EE accurately (e.g., Tharion, Yokota, Buller, DeLany, & Hoyt, 2003). Exploring other alternatives is urgently needed. This study therefore examined the possibility of estimating EE using step frequency, which is easy to measure in practice. Step frequency can be obtained via a pressure sensor attached on the soles of shoes or a portable accelerometer attached to the waist (Moe-Nilssen, 1998; Schutz, Weinsier, Terrier, & Durrer, 2002), shank (Li, Young, Naing, & Donelan, 2010), thigh (Willemsen, van Alste´, & Boom, 1990), head (Kavanagh, Barrett, & Morrison, 2004), or sole of the foot (Kane, Simmons, John, Thompson, & Basset, 2010). Inexpensive pedometers are suitable for this purpose. Once step frequency is obtained, walking or running speed can be predicted and the distance of movement can be calculated. This study, therefore, tried to collect evidence to support the hypothesized relationship between step frequency and velocity and explored the possibility of predicting EE using step frequency and other related variables. The results of this study supported the hypothesized step frequency – velocity relationship (i.e., step frequency increased as velocity increased). A high linear relationship between step frequency and velocity was observed. Moreover, step frequency was found to be influenced by body height and leg length. Step frequency is lower when legs are longer (Grieve & Gear, 1966; Sutherland et al., 1988;
FIGURE 3 Net EE –step frequency relationship. The solid line refers to the step frequency–net EE regression curve, and the dotted lines refer to the lower prediction limit (LPL) and upper prediction limit (UPL) of the 95% prediction/confidence interval.
FIGURE 4 Net EE –step frequency relationship among different age groups.
The relationship between step frequency and net EE was examined using the regression analysis by age and by gender separately. Because a clear age impact (see Figure 4) and gender impact (see Figure 5) were detected, it was confirmed that prediction of net EE using step frequency should include age and gender in the prediction. The prediction equation of net EE included step frequency, age, and gender and was therefore developed as shown: Net EE ¼ 213:7744 þ 1:8004 ðstep frequencyÞ
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2 5:5715 ðageÞ 2 11:5244 ðgenderÞ
ð7Þ
R 2 for Equation 7 was .78 (F ¼ 1,188.66, p , .001), which indicates a moderately high relationship between outcome variable (i.e., net EE) and predictors (i.e., step frequency, age, and gender). The SEE of Equation 7 was 24.03. Compared with Equation 6’s SEE, there was a 2.47-unit improvement in accuracy, which reflects the positive impact of including age and gender in the prediction equation.
DISCUSSION
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Wheelwright et al., 1993), as illustrated in Figure 2. Given the same step frequency, the participant group with the tallest height had the fastest velocity. These results are consistent with previous research findings (Waters et al., 1983), which indicate that the impact of height should be considered when examining the step frequency–velocity relationship. The results of this study also supported the use of step frequency to predict EE. Besides step frequency itself, age and gender were also found to be important for improving the prediction, which is also supported by the literature. For example, Waters et al. (1988) found that as walking speed increases, VO2 increases linearly, and the regression equation slopes among several age groups (children, 6 to 12 years old, teens and adults, 20 to 59 years old, and senior adults, 60 to 80 years old) differed in terms of velocity and VO2 (Waters et al., 1988). They also found that given the same velocity and VO2, EE during physical activity as related to age (Waters et al., 1988); the younger the participants, the more energy was consumed. Adults and adolescents show rather homogenous efficiency when velocity is low. However, the efficiency of young children was lower. When velocity increased, the difference in efficiency became larger. This result indicated that the respiratory functions of either young children or adolescents have not developed to the level of adults, and the movement efficiency of both children and adolescents was relatively low (Waters & Mulroy, 1999). Similarly, Frost, Bar-Or, Dowling, and Dyson (2002) tested the EE of young children and adolescents on treadmills and found that given the same velocity, EE significantly differed among different age groups. Younger individuals consumed more energy. The test also showed that VO2 did not vary considerably, and significant differences only existed between two adjacent older age groups. Krahenbuhl and Williams (1992) investigated the running economy of young children and adolescents. They found that as age increased, movement efficiency also improved. Compared with adults, young children had higher resting metabolic rates and ventilator
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equivalents for oxygen and had unsmoothed cadence and step length compared with their adolescent counterparts (Krahenbuhl & Williams, 1992). Compared with age difference, the gender impact has been understudied. There are many possible reasons for the gender difference (e.g., impact of body composition and body mass). More studies are clearly needed. Although this study made a promising attempt to explore the prediction of youth’s EE during walking and running with a large sample size, several limitations should be acknowledged. First, the EE of the participants at rest was not directly measured but was estimated using a prediction equation, which may lead to an overestimated net EE (Waters & Mulroy, 1999). Another limitation of this study was that walking and running were conducted on a treadmill, which may slightly differ from walking and running in free-living conditions (Brouwer, Parvataneni, & Olney, 2009). Finally, no comparison was made between predictions of this study and other existing predictions, especially from the cost-effect perspective (e.g., tradeoff of making devices to measure velocity vs. step frequency and their accuracy in EE prediction). Future research should examine the possible impacts of these limitations.
CONCLUSION With developing simple devices to predict Chinese youth EE during walking and running in mind, this study examined the relationships between step frequency and velocity as well as between step frequency and EE. Based on the understanding of the relationship, a step frequency-, age-, and gender-based prediction equation with good accuracy was developed. The prediction equation provided a feasible algorithm that can be used to develop a simple device to estimate the youth’s EE during walking and running with good accuracy.
WHAT DOES THIS ARTICLE ADD?
FIGURE 5 Net EE– step frequency relationship between genders.
Walking and running are the most popular human activities. Walking, in fact, has been considered the most commonly used mode when promoting physical activity and public health. Considering the fast-spreading childhood obesity epidemic, promoting walking among youth has become urgently important. Efforts have long been made to estimate EE during walking and running, but variables employed have been difficult to implement in simple devices like a pedometer. To address this gap, this study first examined the hypothesized step frequency –velocity relationship, upon which an EE prediction equation based on step frequency, age, and gender was derived and validated. With the developed algorithm, developing a simple device to measure EE during walking and running becomes possible.
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ACKNOWLEDGMENTS This work was partly supported by the Supporting Programs of the Ministry of Science and Technology of China (2009BAK62B02002 and 2012BAK23B04) and the Shanghai Committee of Science and Technology in China (Grant No. 11dz1503700).
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REFERENCES Ainslie, P., Reilly, T., & Westerterp, K. (2003). Estimating human energy expenditure: A review of techniques with particular reference to doubly labelled water. Sports Medicine, 33, 683 –698. Bernardi, M., Macaluso, A., Sproviero, E., Castellano, V., Coratella, D., Felici, F., . . . Ditunno, J. F., Jr. (1999). Cost of walking and locomotor impairment. Journal of Electromyography and Kinesiology, 9, 149–157. Brooks, A. G., Gunn, S. M., Withers, R. T., Gore, C. J., & Plummer, J. L. (2005). Predicting walking METs and energy expenditure from speed or accelerometry. Medicine & Science in Sports & Exercise, 37, 1216–1223. Brouwer, B., Parvataneni, K., & Olney, S. J. (2009). A comparison of gait biomechanics and metabolic requirements of overground and treadmill walking in people with stroke. Clinical Biomechanics, 24, 729–734. Crouter, S. E., Schneider, P. L., Karabulut, M., & Bassett, D. R. Jr (2003). Validity of 10 electronic pedometers for measuring steps, distance, and energy cost. Medicine & Science in Sports & Exercise, 35, 1455–1460. Derumeaux-Burel, H., Meyer, M., Morin, L., & Boirie, Y. (2004). Prediction of resting energy expenditure in a large population of obese children. American Journal of Clinical Nutrition, 80, 1544–1550. Eston, R. G., Rowlands, A. V., & Ingledew, D. K. (1998). Validity of heart rate, pedometry and accelerometry for predicting the energy cost of children’s activities. Journal of Applied Physiology, 84, 362–371. Foster, R. C., Lanningham-Foster, L. M., Manohar, C., McCrady, S. K., Nysse, L. J., Kaufman, K. R., . . . Levine, J. A. (2005). Precision and accuracy of an ankle-worn accelerometer-based pedometer in step counting and energy expenditure. Preventive Medicine, 41, 778 –783. Franceschini, M., Rampello, A., Agosti, M., Massucci, M., Bovolenta, F., & Sale, P. (2013). Walking performance: Correlation between energy cost of walking and walking participation. New statistical approach concerning outcome measurement. PLoS ONE, 8, e56669. Frankenfield, D. C., Muth, E. R., & Rowe, W. A. (1998). The HarrisBenedict studies of human basal metabolism: History and limitations. Journal of the American Dietetic Association, 98, 439 –445. Frost, G., Bar-Or, O., Dowling, J., & Dyson, K. (2002). Explaining differences in the metabolic cost and efficiency of treadmill locomotion in children. Journal of Sports Sciences, 20, 451–461. Grieve, D. W., & Gear, R. J. (1966). The relationships between length of stride, step frequency, time of swing and speed of walking for children and adults. Ergonomics, 9, 379–399. Hall, C., Figueroa, A., Fernhall, B., & Kanaley, J. A. (2004). Energy expenditure of walking and running: Comparison with prediction equations. Medicine and Science in Sports and Exercise, 36, 2128–2134. Heden, T. D., Le Cheminant, J. D., & Smith, J. D. (2012). Influence of weight classification on walking and jogging energy expenditure prediction in women. Research Quarterly for Exercise and Sport, 83, 391–399. Kane, N. A., Simmons, M. C., John, D., Thompson, D. L., & Basset, D. R. (2010). Validity of the Nike þ device during walking and running. International Journal of Sports Medicine, 31, 101–105. Kavanagh, J. J., Barrett, R. S., & Morrison, S. (2004). Upper body accelerations during walking in healthy young and elderly men. Gait & Posture, 20, 291 –298. Krahenbuhl, G. S., & Williams, T. J. (1992). Running economy: Changes with age during childhood and adolescence. Medicine & Science in Sports & Exercise, 24, 462 –466.
Kramer, P. A., & Sylvester, A. D. (2011). The energetic cost of walking: A comparison of predictive methods. PLoS ONE, 6, e21290. Kuffel, E., Crouter, S. E., Haas, J. D., Frongillo, E. A., & Bassett, D. R. Jr (2011). Validity of estimating minute-by-minute energy expenditure of continuous walking bouts by accelerometry. International Journal of Behavioral Nutrition and Physical Activity, 8, 92. doi:10.1186/14795868-8-92 Li, Q., Young, M., Naing, V., & Donelan, J. M. (2010). Walking speed estimation using a shank-mounted inertial measurement unit. Journal of Biomechanics, 43, 1640–1643. Mark, J. O. M. (1996). Normalization of temporal-distance parameters in pediatric gait. Journal of Biomechanics, 29, 619–625. Matsas, A., Taylor, N., & McBurney, H. (2000). Knee joint kinematics from familiarised treadmill walking can be generalised to overground walking in young unimpaired subjects. Gait & Posture, 11, 46–53. McDuffie, J. R., Adler-Wailes, D. C., Elberg, J., Steinberg, E. N., Fallon, E. M., Tershakovec, A. M., . . . Yanovski, J. A. (2004). Prediction equations for resting energy expenditure in overweight and normalweight Black and White children. American Journal of Clinical Nutrition, 80, 365–373. McLaughlin, J. E., King, G. A., Howley, E. T., Bassett, D. R. Jr, & Ainsworth, B. E. (2001). Validation of the COSMED K4 b2 portable metabolic system. International Journal of Sports Medicine, 22, 280–284. Moe-Nilssen, R. (1998). A new method for evaluating motor control in gait under real-life environmental conditions. Part 1: The instrument. Clinical Biomechanics, 13, 320–327. Pinnington, H. C., Wong, P., Tay, J., Green, D., & Dawson, B. (2001). The level of accuracy and agreement in measures of FEO2, FECO2 and VE between the Cosmed K4b2 portable, respiratory gas analysis system and a metabolic cart. Journal of Science and Medicine in Sport, 4, 324–335. Pols, M. A., Peeters, P. H., Kemper, H. C., & Grobbee, D. E. (1998). Methodological aspects of physical activity assessment in epidemiological studies. European Journal of Epidemiology, 14, 63– 70. Riner, W. F., & Sellhorst, S. H. (2013). Physical activity and exercise in children with chronic health conditions. Journal of Sport and Health Science, 2, 12 –20. Saibene, F., & Minetti, A. E. (2003). Biomechanical and physiological aspects of legged locomotion in humans. European Journal of Applied Physiology, 88, 297–316. Schneider, P. L., Crouter, S. E., & Bassett, D. R. (2004). Pedometer measures of free-living physical activity: Comparison of 13 models. Medicine & Science in Sports & Exercise, 36, 331 –335. Schneider, P. L., Crouter, S. E., Lukajic, O., & Bassett, D. R. Jr (2003). Accuracy and reliability of 10 pedometers for measuring steps over a 400-m walk. Medicine & Science in Sports & Exercise, 35, 1779– 1784. Schutz, Y., Weinsier, S., Terrier, P., & Durrer, D. (2002). A new accelerometric method to assess the daily walking practice. International Journal of Obesity and Related Metabolic Disorders, 26, 111–118. Sekiya, N., & Nagasaki, H. (1998). Reproducibility of the walking patterns of normal young adults: Test –retest reliability of the walk ratio (steplength/step-rate). Gait & Posture, 7, 225– 227. Stoquart, G., Detrembleur, C., & Lejeune, T. (2008). Effect of speed on kinematic, kinetic, electromyographic and energetic reference values during treadmill walking. Clinical Neurophysiology, 38, 105– 116. Sutherland, D., Olshen, R., Biden, E., & Wyatt, M. (1988). The development of mature walking. Cambridge, UK: Cambridge University Press. Tesio, L., Rota, V., Chessa, C., & Perucca, L. (2010). The 3D path of body centre of mass during adult human walking on force treadmill. Journal of Biomechanics, 43, 938–944.
Downloaded by [University of North Dakota] at 00:05 21 November 2014
EE EQUATIONS BASED ON STEP FREQUENCY Tharion, W. J., Yokota, M., Buller, M. J., DeLany, J. P., & Hoyt, R. W. (2003). Prediction of total energy expenditure using a pedometer in male and female sailors aboard ship (Report T03-12). Natick, MA: U.S. Army Research Institute of Environmental Medicine. Trost, S. G., Way, R., & Okely, A. D. (2006). Predictive validity of three ActiGraph energy expenditure equations for children. Medicine & Science in Sports & Exercise, 38, 380–387. Vallis, L. A., Patla, A. E., & Adkin, A. L. (2001). Control of steering in the presence of unexpected head yaw movements. Influence on sequencing of subtasks. Experimental Brain Research, 138, 128–134. Walker, J. L., Murray, T. D., Jackson, A. S., Morrow, J. R., & Michaud, T. J. (1999). The energy cost of horizontal walking and running in adolescents. Medicine & Science in Sports & Exercise, 31, 311–322. Waters, R. L., Hislop, H. J., Perry, J., Thomas, L., & Campbell, J. (1983). Comparative cost of walking in young and old adults. Journal of Orthopaedic Research, 1, 73–76. Waters, R. L., Hislop, H. J., Thomas, L., & Campbell, J. (1983). Energy cost of walking in normal children and teenagers. Developmental Medicine & Child Neurology, 25, 184 –188.
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Waters, R. L., Lunsford, B. R., Perry, J., & Byrd, R. (1988). Energy–speed relationship of walking: Standard tables. Journal of Orthopaedic Research, 6, 215 –222. Waters, R. L., & Mulroy, S. (1999). The energy expenditure of normal and pathologic gait. Gait & Posture, 9, 207–231. Weyand, P. G., Smith, B. R., Puyau, M. R., & Butte, N. F. (2010). The mass-specific energy cost of human walking is set by stature. Journal of Experimental Biology, 213, 3972– 3979. Weyand, P. G., Smith, B. R., Schultz, N. S., Wohlers, L. M., Puyau, M. R., & Butte, N. F. (2013). Predicting metabolic rate across walking speed: One fit for all body sizes? [Abstract]. Journal of Applied Physiology, Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/ 23928111 Wheelwright, E., Minns, R., Law, H., & Elton, R. (1993). Temporal and spatial parameters of gait in children. I: Normal control data. Developmental Medicine & Child Neurology, 35, 102–113. Willemsen, A. T., van Alste´, J. A., & Boom, H. B. (1990). Real-time gait assessment utilizing a new way of accelerometry. Journal of Biomechanics, 23, 859–863.
Research Quarterly for Exercise and Sport Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/urqe20
Prediction Equations of Energy Expenditure in Chinese Youth Based on Step Frequency During Walking and Running Bo Sun
a b
a
, Yu Liu , Jing Xian Li
a
Shanghai University of Sport
b
Liaocheng University
a c
a
, Haipeng Li & Peijie Chen
a
c
University of Ottawa Published online: 04 Dec 2013.
To cite this article: Bo Sun , Yu Liu , Jing Xian Li , Haipeng Li & Peijie Chen (2013) Prediction Equations of Energy Expenditure in Chinese Youth Based on Step Frequency During Walking and Running, Research Quarterly for Exercise and Sport, 84:sup2, S64-S71, DOI: 10.1080/02701367.2013.851155 To link to this article: http://dx.doi.org/10.1080/02701367.2013.851155
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Research Quarterly for Exercise and Sport, 84, S64–S71, 2013 Copyright q AAHPERD ISSN 0270-1367 print/ISSN 2168-3824 online DOI: 10.1080/02701367.2013.851155
Prediction Equations of Energy Expenditure in Chinese Youth Based on Step Frequency During Walking and Running
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Bo Sun Shanghai University of Sport Liaocheng University
Yu Liu Shanghai University of Sport
Jing Xian Li Shanghai University of Sport University of Ottawa
Haipeng Li and Peijie Chen Shanghai University of Sport
Purpose: This study set out to examine the relationship between step frequency and velocity to develop a step frequency-based equation to predict Chinese youth’s energy expenditure (EE) during walking and running. Method: A total of 173 boys and girls aged 11 to 18 years old participated in this study. The participants walked and ran on a treadmill at speeds of 3 km/hr, 4 km/hr, 5 km/hr, 6 km/hr, 7 km/hr, and 8 km/hr. EE was measured using indirect calorimetry of open circuit spirometry (Cosmed K4b2 metabolic analyzer). Using multiple regression analysis, the relationship between step frequency and velocity was first examined, and the prediction equation of EE based on step frequency, age, and gender was derived. Results: The hypothesized relationship between step frequency and velocity was confirmed and an accurate (R 2 ¼ .78) EE prediction equation was derived: Net EE ¼ 213.7744 þ 1.8004 (step frequency) – 5.5715 (age) – 11.5244 (gender). Conclusion: A step frequency-, age-, and gender-based equation was derived to predict the EE of youth during walking and running. The equation can be used to develop a simple device to estimate EE during walking and running in this population. Keywords: energy consumption, physical activity, regression equations, velocity
Estimating the energy expenditure (EE) in walking is necessary for health, rehabilitation, and kinesiology studies, and this is especially true for children and youth considering
Correspondence should be addressed to Yu Liu, Key Laboratory of Exercise and Health Sciences, Ministry of Education, Shanghai University of Sport, 650 Qing Yuan Huan Road, Shanghai, 200438 China. E-mail: [email protected]
the fast-spreading childhood obesity epidemic and chronic health conditions worldwide (Riner & Sellhorst, 2013). The estimation is also an important basis for diagnosing diseases in clinical settings (Pols, Peeters, Kemper, & Grobbee, 1998). However, challenges remain in estimating EE of individuals by means of a system that has a low cost, does not interrupt an individual’s daily life, and is able to measure real-time EE and total EE using simple devices. The most accurate methods to measure and estimate EE of
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EE EQUATIONS BASED ON STEP FREQUENCY
free-living activities are the doubly labeled water (DLW) method (Ainslie, Reilly, & Westerterp, 2003) and respiratory metabolism analysis (McLaughlin, King, Howley, Bassett, & Ainsworth, 2001; Pinnington, Wong, Tay, Green, & Dawson, 2001). DLW, in fact, is considered the “gold standard” in measuring total EE. The limitations of both methods are that they can only be used in clinical and laboratory settings and they are not viable measurement tools during physical activity. In recent years, some simple and convenient devices that measure EE during walking or running, such as pedometers, have been developed and used (Crouter, Schneider, Karabulut, & Bassett, 2003; Foster et al., 2005; Schneider, Crouter, & Bassett, 2004). The design and price of pedometers vary greatly. The pedometer has three basic types of designs, including the spring-suspended lever arm with metal-on-metal contact, a magnetic reed proximity switch, and an accelerometer type (Crouter et al., 2003; Schneider, Crouter, Lukajic, & Bassett, 2003). Nevertheless, walking and running—two of the most common forms of physical activity—can be readily measured by pedometers. Pedometers have become increasingly popular as a measurement tool for physical activity. The accuracy and scope of application, however, are not known completely because of the commercial confidential restraints on computational algorithms used in pedometers. Developing and validating open computational algorithms that can predict EE is thus greatly needed. One of the ideas to develop such an algorithm is to employ the “walk ratio” concept. According to Sekiya and Nagasaki (1998), Walk Ratio ¼ Step Length / Step Rate or Step Frequency. The walk ratio is a speed-independent index of walking patterns, which remains constant within a broad speed range (Sekiya & Nagasaki, 1998). The gait velocity (or simply velocity)/speed of human walking is determined by the product of step length and step rate or step frequency. Thus, in theory, a stable functional relationship between step frequency and velocity exists. Stoquart, Detrembleur, and Lejeune (2008) examined the relationship between step frequency, walking velocity, and EE by analyzing kinematics, kinetics, and muscle activity, as well as energy cost on 12 healthy young men. The participants walked on a treadmill at six different speed settings (1 – 6 km/hr) and on the ground, respectively. The
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study results showed an apparent correlation between step frequency and velocity (R 2 ¼ .98; see also Tesio, Rota, Chessa, & Perucca, 2010). Step frequency is known to be influenced by body height and leg length, and step frequency is lower when legs are longer at the same velocity (Grieve & Gear, 1966; Sutherland, Olshen, Biden, & Wyatt, 1988; Wheelwright, Minns, Law, & Elton, 1993). Moreover, EE is influenced by age at natural customary and fast walking speeds (Waters, Hislop, Perry, Thomas, & Campbell, 1983; Waters, Hislop, Thomas, & Campbell, 1983; Waters, Lunsford, Perry, & Byrd, 1988). For children without movement issues, the rate of EE requirement for walking per kilogram of body weight decreases as the child ages (Waters et al., 1983). Therefore, to predict EE of children and youth using the walking ratio information, influencing factors, such as age and height, should be considered. Although some studies on predicting children’s EE at resting or on issues related to obese children have been done (Derumeaux-Burel, Meyer, Morin, & Boirie, 2004; McDuffie et al., 2004), EE prediction using the walking ratio has not been established for children and youth. The purposes of this study were twofold: (a) to verify the expected relationship between step frequency and velocity based on the concept of walking ratio, and (b) to develop a prediction equation based on step frequency and other possible predictors (e.g., age and gender).
METHOD Participants As part of the Chinese City Children and Youth Physical Activity Study, a total of 173 Chinese participants aged 11 to 18 years old were recruited from local schools in Shanghai, China. Before study commencement, participants assented to participate and all participants’ parents or guardians signed an informed consent form approved by the Ethics Advisory Committee of the Shanghai University of Sport. All participants were recreationally active, had no musculoskeletal disorders or injuries, were disease-free, took no medications that affect metabolism, and were nonsmokers (see Table 1).
TABLE 1 General Information of Participants (M ^ SD) Age (years)
Gender
n
Height (cm)
Weight (kg)
BMI (kg·m22)
11– 12
Male Female Male Female Male Female Male Female
10 15 24 21 36 35 15 17
159.00 ^ 5.58 155.79 ^ 6.38 169.08 ^ 5.86 160.07 ^ 4.43 172.15 ^ 5.43 160.43 ^ 4.93 174.19 ^ 5.07 160.40 ^ 3.50
52.74 ^ 5.85 46.77 ^ 7.77 59.83 ^ 9.70 51.89 ^ 6.32 61.50 ^ 10.39 53.17 ^ 9.34 68.53 ^ 7.80 55.73 ^ 7.39
20.98 ^ 2.61 19.18 ^ 2.21 20.98 ^ 3.29 20.25 ^ 2.32 20.68 ^ 2.77 20.62 ^ 3.28 22.64 ^ 2.88 21.72 ^ 3.25
13– 14 15– 16 17– 18
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Instrumentation A Gaitway motorized treadmill (H/P Cosmos Gaitway II S, Germany), with an appropriate conveyor belt (1,500 mm long £ 500 mm wide) and sufficient motor power (2.2 kW) was used in this study. The treadmill was calibrated before use to ensure that proper speed was maintained. EE was measured by indirect calorimetry utilizing open circuit spirometry of the portable metabolic analyzer (weight ¼ 475 g; Cosmed K4b2, COSMED, Rome, Italy). The K4b2 analyzer has been validated in previous studies (McLaughlin et al., 2001; Pinnington et al., 2001). The Cosmed system was calibrated using standard gas for the gas analyzers. A calibration syringe (3 L) was used to calibrate the turbine, which is flow rateindependent. The values were automatically converted by the software (Cosmed Quark b2 win, Version 5.1a) to standardize temperature, pressure, and dry oxygen uptake (VO2). The respiratory quotient, computed as the ratio between carbon dioxide production and VO2, is always less than 1. POLAR heart rate monitors (T34, Finland) were worn on the participants’ thorax to monitor each participant’s heart rate during the tests. A manual counter was used to count the steps in the last 2 min at each speed to calculate the average step frequency after cadence was stabilized during walking or running. An Omron body composition monitor “body scan” (HBF-375, Japan) was used to measure body fat rate and body weight.
were averaged, and the mean value was used for further analyses. Verification that the participants had reached a steady state was obtained in real time by observing the means of heart rate acquisition and graphic visualization (Bernardi et al., 1999). Data Processing and Analysis The physiological data from K4b2 and the step frequency data of the last 2 min of each speed were taken for EE analysis and for calculation of the average EE (cal·kg21·min21), respectively. The Statistical Package for the Social Sciences Version 13.0 was used to conduct the statistical analysis. The net EE during walking and running was calculated using the following equation (Stoquart et al., 2008): Net EE ¼ ðEgross 2 Erest Þ=Mass;
ð1Þ
where Net EE is net energy expenditure (cal·kg21·min21), Egross is gross energy expenditure, Erest is rest energy expenditure (cal·min21), and Mass is body mass (kg). EE of the participants at rest (Erest) was assumed to be equal to the basal energy expenditure (BEE). Erest was calculated via the empirical equations of Harris-Benedict for BEE, which was modified by Frankenfield, Muth, and Rowe (1998). The modified equation increased the multiple correlation coefficients for women. Harris-Benedict equations have been applied across a wide range of body weight and ages. The equations are as follows:
Protocols When the participants arrived at the lab, the researcher explained the details of the test to them. The participants filled out an information sheet. Researchers measured each participant’s body height, weight, leg length, and resting heart rate. Before the tests, the participants were allowed to walk or run on the treadmill for about 6 min for practice (Matsas, Taylor, & McBurney, 2000). A 5-min rest break was given to allow the participants’ heart rates to recover to a level ^ 5% of their resting heart rates. Formal data collection was then conducted. The participants were asked to walk on the treadmill at speeds of 3 km/hr, 4 km/hr, 5 km/hr, and 6 km/hr, and they were asked to run at 7 km/hr and 8 km/hr. The participants were asked to look straight ahead at a point about 158 downward placed about 2 m away during the treadmill walking and running tests. Erect head posture was required because head stabilization in space during gait has been shown to be important for postural control (Vallis, Patla, & Adkin, 2001). The speed was increased from the lowest to the highest level during walking and running tests. Walking and running at each of the test speeds lasted for at least 5 min to enable metabolism to reach and maintain a steady state condition, and there were no rests between test conditions. Step frequency, heart rate, and EE obtained during the last 2 min at each speed
Male : BEE ðkcald21 Þ ¼ 65 þ 13:40 ðwÞ þ 4:96 ðhÞ 2 5:82 ðaÞ ðR 2 ¼ :76; F ¼ 207:7; p , :001Þ ðFrankenfield et al:; 1998Þ Female :
BEE ðkcal·d21 Þ ¼ 447 þ 9:25 ðwÞ þ 3:10 ðhÞ 2 4:33 ðaÞ; ðR 2 ¼ :68; F ¼ 119:2; p , :001Þ ðFrankenfield et al:; 1998Þ
ð2Þ
ð3Þ
where w represents body mass (kg), h is height (cm), and a is age (years). Besides applying linear regression to examine the relationships, analysis of covariance (ANCOVA) was also employed when the influence of leg length on step frequency and the influence of age and gender on EE were examined. The alpha level of the statistical analysis was set at .05. Given the influence of leg length on step frequency, step frequency decreased as leg length increased at the same speed. Additional analysis is needed to eliminate the influence of leg length on step frequency (Mark, 1996). Based on body height, the participants were grouped into three groups: Group 1 had a body height range of 147 cm to 160 cm, Group 2 had a body height range of 160.1 cm to 173 cm, and Group 3 had a body height range of 173.1 cm to 186 cm. The velocity–step
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frequency relationship was examined using multiple analyses. ANCOVA was also conducted to examine the impact of height. Specifically, the height impact was examined in regression analyses, in which velocity was the dependent variable, step frequency was the independent variable, and height grouping was the covariate. The regression equation was further developed to include age as a covariate because the age of the participants ranged from 11 to 18 years old. The participants were grouped into four groups based on their age: Group 1 included 11- to 12-year-olds, Group 2 included 13- to 14-year-olds, Group 3 included 15- to 16-year-olds, and Group 4 included 17- to 18-year-olds. The slopes of regression equation between step frequency and net EE in movement were also examined for each of the groups. In the ANCOVA, net EE was the dependent variable, age was the covariate, and step frequency was the independent variable or predictor.
RESULTS
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Velocity – Step Frequency Relationship by Height Groups The ANCOVA revealed that there was a statistically significant interaction between step frequency and height ( p , .001), which was an indication of the impact of height (see Figure 2). This means that height influenced the estimation of EE by step frequency. Variance can be better explained if height is also brought into the prediction, which was confirmed by the results of the following regression analysis: Velocity ¼ 27:6993 þ 0:06511 ðstep frequencyÞ þ 0:0285 ðheightÞ;
ð5Þ
where Velocity is recorded in km/hr, step frequency in steps/ min, and height in cm. In the regression Equation 5 (F ¼ 3,177.37, p , .001), R 2 was .87, improving slightly from Equation 4. Accordingly, prediction accuracy improved from a standard error of estimate (SEE) of 0.669 in Equation 4 to an SEE of 0.629 in Equation 5.
Relationship Between Step Frequency and Velocity Figure 1 shows a linear relationship between step frequency and velocity: Velocity ¼ 0:064 ðStep FrequencyÞ 2 2:838;
ð4Þ
where velocity is recorded in kilometers per hour and step frequency is recorded in steps per minute. The multiple correlation coefficients (R 2) were .8. (F ¼ 5,507.93, p , .001), indicating a high linear relationship between velocity and step frequency. The 95% confidence intervals for the regression equation (Number 4) were 23.062 and 22.614 for the constant .062 and .066 for step frequency, respectively.
FIGURE 1 Velocity – step frequency relationship. The solid line represents the regression curve of step frequency and velocity and the dotted lines refer to the lower prediction limit (LPL) and upper prediction limit (UPL) of the 95% prediction/confidence interval.
Relationship Between Step Frequency and Net Energy Expenditure Figure 3 illustrates that as step frequency increased, EE also increased. The result showed a linear relationship between step frequency and EE. The linear regression equation showed as follows: Net EE ¼ 1:7958 ðstep frequencyÞ 2 113:6869
ð6Þ
In the regression Equation 6 (F ¼ 2,754.50, p , .001), the multiple correlation coefficient (R 2) was .74 and the SEE of the estimate was 26.50, showing a high linear relationship.
FIGURE 2 Velocity–step frequency relationship in different groups of body height.
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Relationship Between Step Frequency and Net Energy Expenditure With Consideration of Age and Gender
There has been a long interest in the ability to measure and predict EE during walking or running (Saibene & Minetti, 2003; Weyand, Smith, Puyau, & Butte, 2010), and a variety of EE equations have been developed (see, e.g., Brooks, Gunn, Withers, Gore, & Plummer, 2005; Hall, Figueroa, Fernhall, & Kanaley, 2004; Kramer & Sylvester, 2011; Kuffel, Crouter, Haas, Frongillo, & Bassett, 2011), including ones specifically targeting children and youth (Eston, Rowlands, & Ingledew, 1998; Trost, Way, & Okely, 2006; Walker, Murray, Jackson,
Morrow, & Michaud, 1999), gender (Heden, LeCheminant, & Smith, 2012), or special populations (Franceschini et al., 2013). Many different predictors (e.g., velocity, body mass, ground reaction force, acceleration, motion of ankle, knee, and hip joints, etc.; Kramer & Sylvester, 2011) have been employed, and among them, velocity has been the most popular (Weyand et al., 2013). With the development of a simple device, like a pedometer, to measure Chinese children and youth, measuring velocity in practice is not easy and current pedometers cannot predict EE accurately (e.g., Tharion, Yokota, Buller, DeLany, & Hoyt, 2003). Exploring other alternatives is urgently needed. This study therefore examined the possibility of estimating EE using step frequency, which is easy to measure in practice. Step frequency can be obtained via a pressure sensor attached on the soles of shoes or a portable accelerometer attached to the waist (Moe-Nilssen, 1998; Schutz, Weinsier, Terrier, & Durrer, 2002), shank (Li, Young, Naing, & Donelan, 2010), thigh (Willemsen, van Alste´, & Boom, 1990), head (Kavanagh, Barrett, & Morrison, 2004), or sole of the foot (Kane, Simmons, John, Thompson, & Basset, 2010). Inexpensive pedometers are suitable for this purpose. Once step frequency is obtained, walking or running speed can be predicted and the distance of movement can be calculated. This study, therefore, tried to collect evidence to support the hypothesized relationship between step frequency and velocity and explored the possibility of predicting EE using step frequency and other related variables. The results of this study supported the hypothesized step frequency – velocity relationship (i.e., step frequency increased as velocity increased). A high linear relationship between step frequency and velocity was observed. Moreover, step frequency was found to be influenced by body height and leg length. Step frequency is lower when legs are longer (Grieve & Gear, 1966; Sutherland et al., 1988;
FIGURE 3 Net EE –step frequency relationship. The solid line refers to the step frequency–net EE regression curve, and the dotted lines refer to the lower prediction limit (LPL) and upper prediction limit (UPL) of the 95% prediction/confidence interval.
FIGURE 4 Net EE –step frequency relationship among different age groups.
The relationship between step frequency and net EE was examined using the regression analysis by age and by gender separately. Because a clear age impact (see Figure 4) and gender impact (see Figure 5) were detected, it was confirmed that prediction of net EE using step frequency should include age and gender in the prediction. The prediction equation of net EE included step frequency, age, and gender and was therefore developed as shown: Net EE ¼ 213:7744 þ 1:8004 ðstep frequencyÞ
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2 5:5715 ðageÞ 2 11:5244 ðgenderÞ
ð7Þ
R 2 for Equation 7 was .78 (F ¼ 1,188.66, p , .001), which indicates a moderately high relationship between outcome variable (i.e., net EE) and predictors (i.e., step frequency, age, and gender). The SEE of Equation 7 was 24.03. Compared with Equation 6’s SEE, there was a 2.47-unit improvement in accuracy, which reflects the positive impact of including age and gender in the prediction equation.
DISCUSSION
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EE EQUATIONS BASED ON STEP FREQUENCY
Wheelwright et al., 1993), as illustrated in Figure 2. Given the same step frequency, the participant group with the tallest height had the fastest velocity. These results are consistent with previous research findings (Waters et al., 1983), which indicate that the impact of height should be considered when examining the step frequency–velocity relationship. The results of this study also supported the use of step frequency to predict EE. Besides step frequency itself, age and gender were also found to be important for improving the prediction, which is also supported by the literature. For example, Waters et al. (1988) found that as walking speed increases, VO2 increases linearly, and the regression equation slopes among several age groups (children, 6 to 12 years old, teens and adults, 20 to 59 years old, and senior adults, 60 to 80 years old) differed in terms of velocity and VO2 (Waters et al., 1988). They also found that given the same velocity and VO2, EE during physical activity as related to age (Waters et al., 1988); the younger the participants, the more energy was consumed. Adults and adolescents show rather homogenous efficiency when velocity is low. However, the efficiency of young children was lower. When velocity increased, the difference in efficiency became larger. This result indicated that the respiratory functions of either young children or adolescents have not developed to the level of adults, and the movement efficiency of both children and adolescents was relatively low (Waters & Mulroy, 1999). Similarly, Frost, Bar-Or, Dowling, and Dyson (2002) tested the EE of young children and adolescents on treadmills and found that given the same velocity, EE significantly differed among different age groups. Younger individuals consumed more energy. The test also showed that VO2 did not vary considerably, and significant differences only existed between two adjacent older age groups. Krahenbuhl and Williams (1992) investigated the running economy of young children and adolescents. They found that as age increased, movement efficiency also improved. Compared with adults, young children had higher resting metabolic rates and ventilator
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equivalents for oxygen and had unsmoothed cadence and step length compared with their adolescent counterparts (Krahenbuhl & Williams, 1992). Compared with age difference, the gender impact has been understudied. There are many possible reasons for the gender difference (e.g., impact of body composition and body mass). More studies are clearly needed. Although this study made a promising attempt to explore the prediction of youth’s EE during walking and running with a large sample size, several limitations should be acknowledged. First, the EE of the participants at rest was not directly measured but was estimated using a prediction equation, which may lead to an overestimated net EE (Waters & Mulroy, 1999). Another limitation of this study was that walking and running were conducted on a treadmill, which may slightly differ from walking and running in free-living conditions (Brouwer, Parvataneni, & Olney, 2009). Finally, no comparison was made between predictions of this study and other existing predictions, especially from the cost-effect perspective (e.g., tradeoff of making devices to measure velocity vs. step frequency and their accuracy in EE prediction). Future research should examine the possible impacts of these limitations.
CONCLUSION With developing simple devices to predict Chinese youth EE during walking and running in mind, this study examined the relationships between step frequency and velocity as well as between step frequency and EE. Based on the understanding of the relationship, a step frequency-, age-, and gender-based prediction equation with good accuracy was developed. The prediction equation provided a feasible algorithm that can be used to develop a simple device to estimate the youth’s EE during walking and running with good accuracy.
WHAT DOES THIS ARTICLE ADD?
FIGURE 5 Net EE– step frequency relationship between genders.
Walking and running are the most popular human activities. Walking, in fact, has been considered the most commonly used mode when promoting physical activity and public health. Considering the fast-spreading childhood obesity epidemic, promoting walking among youth has become urgently important. Efforts have long been made to estimate EE during walking and running, but variables employed have been difficult to implement in simple devices like a pedometer. To address this gap, this study first examined the hypothesized step frequency –velocity relationship, upon which an EE prediction equation based on step frequency, age, and gender was derived and validated. With the developed algorithm, developing a simple device to measure EE during walking and running becomes possible.
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ACKNOWLEDGMENTS This work was partly supported by the Supporting Programs of the Ministry of Science and Technology of China (2009BAK62B02002 and 2012BAK23B04) and the Shanghai Committee of Science and Technology in China (Grant No. 11dz1503700).
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REFERENCES Ainslie, P., Reilly, T., & Westerterp, K. (2003). Estimating human energy expenditure: A review of techniques with particular reference to doubly labelled water. Sports Medicine, 33, 683 –698. Bernardi, M., Macaluso, A., Sproviero, E., Castellano, V., Coratella, D., Felici, F., . . . Ditunno, J. F., Jr. (1999). Cost of walking and locomotor impairment. Journal of Electromyography and Kinesiology, 9, 149–157. Brooks, A. G., Gunn, S. M., Withers, R. T., Gore, C. J., & Plummer, J. L. (2005). Predicting walking METs and energy expenditure from speed or accelerometry. Medicine & Science in Sports & Exercise, 37, 1216–1223. Brouwer, B., Parvataneni, K., & Olney, S. J. (2009). A comparison of gait biomechanics and metabolic requirements of overground and treadmill walking in people with stroke. Clinical Biomechanics, 24, 729–734. Crouter, S. E., Schneider, P. L., Karabulut, M., & Bassett, D. R. Jr (2003). Validity of 10 electronic pedometers for measuring steps, distance, and energy cost. Medicine & Science in Sports & Exercise, 35, 1455–1460. Derumeaux-Burel, H., Meyer, M., Morin, L., & Boirie, Y. (2004). Prediction of resting energy expenditure in a large population of obese children. American Journal of Clinical Nutrition, 80, 1544–1550. Eston, R. G., Rowlands, A. V., & Ingledew, D. K. (1998). Validity of heart rate, pedometry and accelerometry for predicting the energy cost of children’s activities. Journal of Applied Physiology, 84, 362–371. Foster, R. C., Lanningham-Foster, L. M., Manohar, C., McCrady, S. K., Nysse, L. J., Kaufman, K. R., . . . Levine, J. A. (2005). Precision and accuracy of an ankle-worn accelerometer-based pedometer in step counting and energy expenditure. Preventive Medicine, 41, 778 –783. Franceschini, M., Rampello, A., Agosti, M., Massucci, M., Bovolenta, F., & Sale, P. (2013). Walking performance: Correlation between energy cost of walking and walking participation. New statistical approach concerning outcome measurement. PLoS ONE, 8, e56669. Frankenfield, D. C., Muth, E. R., & Rowe, W. A. (1998). The HarrisBenedict studies of human basal metabolism: History and limitations. Journal of the American Dietetic Association, 98, 439 –445. Frost, G., Bar-Or, O., Dowling, J., & Dyson, K. (2002). Explaining differences in the metabolic cost and efficiency of treadmill locomotion in children. Journal of Sports Sciences, 20, 451–461. Grieve, D. W., & Gear, R. J. (1966). The relationships between length of stride, step frequency, time of swing and speed of walking for children and adults. Ergonomics, 9, 379–399. Hall, C., Figueroa, A., Fernhall, B., & Kanaley, J. A. (2004). Energy expenditure of walking and running: Comparison with prediction equations. Medicine and Science in Sports and Exercise, 36, 2128–2134. Heden, T. D., Le Cheminant, J. D., & Smith, J. D. (2012). Influence of weight classification on walking and jogging energy expenditure prediction in women. Research Quarterly for Exercise and Sport, 83, 391–399. Kane, N. A., Simmons, M. C., John, D., Thompson, D. L., & Basset, D. R. (2010). Validity of the Nike þ device during walking and running. International Journal of Sports Medicine, 31, 101–105. Kavanagh, J. J., Barrett, R. S., & Morrison, S. (2004). Upper body accelerations during walking in healthy young and elderly men. Gait & Posture, 20, 291 –298. Krahenbuhl, G. S., & Williams, T. J. (1992). Running economy: Changes with age during childhood and adolescence. Medicine & Science in Sports & Exercise, 24, 462 –466.
Kramer, P. A., & Sylvester, A. D. (2011). The energetic cost of walking: A comparison of predictive methods. PLoS ONE, 6, e21290. Kuffel, E., Crouter, S. E., Haas, J. D., Frongillo, E. A., & Bassett, D. R. Jr (2011). Validity of estimating minute-by-minute energy expenditure of continuous walking bouts by accelerometry. International Journal of Behavioral Nutrition and Physical Activity, 8, 92. doi:10.1186/14795868-8-92 Li, Q., Young, M., Naing, V., & Donelan, J. M. (2010). Walking speed estimation using a shank-mounted inertial measurement unit. Journal of Biomechanics, 43, 1640–1643. Mark, J. O. M. (1996). Normalization of temporal-distance parameters in pediatric gait. Journal of Biomechanics, 29, 619–625. Matsas, A., Taylor, N., & McBurney, H. (2000). Knee joint kinematics from familiarised treadmill walking can be generalised to overground walking in young unimpaired subjects. Gait & Posture, 11, 46–53. McDuffie, J. R., Adler-Wailes, D. C., Elberg, J., Steinberg, E. N., Fallon, E. M., Tershakovec, A. M., . . . Yanovski, J. A. (2004). Prediction equations for resting energy expenditure in overweight and normalweight Black and White children. American Journal of Clinical Nutrition, 80, 365–373. McLaughlin, J. E., King, G. A., Howley, E. T., Bassett, D. R. Jr, & Ainsworth, B. E. (2001). Validation of the COSMED K4 b2 portable metabolic system. International Journal of Sports Medicine, 22, 280–284. Moe-Nilssen, R. (1998). A new method for evaluating motor control in gait under real-life environmental conditions. Part 1: The instrument. Clinical Biomechanics, 13, 320–327. Pinnington, H. C., Wong, P., Tay, J., Green, D., & Dawson, B. (2001). The level of accuracy and agreement in measures of FEO2, FECO2 and VE between the Cosmed K4b2 portable, respiratory gas analysis system and a metabolic cart. Journal of Science and Medicine in Sport, 4, 324–335. Pols, M. A., Peeters, P. H., Kemper, H. C., & Grobbee, D. E. (1998). Methodological aspects of physical activity assessment in epidemiological studies. European Journal of Epidemiology, 14, 63– 70. Riner, W. F., & Sellhorst, S. H. (2013). Physical activity and exercise in children with chronic health conditions. Journal of Sport and Health Science, 2, 12 –20. Saibene, F., & Minetti, A. E. (2003). Biomechanical and physiological aspects of legged locomotion in humans. European Journal of Applied Physiology, 88, 297–316. Schneider, P. L., Crouter, S. E., & Bassett, D. R. (2004). Pedometer measures of free-living physical activity: Comparison of 13 models. Medicine & Science in Sports & Exercise, 36, 331 –335. Schneider, P. L., Crouter, S. E., Lukajic, O., & Bassett, D. R. Jr (2003). Accuracy and reliability of 10 pedometers for measuring steps over a 400-m walk. Medicine & Science in Sports & Exercise, 35, 1779– 1784. Schutz, Y., Weinsier, S., Terrier, P., & Durrer, D. (2002). A new accelerometric method to assess the daily walking practice. International Journal of Obesity and Related Metabolic Disorders, 26, 111–118. Sekiya, N., & Nagasaki, H. (1998). Reproducibility of the walking patterns of normal young adults: Test –retest reliability of the walk ratio (steplength/step-rate). Gait & Posture, 7, 225– 227. Stoquart, G., Detrembleur, C., & Lejeune, T. (2008). Effect of speed on kinematic, kinetic, electromyographic and energetic reference values during treadmill walking. Clinical Neurophysiology, 38, 105– 116. Sutherland, D., Olshen, R., Biden, E., & Wyatt, M. (1988). The development of mature walking. Cambridge, UK: Cambridge University Press. Tesio, L., Rota, V., Chessa, C., & Perucca, L. (2010). The 3D path of body centre of mass during adult human walking on force treadmill. Journal of Biomechanics, 43, 938–944.
Downloaded by [University of North Dakota] at 00:05 21 November 2014
EE EQUATIONS BASED ON STEP FREQUENCY Tharion, W. J., Yokota, M., Buller, M. J., DeLany, J. P., & Hoyt, R. W. (2003). Prediction of total energy expenditure using a pedometer in male and female sailors aboard ship (Report T03-12). Natick, MA: U.S. Army Research Institute of Environmental Medicine. Trost, S. G., Way, R., & Okely, A. D. (2006). Predictive validity of three ActiGraph energy expenditure equations for children. Medicine & Science in Sports & Exercise, 38, 380–387. Vallis, L. A., Patla, A. E., & Adkin, A. L. (2001). Control of steering in the presence of unexpected head yaw movements. Influence on sequencing of subtasks. Experimental Brain Research, 138, 128–134. Walker, J. L., Murray, T. D., Jackson, A. S., Morrow, J. R., & Michaud, T. J. (1999). The energy cost of horizontal walking and running in adolescents. Medicine & Science in Sports & Exercise, 31, 311–322. Waters, R. L., Hislop, H. J., Perry, J., Thomas, L., & Campbell, J. (1983). Comparative cost of walking in young and old adults. Journal of Orthopaedic Research, 1, 73–76. Waters, R. L., Hislop, H. J., Thomas, L., & Campbell, J. (1983). Energy cost of walking in normal children and teenagers. Developmental Medicine & Child Neurology, 25, 184 –188.
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Waters, R. L., Lunsford, B. R., Perry, J., & Byrd, R. (1988). Energy–speed relationship of walking: Standard tables. Journal of Orthopaedic Research, 6, 215 –222. Waters, R. L., & Mulroy, S. (1999). The energy expenditure of normal and pathologic gait. Gait & Posture, 9, 207–231. Weyand, P. G., Smith, B. R., Puyau, M. R., & Butte, N. F. (2010). The mass-specific energy cost of human walking is set by stature. Journal of Experimental Biology, 213, 3972– 3979. Weyand, P. G., Smith, B. R., Schultz, N. S., Wohlers, L. M., Puyau, M. R., & Butte, N. F. (2013). Predicting metabolic rate across walking speed: One fit for all body sizes? [Abstract]. Journal of Applied Physiology, Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/ 23928111 Wheelwright, E., Minns, R., Law, H., & Elton, R. (1993). Temporal and spatial parameters of gait in children. I: Normal control data. Developmental Medicine & Child Neurology, 35, 102–113. Willemsen, A. T., van Alste´, J. A., & Boom, H. B. (1990). Real-time gait assessment utilizing a new way of accelerometry. Journal of Biomechanics, 23, 859–863.
