1. Prediction of Energy Expenditure from Overground and
Treadmill Walking Speed in Healthy Adults
D.A. Rowe1,2 FACSM, G.J. Welk3 FACSM, D.P. Heil4 FACSM, M.T. Mahar1, C.D., Kemble1, J.L. Aycock1, A.M. Guerieri1, A. Calabro3, & K. Camenisch4
1Department of Exercise and Sport Science, East Carolina University, 2Department of Sport, Culture and the Arts, University of Strathclyde, 3Department of Health and Human Performance, Iowa State University, 4Dept. of Health and Human Development, Montana State University
Figure 1
Regression of walking speed (mph) and EE (ml/kg/min) - Overground
Background
Results
r = .82; SEE = 1.70 ml/kg/min
VO2pred (ml/kg/min) = speed (mph)
Several accelerometer equations have been developed for predicting energy expenditure (EE) during ambulatory activity such as walking
In many such studies, walking movement and EE have been measured during treadmill (TM) walking rather than during overground (OG) walking
Few studies have been conducted to determine whether the energy cost of TM and OG walking are similar
OG vs. TM speed comparison:
OG walking speed significantly (p < .05) faster than TM walking speed during the slow walk (2.65 ± 0.33 mph vs ± 0.21 mph)
No significant difference (p > .05) during the intermediate walk (3.11 ± 0.29 mph vs ± 0.22 mph)
OG walking speed significantly (p < .05) slower than TM walking speed during the fast walk (3.58 ± 0.35 mph vs ± 0.22 mph).
Conclusion: TM walking speed was not replicated during the OG slow and fast trials.
Linear regression:
3 outliers (> 3 SD) deleted
Prediction accuracy of EE from walking speed was significantly greater than zero (p < .05)
TM walking: r = .77, SEE = 2.27 ml/kg/min
OG walking: r = .82; SEE = 1.70 ml/kg/min
Regression equations:
Similar observed slope (4.47 TM vs OG)
Different observed intercepts (– 0.56 TM vs. – 1.58 OG)
However, neither the slope nor the intercept was significantly different (p > .05) between TM and OG walking trials
Conclusion: Similar relationship between walking speed and EE during OG and TM walking
Purpose
To investigate whether the relationship between speed (mph) and EE (ml/kg/min) is similar during TM and OG walking in healthy adults
Summary/Discussion i
Methods
In this study, the metronome method described by Gordon et al(1) was not successful in replicating TM walking speed during OG walking trials
There was a tendency to walk faster OG at slow speeds and walk slower OG at fast speeds, compared to TM walking
May indicate a systematic pattern when comparing TM and OG walking speeds
Alternatively, because participants were not allowed to self-select their TM speeds, the results may simply reflect that the assigned lower and upper TM walking speeds used in this study were beyond the comfortable range of some participants, and they adjusted to a more customary speed during the slow and fast OG trials
From the regression analysis, the similar slopes indicated that EE increases with walking speed similarly during TM and OG walking
Although the different intercepts in this sample indicated that, at a given speed, OG EE was slightly lower (by approximately 1 ml/kg/min) than TM EE, this comparison was within the boundaries of chance probability
Interestingly, 3 METs (a commonly used EE cutpoint for moderate intensity activity) corresponded on average to walking at 2.58 mph, similar to the value of 2.66 mph derived from data in a similar TM walking study(2)
In summary, the evidence of this study implies that walking speed and EE are similarly related during TM and OG walking
This implies that accelerometry equations developed from TM walking protocols may be appropriate for predicting EE during OG walking
Design:
Laboratory study conducted at 3 sites (ECU, ISU, MSU)
Three 6-minute treadmill walking trials were completed at randomly assigned slow ( mph), intermediate ( mph), and fast ( mph) speeds
Followed by three OG walking trials (slow, intermediate, fast)
Clip-on metronome used during OG trials, to replicate the stride rate from each TM trial according to the protocol of Gordon et al(1)
Trial order counterbalanced
Rest given between trials, until HR < 100 b/min
Participants: Three equally sized samples (n = 25), recruited from the university and local community at three sites
ECU (12 M 13 F; Age 47.2 ± 9.5 yr; Height ± 9.3 cm; weight 80.5 ± 15.4 kg)
ISU (15 M 10 F; Age 27.4 ± 4.6 yr; Height ± 8.7 cm; weight 75.1 ± 15.8 kg)
MSU (10 M 15 F; Age 24.0 ± 6.2 yr; Height ± 7.9 cm; weight 71.3 ± 14.3 kg)
Instruments:
Metabolic systems:
Cosmed K4b2 [ECU; Cosmed SRL, Rome, Italy]
Oxycon Mobile [ISU OG trials and MSU; Viasys Healthcare Inc, Yorba Linda, CA])
Cosmed Quark b2 (ISU TM trials; Cosmed SRL, Rome, Italy)
Heart rate monitor (Polar Inc., Lake Success, NY)
Accusplit AH120 M9 pedometer (Accusplit, Silicon Valley, CA)
Data processing and analysis:
EE data from the last 2 minutes of each trial were analyzed
TM speed was determined using a tachometer and OG speed was calculated from measured distance and time to complete the trial
Walking speed for TM and OG trials compared using dependent t-tests
Linear regression used to determine the relationship between walking speed and EE during TM and OG walking
data from only one speed was selected for each participant, to avoid violating the assumption of independence of observations,
Figure 1
Regression of walking speed (mph) and EE (ml/kg/min) - Treadmill
r = .77, SEE = 2.27 ml/kg/min
VO2pred (ml/kg/min) = speed (mph)
References
Activity Promotion Lab
Promoting Active Lifestyles
Gordon, J.S., et al. (1999). Energy expenditure prediction accuracy of the CSA accelerometer for overground walking. Medicine & Science in Sports & Exercise, 31(5), s143.
Tudor-Locke, C, et al (2005). Pedometer-determined step count guidelines for classifying walking intensity in a young ostensibly healthy population. Canadian Journal of Applied Physiology, 30(6),
Acknowledgments: This study was part-funded by support from Accusplit, Inc.