1. Investigating the relationship between physiological measures and energy expenditure Yash Upadhyay Mentored by Dr. Angela Boynton and Dr. Jennifer Neugebauer
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Introduction
Materials and Methods (cont.)
Results (cont.)
Soldiers today carry heavy loads- up to 100 pounds. Strenuous physical activity greatly increases energy expenditure (EE) while carrying these loads. Existing methods of measuring EE are difficult and tedious to apply in the field so being able to simply calculate it would be helpful. Further research could be done on how to mitigate the effects of load carriage on EE which would be especially beneficial for military applications.
Most EE prediction models used today are inaccurate because they do not account for the proper variables or changes over an extended period of time. Keytel et al. (2005) found a linear relationship between HR and EE but only in a narrow range of 90 to 150 beats per minute (BPM). Pandolf’s equation is the most commonly used load carriage EE equation today. This equation uses body weight, external load, terrain, and walking gradient as variables (Keytel et al., 2005). However, it has been found to underestimate EE by 15% (Patton, Kaszuba, Mello, & Reynolds, 1991).
The ultimate goal of this project was to investigate the relationship between physiological measures and EE. The relationship between heart rate and EE over time was investigated. The relationship between various physiological variables and EE independent of time was also examined. Finally, an equation to estimate EE during prolonged load carriage was developed.
Aim 2: To investigate the linear relationship between EEhtotal and each of the six other physiological variables, Pearson correlation coefficients were calculated for each pair. The data used spanned all five time points because the purpose of this aim was to determine the relationship independent of time. Correlation values of indicate a weak linear relationship, indicate a moderate linear relationship, and indicate a strong linear relationship.
Aim 3: A linear mixed effects model was created to predict EEhtotal. The six recorded variables were used as well as a terrain factor (cross-country or treadmill). Non-significant variables (p < 0.05) were removed from the model. RMSE analysis was then performed on the final model to analyze how effective the model was for predicting EEhtotal. The RMSE analysis was also used to evaluate the model against the basic EEhtotal vs. HR model from Aim 1.
The linear mixed effects model created for Aim 3 is shown below. Table 2 compares the error of the Aim 1 EEhtotal vs. HR model with the model from Aim 3.
EEhtotal = − × Rf × VE− × Terrain − × Total
EEhtotal = Energy expenditure (kcal/hour/kg)
Rf = Respiratory frequency (breaths/minute)
VE = Ventilation rate (l/min)
Terrain = Treadmill or cross country, 1 or 0
Total = Total mass (subject weight + load, kg)
Figure 1 (above): Aim 3 linear mixed effects model to predict EEhtotal.
Time Point
Aim 1 Equation
RMSE
Aim 3 Equation
PRT
0.290
0.212 OP
1.952
0.385 M
1.761
0.457 E
1.480
0.490
POT
0.416
0.374
Table 2 (left): RMSE values sorted by time point for the Aim 1 and Aim 3 energy expenditure prediction equations. Error in predicting energy expenditure was reduced up to 30% by using the Aim 3 linear mixed effects model instead of the Aim 1 EEhtotal vs. HR model.
Results
Terrain was found to play a major role in EEhtotal. The red line in Graph 1 below shows the EEhtotal vs. HR prediction model created using the HR data.
Materials and Methods
Conclusions
Previously recorded data of Soldiers walking with a military load was used. Twenty-four Soldiers walked a 4.3 km cross country course (paved and wooded terrain, natural obstacles) with a load of 38 kg at Aberdeen Proving Ground. Before and after walking the course, participants walked for five minutes on a treadmill. The following physiological variables were measured and recorded continuously by a portable cardio-pulmonary exercise testing system: O2 consumption (VO2, ml/min), CO2 production (VCO2, ml/min), ratio between O2 and CO2 consumption (R), ventilation rate (VE, l/min), heart rate (HR, BPM), respiratory frequency (Rf, breaths/minute), and energy expenditure (EE, Kcal/min). Data for 60-second intervals from five time points were used in the analyses: Minute 4-5 of the pre-course treadmill walk (PRT), the minute they got off the pavement (OP), the minute they reached the mid-point (M), the end of the course before entering the lab (E), and minute 4-5 of the post-course treadmill walk (POT).
Aim 1: EE was multiplied by 60 to get units in Kcal/hour (EEh). To determine how the relationship between EEh and HR changes over time, a linear model was made for EEh vs. HR using the data from the PRT time point. The resulting prediction equation was applied to the HR data for each of the five time points and the root mean squared error (RMSE) between actual and predicted EEh values was calculated. This was repeated with EEh values divided by body weight + carried load (EEhtotal) to determine the effect of load carriage on the relationship between HR and EEhtotal.
The purpose was to determine how energy expenditure changes over time and its relationship with other variables. Aim 1 results clearly show that the relationship between EEhtotal and HR changes across different terrain. Aim 2 showed that Rf had no linear relationship with EEhtotal and VE had a strong relationship. VO2 and VCO2 had a very high correlation because they are used in the calculation of EEhtotal. The results of Aim 3 were interesting because Rf was a significant variable in the mixed effects model despite its weak correlation with EEhtotal. The developed equation was more accurate than the initial EEhtotal vs. HR model used in Aim 1, and the end goal of accurately predicting EE during prolonged load carriage was achieved. This equation can be used to predict EE more accurately, provide a better understanding of it, and lead follow-up studies on how to mitigate it during load carriage. This would be incredibly useful to not just the armed forces but also to potential future research to lower EE and increase efficiency.
Graph 1 (above): Actual treadmill and cross country EEhtotal (green square, yellow triangle) and predicted EEhtotal (red line created from the treadmill condition data) as a function of HR. Cross country EEhtotal is higher than treadmill EEhtotal.
Variable
Pearson Correlation
VO2
r = 0.995
VCO2
r = 0.968 VE
r = 0.849 HR
r = 0.538 R
r = 0.380 Rf
r = 0.259
Table 1 (left): Recorded variables’ Pearson correlations with EEhtotal from strongest to weakest. Respiratory frequency had the weakest linear correlation while VO2 and VCO2 had the strongest correlations.
References
Keytel, L., Goedecke, J., Noakes, T., Hiiloskorpi, H., Laukkanen, R., Van Der Merwe, L., & Lambert, E. (2005). Prediction of energy expenditure from heart rate monitoring during submaximal exercise. Journal Of Sports Sciences, 23(3),
Patton, J. F., Kaszuba, J., Mello, R. P., & Reynolds, K. L. (1991). Physiological responses to prolonged treadmill walking with external loads. European Journal of Applied Physiology and Occupational Physiology, 63(2),