Parameters Selected by the Stepwise Regression

The vertical accelerometer counts parameter was selected by the stepwise regressions for both MET and kilocalorie models, for both TEE and PAEE. For the TEE kilocalorie model, BMI was chosen by the stepwise regression as the most significant parameter other than accelerometer counts, although weight was expected to fill this position. The selection of BMI over weight appears to be a consequence of applying the Weir equation (223) to calculate EE from both VO2 and CO2; preliminary testing of the MATLAB code had shown

weight to be the second most significant parameter in the model when kilocalories were calculated using VO2 alone. In terms of the MET models, weight was already factored in to

the units of measurement of the independent variable, so weight related measurements are not expected to feature highly in the model. However, for the PA kilocalorie model, lean mass was selected instead of weight. It may be that lean mass in combination with parameters such as BMI and body fat percentage (as chosen by the model) is a better variable for the PAEE prediction model than weight in this particular case.

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The triceps fat thickness is common to both the MET and kilocalorie models that were identified by stepwise regression, and was considered the most significant parameter other than accelerometer counts and weight in the TEE and PAEE MET models. It may be that the triceps fat thickness is a good representative quantity of upper body fat distribution. Additionally, it is possible that the triceps fat measurement is more reliable than the other ultrasound measurements. Triceps fat thickness did also factor in the TEE and PAEE kilocalorie models, though it was not considered as significant as in the MET models. Waist circumference appears in all models except the PAEE MET model. Waist circumference in conjunction with triceps fat thickness may give a good indication of how subjects‘ weight is distributed about their bodies. This in turn may influence energy expenditure directly or indirectly due to the effect of weight distribution on walking economy.

Resting VO2 was common to the TEE MET and TEE kilocalorie models derived by stepwise

regression. This parameter is a representation of the amount of energy that an individual consumes when at rest. The difference between resting VO2 and the amount of oxygen

consumed performing an activity such as treadmill walking represents the energy cost of the physical activity, and some studies have removed base level energy consumption in order to predict the physical activity energy expenditure directly (77). By including resting VO2 in the

prediction model, the model may be effectively accounting for the difference between resting EE and that which is due to physical activity, and a consequence of this may be an improvement in EE prediction. Unsurprisingly, the resting VO2 parameter does not feature in

the PAEE models as it has already been factored into the dependent variable (resting VO2

was first removed from the measured VO2 for the PAEE MET models, and a combination of

resting VO2 and resting CO2 was removed from the measured PAEE in the PAEE kilocalorie

model).

Diastolic blood pressure was selected by the stepwise regression for all models except the TEE kilocalorie model where systolic blood pressure was chosen. These parameters were added by the stepwise regression in the latter steps of the algorithm, which suggests that blood pressure measurements may have lower explanatory power in the model but high statistical significance. This reflects the findings of Snodgrass et al. (279) who reported statistically significant correlations between both systolic and diastolic blood pressure with the basal metabolic rate (BMR) of a population of Siberians.

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It was unexpected that the mediolateral counts parameter was identified by the stepwise regression before the vertical counts parameter in the both TEE and PA kilocalorie models. However, this may be due to the models requiring a measure of both accelerometer output and weight before a good correlation between parameters and kilocalories is observed – to support this, it can be seen that once weight is added to the TEE model the RMSE reduces by 22% (Table 30). It was also unexpected that the thigh fat thickness did not appear in any of the models, as there is research to suggest that the energy cost of walking in the obese may be increased due to greater weight of the leg (240). It is possible, in this case, that differences in the EE model which are due to fat distribution may have been explained by other parameters, and that thigh fat thickness may not have any further explanatory power.

Gender and age do not feature in either the TEE MET or TEE kilocalorie model, which is also unexpected as previous research has identified these as factors affecting energy expenditure between individuals (234-237), and the subject group was sufficiently diverse in these areas to expect them to have a bearing on the estimation models. It may be that the other parameters have better explanatory power, and gender and age do not significantly improve the model once the other parameters have been selected. Age factored in the PAEE MET model, which again is unexpected because while there is a correlation between age and BMR (290), resting energy expenditure has already been accounted for in this model.

Lean mass was another parameter unexpected excluded from the TEE model, as it has been identified as a major determinant of energy expenditure (237). It may be that for the TEE models lean mass did not add greater predictive power than the weight parameter in combination with other parameters. However, lean mass was substituted for weight in the PAEE kilocalorie model. Several other candidate parameters, such as heart rate and height were also absent from the stepwise regression models, which may simply suggest that these parameters are not useful in improving EE prediction accuracy, or it may be that some are correlated with one of the selected model parameters, and therefore do not provide additional explanatory power to the regression.