Concentración de mercado

Research has shown that off-the-shelf activity monitors cannot accurately predict EE in MWUs with SCI [87, 122]. Our previous study [87] using SenseWear has found large EE estimation errors ranging from 24.4% to 125.8% among 24 MWUs with SCI. Davis [122] showed that the mean signed EE by SenseWear (14.3 ± 6.0kJ/min) was much higher than the EE from a metabolic cart (11.4 ± 4.0kJ/min) during wheelchair propulsion on a treadmill. This study with a larger cohort also showed a consistent trend of large EE estimation errors and has led to the development of new EE prediction models.

The new activity-specific and general models developed in this study integrated all- possible-regression and cross-validation techniques to leverage the benefits of exhaustive model search, avoid overfitting, and increase the robustness of the model’s performance on

unseen data. Our validation results showed high intraclass correlation coefficients between the predicted EE by the new models and the criterion EE, indicating that there is a good agreement between the estimated EE by the new models and the criterion EE [96]. We also compared the two new prediction models and the manufacturer’s model with the criterion EE and found that the two new models significantly improved the EE prediction accuracy over the manufacturer’s model. We used four measures for the comparison including minute-by-minute MAE and MSE that assess the model’s ability to predict EE of performing a single activity for each minute, and per-session MSE that assesses the model’s ability to predict EE of performing multiple activities over a period of time. The MAE provides information regarding the magnitude of the prediction error, and the MSE indicates whether the predictions are biased—that is, whether they tend to be disproportionately positive or negative. The minute-by-minute prediction error for the manufacturer’s model was much higher than that for the generalized and activity-specific models (see Table 3and Table 4). We also found that the manufacturer’s model tended to overestimate EE, while the generalized and activity-specific models underestimated EE with smaller biases (see Table 4 and Table 5).

When further examining the two new models, we found the activity-specific model outperformed the general model, with a smaller absolute minute-by-minute EE prediction error for each individual activity and for all the activities combined (see Table 3). The activity-specific model also performed better than the general model, with a smaller bias for each individual activity (see Table 4). The general model selected the predictors that are sensitive to all activities as a whole, which compromises EE estimation for individual activities. However, we noticed that the general model had a smaller overall bias than the activity-specific model. This could be because the overestimations and underestimations

from different activities tend to cancel each other out. For example, the general model tended to overestimate EE for wheelchair propulsion but underestimate EE for arm- ergometry. For the same reason, the general model performed similarly as the activity- specific model for the per-session evaluation where the EE was estimated for multiple activities combined. These results suggested that the activity-specific model is more suitable for predicting minute-by-minute EE of individual activities, while the general model could be used when predicting the total EE over a group of activities together.

As for the predictors of the general and activity-specific models, of all the demographic variables, the body mass to the power of .75 (MASS_E_point75) was selected as a predictor by all the models. This is consistent with Kleiber’s law, which states that any mammal’s metabolic rate is proportional to the mammal’s mass raised to the power of .75 [123]. The upper limb movements captured by the accelerometer in the SenseWear also played important roles in the EE estimation. The mean absolute deviation variables (LMAD, SQRT_LMAD, TLMAD) captured the variability of the upper limb movements in different directions. The number of peaks (LPEAKS) captured the change of direction in the arm movements. The height divided by mean absolute deviation (HTDivLMAD) was chosen for the resting activity possibly because subjects were not sitting completely still and small arm movements as well as fidgeting were picked up by the accelerometer. Taller individuals tend to have longer arms, and their arm movements are more easily detected by the accelerometer during resting. The predictors chosen in the models are specific for wheelchair-related activities, including the average number of peaks in longitudinal acceleration (LPEAKS) and the product of mean absolute deviation in transverse and longitudinal acceleration (TLMAD) for wheelchair propulsion and arm-ergometry activities. Because the predictors included in the models were not SCI specific (eg, injury

level), the models can be used to estimate EE for all MWUs with SCI during the activities discussed in this study.

2.4.1 Study Limitations

Although the EE prediction accuracy was significantly improved with the new predictions models, it was relatively low when compared with the performance of the SenseWear among the ambulatory populations. Johannsen et al. [26] evaluated the validity of the SenseWear in estimating total EE for 14 consecutive days among 30 healthy adults aged 24 to 60 years and found that the absolute prediction error rate when compared with the criterion measure using doubly labeled water was 8.1% ± 6.8%. Berntsen et al. [33] found that the SenseWear underestimated total EE with a minute-by-minute MAE of 9% compared with an indirect calorimeter in 20 adults for a period of 120 minutes during various types of activities and intensities. One possible reason for the relatively low prediction accuracy of the new models could be the small sample size for both the training and validation groups. The small sample size in the training group further limited our ability to achieve a balanced distribution of subjects with different ranges of weight. Subjects in certain ranges may be underrepresented compared with those in other ranges, possibly leading to relatively large prediction errors for validation subjects who fell under the underrepresented ranges. Other limitations include collecting resting EE while the subjects were seated still in their wheelchairs, limited number of PAs, PA trials tested in a structured laboratory setting, and other demographic factors related to the inclusion criteria. It is not yet clear whether the new models can be used to predict EE for free-living activities, and how the models will perform under various conditions (velocities,

resistances, and environments). We hope our research will stimulate interest and efforts of other researchers to validate and refine the EE prediction models for MWUs with SCI. We recommend that researchers evaluate our models in their subject groups before use. Recommendations for future studies include testing more subjects with carefully planned recruitment strategies and experimental protocols containing free-living activities, testing the SenseWear among MWUs with other types of disabilities, and possibly developing PA monitors particularly suitable for MWUs.