OBJECTIVE: To develop and evaluate new energy expenditure (EE) prediction models for manual wheelchair users (MWUs) with spinal cord injury (SCI) based on a commercially available multisensor-based activity monitor. DESIGN: Cross-sectional. SETTING: Laboratory. PARTICIPANTS: Volunteer sample of MWUs with SCI (N=45). INTERVENTION: Subjects were asked to perform 4 activities including resting, wheelchair propulsion, arm-ergometer exercise, and deskwork. Criterion EE using a metabolic cart and raw sensor data from a multisensor activity monitor was collected during each of these activities. MAIN OUTCOME MEASURES: Two new EE prediction models including a general model and an activity-specific model were developed using enhanced all-possible regressions on 36 MWUs and tested on the remaining 9 MWUs. RESULTS: The activity-specific and general EE prediction models estimated the EE significantly better than the manufacturer's model. The average EE estimation error using the manufacturer's model and the new general and activity-specific models for all activities combined was -55.31% (overestimation), 2.30% (underestimation), and 4.85%, respectively. The average EE estimation error using the manufacturer's model, the new general model, and activity-specific models for various activities varied from -19.10% to -89.85%, -18.13% to 25.13%, and -4.31% to 9.93%, respectively. CONCLUSIONS: The predictors for the new models were based on accelerometer and demographic variables, indicating that movement and subject parameters were necessary in estimating the EE. The results indicate that the multisensor activity monitor with new prediction models can be used to estimate EE in MWUs with SCI during wheelchair-related activities mentioned in this study.
OBJECTIVE: To develop and evaluate new energy expenditure (EE) prediction models for manual wheelchair users (MWUs) with spinal cord injury (SCI) based on a commercially available multisensor-based activity monitor. DESIGN: Cross-sectional. SETTING: Laboratory. PARTICIPANTS: Volunteer sample of MWUs with SCI (N=45). INTERVENTION: Subjects were asked to perform 4 activities including resting, wheelchair propulsion, arm-ergometer exercise, and deskwork. Criterion EE using a metabolic cart and raw sensor data from a multisensor activity monitor was collected during each of these activities. MAIN OUTCOME MEASURES: Two new EE prediction models including a general model and an activity-specific model were developed using enhanced all-possible regressions on 36 MWUs and tested on the remaining 9 MWUs. RESULTS: The activity-specific and general EE prediction models estimated the EE significantly better than the manufacturer's model. The average EE estimation error using the manufacturer's model and the new general and activity-specific models for all activities combined was -55.31% (overestimation), 2.30% (underestimation), and 4.85%, respectively. The average EE estimation error using the manufacturer's model, the new general model, and activity-specific models for various activities varied from -19.10% to -89.85%, -18.13% to 25.13%, and -4.31% to 9.93%, respectively. CONCLUSIONS: The predictors for the new models were based on accelerometer and demographic variables, indicating that movement and subject parameters were necessary in estimating the EE. The results indicate that the multisensor activity monitor with new prediction models can be used to estimate EE in MWUs with SCI during wheelchair-related activities mentioned in this study.
Authors: Werner L Popp; Sophie Schneider; Jessica Bär; Philipp Bösch; Christina M Spengler; Roger Gassert; Armin Curt Journal: Front Neurol Date: 2019-11-01 Impact factor: 4.003
Authors: Werner L Popp; Lea Richner; Michael Brogioli; Britta Wilms; Christina M Spengler; Armin E P Curt; Michelle L Starkey; Roger Gassert Journal: Front Neurol Date: 2018-07-03 Impact factor: 4.003