Shivayogi V Hiremath1, Stephen S Intille2, Annmarie Kelleher3, Rory A Cooper4, Dan Ding4. 1. Department of Veterans Affairs, Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA; Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA; Department of Physical Therapy, Temple University, Philadelphia, PA. Electronic address: Shiv.Hiremath@temple.edu. 2. College of Computer and Information Science, Northeastern University, Boston, MA; Department of Health Sciences, Northeastern University, Boston, MA. 3. Department of Veterans Affairs, Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA. 4. Department of Veterans Affairs, Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA.
Abstract
OBJECTIVE: To develop and evaluate energy expenditure (EE) estimation models for a physical activity monitoring system (PAMS) in manual wheelchair users with spinal cord injury (SCI). DESIGN: Cross-sectional study. SETTING: University-based laboratory environment, a semistructured environment at the National Veterans Wheelchair Games, and the participants' home environments. PARTICIPANTS: Volunteer sample of manual wheelchair users with SCI (N=45). INTERVENTION: Participants were asked to perform 10 physical activities (PAs) of various intensities from a list. The PAMS consists of a gyroscope-based wheel rotation monitor (G-WRM) and an accelerometer device worn on the upper arm or on the wrist. Criterion EE using a portable metabolic cart and raw sensor data from PAMS were collected during each of these activities. MAIN OUTCOME MEASURES: Estimated EE using custom models for manual wheelchair users based on either the G-WRM and arm accelerometer (PAMS-Arm) or the G-WRM and wrist accelerometer (PAMS-Wrist). RESULTS: EE estimation performance for the PAMS-Arm (average error ± SD: -9.82%±37.03%) and PAMS-Wrist (-5.65%±32.61%) on the validation dataset indicated that both PAMS-Arm and PAMS-Wrist were able to estimate EE for a range of PAs with <10% error. Moderate to high intraclass correlation coefficients (ICCs) indicated that the EE estimated by PAMS-Arm (ICC3,1=.82, P<.05) and PAMS-Wrist (ICC3,1=.89, P<.05) are consistent with the criterion EE. CONCLUSIONS: Availability of PA monitors can assist wheelchair users to track PA levels, leading toward a healthier lifestyle. The new models we developed can estimate PA levels in manual wheelchair users with SCI in laboratory and community settings.
OBJECTIVE: To develop and evaluate energy expenditure (EE) estimation models for a physical activity monitoring system (PAMS) in manual wheelchair users with spinal cord injury (SCI). DESIGN: Cross-sectional study. SETTING: University-based laboratory environment, a semistructured environment at the National Veterans Wheelchair Games, and the participants' home environments. PARTICIPANTS: Volunteer sample of manual wheelchair users with SCI (N=45). INTERVENTION: Participants were asked to perform 10 physical activities (PAs) of various intensities from a list. The PAMS consists of a gyroscope-based wheel rotation monitor (G-WRM) and an accelerometer device worn on the upper arm or on the wrist. Criterion EE using a portable metabolic cart and raw sensor data from PAMS were collected during each of these activities. MAIN OUTCOME MEASURES: Estimated EE using custom models for manual wheelchair users based on either the G-WRM and arm accelerometer (PAMS-Arm) or the G-WRM and wrist accelerometer (PAMS-Wrist). RESULTS: EE estimation performance for the PAMS-Arm (average error ± SD: -9.82%±37.03%) and PAMS-Wrist (-5.65%±32.61%) on the validation dataset indicated that both PAMS-Arm and PAMS-Wrist were able to estimate EE for a range of PAs with <10% error. Moderate to high intraclass correlation coefficients (ICCs) indicated that the EE estimated by PAMS-Arm (ICC3,1=.82, P<.05) and PAMS-Wrist (ICC3,1=.89, P<.05) are consistent with the criterion EE. CONCLUSIONS: Availability of PA monitors can assist wheelchair users to track PA levels, leading toward a healthier lifestyle. The new models we developed can estimate PA levels in manual wheelchair users with SCI in laboratory and community settings.
Authors: Shivayogi V Hiremath; Amir Mohammad Amiri; Binod Thapa-Chhetry; Gretchen Snethen; Mary Schmidt-Read; Marlyn Ramos-Lamboy; Donna L Coffman; Stephen S Intille Journal: PLoS One Date: 2019-10-15 Impact factor: 3.240
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: Wiebe H K de Vries; Sabrina Amrein; Ursina Arnet; Laura Mayrhuber; Cristina Ehrmann; H E J Veeger Journal: Sensors (Basel) Date: 2022-09-29 Impact factor: 3.847
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