Category: Technology (e.g. robotics, assistive technology, mHealth); Spinal Cord Injury; Lifestyle Medicine
This study aims to validate the accuracy of predictive Energy Expenditure equations which are based on Actigraph activity monitors worn on the wrist of Manual Wheelchair Users with Spinal Cord Injury.
Cross-Sectional study design uses a portable metabolic cart as the criterion standard for measuring Energy Expenditure. Participants performed a variety of sedentary activities (Eg. resting, watching TV), light activities (Eg. vacuuming, using a dishwasher), and moderate-to-vigorous activities (Eg. propulsion at varying speeds). All subjects were equipped with an Actigraph GT3X+ on their dominant wrist.
Setting : Spinal Cord Injury individuals in a University testing facility.
Participants (or Animals, Specimens, Cadavers) :
30 subjects were recruited for the study. The inclusion/exclusion criteria for individuals included that 1) they have a spinal cord injury, 2) were between the age of 18 and 65, 3) used a manual wheelchair as their primary means of mobility for at least 40 hours/week, 4) were at least one-year post injury, and 5) were medically stable.
Interventions : Not Applicable.
Main Outcome Measure(s) :
The predictive Energy Expenditure equations and criterion standard generated per minute data during each of the activities. The accuracies across different activities for the same subjects along with the accuracies across the entire visit for each subject was calculated.
Results : 6 wrist based Energy Expenditure equations using Actigraph activity monitors were used from 5 different studies. The predicted Energy Expenditure from these equations were compared to the Energy Expenditure outputs from the metabolic cart. Mean Absolute Error and Mean Absolute Deviation was calculated for different activities as well as across participants. The accuracies of different predictive Energy Expenditure equations were compared.
Equations using ActiGraph activity monitors were less accurate for predicting Energy Expenditure from resistance based activities.
Yousif Shwetar– Co-op Student, Human Engineering Research Laboratory, University of Pittsburgh, Pittsburgh, Pennsylvania
Dan Ding– Associate Professor, Human Engineering Research Lab, University of Pittsburgh, Pittsburgh, Pennsylvania
Akhila Veerubhotla– Doctoral Student, Human Engineering Research Lab, University of Pittsburgh, Pittsburgh, Pennsylvania