Huang, Zijian
(2025)
Quantifying Free-Living Physical Activity in People with Spinal Cord Injury or Disorder Using Wearable Devices.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
People with Spinal Cord Injury/Disorder (SCI/D) have a significantly higher obesity prevalence, which elevates their risk of developing secondary conditions. Accurate objective measurements of energy expenditure (EE) and physical activity (PA) are essential for obesity prevention and weight management. While wearable devices have been widely validated in the general population, and sophisticated PA pattern analysis using wearable device data has been proposed, relative validation studies and the use of wearable devices to analyze PA patterns in people with SCI/D are lacking. This dissertation focuses on improving the use of wearable devices, specifically the ActiGraph wearable accelerometer, to assess PA and EE in two specific SCI/D groups: adults with spinal cord injury (SCI) and children with Spina Bifida (SB). For adults with SCI, we developed a custom raw acceleration signal-based algorithm to predict total daily energy expenditure (TDEE). We evaluated its field validity against the criterion measure of doubly labeled water (DLW) and compared it with other existing ActiGraph Count-based custom prediction algorithms in this population. In children with SB, we utilized data from an ongoing study on body composition and EE. We evaluated existing TDEE prediction equations in this population and improved prediction accuracy by developing custom equations that use demographic variables alone or in combination with ActiGraph outputs. We also developed new sets of custom ActiGraph PA intensity classification cut-points for children with SB. Using these cut-points and free-living PA data, we conducted an exploratory analysis to understand PA patterns and their preliminary relationship with obesity in children with SB. In summary, our validated custom TDEE prediction algorithms for adults with SCI and children with SB provide new methods for accurately assessing free-living TDEE in these groups. Additionally, the custom ActiGraph PA intensity cut-points and our analytical approach to PA patterns provide a solid foundation for future studies that further investigate the links between free-living PA and health outcomes in these populations.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
25 February 2025 |
| Date Type: |
Publication |
| Defense Date: |
10 October 2024 |
| Approval Date: |
25 February 2025 |
| Submission Date: |
21 November 2024 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
145 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Health and Rehabilitation Sciences > Rehabilitation Science |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
spinal cord injury, spina bifida, ActiGraph, wearable, machine learning, energy expenditure, physical activity, children |
| Date Deposited: |
25 Feb 2025 16:01 |
| Last Modified: |
25 Feb 2025 16:01 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/47113 |
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