Hoydick, Jordan
(2023)
Development, Validation, and Implementation of Actigraphy Algorithms for Accelerometry Data Collected through an Alternative Brand Inertial Measurement Unit Mounted to the Spine in Participants with Chronic Low Back Pain and Healthy Controls.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Assessing physical activity is important in the treatment of chronic conditions, including chronic low back pain (cLBP). ActiGraph, a widely used clinical physical activity monitor, collects raw acceleration data, and processes it through proprietary algorithms to produce physical activity measures including activity counts, counts per minute, activity cut points, wear time, and step counts. This thesis aims to replicate ActiGraph algorithms in MATLAB and test the validity of this method with both healthy controls and participants with cLBP. The algorithms will then be modified and applied to two alternative brand inertial measurement units (IMUs) placed on L1 and L5 of the spine. ActiGraph waist results will be compared to IMU L1 and L5 results to interpret differences in device and location and to determine the feasibility of this method in participants with cLBP. A free-living validation was performed where 24 participants, 12 cLBP and 12 healthy, wore an ActiGraph GT9X on the non-dominant hip, and alternative brand IMUs on L1 and L5 of the spine for up to seven days. The raw acceleration data collected with the ActiGraph was processed in both ActiLife, ActiGraph’s data analysis software platform, and through the developed MATLAB algorithms. Percent errors between methods for all 24 participants, as well as separated by cLBP and healthy, are all less than 2%. ActiGraph algorithms were replicated and validated for both populations, based on minimal error differences between ActiLife and MATLAB. After validation, the raw acceleration data collected through the IMUs were processed in MATLAB for ten participants, five cLBP and five healthy, with full datasets. The IMUs placed on L1 and L5 had more active wear time than the ActiGraph on the waist. Comparing device location, the ActiGraph on the waist showed higher physical activity and less sedentary time than IMUs on L1 and L5 in all participants and separated by groups. Ultimately, the validated algorithms allow researchers to calculate physical activity metrics with any collected accelerometry data, regardless of device used. The application of these algorithms on participants with cLBP and healthy controls revealed similar trends across device locations, suggesting the feasibility of this method.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
13 June 2023 |
| Date Type: |
Publication |
| Defense Date: |
24 March 2023 |
| Approval Date: |
13 June 2023 |
| Submission Date: |
15 March 2023 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
73 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Bioengineering |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Actigraphy, ActiGraph, Inertial Measurement Units (IMU), Chronic Low Back Pain (cLBP) |
| Date Deposited: |
13 Jun 2023 14:18 |
| Last Modified: |
13 Jun 2024 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/44277 |
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