The objectives of this dissertation include 1) to review accelerometry and Global Navigation Satellite System (GNSS) derived measures used to monitor training load, 2) to investigate the validity and reliability of accelerometers (ACCs) to identify stepping events and quantify training load, 3) to assess the relationship between accelerometry and Global Navigation Satellite Systems (GNSS) derived measures in quantifying training load. In Study I, acceleration data was collected via two tri-axial ACC (Device A and Device B) sampling at 100Hz mounted closely together at the xiphoid process level. Each participant (n=30) performed two trials of straight-line walking, running, and sprinting on a 20m course. Device A was used to assess ACC validity to identify steps and the test-retest reliability of the instrument to quantify the external load. Device A and Device B were used to assess inter-device reliability. The reliability of accelerometry derived metrics Impulse Load (IL) and Magnitude g (MAG) were assessed. In Study II, known distance (DIST) was predicted via acceleration data collected by a tri-axial ACC sampling at 100Hz mounted at the xiphoid process level, simultaneously positional data collected using a triple GNSS unit sampling at 10Hz placed between scapulae. Each participant (n=30) walked different DIST around various movement constraints (small and large circles). Thirty distances were completed around each circle and ranged from 12.57–376.99m. In Study I, the instrument demonstrated a positive predictive value (PPV) of 96.98-99.41% and an agreement of 93.08-96.29% for step detection during all conditions. Good test-retest reliability was found with a coefficient of variation (CV) < 5% for IL and MAG during all locomotor conditions. Good inter-device reliability was also found for all locomotor conditions (IL and MAG CV < 5%). These results indicated that tri-axial ACCs are a valid and reliable tool used to identify steps and quantify external load when movement is completed at a range of speeds. In Study 2, all linear regression models performed well for both movement constraints (R2=0.922-0.999; RMSE=0.047-0.242, p
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:etd-5444 |
| Date | 01 August 2021 |
| Creators | Bursais, Abdulmalek |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
| Rights | Copyright by the authors. |
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