The Relationship between Accelerometry, Global Navigation Satellite System, and Known Distance: A Correlational Design Study

Abstract

Background: Previous research has explored associations between accelerometry and Global Navigation Satellite System (GNSS) derived loads. However, to our knowledge, no study has investigated the relationship between these measures and a known distance. Thus, the current study aimed to assess and compare the ability of four accelerometry based metrics and GNSS to predict known distance completed using different movement constraints. Method: A correlational design study was used to evaluate the association between the dependent and independent variables. A total of 30 physically active college students participated. Participants were asked to walk two different known distances (DIST) around a 2 m diameter circle (small circle) and a different distance around an 8 m diameter circle (large circle). Each distance completed around the small circle by one participant was completed around the large circle by a different participant. The same 30 distances were completed around each circle and ranged from 12.57 to 376.99 m. Instrumentation: Acceleration data was collected via a tri-axial accelerometer sampling at 100 Hz. Accelerometry derived measures included the sum of the absolute values of acceleration (SUM), the square root of the sum of squared accelerations (MAG), Player Load (PL), and Impulse Load (IL). Distance (GNSSD) was measured from positional data collected using a triple GNSS unit sampling at 10 Hz. Results: Separate simple linear regression models were created to assess the ability of each independent variable to predict DIST. The results indicate that all regression models performed well (R = 0.960–0.999, R2 = 0.922–0.999; RMSE = 0.047–0.242, p < 0.001), while GNSSD (small circle, R = 0.999, R2 = 0.997, RMSE = 0.047 p < 0.001; large circle, R = 0.999, R2 = 0.999, RMSE = 0.027, p < 0.001) and the accelerometry derived metric MAG (small circle, R = 0.992, R2 = 0.983, RMSE = 0.112, p < 0.001; large circle, R = 0.997, R2 = 0.995, RMSE = 0.064, p < 0.001) performed best among all models. Conclusions: This research illustrates that both GNSS and accelerometry may be used to indicate total distance completed while walking.

Field based assessment of a tri-axial accelerometers validity to identify steps and reliability to quantify external load

“…This investigation revealed promising test-retest (IL CV = 3.22%–4.99%; MAG CV = 3.12%–3.49%) and inter-device (IL CV = 1.13%–2.67%; MAG CV = 1.61%–2.10%) reliability during all conditions. In accordance with the present results, our earlier observations demonstrated that MAG had lower intra- and inter-device CV when compared to IL and two other accelerometry-derived metrics when assessing external load while walking ( Bursais et al, 2022 ). Future studies should investigate which accelerometry derived metrics best quantify external load in sport.…”

“…Different accelerometry derived metrics have been used to quantity external loads and reported in the literature (Bursais et al, 2022). Accelerometry derived external loads have been assessed in field and laboratory environments (Johnstone et al, 2012a;Johnstone et al, 2012b;Johnstone et al, 2012c;Barrett, Midgley and Lovell, 2014).…”

Arabic translation and cultural adaptation of a training load and player monitoring in high-level football questionnaire: A cognitive interview study

“…Advancements in technology and analytical techniques have ushered in new opportunities within the applied sports environment. Nowadays, sports practitioners have the capability to monitor TL through the utilization of global navigation satellite systems and other microtechnology [ 5 , 6 ]. In the past, elite and professional teams might have been hesitant to share their methods, but recent research indicates their increasing willingness to participate in and publish applied research [ 7 , 8 ].…”