Abstract

It is important for coaches to be able to quantify training load in order to optimize performance and reduce injury risk in their athletes (Malone, 2014). Training load can be defined as “the cumulative amount of stress placed on an individual from multiple sessions and games over a period of time” (Gabbett, 2014). The most common way and economical method (free) of determining training load is session rating of perceived exertion (RPE) (Foster et al., 2001), a subjective measurement of training load. Other measures of training load include data collected from global positioning systems (GPS). These technologies have improved over the last decade and have become more affordable. GPS devices allow a coach to measure the distance covered during practices and competitions. It is important for coaches to be able to understand the relationship between these training load variables, especially if they aren't fortunate enough to afford wearable GPS technology for their athletes. Therefore, the purpose of this study was twofold: 1) to compare minutes played during games to the GPS’s active time measures, and 2) to determine the relationship between GPS-derived training loads and sRPE. Data from 14 NCAA Division female soccer players were collected and analyzed (age 20.1 ± 1.3years height 166.7 ± 6.6cm, weight 61.5 ± 8.3kg). These data included the first 13 matches of the 2021 season. A total of 148 data points were recorded for analysis. The investigation was approved by the Institutional Review Board and all participants completed and signed the approved informed consent. Training load was measured using a 10Hz GPS (Titan 1+; Integrated Bionics, Houston, TX, USA). GPS variables used in the present study included 1)total distance (m), 2) total time moving at >1m/s (active time), and 3) GPS load, a proprietary value using arbitrary units that accounts for the intensity and duration of the session. Minutes played (MP) included only the time spent playing during the match. The entire match duration included an approximate 20-minute warm-up and a 90-minute game (overtime periods were included if games went into overtime). Another measure called 'Active Time' (AT) was recorded by the GPS. These three durations were multiplied by the RPE to calculate session RPE (training load). Mean and standard deviations for total distance were 5008m and 1991m, respectively. Mean and standard deviations for RPE were 7.8 and 2.0, respectively. There was a statistically significant difference between minutes played (43.1 ± 29.6 minutes) and active time (55.7 ± 23.3 minutes) (p < 0.01). The present study demonstrated that both session RPE calculated using AT (r= 0.93 or MP (r= 0.89) are related to the total distance. Coaches and Practitioners should understand that MP may underestimate the duration of physical activity for low-minute players, but is a good determinant of s session RPE for players that play more than 70 minutes of a match.