Metabolic Power May Be a Useful Tool for Monitoring Locomotor Load in Team Sports Involving Repetitive, Intermittent, Intensive Running Sprints

Background: Global positioning systems (GPS) are regularly used by team sports where distance in a high speed zone (e.g. > 5.5 m/s) and acceleration zone count (e.g. > 3 m/s/s) are common metrics of locomotion workload. Many team sports consist of repetitive, short, intermittent, intensive sprints, which severely limits the ability of the above metrics to accurately measure running workload. To overcome these limitations, some sports GPS include the high metabolic workload distance (HMLD) metric, despite studies claiming its mechanistic unsuitability for this task. HMLD is the distance covered where power output estimated from velocity and acceleration/deceleration measures, exceeds a specific power output (e.g. ≥25 watts/kg).

Aim: We sought to explore the relationship between HMLD measured by a ViperPod GPS (STATSports Technology Ltd, Courtney Hill, NI) and the time to complete various intermittent repeated sprinting tasks common to game activities and testing protocols in a Rugby union.

Methods: Elite rugby union players and physically fit university students participated in different types of maximal intermittent rugby specific out-and-back shuttle run tests, ranging from 60 to 1440 meters. The shuttle run time and HMLD for each participant were plotted to determine the Pearson’s correlation coefficient (r) between the two variables for each shuttle run. The shuttle runs (SR) were 60m (10-20m shuttle run), SR180m (15-30-45m), SR240m (20-40-60m), SR600m (10-20m x 10, each 10-20m starting on 60 sec repeats), SR900m (15-30-45m x 5 on 2 minute repeats) and SR1440m (20-40-60m x 6 on 2 minute repeats).

Results: Results for the shuttle runs were: SR60m, r = 0.85, sample size (n) = 170; SR180m, r = 0.87, n = 65; SR240m, r = 0.87, n = 144; SR600m, r = 0.88, n = 17; SR900m, r = 0.92, n = 13; SR1440m, r = 0.96, n = 24.

Discussion and Conclusion: The criterion measure of performance for a running task is time to complete that task. As such, one would expect any measure that correlates strongly with participants` finish time in an intensive running task to be an effective predictor of running performance. HMLD has a strong relationship with time to complete intensive shuttle runs and distance commonly found in matches and fitness tests in rugby union. The longer the distance covered for the shuttle runs the better the correlation, with a very strong relationship (r > 0.95) between time and HMLD for 1440m. From these results one could surmise that HMLD has the capacity to accurately monitor intensive running workload in rugby union where average match distances covered for the various player positions range from 4.5 – 7 km. It is also possible that HMLD may be an effective running workload monitoring tool in other sports where intensive intermittent running activities abound.