Purpose: To examine the ability of multivariate models to predict the heart-rate (HR) responses to some specific training drills from various global positioning system (GPS) variables and to examine the usefulness of the difference in predicted vs actual HR responses as an index of fitness or readiness to perform. Method: All data were collected during 1 season (2016–17) with players’ soccer activity recorded using 5-Hz GPS and internal load monitored using HR. GPS and HR data were analyzed during typical small-sided games and a 4-min standardized submaximal run (12 km·h−1). A multiple stepwise regression analysis was used to identify which combinations of GPS variables showed the largest correlations with HR responses at the individual level (HRACT, 149  GPS/HR pairs per player) and was further used to predict HR during individual drills (HRPRED). Then, HR predicted was compared with actual HR to compute an index of fitness or readiness to perform (HRΔ, %). The validity of HRΔ was examined while comparing changes in HRΔ with the changes in HR responses to a submaximal run (HRRUN, fitness criterion) and as a function of the different phases of the season (with fitness being expected to increase after the preseason). Results: HRPRED was very largely correlated with HRACT (r = .78 [.04]). Within-player changes in HRΔ were largely correlated with within-player changes in HRRUN (r = .66, .50–.82). HRΔ very likely decreased from July (3.1% [2.0%]) to August (0.8% [2.2%]) and most likely decreased further in September (−1.5% [2.1%]). Conclusions: HRΔ is a valid variable to monitor elite soccer players’ fitness and allows fitness monitoring on a daily basis during normal practice, decreasing the need for formal testing.
Mathieu Lacome, Ben Simpson, Nick Broad and Martin Buchheit
Ronald J. Maughan, Stuart J. Merson, Nick P. Broad and Susan M. Shirreffs
This study measured fluid balance during a 90-min preseason training session in the first team squad (24 players) of an English Premier League football team. Sweat loss was assessed from changes in body mass after correction for ingested fluids and urine passed. Sweat composition was measured by collection from patches attached to the skin at 4 sites. The weather was warm (24-29 °C), with moderate humidity (46–64%). The mean ± SD body mass loss over the training session was 1.10 ± 0.43 kg, equivalent to a level of dehydration of 1.37 ± 0.54% of the pre-training body mass. Mean fluid intake was 971 ± 303 ml. Estimated total mean sweat loss was 2033 ±413 ml. Mean sweat electrolyte concentrations (mmol/L) were: sodium,49± 12; potassium,6.0± 1.3;chloride, 43 ± 10. Total sweat sodium loss of 99 ± 24 mmol corresponds to a salt (sodium chloride) loss of 5.8 ± 1.4 g. Mean urine osmolality measured on pre-training samples provided by the players was 666 ±311 mosmol/kg (n=21). These data indicate that sweat losses of water and solute in football players in training can be substantial but vary greatly between players even with the same exercise and environmental conditions. Voluntary fluid intake also shows wide inter-individual variability and is generally insufficient to match fluid losses.