The effect of three days accumulated workload on football players’ perceived fatigue during Pre-Season

Abstract: Aim: The aim of the study was to improve the understanding on the relationship between different training- and match load variables and subsequent perceived rating of fatigue. This was done by investigating whether traditionally used workload variables (e.g GPS, HR data) functions as a good indicator of the players' perceived ratings of fatigue. A secondary aim was to quantify the size and distribution of the accumulated workload variables effects, in a three-days sequence, on perceived rating of fatigue by using a distributed lag model. That is the workload measured the day before, two days before and three days before the perceived rating of fatigue. Method: The current study is a retrospective observational study. Heart Rate data, data from a 10Hz GPS tracking system and data from Wellness Questionnaire were collected from a convenience sample of 13 professional male football players (average age ± SD: 24.62 ± 3.01 years, height 180.23 ± 5.04 cm, weight 74.77 ± 5.89 kg) from Swedish Allsvenskan team during a pre-season. A correlation analysis and distributed lag regression were used to detect the association between training- and match load variables and perceived rating of fatigue. Results: Moderate correlations were found between fatigue and several internal- and external- training load variables from the previous day session. A model with Fatigue explained by Training load Score (TLS) showed a significant positive (higher TLS = more fatigue) effect from all of the three previous days training sessions or matches with the largest effect from the session closest in time, i.e the day before (size of coefficient = 0.0100) followed by decreasing effect for the session two days ago (size of coefficient = 0.0074) and three days ago (size of coefficient = 0.0036).  Conclusions: The results showed in this study provides practitioners with a helpful tool to plan training and to estimate the dose-response relationship based on the group and training methods used. Diverse internal- and external training load variables can be used effectively to quantify the training load. The size of the coefficients can be used as an index or multiplier when estimating the effect from the last three days training load on fatigue. Nevertheless, a large variation in the group response depends on individuals responding differently which gives extra weight on monitoring the load on individual level and not only team-level. A larger sample, with fixed characteristics like age, playing position and gender, could provide more general conclusions.