Circulating, Cell-Free DNA for Monitoring Player Load in Professional Football

Abstract

Purpose: Player monitoring in elite sport settings is becoming increasingly important. Questionnaire-based methods and biomarkers such as circulating, cell-free DNA (cfDNA) are suggested for load monitoring. cfDNA concentrations were shown to increase depending on total distance covered in football and were associated with overtraining in weight lifters. Thus, the objective of this study was to examine whether cfDNA is feasible as a monitoring tool in elite football players. Methods: Capillary blood samples from 22 male elite football players were collected over 4 mo of a regular season. Sampling was conducted the day before, 1 day after, or several days after regular-season games and/or training. In addition, each player filled in a visual analogue scale (VAS) questionnaire including the items “general perceived exertion,” “muscular fatigue,” and “mental fatigue.” Performance during training and games was tracked by the Catapult system and with the OPTA system, respectively. Results: cfDNA values were significantly elevated in players the day after regular-season games (1.4-fold; P = .0004) in line with the scores of the VAS. Both parameters showed significantly higher values during midweek-game weeks. cfDNA concentrations correlated with training data, and VAS was correlated with the tracking of the season games. However, cfDNA and VAS did not correlate with each other. Conclusions: cfDNA concentrations at rest and VAS scores are influenced by previous load in professional football players. Future studies will reveal whether cfDNA might serve as a practically applicable marker for player load in football players.

Single-Item Self-Report Measures of Team-Sport Athlete Wellbeing and Their Relationship With Training Load: A Systematic Review

“…The association between sleep quality and training load was provided by 13 groups. Pearson and Spearman correlations indicated no associations 25 to trivial, 17 small, 16,18,30 large, 20 and very large 31 negative and positive associations between measures of sleep quality and measures of training load.…”

“…Associations between measures of fatigue and training load were reported in 13 studies. Pearson and Spearman correlations yielded results that varied from no correlation 29 to small, [16][17][18][19] moderate, 25,30 large, 20 and very large 31 negative and positive associations between measures of fatigue and measures of training load.…”

“…The statistical tests used were determined to be appropriate in all cases. A total of 15 groups 16,[20][21][22][23][24][25]27,[30][31][32][33][34][35][36] described participants lost to followup; this score was not awarded when the reason for missing data was not reported.…”

“…When the direction of the scale was evident (n ¼ 18), a higher score reflected better wellness in 11 studies, [16][17][18]23,24,28,[32][33][34][35][36] and a lower score represented better wellness in 7 studies. 19,20,25,27,[29][30][31] The authors of 12 investigations † cited a 1995 review by Hooper and Mackinnon 6 in describing the design of their self-report measure: 4 sets of authors 17,18,20,31 labeled the summed wellness score the Hooper Index, and 1 set of authors 30 labeled it the Hooper Score (Table 4).…”

“…Thirteen studies explored (1) the relationship between pretraining wellness and subsequent load output (ie, same day) 16,18,21,24,26-28 and (2) the relationship between training load and subsequent wellness (ie, next day) 18,22,[32][33][34][35][36] ; whereas in 8 studies, 17,19,20,23,25,[29][30][31] the combination of data used for the correlation analysis (ie, whether they used same-day versus next-day data) was not clear. Researchers used a variety of derived variables, true values, changes in values and z scores, means and group means, normalization, and fixed and random effects in their analyses.…”