The aim of this study was two-fold: (1) to analyze the variations of acute load, training monotony, and training strain among early (pre-season), mid (first half of season), and end season (second half of season) periods; (2) to compare these training indicators for playing positions in different moments of the season. Nineteen professional players (age: 26.5 ± 4.3 years; experience as professional: 7.5 ± 4.3 years) from a European First League team participated in this study. The players were monitored daily over a 45-week period for the total distance (TD), distance covered (DC) at 14 km/h−1 or above (DC > 14 km/h), high-speed running above 19.8 km/h−1 (HSR) distance, and number of sprints above 25.2 km/h−1. The acute load (sum of load during a week), training monotony (mean of training load during the seven days of the week divided by the standard deviation of the training load of the seven days), and training strain (sum of the training load for all training sessions and matches during a week multiplied by training monotony) workload indices were calculated weekly for each measure and per player. Results revealed that training monotony and training strain for HSR were meaningfully greater in pre-season than in the first half of the in-season (p ≤ 0.001; d = 0.883 and p ≤ 0.001; d = 0.712, respectively) and greater than the second half of the in-season (p ≤ 0.001; d = 0.718 and p ≤ 0.001; d = 0.717). The training monotony for the sprints was meaningfully greater in pre-season than in the first half of in-season (p < 0.001; d = 0.953) and greater than the second half of in-season (p ≤ 0.001; d = 0.916). Comparisons between playing positions revealed that small-to-moderate effect sizes differences mainly for the number of sprints in acute load, training monotony, and training strain. In conclusion, the study revealed that greater acute load, training monotony, and training strain occurred in the pre-season and progressively decreased across the season. Moreover, external defenders and wingers were subjected to meaningfully greater acute load and training strain for HSR and number of sprints during the season compared to the remaining positions.

The aim of this study was to provide reference data of variation in external training loads for weekly periods within the annual season. Specifically, we aimed to compare the weekly acute load, monotony, and training strain of accelerometry-based measures across a professional soccer season (pre-season, first and second halves of the season) according to players’ positions. Nineteen professional players were monitored daily for 45 weeks using an 18-Hz global positioning system to obtain measures of high metabolic load distance (HMLD), impacts, and high intensity accelerations and decelerations. Workload indices of acute load, training monotony, and training strain were calculated weekly for each of the measures. The HMLD had greater training strain values in the pre-season than in the first (p ≤ 0.001; d = 0.793) and second halves of the season (p ≤ 0.001; d = 0.858). Comparisons between playing positions showed that midfielders had the highest weekly acute load of HMLD (6901 arbitrary units [AU]), while central defenders had the lowest (4986 AU). The pre-season period was associated with the highest acute and strain load of HMLD and number of impacts, with a progressive decrease seen during the season. In conclusion, coaches should consider paying greater attention to variations in HMLD and impacts between periods of the season and between players to individualize training accordingly.

The aim of this study was to analyze the variations of acute load (AL), acute: chronic workload ratio (ACWR), training monotony (TM), and training strain (TS) of accelerometry-based GPS measures in players who started in three matches (S3M), two matches (S2M), and one match (S1M) during congested weeks. Nineteen elite professional male players from a Portuguese team (age: 26.5 ± 4.3 years) were monitored daily using global positioning systems (GPSs) over a full season (45 weeks). Accelerometry-derived measures of high metabolic load distance (HMLD), high accelerations (HA), and high decelerations (HD) were collected during each training session and match. Seven congested weeks were classified throughout the season, and the participation of each player in matches played during these weeks was codified. The workload indices of AL (classified as ACWR, TM, and TS) were calculated weekly for each player. The AL of HMLD was significantly greater for S2M than S1M (difference = 42%; p = 0.002; d = 0.977) and for S3M than S1M (difference = 44%; p = 0.001; d = 1.231). Similarly, the AL of HA was significantly greater for S2M than S1M (difference = 25%; p = 0.023; d = 0.735). The TM of HD was significantly greater for S2M than S3M (difference = 25%; p = 0.002; d = 0.774). Accelerometry-based measures were dependent on congested fixtures. S2M had the greatest TS values, while S3M had the greatest TM.

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The aims of this study were: (i) to describe weekly variations of acute load (AL), acute:chronic workload ratio, delayed onset muscle soreness (DOMS), and fatigue; (ii) to analyze variations of weekly workload and well-being in three periods of the season (P1, P2, and P3); and (iii) to analyze the relationships between workload and well-being measures. Fifteen professional basketball players from a first-league European club were monitored throughout the season using the CR-10 Borg scale and the Hooper questionnaire. Weekly AL and acute:chronic workload ratio (ACWR) were weekly calculated for monitoring of the internal load. In addition, DOMS and fatigue values were weekly calculated. Greater AL, DOMS, and fatigue values were found during the early season, and the highest ACWR value was found during the second period. Overall, AL presented large correlations with DOMS (r=0.60) and fatigue (r=0.62). The results of this study indicate that load is higher in the first period and then decreases throughout the season. The results also showed that AL is more closely related to well-being parameters than ACWR.

The purpose of this study was to compare the variations of weekly workload indices of internal and external load measures across the three weeks prior to injury occurrences in trail runners. Twenty-five trail runners (age: 36.23 ± 8.30 years old; body mass: 67.24 ± 5.97 kg; height: 172.12 ± 5.12 cm) were monitored daily for 52 weeks using global positioning systems (GPSs) to determine the total distance covered. Additionally, a rate of perceived exertion (RPE) scale was applied to determine session-RPE (sRPE: RPE multiplied by training time). The accumulated load (AL), acute: chronic workload ratio (ACWR), training monotony (TM), and training strain (TS) indices were calculated weekly for each runner. During the period of analysis, the injury occurrences were recorded. The differences were observed in AL and ACWR for sRPE and training time were significantly greater during the injury week when compared to the previous weeks. Similar evidence was found in TM and TS indices for sRPE, training time, and total distance. Furthermore, no meaningful differences were observed in AL and ACWR for total distance in the weeks prior to injury occurrence. Nevertheless, significant between-subjects variability was found, and this should be carefully considered. For that reason, an individualized analysis of the workload dynamics is recommended, avoiding greater spikes in load by aiming to keep a progressive increment of load without consequences for injury risk.

The purposes of this study were (1) to analyze between-session variations of external and internal load measures during small-sided games (SSGs) and (2) to test the relationships between the maximum speed reached (VIFT) during the last stage of the 30-15 Intermittent Fitness Test, hemoglobin levels, and training load measures during SSG intervals among professional soccer players. Sixteen professional soccer players (mean ± SD; age 27.2 ± 3.4 years, height 174.2 ± 3.6 cm, body mass 69.1 ± 6.4 kg, and body fat 10.4 ± 4.1%) participated in this study. Hemoglobin and aerobic performance were first tested, and then a 3-week SSG program was applied using a 3 vs. 3 format. During those 3 weeks, internal and external load of entire sessions were also monitored for all training sessions. Trivial-to-small, standardized differences were observed between sessions for external and internal measures during SSGs. Total distance (TD) and mechanical work (MW) were the only variables that indicated small changes. Large-to-very-large relationships were found between VIFT and external loads: TD (r range: 0.69; 0.87), high-intensity running (HIR; r range: 0.66; 0.75), and MW (r range: 0.56; 0.68). Moderate-to-large negative relationships were found between hemoglobin levels and internal loads: Edwards’ TRIMP (r range: −0.36; −0.63), %HRmax (r range: −0.50; −0.61), and red zone (r range: −0.50; −0.61). VIFT had unclear relationships with overall internal loads, while hemoglobin levels presented unclear relationships with overall external loads. In conclusion, no meaningful changes were found between sessions considering the format of play used. Additionally, the detected relationships indicate that VIFT and hemoglobin levels are good indicators of the performance capacity and physiological profile of players during SSGs. Also, the use of SSGs protocols as a monitoring complement of the 30-15IFT is suggested.

Purpose The purposes of this study were to describe the fitness and hormonal levels according to playing time (PT) (i.e., PT during season less (PT1) or more (PT2) than 50% of the total time) and maturation level (ML) (i.e., normal (ML1) and early maturity levels (ML2)), and to analyze the differences between groups for the measures of aerobic capacity, anaerobic power, power performance, and hormonal concentrations. Methods Twenty-four youth footballers of a U16 team participated in this study. Anthropometric measures, maturity status, growth hormone, insulin-like growth factor (IGF-1), maximal oxygen uptake, fatigue index, and countermovement jump were collected. Results Significant differences were found between both PT and ML groups for maturational status, aerobic capacity, power performance, and IGF1 concentrations. The interaction of PT and ML revealed significant differences for maturity offset and power performance. When using the skeletal age as a covariant, the previously significant differences found were reduced only to the fatigue index measure. Conclusions The response variables analyzed in the present study seem to be influenced by PT and ML. This must be considered when planning training, and coaches must be sensible to these effects as they may assume a preponderant role in PT.