scholarly journals Monitoring and Behavior of Biomotor Skills in Futsal Athletes During a Season

2021 ◽  
Vol 12 ◽  
Author(s):  
Ricardo Stochi de Oliveira ◽  
João Paulo Borin
Keyword(s):  
Perceived Exertion ◽  
Aerobic Power ◽  
Training Load ◽  
Lower Limbs ◽  
Preparatory Period ◽  
Bonferroni Test ◽  
Displacement Speed ◽  
And Behavior ◽  
The Relationship ◽  
Training Organization

Futsal is a sport that presents alternation of high- and low-intensity moments, which lacks investigations regarding the effects of the organization of the training load on biomotor skills. In this sense, this study aims to verify the monitoring of the training load throughout the season and the behavior of biomotor skills in futsal athletes. Twelve futsal athletes (24.5 ± 4.9 years, 1.79 ± 0.6 m, 72.4 ± 9.4 kg, and 9.4 ± 4.3% fat) from the adult category, who competed in the first division of the Paulista championship, participated in the study. Throughout the season, the internal training load (ITL) was calculated, through the relationship between volume (minutes) and the rate of perceived exertion (RPE), monotony, and training strain. The training periods were divided into preparatory, competitive, and competitive II for a total of four moments of evaluation: M1: at the beginning of the preparatory period; M2: 50 week, at the end of the preparatory period; M3: 13th week, in the middle of the competitive period; and M4: at the start of the competitive period II. The tests used were: (i) Power of lower limbs: countermovement jump (CMJ); (ii) Displacement speed, over the 10-meter distance (V10 m), and (iii) Aerobic power by the Carminatti test (T-CAR). The variables analyzed were compared at different moments of evaluation using the two-way ANOVA completed by the Bonferroni test. For monotony, training strain, and ILT, the ANOVA ONE-WAY test was used between the evaluation periods completed by the Bonferroni test. The significance value adopted was p < 0.05. A significant improvement (p < 0.05) was observed in the power of lower limbs from M1 (37.5 ± 5.5 cm) to M3 (40.8 ± 5.7 cm), from M2 (38.9 ± 5.5 cm) to M3 (40.8 ± 5.7 cm), and from M1 (37.5 ± 5.5 cm) to M4 (40.2 ± 5.4 cm). Aerobic power showed a significant increase (p < 0.05) from M1 (12.1 ± 0.7 km/h) to M3 (12.7 ± 7 km/h) and from M1 (12.1 ± 0.7 km/h) to M4 (12.73 ± 1.04 km/h). The ITL showed a difference between competitive I and II in relation to the preparatory period (p < 0.05). In conclusion, the proposed training organization was sufficient to improve the power of the lower limbs and the aerobic power.

Technical and Tactical Training Load in Professional Volleyball Players

2019 ◽  
Vol 14 (10) ◽  
pp. 1338-1343
Author(s):  
Thiago S. Duarte ◽  
Danilo L. Alves ◽  
Danilo R. Coimbra ◽  
Bernardo Miloski ◽  
João C. Bouzas Marins ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Training Load ◽  
Technical Training ◽  
Rating Of Perceived Exertion ◽  
Different Types ◽  
Volleyball Players ◽  
Tactical Training ◽  
Training Impulse ◽  
The Relationship ◽  
And Training

Purpose: To analyze the technical and tactical training load in professional volleyball players, using subjective internal training load (session rating of perceived exertion  [SRPE]) and objective internal training load (training impulse of the heart rate [HR]) and the relationship between them. Methods: The sample was composed of 15 male professional volleyball players. They were monitored during 37 training sessions that included both technical (n = 23) and tactical (n = 14) training. Technical and training load was calculated using SRPE and training impulse of the HR. Results: Significant correlations were found between the methods in tactical (r = .616) and technical training (r = −.414). Furthermore, it was noted that technical training occurs up to 80% of HRmax (zone 3) and tactical training between 70% and 90% of HRmax (zones 3–4). Conclusions: The training impulse of the HR method has proved to be effective for training-load control during tactical training. However, it was limited compared with technical training. Thus, the use of SRPE is presented as a more reliable method in the different types of technical training in volleyball.

scholarly journals Relationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load

2019 ◽  
Vol 9 (23) ◽  
pp. 5174
Author(s):  
Alessio Rossi ◽  
Enrico Perri ◽  
Luca Pappalardo ◽  
Paolo Cintia ◽  
F. Iaia
Keyword(s):  
Perceived Exertion ◽  
Selection Process ◽  
Team Sports ◽  
Training Load ◽  
Soccer Players ◽  
Gps Tracking ◽  
Individual Response ◽  
Rate Of Perceived Exertion ◽  
The Relationship ◽  
And Training

The use of machine learning (ML) in soccer allows for the management of a large amount of data deriving from the monitoring of sessions and matches. Although the rate of perceived exertion (RPE), training load (S-RPE), and global position system (GPS) are standard methodologies used in team sports to assess the internal and external workload; how the external workload affects RPE and S-RPE remains still unclear. This study explores the relationship between both RPE and S-RPE and the training workload through ML. Data were recorded from 22 elite soccer players, in 160 training sessions and 35 matches during the 2015/2016 season, by using GPS tracking technology. A feature selection process was applied to understand which workload features influence RPE and S-RPE the most. Our results show that the training workloads performed in the previous week have a strong effect on perceived exertion and training load. On the other hand, the analysis of our predictions shows higher accuracy for medium RPE and S-RPE values compared with the extremes. These results provide further evidence of the usefulness of ML as a support to athletic trainers and coaches in understanding the relationship between training load and individual-response in team sports.

Relationship Between Pretraining Subjective Wellness Measures, Player Load, and Rating-of-Perceived-Exertion Training Load in American College Football

2018 ◽  
Vol 13 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Andrew D. Govus ◽  
Aaron Coutts ◽  
Rob Duffield ◽  
Andrew Murray ◽  
Hugh Fullagar
Keyword(s):  
Muscle Soreness ◽  
Perceived Exertion ◽  
Training Session ◽  
College Football ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Football Players ◽  
Unit Increase ◽  
Relationship Of ◽  
The Relationship

Context:The relationship between pretraining subjective wellness and external and internal training load in American college football is unclear.Purpose:To examine the relationship of pretraining subjective wellness (sleep quality, muscle soreness, energy, wellness Z score) with player load and session rating of perceived exertion (s-RPE-TL) in American college football players.Methods:Subjective wellness (measured using 5-point, Likert-scale questionnaires), external load (derived from GPS and accelerometry), and s-RPE-TL were collected during 3 typical training sessions per week for the second half of an American college football season (8 wk). The relationship of pretraining subjective wellness with player load and s-RPE training load was analyzed using linear mixed models with a random intercept for athlete and a random slope for training session. Standardized mean differences (SMDs) denote the effect magnitude.Results:A 1-unit increase in wellnessZscore and energy was associated with trivial 2.3% (90% confidence interval [CI] 0.5, 4.2; SMD 0.12) and 2.6% (90% CI 0.1, 5.2; SMD 0.13) increases in player load, respectively. A 1-unit increase in muscle soreness (players felt less sore) corresponded to a trivial 4.4% (90% CI −8.4, −0.3; SMD −0.05) decrease in s-RPE training load.Conclusion:Measuring pretraining subjective wellness may provide information about players’ capacity to perform in a training session and could be a key determinant of their response to the imposed training demands American college football. Hence, monitoring subjective wellness may aid in the individualization of training prescription in American college football players.

Sport-Specific Outdoor Rehabilitation in a Group Setting: Do the Intentions Match Actual Training Load?

2018 ◽  
Vol 27 (2) ◽  
pp. 151-156 ◽  
Author(s):  
Jeroen de Bruijn ◽  
Henk van der Worp ◽  
Mark Korte ◽  
Astrid de Vries ◽  
Rick Nijland ◽  
...  
Keyword(s):  
Heart Rate ◽  
Perceived Exertion ◽  
Training Load ◽  
Soccer Players ◽  
Group Setting ◽  
Ratings Of Perceived Exertion ◽  
Lower Limb Injury ◽  
Distance Data ◽  
Group Rehabilitation ◽  
The Relationship

Context: Previous research has shown a weak relationship between intended and actual training load in various sports. Due to variety in group and content, this relationship is expected to be even weaker during group rehabilitation. Objective: The goal of this study was to examine the relationship between intended and actual training load during sport-specific rehabilitation in a group setting. Design: Observational study. Setting: Three outdoor rehabilitation sessions. Participants: Nine amateur soccer players recovering from lower limb injury participated in the study (age 22 ± 3 y, height 179 ± 9 cm, body mass 75 ± 13 kg). Main Outcome Measures: We collected physiotherapists’ ratings of intended exertion (RIE) and players’ ratings of perceived exertion (RPE). Furthermore, Zephyr Bioharness 3 equipped with GPS-trackers provided heart rate and distance data. We computed heart rate–based training loads using Edwards’ method and a modified TRIMP. Results: Overall, we found weak correlations (N = 42) between RIE and RPE (r = 0.35), Edwards’ (r = 0.34), TRIMPMOD (r = 0.07), and distance (r = 0.26). Conclusions: In general, physiotherapists tended to underestimate training loads. To check whether intended training loads are met, it is thus recommended to monitor training loads during rehabilitation.

Relationship Between Various Training-Load Measures in Elite Cyclists During Training, Road Races, and Time Trials

2019 ◽  
Vol 14 (4) ◽  
pp. 493-500 ◽  
Author(s):  
Teun van Erp ◽  
Carl Foster ◽  
Jos J. de Koning
Keyword(s):  
Perceived Exertion ◽  
Mechanical Energy ◽  
Training Load ◽  
The Other ◽  
Rating Of Perceived Exertion ◽  
Heart Rate Data ◽  
Training Stress ◽  
Training Impulse ◽  
Road Races ◽  
The Relationship

Purpose: The relationship between various training-load (TL) measures in professional cycling is not well explored. This study investigated the relationship between mechanical energy spent (in kilojoules), session rating of perceived exertion (sRPE), Lucia training impulse (LuTRIMP), and training stress score (TSS) in training, races, and time trials (TT). Methods: For 4 consecutive years, field data were collected from 21 professional cyclists and categorized as being collected in training, racing, or TTs. Kilojoules (kJ) spent, sRPE, LuTRIMP, and TSS were calculated, and the correlations between the various TLs were made. Results: 11,655 sessions were collected, from which 7596 sessions had heart-rate data and 5445 sessions had an RPE score available. The r between the various TLs during training was almost perfect. The r between the various TLs during racing was almost perfect or very large. The r between the various TLs during TTs was almost perfect or very large. For all relationships between TSS and 1 of the other measurements of TL (kJ spent, sRPE, and LuTRIMP), a significant different slope was found. Conclusion: kJ spent, sRPE, LuTRIMP, and TSS all have a large or almost perfect relationship with each other during training, racing, and TTs, but during racing, both sRPE and LuTRIMP have a weaker relationship with kJ spent and TSS. Furthermore, the significant different slope of TSS vs the other measurements of TL during training and racing has the effect that TSS collected in training and road races differs by 120%, whereas the other measurements of TL (kJ spent, sRPE, and LuTRIMP) differ by only 73%, 67%, and 68%, respectively.

Quantifying Training Load: A Comparison of Subjective and Objective Methods

2008 ◽  
Vol 3 (1) ◽  
pp. 16-30 ◽  
Author(s):  
Jill Borresen ◽  
Michael I. Lambert
Keyword(s):  
Heart Rate ◽  
High Intensity ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Regression Equations ◽  
Training Time ◽  
Ad Libitum ◽  
Training Impulse ◽  
The Relationship

Purpose:To establish the relationship between a subjective (session rating of perceived exertion [RPE]) and 2 objective (training impulse [TRIMP]) and summated-heart-rate-zone (SHRZ) methods of quantifying training load and explain characteristics of the variance not accounted for in these relationships.Methods:Thirty-three participants trained ad libitum for 2 wk, and their heart rate (HR) and RPE were recorded to calculate training load. Subjects were divided into groups based on whether the regression equations over- (OVER), under- (UNDER), or accurately predicted (ACCURATE) the relationship between objective and subjective methods.Results:A correlation of r = .76 (95% CI: .56 to .88) occurred between TRIMP and session-RPE training load. OVER spent a greater percentage of training time in zone 4 of SHRZ (ie, 80% to 90% HRmax) than UNDER (46% ± 8% vs 25% ± 10% [mean ± SD], P = .008). UNDER spent a greater percentage of training time in zone 1 of SHRZ (ie, 50% to 60% HRmax) than OVER (15% ± 8% vs 3% ± 3%, P = .005) and ACCURATE (5% ± 3%, P = .020) and more time in zone 2 of SHRZ (ie, 60% to 70%HRmax) than OVER (17% ± 6% vs 7% ± 6%, P = .039). A correlation of r = .84 (.70 to .92) occurred between SHRZ and session-RPE training load. OVER spent proportionally more time in Zone 4 than UNDER (45% ± 8% vs 25% ± 10%, P = .018). UNDER had a lower training HR than ACCURATE (132 ± 10 vs 148 ± 12 beats/min, P = .048) and spent more time in zone 1 than OVER (15% ± 8% vs 4% ± 3%, P = .013) and ACCURATE (5% ± 3%, P = .015).Conclusions:The session-RPE method provides reasonably accurate assessments of training load compared with HR-based methods, but they deviate in accuracy when proportionally more time is spent training at low or high intensity.

scholarly journals Quantifying Training Load During Physically Demanding Tasks in U.S. Army Soldiers: A Comparison of Physiological and Psychological Measurements

2020 ◽  
Vol 185 (5-6) ◽  
pp. e847-e852
Author(s):  
Maria C Canino ◽  
Stephen A Foulis ◽  
Bruce S Cohen ◽  
Leila A Walker ◽  
Kathryn M Taylor ◽  
...  
Keyword(s):  
Heart Rate ◽  
Indirect Calorimetry ◽  
Perceived Exertion ◽  
Training Load ◽  
Maximal Heart Rate ◽  
Rank Tests ◽  
Institutional Review ◽  
Signed Rank ◽  
Positive Correlations ◽  
The Relationship

Abstract Introduction There are many ways to quantify the training loads required to perform soldiering tasks. Although indirect calorimetry may provide the most accurate measures, the equipment can be burdensome and expensive. Simpler measures may provide sufficient data, while being more practical for measuring soldiers in the field. The purpose of this study was to examine the relationship between total relative oxygen uptake (TotalRelVO2) measured by indirect calorimetry during three soldiering tasks, with two field-expedient measures of training load: summated heart rate zone (sumHR) and session rate of perceived exertion (sRPE). Materials and Methods 33 male and 28 female soldiers performed three soldiering tasks while wearing a 32.3-kg fighting load: sandbag fill, sandbag carry, and ammunition can carry. Metabolic measurements were monitored and completion times were recorded (min). TotalRelVO2 (average relative VO2*time) and age-predicted maximal heart rate (220-age) were calculated. SumHR was calculated by multiplying time spent in each of the five heart rate zones by a multiplier factor for each zone (50–59% = 1, 60–69% = 2, 70–79% = 3, 80–89% = 4, and ≥90% = 5). RPE (Borg 6–20 scale) was collected at the end of each task, then sRPE was calculated (RPE*time). Pearson and Spearman correlations were performed to examine the relationship between TotalRelVO2, sumHR and sRPE. Wilcoxon signed rank tests were conducted to determine if there was a difference in median rankings between the three variables for each task. Linear regressions were performed to determine predictability of TotalRelVO2 from sumHR and sRPE. The study was approved by the U.S. Army Research Institute of Environmental Medicine Institutional Review Board. Results Significant, positive correlations were revealed for all three tasks between TotalRelVO2, sumHR and sRPE (r ≥ 0.67, p ≤ 0.01; rho≥0.74, p ≤ 0.01). Wilcoxon signed rank tests revealed no significant differences in rankings between TotalRelVO2, sumHR and sRPE for all three tasks (p ≥ 0.43). Both sumHR and sRPE are significant predictors of TotalRelVO2 (p ≤ 0.01). Conclusions SumHR and sRPE are acceptable alternatives to TotalRelVO2 when attempting to quantify and/or monitor training load during soldiering tasks.

The Relationship Between Training Load and Injury in Men’s Professional Basketball

2017 ◽  
Vol 12 (9) ◽  
pp. 1238-1242 ◽  
Author(s):  
Kaitlyn J. Weiss ◽  
Sian V. Allen ◽  
Mike R. McGuigan ◽  
Chris S. Whatman
Keyword(s):  
Regression Model ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Self Report ◽  
Basketball Players ◽  
Professional Basketball ◽  
Trauma Research ◽  
Mean Differences ◽  
The Relationship

Purpose:To establish the relationship between the acute:chronic workload ratio and lower-extremity overuse injuries in professional basketball players over the course of a competitive season. Methods:The acute:chronic workload ratio was determined by calculating the sum of the current week’s session rating of perceived exertion of training load (acute load) and dividing it by the average weekly training load over the previous 4 wk (chronic load). All injuries were recorded weekly using a self-report injury questionnaire (Oslo Sports Trauma Research Center Injury Questionnaire20). Workload ratios were modeled against injury data using a logistic-regression model with unique intercepts for each player. Results:Substantially fewer team members were injured after workload ratios of 1 to 1.49 (36%) than with very low (≤0.5; 54%), low (0.5–0.99; 51%), or high (≥1.5; 59%) workload ratios. The regression model provided unique workload–injury trends for each player, but all mean differences in likelihood of being injured between workload ratios were unclear. Conclusions:Maintaining workload ratios of 1 to 1.5 may be optimal for athlete preparation in professional basketball. An individualized approach to modeling and monitoring the training load–injury relationship, along with a symptom-based injury-surveillance method, should help coaches and performance staff with individualized training-load planning and prescription and with developing athlete-specific recovery and rehabilitation strategies.

Seasonal Training Load and Wellness Monitoring in a Professional Soccer Goalkeeper

2018 ◽  
Vol 13 (5) ◽  
pp. 672-675 ◽  
Author(s):  
James J. Malone ◽  
Arne Jaspers ◽  
Werner Helsen ◽  
Brenda Merks ◽  
Wouter G.P. Frencken ◽  
...  
Keyword(s):  
Global Positioning System ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Total Distance ◽  
Professional Soccer ◽  
Competitive Season ◽  
Global Positioning ◽  
The Relationship

The purpose of this investigation was to (1) quantify the training load practices of a professional soccer goalkeeper and (2) investigate the relationship between the training load observed and the subsequent self-reported wellness response. One male goalkeeper playing for a team in the top league of the Netherlands participated in this case study. Training load data were collected across a full season using a global positioning system device and session-RPE (rating of perceived exertion). Data were assessed in relation to the number of days to a match (MD− and MD+). In addition, self-reported wellness response was assessed using a questionnaire. Duration, total distance, average speed, PlayerLoad™, and load (derived from session-RPE) were highest on MD. The lowest values for duration, total distance, and PlayerLoad™ were observed on MD−1 and MD+1. Total wellness scores were highest on MD and MD−3 and were lowest on MD+1 and MD−4. Small to moderate correlations between training load measures (duration, total distance covered, high deceleration efforts, and load) and the self-reported wellness response scores were found. This exploratory case study provides novel data about the physical load undertaken by a goalkeeper during 1 competitive season. The data suggest that there are small to moderate relationships between training load indicators and self-reported wellness response. This weak relation indicates that the association is not meaningful. This may be due to the lack of position-specific training load parameters that practitioners can currently measure in the applied context.

Relationship Between Wellness Index and Internal Training Load in Soccer: Application of a Machine Learning Model

2020 ◽  
pp. 1-9
Author(s):  
Enrico Perri ◽  
Carlo Simonelli ◽  
Alessio Rossi ◽  
Athos Trecroci ◽  
Giampietro Alberti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Perceived Exertion ◽  
Training Load ◽  
Supervised Machine Learning ◽  
Training Time ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Learning Technique ◽  
The One ◽  
The Relationship

Purpose: To investigate the relationship between the training load (TL = rate of perceived exertion × training time) and wellness index (WI) in soccer. Methods: The WI and TL data were recorded from 28 subelite players (age = 20.9 [2.4] y; height = 181.0 [5.8] cm; body mass = 72.0 [4.4] kg) throughout the 2017/2018 season. Predictive models were constructed using a supervised machine learning method that predicts the WI according to the planned TL. The validity of our predictive model was assessed by comparing the classification’s accuracy with the one computed from a baseline that randomly assigns a class to an example by respecting the distribution of classes (B1). Results: A higher TL was reported after the games and during match day (MD)-5 and MD-4, while a higher WI was recorded on the following days (MD-6, MD-4, and MD-3, respectively). A significant correlation was reported between daily TL (TLMDi) and WI measured the day after (WIMDi+1) (r = .72, P < .001). Additionally, a similar weekly pattern seems to be repeating itself throughout the season in both TL and WI. Nevertheless, the higher accuracy of ordinal regression (39% [2%]) compared with the results obtained by baseline B1 (21% [1%]) demonstrated that the machine learning approach used in this study can predict the WI according to the TL performed the day before (MD<i). Conclusion: The machine learning technique can be used to predict the WI based on a targeted weekly TL. Such an approach may contribute to enhancing the training-induced adaptations, maximizing the players’ readiness and reducing the potential drops in performance associated with poor wellness scores.

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