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.

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.

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.

Player Session Rating of Perceived Exertion: A More Valid Tool Than Coaches’ Ratings to Monitor Internal Training Load in Elite Youth Female Basketball

2020 ◽  
Vol 15 (4) ◽  
pp. 548-553 ◽  
Author(s):  
Corrado Lupo ◽  
Alexandru Nicolae Ungureanu ◽  
Riccardo Frati ◽  
Matteo Panichi ◽  
Simone Grillo ◽  
...  
Keyword(s):  
Body Mass ◽  
Perceived Exertion ◽  
Team Sports ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Basketball Players ◽  
Mixed Effect ◽  
Maximal Intensity ◽  
Session Duration ◽  
Post Hoc

Purpose: To monitor elite youth female basketball training to verify whether players’ and coaches’ (3 technical coaches and 1 physical trainer) session rating of perceived exertion (s-RPE) has a relationship with Edwards’ method. Methods: Heart rate of 15 elite youth female basketball players (age 16.7 [0.5] y, height 178 [9] cm, body mass 72 [9] kg, body mass index 22.9 [2.2] kg·m−2) was monitored during 19 team (268 individual) training sessions (102 [15] min). Mixed effect models were applied to evaluate whether s-RPE values were significantly (P ≤ .05) related to Edwards’ data, total session duration, maximal intensity (session duration at 90–100% HRmax), type of training (ie, strength, conditioning, and technique), and whether differences emerged between players’ and coaches’ s-RPE values. Results: The results showed that there is a relationship between s-RPE and Edwards’ methods for the players’ RPE scores (P = .019) but not for those of the trainers. In addition, as expected, both players’ (P = .014) and coaches’ (P = .002) s-RPE scores were influenced by total session duration but not by maximal intensity and type of training. In addition, players’ and coaches’ s-RPE values differed (P < .001)—post hoc differences emerged for conditioning (P = .01) and technique (P < .001) sessions. Conclusions: Elite youth female basketball players are better able to quantify the internal training load of their sessions than their coaches, strengthening the validity of s-RPE as a tool to monitor training in team sports.

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.

Monitoring Training Load and Well-Being During the In-Season Phase in National Collegiate Athletic Association Division I Men’s Basketball

2018 ◽  
Vol 13 (8) ◽  
pp. 1067-1074 ◽  
Author(s):  
Daniele Conte ◽  
Nicholas Kolb ◽  
Aaron T. Scanlan ◽  
Fabrizio Santolamazza
Keyword(s):  
Perceived Exertion ◽  
National Collegiate Athletic Association ◽  
Well Being ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Division I ◽  
Basketball Players ◽  
Men's Basketball ◽  
College Basketball ◽  
Basketball Coaches

Purpose: To characterize the weekly training load (TL) and well-being of college basketball players during the in-season phase. Methods: Ten (6 guards and 4 forwards) male basketball players (age 20.9 [0.9] y, stature 195.0 [8.2] cm, and body mass 91.3 [11.3] kg) from the same Division I National Collegiate Athletic Association team were recruited to participate in this study. Individualized training and game loads were assessed using the session rating of perceived exertion at the end of each training and game session, and well-being status was collected before each session. Weekly changes (%) in TL, acute-to-chronic workload ratio, and well-being were determined. Differences in TL and well-being between starting and bench players and between 1-game and 2-game weeks were calculated using magnitude-based statistics. Results: Total weekly TL and acute-to-chronic workload ratio demonstrated high week-to-week variation, with spikes up to 226% and 220%, respectively. Starting players experienced a higher (most likely negative) total weekly TL and similar (unclear) well-being status compared with bench players. Game scheduling influenced TL, with 1-game weeks demonstrating a higher (likely negative) total weekly TL and similar (most likely trivial) well-being status compared with 2-game weeks. Conclusions: These findings provide college basketball coaches information to optimize training strategies during the in-season phase. Basketball coaches should concurrently consider the number of weekly games and player status (starting vs bench player) when creating individualized periodization plans, with increases in TL potentially needed in bench players, especially in 2-game weeks.

scholarly journals Differences in Weekly Load Distribution Over Two Euroleague Seasons with a Different Head Coach

2020 ◽  
Vol 17 (8) ◽  
pp. 2812
Author(s):  
Hugo Salazar ◽  
Luka Svilar ◽  
Ane Aldalur-Soto ◽  
Julen Castellano
Keyword(s):  
External Load ◽  
Perceived Exertion ◽  
Management Program ◽  
Rating Of Perceived Exertion ◽  
Load Management ◽  
Head Coach ◽  
Basketball Players ◽  
Professional Basketball ◽  
Coaching Strategies ◽  
Weekly Training

The weekly training management and competition loads are important aspects to optimize the performance of professional basketball players. The objectives of the study were (a) to describe the weekly external load (EL), as well as the internal response (IR), of elite basketball players over two consecutive seasons with a different head coach and (b) to compare weekly loads of different competitive densities. The data were collected from 27 elite players from the same team competing in the Spanish first division league (ACB) and EuroLeague during 2017–2018 and 2018–2019 seasons. EL was measured using microsensor technology to determine PlayerLoad values, expressed in arbitrary units (AU). Session rating of perceived exertion (sRPE) was used for IR quantification. Comparisons between the two seasons and of weeks with different competitive densities were made. The inter-week load variability was moderate-high for both seasons. The highest EL values were measured during the weeks with three games (W3) (W3 > W0 > W2 > W1), while the most demanding week for players’ IR was observed during weeks with no competition (W0). Additionally, higher EL (d = 0.31) and IR (d = 0.37) values were observed in season 2018–2019 compared to 2017–2018. The results obtained in this study contributed new data on the internal and external load required by professional basketball players in weeks with different number of games and showed that different coaching strategies may demand a different external and internal workload in consecutive seasons. Furthermore, the results highlighted the need to carry out an adequate load management program.

scholarly journals Can the talk test be used to predict training induced changes in ventilatory threshold?

Kinesiology ◽  
2020 ◽  
Vol 52 (2) ◽  
pp. 163-168
Author(s):  
Kristen M. Deal ◽  
Carl Foster ◽  
Salvador Jaime ◽  
Richard P. Mikat ◽  
Kim Radtke ◽  
...  
Keyword(s):  
Gas Exchange ◽  
Repeated Measures ◽  
Perceived Exertion ◽  
Ventilatory Threshold ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Exercise Tests ◽  
Before And After ◽  
Mean Differences ◽  
The Individual

This study was designed to assess the ability of the Talk Test (TT) to track training-related changes in ventilatory threshold (VT). Thirteen recreational athletes (20.5±1.91 years, males=7) completed two incremental exercise tests (one with respiratory gas exchange and one with the TT) before and after six weeks of self-directed increases in training load. The TT was used to predict VT by assessing the ability to speak comfortably after three-minute exercise stages, based on speech comfort while reciting a 100-word passage. Training load was documented from exercise logs based on session rating of perceived exertion (sRPE) and training duration. Repeated measures ANOVA, with the Tukey’s post-hoc analysis, was used to detect differences between the changes in power output (PO) at the equivocal stage of the Talk Test (EQ) and VT measured by gas exchange (p&lt;.05). Significant mean differences were found between pre- vs. post-training PO and measured VT (116+32.4 vs. 134+32.4 Watts) (p&lt;.05) but not at the EQ stage of the TT (125+40.8 vs. 135+29.8 Watts). The increase in PO at VT (+15.5%) was significantly underestimated by the change in PO at the EQ stage of the TT (+8.0%). The correlation between changes in PO at VT and PO at the EQ stage of the TT was r=0.66, p&lt;.01. However, about 50% of participants did not change their PO at the EQ stage of the TT, so the individual correspondence between TT and measured VT was only moderately strong.

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.

scholarly journals Impaired Player–Coach Perceptions of Exertion and Recovery During Match Congestion

2017 ◽  
Vol 12 (9) ◽  
pp. 1151-1156 ◽  
Author(s):  
Steven H. Doeven ◽  
Michel S. Brink ◽  
Wouter G.P. Frencken ◽  
Koen A.P.M. Lemmink
Keyword(s):  
Perceived Exertion ◽  
Training Session ◽  
Rating Of Perceived Exertion ◽  
Total Quality ◽  
Basketball Players ◽  
Subsequent Recovery ◽  
Professional Basketball ◽  
Quality Of Recovery ◽  
Degree Of Recovery

During intensified phases of competition, attunement of exertion and recovery is crucial to maintain performance. Although a mismatch between coach and player perceptions of training load is demonstrated, it is unknown if these discrepancies also exist for match exertion and recovery. Purpose:To determine match exertion and subsequent recovery and to investigate the extent to which the coach is able to estimate players’ match exertion and recovery. Methods:Rating of perceived exertion (RPE) and total quality of recovery (TQR) of 14 professional basketball players (age 26.7 ± 3.8 y, height 197.2 ± 9.1 cm, weight 100.3 ± 15.2 kg, body fat 10.3% ± 3.6%) were compared with observations of the coach. During an in-season phase of 15 matches within 6 wk, players gave RPEs after each match. TQR scores were filled out before the first training session after the match. The coach rated observed exertion (ROE) and recovery (TQ-OR) of the players. Results:RPE was lower than ROE (15.6 ± 2.3 and 16.1 ± 1.4; P = .029). Furthermore, TQR was lower than TQ-OR (12.7 ± 3.0 and 15.3 ± 1.3; P < .001). Correlations between coach- and player-perceived exertion and recovery were r = .25 and r = .21, respectively. For recovery within 1 d the correlation was r = .68, but for recovery after 1–2 d no association existed. Conclusion:Players perceive match exertion as hard to very hard and subsequent recovery reasonable. The coach overestimates match exertion and underestimates degree of recovery. Correspondence between coach and players is thus not optimal. This mismatch potentially leads to inadequate planning of training sessions and decreases in performance during fixture congestion in basketball.

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