Within-Subject Correlation Between Salivary IgA and Measures of Training Load in Elite Football Players

2019 ◽  
Vol 14 (6) ◽  
pp. 847-849 ◽  
Author(s):  
Pedro Figueiredo ◽  
George P. Nassis ◽  
João Brito
Keyword(s):  
High Speed ◽  
Perceived Exertion ◽  
Immunoglobulin A ◽  
Correlation Coefficients ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Football Players ◽  
Positioning Systems ◽  
External Training ◽  
Salivary Secretory Immunoglobulin A

Purpose: To quantify the association between salivary secretory immunoglobulin A (sIgA) and training load in elite football players. Methods: Data were obtained on 4 consecutive days during the preparation camp for the Rio 2016 Olympic Games. Saliva samples of 18 elite male football players were collected prior to breakfast. The session rating of perceived exertion (s-RPE) and external training-load metrics from global positioning systems (GPS) were recorded. Within-subject correlation coefficients between training load and sIgA concentration, and magnitude of relationships, were calculated. Results: sIgA presented moderate to large negative correlations with s-RPE (r = −.39), total distance covered (r = −.55), accelerations (r = −.52), and decelerations (r = −.48). Trivial to small associations were detected between sIgA and distance covered per minute (r = .01), high-speed distance (r = −.23), and number of sprints (r = −.18). sIgA displayed a likely moderate decrease from day 1 to day 2 (d = −0.7) but increased on day 3 (d = 0.6). The training-load variables had moderate to very large rises from day 1 to day 2 (d = 0.7 to 3.2) but lowered from day 2 to day 3 (d = −5.0 to −0.4), except for distance per minute (d = 0.8) and sprints (unclear). On day 3, all training-load variables had small to large increments compared with day 1 (d = 0.4 to 1.5), except for accelerations (d = −0.8) and decelerations (unclear). Conclusions: In elite football, sIgA might be more responsive to training volume than to intensity. External load such as GPS-derived variables presented stronger association with sIgA than with s-RPE. sIgA can be used as an additional objective tool in monitoring football players.

scholarly journals In-season internal and external training load quantification of an elite European soccer team

2018 ◽  
Author(s):  
Rafael Soares Oliveira ◽  
João Paulo Brito ◽  
Alexandre Martins ◽  
Bruno Mendes ◽  
Francisco Calvete ◽  
...  
Keyword(s):  
High Speed ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Soccer Players ◽  
Soccer Teams ◽  
External Training ◽  
Daily Training ◽  
Player Positions ◽  
Level Of Effort

Elite soccer teams that participate in European competitions often have a difficult schedule, involving weeks in which they play up to three matches, which leads to acute and transient subjective, biochemical, metabolic and physical disturbances in players over the subsequent hours and days. Inadequate time recovery between matches can expose players to the risk of training and competing whilst not fully recovered. Controlling the level of effort and fatigue of players to reach higher performances during the matches is therefore critical. Therefore, the aim of the current study was to provide the first report of seasonal internal and external training load (TL) that included Hooper Index (HI) scores in elite soccer players during an in-season period. Sixteen elite soccer players were sampled, using global position system, session rating of perceived exertion (s-RPE) and HI scores during the daily training sessions throughout the 2015-2016 in-season period. Data were analysed across ten mesocycles (M: 1 to 10) and collected according to the number of days prior to a match. Total daily distance covered was higher at the start (M1 and M3) compared to the final mesocycle (M10) of the season. M1 (5589m) reached a greater distance than M5 (4473m) (ES = 9.33 [12.70, 5.95]) and M10 (4545m) (ES = 9.84 [13.39, 6.29]). M3 (5691m) reached a greater distance than M5 (ES = 9.07 [12.36, 5.78]), M7 (ES = 6.13 [8.48, 3.79]) and M10 (ES = 9.37 [12.76, 5.98]). High-speed running distance was greater in M1 (227m), than M5 (92m) (ES = 27.95 [37.68, 18.22]) and M10 (138m) (ES = 8.46 [11.55, 5.37]). Interestingly, the s-RPE response was higher in M1 (331au) in comparison to the last mesocycle (M10, 239au). HI showed minor variations across mesocycles and in days prior to the match. Every day prior to a match, all internal and external TL variables expressed significant lower values to other days prior to a match (p<0.01). In general, there were no differences between player positions. Conclusions: Our results reveal that despite the existence of some significant differences between mesocycles, there were minor changes across the in-season period for the internal and external TL variables used. Furthermore, it was observed that MD-1 presented a reduction of external TL (regardless of mesocycle) while internal TL variables did not have the same record during in-season match-day-minus.

scholarly journals Combining Internal- and External-Training-Load Measures in Professional Rugby League

2014 ◽  
Vol 9 (6) ◽  
pp. 905-912 ◽  
Author(s):  
Dan Weaving ◽  
Phil Marshall ◽  
Keith Earle ◽  
Alan Nevill ◽  
Grant Abt
Keyword(s):  
Principal Components ◽  
High Speed ◽  
Perceived Exertion ◽  
Principal Component ◽  
Rugby League ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Training Dose ◽  
External Training ◽  
Training Impulse

Purpose:This study investigated the effect of training mode on the relationships between measures of training load in professional rugby league players.Methods:Five measures of training load (internal: individualized training impulse, session rating of perceived exertion; external—body load, high-speed distance, total impacts) were collected from 17 professional male rugby league players over the course of two 12-wk preseason periods. Training was categorized by mode (small-sided games, conditioning, skills, speed, strongman, and wrestle) and subsequently subjected to a principal-component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Modes that extracted more than 1 principal component were subjected to a varimax rotation.Results:Small-sided games and conditioning extracted 1 principal component, explaining 68% and 52% of the variance, respectively. Skills, wrestle, strongman, and speed extracted 2 principal components each explaining 68%, 71%, 72%, and 67% of the variance, respectively.Conclusions:In certain training modes the inclusion of both internal and external training-load measures explained a greater proportion of the variance than any 1 individual measure. This would suggest that in training modes where 2 principal components were identified, the use of only a single internal or external training-load measure could potentially lead to an underestimation of the training dose. Consequently, a combination of internal- and external-load measures is required during certain training modes.

scholarly journals Internal and External Training Load in Under-19 versus Professional Soccer Players during the In-Season Period

2021 ◽  
Vol 18 (2) ◽  
pp. 558
Author(s):  
Sullivan Coppalle ◽  
Guillaume Ravé ◽  
Jason Moran ◽  
Iyed Salhi ◽  
Abderraouf Ben Abderrahman ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Training Session ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Soccer Players ◽  
Time Intervals ◽  
Professional Soccer ◽  
Global Positioning ◽  
External Training ◽  
Soccer Team

This study aimed to compare the training load of a professional under-19 soccer team (U-19) to that of an elite adult team (EAT), from the same club, during the in-season period. Thirty-nine healthy soccer players were involved (EAT [n = 20]; U-19 [n = 19]) in the study which spanned four weeks. Training load (TL) was monitored as external TL, using a global positioning system (GPS), and internal TL, using a rating of perceived exertion (RPE). TL data were recorded after each training session. During soccer matches, players’ RPEs were recorded. The internal TL was quantified daily by means of the session rating of perceived exertion (session-RPE) using Borg’s 0–10 scale. For GPS data, the selected running speed intensities (over 0.5 s time intervals) were 12–15.9 km/h; 16–19.9 km/h; 20–24.9 km/h; >25 km/h (sprint). Distances covered between 16 and 19.9 km/h, > 20 km/h and >25 km/h were significantly higher in U-19 compared to EAT over the course of the study (p =0.023, d = 0.243, small; p = 0.016, d = 0.298, small; and p = 0.001, d = 0.564, small, respectively). EAT players performed significantly fewer sprints per week compared to U-19 players (p = 0.002, d = 0.526, small). RPE was significantly higher in U-19 compared to EAT (p =0.001, d = 0.188, trivial). The external and internal measures of TL were significantly higher in the U-19 group compared to the EAT soccer players. In conclusion, the results obtained show that the training load is greater in U19 compared to EAT.

Positional Differences in Elite Basketball: Selecting Appropriate Training-Load Measures

2018 ◽  
Vol 13 (7) ◽  
pp. 947-952 ◽  
Author(s):  
Luka Svilar ◽  
Julen Castellano ◽  
Igor Jukic ◽  
David Casamichana
Keyword(s):  
Principal Components ◽  
Perceived Exertion ◽  
Principal Component ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Change Of Direction ◽  
External Variables ◽  
Load Monitoring ◽  
External Training ◽  
Almost All

Purpose: To study the structure of interrelationships among external-training-load measures and how these vary among different positions in elite basketball. Methods: Eight external variables of jumping (JUMP), acceleration (ACC), deceleration (DEC), and change of direction (COD) and 2 internal-load variables (rating of perceived exertion [RPE] and session RPE) were collected from 13 professional players with 300 session records. Three playing positions were considered: guards (n = 4), forwards (n = 4), and centers (n = 5). High and total external variables (hJUMP and tJUMP, hACC and tACC, hDEC and tDEC, and hCOD and tCOD) were used for the principal-component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Varimax rotation mode was used to extract multiple principal components. Results: The analysis showed that all positions had 2 or 3 principal components (explaining almost all of the variance), but the configuration of each factor was different: tACC, tDEC, tCOD, and hJUMP for centers; hACC, tACC, tCOD, and hJUMP for guards; and tACC, hDEC, tDEC, hCOD, and tCOD for forwards are specifically demanded in training sessions, and therefore these variables must be prioritized in load monitoring. Furthermore, for all playing positions, RPE and session RPE have high correlation with the total amount of ACC, DEC, and COD. This would suggest that although players perform the same training tasks, the demands of each position can vary. Conclusion: A particular combination of external-load measures is required to describe the training load of each playing position, especially to better understand internal responses among players.

scholarly journals We know they train, but what do they do? Implications for coaches working with adolescent rugby union players

2017 ◽  
Vol 12 (2) ◽  
pp. 175-182 ◽  
Author(s):  
Padraic J Phibbs ◽  
Ben Jones ◽  
Gregory AB Roe ◽  
Dale B Read ◽  
Joshua Darrall-Jones ◽  
...  
Keyword(s):  
Heart Rate ◽  
High Speed ◽  
Injury Risk ◽  
Perceived Exertion ◽  
Rugby Union ◽  
Rating Of Perceived Exertion ◽  
Limited Information ◽  
Positioning Systems ◽  
Global Positioning ◽  
School Club

Limited information is available regarding the training loads of adolescent rugby union players. One-hundred and seventy male players (age 16.1 ± 1.0 years) were recruited from 10 teams representing two age categories (under-16 and under-18) and three playing standards (school, club and academy). Global positioning systems, accelerometers, heart rate and session-rating of perceived exertion (s-RPE) methods were used to quantify mean session training loads. Session demands differed between age categories and playing standards. Under-18 academy players were exposed to the highest session training loads in terms of s-RPE (236 ± 42 AU), total distance (4176 ± 433 m), high speed running (1270 ± 288 m) and PlayerLoad™ (424 ± 56 AU). Schools players had the lowest session training loads in both respective age categories. Training loads and intensities increased with age and playing standard. Individual monitoring of training load is key to enable coaches to maximise player development and minimise injury risk.

Individual Response to External Training Load in Elite Football Players

2020 ◽  
Vol 15 (5) ◽  
pp. 696-704
Author(s):  
Håvard Wiig ◽  
Thor Einar Andersen ◽  
Live S. Luteberget ◽  
Matt Spencer
Keyword(s):  
External Load ◽  
High Speed ◽  
Training Load ◽  
Effect Sizes ◽  
Football Players ◽  
Individual Response ◽  
Total Distance ◽  
External Training ◽  
Tracking Devices ◽  
Very High

Purpose: To investigate within-player effect, between-player effect, and individual response of external training load from player tracking devices on session rating of perceived exertion training load (sRPE-TL) in elite football players. Methods: The authors collected sRPE-TL from 18 outfield players in 21 training sessions. Total distance, high-speed running distance (>14.4 m/s), very high-speed running distance (>19.8 m/s), PlayerLoad™, PlayerLoad2D™, and high-intensity events (HIE > 1.5, HIE > 2.5, and HIE > 3.5 m/s) were extracted from the tracking devices. The authors modeled within-player and between-player effects of single external load variables on sRPE-TL, and multiple levels of variability, using a linear mixed model. The effect of 2 SDs of external load on sRPE-TL was evaluated with magnitude-based inferences. Results: Total distance, PlayerLoad™, PlayerLoad2D™, and HIE > 1.5 had most likely substantial within-player effects on sRPE-TL (100%–106%, very large effect sizes). Moreover, the authors observed likely substantial between-player effects (12%–19%, small to moderate effect sizes) from the majority of the external load variables and likely to very likely substantial individual responses of PlayerLoad™, high-speed running distance, very high-speed running distance, and HIE > 1.5 (19%–30% coefficient of variation, moderate to large effect sizes). Finally, sRPE-TL showed large to very large between-session variability with all external load variables. Conclusions: External load variables with low intensity-thresholds had the strongest relationship with sRPE-TL. Furthermore, the between-player effect of external load and the individual response to external load advocate for monitoring sRPE-TL in addition to external load. Finally, the large between-session variability in sRPE-TL demonstrates that substantial amounts of sRPE-TL in training sessions are not explained by single external load variables.

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.

scholarly journals Multiple Measures are Needed to Quantify Training Loads in Professional Rugby League

2017 ◽  
Vol 38 (10) ◽  
pp. 735-740 ◽  
Author(s):  
Daniel Weaving ◽  
Ben Jones ◽  
Phil Marshall ◽  
Kevin Till ◽  
Grant Abt
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
High Speed ◽  
Perceived Exertion ◽  
Principal Component ◽  
Component Analysis ◽  
Rugby League ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Training Mode

AbstractThis study aims to investigate the effect of training mode (conditioning and skills) on multivariate training load relationships in professional rugby league via principal component analysis. Four measures of training load (internal: heart rate exertion index, session rating of perceived exertion; external: PlayerLoad™, individualised high-speed distance) were collected from 23 professional male rugby league players over the course of one 12 wk preseason period. Training was categorised by mode (skills or conditioning) and then subjected to a principal component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Modes that extracted more than 1 principal component were subject to a varimax rotation. Skills extracted 1 principal component, explaining 57% of the variance. Conditioning extracted 2 principal components (1st: internal; 2nd: external), explaining 85% of the variance. The presence of multiple training load dimensions (principal components) during conditioning training provides further evidence of the influence of training mode on the ability of individual measures of external or internal training load to capture training variance. Consequently, a combination of internal and external training-load measures is required during certain training modes.

scholarly journals Salivary Biomarkers and Training Load During Training and Competition in Paralympic Swimmers

2018 ◽  
Vol 13 (7) ◽  
pp. 839-843 ◽  
Author(s):  
Ciara Sinnott-O’Connor ◽  
Thomas M. Comyns ◽  
Alan M. Nevill ◽  
Giles D. Warrington
Keyword(s):  
Stress Response ◽  
Stress Responses ◽  
Salivary Cortisol ◽  
Perceived Exertion ◽  
Immunoglobulin A ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Alpha Amylase ◽  
Salivary Biomarkers ◽  
Paralympic Games

Context: Stress responses in athletes can be attributed to training and competition, where increased physiological and psychological stress may negatively affect performance and recovery. Purpose: To examine the relationship between training load (TL) and salivary biomarkers immunoglobulin A (IgA), alpha-amylase (AA), and cortisol across a 16-wk preparation phase and 10-d competition phase in Paralympic swimmers. Methods: Four Paralympic swimmers provided biweekly saliva samples during 3 training phases—(1) normal training, (2) intensified training, and (3) taper—as well as daily saliva samples in the 10-d Paralympic competition (2016 Paralympic Games). TL was measured using session rating of perceived exertion. Results: Multilevel analysis identified a significant increase in salivary immunoglobulin A (sIgA: 94.98 [27.69] μg·mL−1), salivary alpha-amylase (sAA: 45.78 [19.07] μg·mL−1), and salivary cortisol (7.92 [2.17] nM) during intensified training concurrent with a 38.3% increase in TL. During the taper phase, a 49.5% decrease in TL from the intensified training phase resulted in a decrease in sIgA, sAA, and salivary cortisol; however, all 3 remained higher than baseline levels. A further significant increase was observed during competition in sIgA (168.69 [24.19] μg·mL−1), sAA (35.86 [16.67] μg·mL−1), and salivary cortisol (10.49 [1.89] nM) despite a continued decrease (77.8%) in TL from the taper phase. Conclusions: Results demonstrate that performance in major competition such as Paralympic games, despite a noticeable reduction in TL, induces a stress response in athletes. Because of the elevated stress response observed, modifications to individual postrace recovery protocols may be required to enable athletes to maximize performance across all 10 d of competition.

scholarly journals The Association Between Internal and External Measures of Training Load in Batsmen and Medium-Fast Bowlers During Net-Based Cricket Training

2017 ◽  
Vol 12 (2) ◽  
pp. 247-253 ◽  
Author(s):  
Will Vickery ◽  
Ben Dascombe ◽  
Rob Duffield
Keyword(s):  
Heart Rate ◽  
Multiple Regression ◽  
High Intensity ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Stepwise Multiple Regression ◽  
Total Distance ◽  
Regression Techniques ◽  
External Training

Purpose:To examine the relationship between session rating of perceived exertion (sRPE) and measures of internal and external training load (TL) in cricket batsmen and medium-fast bowlers during net-based training sessions.Methods:The internal (heart rate), external (movement demands, PlayerLoad), and technical (cricket-specific skills) loads of 30 male cricket players (age 21.2 ± 3.8 y, height 1.82 ± 0.07 m, body mass 79.0 ± 8.7 kg) were determined from net-based cricket-training sessions (n = 118). The relationships between sRPE and measures of TL were quantified using Pearson product–moment correlations respective to playing position. Stepwise multiple-regression techniques provided key internal- and external-load determinants of sRPE in cricket players.Results:Significant correlations were evident (r = -.34 to .87, P < .05) between internal and external measures of TL and sRPE, with the strongest correlations (r ≥ .62) for GPS-derived measures for both playing positions. In batsmen, stepwise multiple-regression analysis revealed that 67.8% of the adjusted variance in sRPE could be explained by PlayerLoad and high-intensity distance (y = 27.43 + 0.81 PlayerLoad + 0.29 high-intensity distance). For medium-fast bowlers, 76.3% of the adjusted variance could be explained by total distance and mean heart rate (y = 101.82 + total distance 0.05 + HRmean – 0.48).Conclusion:These results suggest that sRPE is a valid method of reporting TL among cricket batsmen and medium-fast bowlers. Position-specific responses are evident and should be considered when monitoring the TL of cricket players.

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