scholarly journals Methods of Monitoring Training Load and Their Relationships to Changes in Fitness and Performance in Competitive Road Cyclists

2017 ◽  
Vol 12 (5) ◽  
pp. 668-675 ◽  
Author(s):  
Dajo Sanders ◽  
Grant Abt ◽  
Matthijs K.C. Hesselink ◽  
Tony Myers ◽  
Ibrahim Akubat

Purpose:To assess the dose-response relationships between different training-load methods and aerobic fitness and performance in competitive road cyclists.Methods:Training data from 15 well-trained competitive cyclists were collected during a 10-wk (December–March) preseason training period. Before and after the training period, participants underwent a laboratory incremental exercise test with gas-exchange and lactate measures and a performance assessment using an 8-min time trial (8MT). Internal training load was calculated using Banister TRIMP, Edwards TRIMP, individualized TRIMP (iTRIMP), Lucia TRIMP (luTRIMP), and session rating of perceived exertion (sRPE). External load was measured using Training Stress Score (TSS).Results:Large to very large relationships (r = .54–.81) between training load and changes in submaximal fitness variables (power at 2 and 4 mmol/L) were observed for all training-load calculation methods. The strongest relationships with changes in aerobic fitness variables were observed for iTRIMP (r = .81 [95% CI .51–.93, r = .77 [95% CI .43–.92]) and TSS (r = .75 [95% CI .31–.93], r = .79 [95% CI .40–.94]). The strongest dose-response relationships with changes in the 8MT test were observed for iTRIMP (r = .63 [95% CI .17–.86]) and luTRIMP (r = .70 [95% CI .29–.89).Conclusions:Training-load quantification methods that integrate individual physiological characteristics have the strongest dose-response relationships, suggesting this to be an essential factor in the quantification of training load in cycling.

2019 ◽  
Vol 66 (1) ◽  
pp. 131-141
Author(s):  
Petros G. Botonis ◽  
Argyris G. Toubekis ◽  
Theodoros I. Platanou

AbstractWe investigated the effectiveness of a short-duration training period including an overloaded (weeks 1 and 2) and a reduced training load period (weeks 3 and 4) on wellness, swimming performance and a perceived internal training load in eight high-level water-polo players preparing for play-offs. The internal training load was estimated daily using the rating of perceived exertion (RPE) and session duration (session-RPE). Perceived ratings of wellness (fatigue, muscle soreness, sleep quality, stress level and mood) were assessed daily. Swimming performance was evaluated through 400-m and 20-m tests performed before (baseline) and after the end of weeks 2 and 4. In weeks 3 and 4, the internal training load was reduced by 19.0 ± 3.8 and 36.0 ± 4.7%, respectively, compared to week 1 (p = 0.00). Wellness was improved in week 4 (20.4 ± 2.8 AU) compared to week 1 and week 2 by 16.0 ± 2.2 and 17.3 ± 2.9 AU, respectively (p =0.001). At the end of week 4, swimming performance at 400-m and 20-m tests (299.0 ± 10.2 and 10.2 ± 0.3 s) was improved compared to baseline values (301.4 ± 10.9 and 10.4 ± 0.4 s, p < 0.05) and the overloading training period (week 2; 302.9 ± 9.0 and 10.4 ± 0.4 s, p < 0.05). High correlations were observed between the percentage reduction of the internal training load from week 4 to week 1 (-25.3 ± 5.5%) and the respective changes in 20-m time (-2.1 ± 2.2%, r = 0.88, p < 0.01), fatigue perception (39.6 ± 27.1%), muscle soreness (32.5 ± 26.6%), stress levels (25.6 ± 15.1%) and the overall wellness scores (28.6 ± 21.9%, r = 0.74-0.79, p < 0.05). The reduction of the internal training load improved the overall perceived wellness and swimming performance of players. The aforementioned periodization approach may be an effective training strategy in the lead-up to play-off tournaments.


2021 ◽  
Vol 12 ◽  
Author(s):  
Rasmus Pind ◽  
Peter Hofmann ◽  
Evelin Mäestu ◽  
Eno Vahtra ◽  
Priit Purge ◽  
...  

Purpose: The aim of this study was to investigate the interaction of training load quantification using heart rate (HR) and rating of perceived exertion (RPE)-based methodology, and the relationship between internal training load parameters and subjective training status (Fatigue) in high-level rowers during volume increased low-intensity training period.Methods: Training data from 19 high-level rowers (age 23.5 ± 5.9 years; maximal oxygen uptake 58.9 ± 5.8 ml·min−1·kg−1) were collected during a 4-week volume increased training period. All individual training sessions were analyzed to quantify training intensity distribution based on the HR time-in-zone method (i.e., HR Z1, HR Z2, and HR Z3) determined by the first and second ventilatory thresholds (VT1/VT2). Internal training load was calculated using session RPE (sRPE) to categorize training load by effort (i.e., sRPE1, sRPE2, and sRPE3). The Recovery-Stress Questionnaire for Athletes (RESTQ-Sport) questionnaire was implemented after every week of the study period.Results: No differences were found between the respective HR and effort-based zone distributions during the baseline week (p &gt; 0.05). Compared to HR Z1, sRPE1 was significantly lower in weeks 2–4 (p &lt; 0.05), while sRPE2 was higher in weeks 2–3 compared to HR Z2 (p &lt; 0.05) and, in week 4, the tendency (p = 0.06) of the higher amount of sRPE3 compared to HR Z3 was found. There were significant increases in RESTQ-Sport stress scales and decreases in recovery scales mostly during weeks 3 and 4. Increases in the Fatigue scale were associated with the amounts of sRPE2 and sRPE3 (p = 0.011 and p = 0.008, respectively), while no associations with Fatigue were found for HR-based session quantification with internal or external training load variables.Conclusion: During a low-intensity 4-week training period with increasing volume, RPE-based training quantification indicated a shift toward the harder rating of sessions with unchanged HR zone distributions. Moderate and Hard rated sessions were related to increases in Fatigue. Session rating of perceived exertion and effort-based training load could be practical measures in combination with HR to monitor adaptation during increased volume, low-intensity training period in endurance athletes.


Author(s):  
Paula B. Debien ◽  
Thiago F. Timoteo ◽  
Tim J. Gabbett ◽  
Maurício G. Bara Filho

Purpose: This study described and analyzed practices and perceptions of rhythmic gymnastics coaches, medical staff, and athletes on training-load management. Methods: Online surveys were distributed among professionals and gymnasts involved in rhythmic gymnastics training across the world. One hundred (50 coaches, 12 medical staff, and 38 gymnasts) participants from 25 different countries completed the surveys. Results: Respondents stated using coaches’ perception on a daily basis as a method of monitoring external (57%) and internal (58%) load, recovery/fatigue (52%), and performance (64%). Variables and methods (eg, wearable devices, athlete self-reported measures, session rating of perceived exertion), and metrics (eg, acute and chronic load) commonly reported in the training-load literature and other sports were not frequently used in rhythmic gymnastics. The majority of coaches (60.3% [17%]) perceived that maladaptation rarely or never occurred. Medical staff involvement in sharing and discussing training-load information was limited, and they perceived that the measurement of athletes’ recovery/fatigue was poor. Gymnasts noted good quality in relation to the measurement of performance. Most participants (≥85%) believed that a specific training-load management model for rhythmic gymnastics could be very or extremely effective. Conclusions: In conclusion, rhythmic gymnastics coaches’ perception is the most commonly used strategy to monitor load, recovery/fatigue, and performance; although, this could be a limited method to guarantee effective training-load management in this sport.


2018 ◽  
Vol 13 (9) ◽  
pp. 1182-1189 ◽  
Author(s):  
Paula B. Debien ◽  
Marcelly Mancini ◽  
Danilo R. Coimbra ◽  
Daniel G.S. de Freitas ◽  
Renato Miranda ◽  
...  

Purpose: To describe and analyze the distribution of internal training load (ITL), recovery, and physical performance of professional volleyball players throughout 1 season. Methods: Fifteen male professional Brazilian volleyball players participated in this study. The session rating of perceived exertion (s-RPE) and Total Quality Recovery (TQR) score were collected daily for 36 wk. s-RPE was collected after each training session, and TQR, before the first session of the day. The sum of the ITL of each session during the week, training monotony, strain, acute∶chronic workload ratio, match difficulty score, and average of the TQR scores were recorded for the analysis. In addition, the athletes performed countermovement-jump (CMJ) tests with and without the use of the arms 4 times over the season. Results: The season mean weekly ITL was 3733 (1228) AU and the TQR was 15.02 (0.71). The ITL and recovery demonstrated undulating dynamics over the 36 wk, with higher weekly ITL in the preparatory periods (F = 50.32; P < .001) and worse recovery during the main competition (F = 6.47; P = .004). Negative correlations were found between TQR and ITL variables (P < .05). There was improvement and maintenance in CMJ tests without (F = 11.88; P < .001) and with (F = 16.02; P < .001) the use of the arms after the preparatory periods. Conclusions: The ITL variables, recovery, and physical performance changed significantly throughout a professional volleyball season. Despite the decrease in ITL during the main competitive period, the correct distribution of weekly ITL seems to be very important to guarantee the best recovery of athletes.


Author(s):  
Diogo Hilgemberg Figueiredo ◽  
Diego Hilgemberg Figueiredo ◽  
Francisco De Assis Manoel ◽  
Helcio Rossi Gonçalves ◽  
Antonio Carlos Dourado

Objective: To our Knowledge, information about the agreement between coaches’ and the young soccer players’ session rating of perceived exertion is not consistent during specific periods of training (intensification and taper) and has not been established. The purpose of this study was to examine and compare the internal training load and session rating of perceived exertion between coaches’ and young soccer players’ during three weeks in different training phases. Method: Participants were 16 male elite Under19 soccer players and their coaches. Before each training session, the coaches reported a session rating of perceived exertion using the Borg CR-10 scale as well as the planned duration (min) of the training based on prior planning, while the athletes responded the scale after each training session. Results: No differences in intensity session rating of perceived exertion (t = 0.49; p = 0.62) and training load (t = 0.18; p = 0.86) were observed between coaches and players during the training period analyzed. During different training phases, no significant differences were found during intensification (t = 0.18; p = 0.85) and taper (t = -0.19; p = 0.85) in training loads and in the session rating of perceived exertion prescribed by coaches and perceived by players. A very large correlation was observed between coaches training load (r= 0.84) and players training load. However, a trivial correlation was found between players training load and changes in the Yo-yo IR1 performance (r= -0.09), age (r= -0.06) and years of competitive experience (r= -0.08). Stepwise linear regression revealed that coaches training load (F1; 238= 582.7; R2= 0.710; p<0.001) explained 71% of the variance in players training load. Conclusion: The results suggest that the session rating of perceived exertion and training load prescribed during three weeks in different training phases (by coaches) was not different from perceived by young soccer players. Moreover, coaches training load seem to be effective to predict the training load in soccer players.


2011 ◽  
Vol 6 (3) ◽  
pp. 358-366 ◽  
Author(s):  
Vinícius F. Milanez ◽  
Rafael E. Pedro ◽  
Alexandre Moreira ◽  
Daniel A. Boullosa ◽  
Fuad Salle-Neto ◽  
...  

Purpose:The aim of this study was to verify the influence of aerobic fitness (VO2max) on internal training loads, as measured by the session rating of perceived exertion (session-RPE) method.Methods:Nine male professional outfeld futsal players were monitored for 4 wk of the in-season period with regards to the weekly accumulated session-RPE, while participating in the same training sessions. Single-session-RPE was obtained from the product of a 10-point RPE scale and the duration of exercise. Maximal oxygen consumption was determined during an incremental treadmill test.Results:The average training load throughout the 4 wk period varied between 2,876 and 5,035 arbitrary units. Technical-tactical sessions were the predominant source of loading. There was a significant correlation between VO2max (59.6 ± 2.5 mL·kg–1 ·min–1) and overall training load accumulated over the total period (r = –0.75).Conclusions:The VO2max plays a key role in determining the magnitude of an individual’s perceived exertion during futsal training sessions.


2017 ◽  
Vol 12 (9) ◽  
pp. 1232-1237 ◽  
Author(s):  
Dajo Sanders ◽  
Tony Myers ◽  
Ibrahim Akubat

Purpose:To evaluate training-intensity distribution using different intensity measures based on rating of perceived exertion (RPE), heart rate (HR), and power output (PO) in well-trained cyclists. Methods:Fifteen road cyclists participated in the study. Training data were collected during a 10-wk training period. Training-intensity distribution was quantified using RPE, HR, and PO categorized in a 3-zone training-intensity model. Three zones for HR and PO were based around a 1st and 2nd lactate threshold. The 3 RPE zones were defined using a 10-point scale: zone 1, RPE scores 1–4; zone 2, RPE scores 5–6; zone 3, RPE scores 7–10. Results:Training-intensity distributions as percentages of time spent in zones 1, 2, and 3 were moderate to very largely different for RPE (44.9%, 29.9%, 25.2%) compared with HR (86.8%, 8.8%, 4.4%) and PO (79.5%, 9.0%, 11.5%). Time in zone 1 quantified using RPE was largely to very largely lower for RPE than PO (P < .001) and HR (P < .001). Time in zones 2 and 3 was moderately to very largely higher when quantified using RPE compared with intensity quantified using HR (P < .001) and PO (P < .001). Conclusions:Training-intensity distribution quantified using RPE demonstrates moderate to very large differences compared with intensity distributions quantified based on HR and PO. The choice of intensity measure affects intensity distribution and has implications for training-load quantification, training prescription, and the evaluation of training characteristics.


2019 ◽  
Vol 14 (1) ◽  
pp. 68-75 ◽  
Author(s):  
Callum J. McCaskie ◽  
Warren B. Young ◽  
Brendan B. Fahrner ◽  
Marc Sim

Purpose: To examine the association between preseason training variables and subsequent in-season performance in an elite Australian football team. Methods: Data from 41 elite male Australian footballers (mean [SD] age = 23.4 [3.1] y, height =188.4 [7.1] cm, and mass = 86.7 [7.9] kg) were collected from 1 Australian Football League (AFL) club. Preseason training data (external load, internal load, fitness testing, and session participation) were collected across the 17-wk preseason phase (6 and 11 wk post-Christmas). Champion Data© Player Rank (CDPR), coaches’ ratings, and round 1 selection were used as in-season performance measures. CDPR and coaches’ ratings were examined over the entire season, first half of the season, and the first 4 games. Both Pearson and partial (controlling for AFL age) correlations were calculated to assess if any associations existed between preseason training variables and in-season performance measures. A median split was also employed to differentiate between higher- and lower-performing players for each performance measure. Results: Preseason training activities appeared to have almost no association with performance measured across the entire season and the first half of the season. However, many preseason training variables were significantly linked with performance measured across the first 4 games. Preseason training variables that were measured post-Christmas were the most strongly associated with in-season performance measures. Specifically, total on-field session rating of perceived exertion post-Christmas, a measurement of internal load, displayed the greatest association with performance. Conclusion: Late preseason training (especially on-field match-specific training) is associated with better performance in the early season.


2020 ◽  
Vol 15 (1) ◽  
pp. 113-118 ◽  
Author(s):  
Caoimhe Tiernan ◽  
Mark Lyons ◽  
Tom Comyns ◽  
Alan M. Nevill ◽  
Giles Warrington

Purpose: Insufficient recovery can lead to a decrease in performance and increase the risk of injury and illness. The aim of this study was to evaluate salivary cortisol as a marker of recovery in elite rugby union players. Method: Over a 10-wk preseason training period, 19 male elite rugby union players provided saliva swabs biweekly (Monday and Friday mornings). Subjective markers of recovery were collected every morning of each training day. Session rating of perceived exertion (sRPE) was taken after every training session, and training load was calculated (sRPE × session duration). Results: Multilevel analysis found no significant association between salivary cortisol and training load or subjective markers of recovery (all P > .05) over the training period. Compared with baseline (wk 1), Monday salivary cortisol significantly increased in wk 4 (14.94 [7.73] ng/mL; P = .04), wk 8 (16.39 [9.53] ng/mL; P = .01), and wk 9 (15.41 [9.82] ng/mL; P = .02), and Friday salivary cortisol significantly increased in wk 5 (14.81 [8.74] ng/mL; P = .04) and wk 10 (15.36 [11.30] ng/mL; P = .03). Conclusions: The significant increase in salivary cortisol on certain Mondays may indicate that players did not physically recover from the previous week of training or match at the weekend. The increased Friday cortisol levels and subjective marker of perceived fatigue indicated increased physiological stress from that week’s training. Regular monitoring of salivary cortisol combined with appropriate planning of training load may allow sufficient recovery to optimize training performance.


2018 ◽  
Vol 13 (3) ◽  
pp. 339-346 ◽  
Author(s):  
Stuart R. Graham ◽  
Stuart Cormack ◽  
Gaynor Parfitt ◽  
Roger Eston

Purpose: To assess and compare the validity of internal and external Australian football (AF) training-load measures for predicting match exercise intensity (MEI/min) and player-rank score (PRScore) using a variable dose-response model. Methods: A cohort of 25 professional AF players (23 ± 3 y, 188.3 ± 7.2 cm, 87.7 ± 8.4 kg) completed a 24-wk in-season macrocycle. In-season internal training and match load were quantified using session rating of perceived exertion (sRPE) and external load from satellite and accelerometer data. Using a training-impulse (TRIMP) calculation, external load (au) was represented as distance (TRIMPDist), distance ≥4.16 m/s (TRIMPHSDist), and PlayerLoad (TRIMPPL). In-season training load, MEI/min, and PRScore were applied to a variable dose-response model, which provided estimates of MEI/min and PRScore. Predicted MEI/min and PRScore were correlated with actual measures from each match. The magnitude of the difference between MEI/min and PRScore estimates for each model input and the difference between the precision of internal and external load measures to predict MEI/min and PRScore were calculated using the effect size ± 90% confidence interval (CI). Results: Estimates of MEI/min demonstrated very large associations with actual MEI/min (r, 90% CI) (eg, TRIMPDist .76 ± .13, and sRPESkills .73 ± .14). Estimates of PRScore demonstrated associations of large magnitude with actual PRScore using the same inputs. Precision of actual MEI/min was lowest using sRPE compared with (ES ± 90% CI) TRIMPDist, −.67 ± .34, and TRIMPPL, −.91 ± .39. There were trivial and unclear differences in the precision of PRScore estimates between TRIMP and sRPE inputs. Conclusions: Dose-response models from multiple training-load inputs can predict within-individual variation of MEI/min and PRScore. Internal and external training-input methods exhibited comparable predictive power.


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