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Relationship Between External Training Load and Session Rating of Perceived Exertion Training Impulse in Elite Sprinters

Matthew Thome, Sophia Nimphius, Matthew J. Jordan, and Robin T. Thorpe

accumulated at high running velocities is also a key determinant of the internal, physiological training load, which can be measured via heart rate response and the session rating of perceived exertion training impulse method (RPE-TRIMP) in field sport athletes. 4 , 17 – 19 Heart rate monitoring is a

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Technical and Tactical Training Load in Professional Volleyball Players

Thiago S. Duarte, Danilo L. Alves, Danilo R. Coimbra, Bernardo Miloski, João C. Bouzas Marins, and Maurício G. Bara Filho

between SITL (session-RPE) and OITL (training impulse of the HR [TRIMP-HR]). Methods Subjects The sample consisted of 15 male athletes who were members of a professional volleyball team (4 outside hitters, 2 opposites, 3 setters, 2 liberos, and 4 middle blockers) that plays in Superliga. The physical

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The Dose-Response Relationship Between Training Load and Aerobic Fitness in Academy Rugby Union Players

Richard J. Taylor, Dajo Sanders, Tony Myers, Grant Abt, Celia A. Taylor, and Ibrahim Akubat

systems and their derived measures have been reported in the rugby-specific literature, the focus to date has been on identifying potential differences in movement patterns based on position, age, and different competition standards. 5 While research in rugby has reported use of HR-based training-impulse

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Intensity and Load Characteristics of Professional Road Cycling: Differences Between Men’s and Women’s Races

Dajo Sanders, Teun van Erp, and Jos J. de Koning

: Edwards training impulse (TRIMP), 9 Training Stress Score (TSS), 11 and session rating of perceived exertion (sRPE). 12 Edwards TRIMP was calculated based on the time spent in the 5 predefined HR zones described above and multiplied by a zone-specific arbitrary weighting factor (zone 1: weighting

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Internal Load of Male Varsity Ice Hockey Players During Training and Games Throughout an Entire Season

Jessica L. Bigg, Alexander S.D. Gamble, and Lawrence L. Spriet

as it leads to competitions, higher game internal loads than training, and positional differences. The physiological component of internal load is commonly assessed using the heart rate (HR)–derived training impulse (TRIMP). 16 – 19 The most commonly used models are the Banister TRIMP 16 and

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Training Load and Acute Performance Decrements Following Different Training Sessions

Kobe M. Vermeire, Kevin Caen, Jan G. Bourgois, and Jan Boone

performance modeling 1 – 3 and at the same time help to diminish the injury risk or the risk of nonfunctional overreaching. 4 It is thus essential that the chosen TL method capture the true load of each training session. In the 70s, Banister et al 5 developed the training impulse (TRIMP) as a first TL

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Integrating the Internal and External Training Loads in Soccer

Ibrahim Akubat, Steve Barrett, and Grant Abt

Purpose:

This study aimed to assess the relationships of fitness in soccer players with a novel integration of internal and external training load (TL).

Design:

Ten amateur soccer players performed a lactate threshold (LT) test followed by a soccer simulation (Ball-Sport Endurance and Sprint Test [BEAST90mod]).

Methods:

The results from the LT test were used to determine velocity at lactate threshold (vLT), velocity at onset of blood lactate accumulation (vOBLA), maximal oxygen uptake (VO2max), and the heart rate–blood lactate profile for calculation of internal TL (individualized training impulse, or iTRIMP). The total distance (TD) and high intensity distance (HID) covered during the BEAST90mod were measured using GPS technology that allowed measurement of performance and external TL. The internal TL was divided by the external TL to form TD:iTRIMP and HID:iTRIMP ratios. Correlation analyses assessed the relationships between fitness measures and the ratios to performance in the BEAST90mod.

Results:

vLT, vOBLA, and VO2max showed no significant relationship to TD or HID. HID:iTRIMP significantly correlated with vOBLA (r = .65, P = .04; large), and TD:iTRIMP showed a significant correlation with vLT (r = .69, P = .03; large).

Conclusions:

The results suggest that the integrated use of ratios may help in the assessment of fitness, as performance alone showed no significant relationships with fitness.

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Training Mode’s Influence on the Relationships between Training-Load Models During Basketball Conditioning

Aaron T. Scanlan, Neal Wen, Patrick S. Tucker, Nattai R. Borges, and Vincent J. Dalbo

Purpose:

To compare perceptual and physiological training-load responses during various basketball training modes.

Methods:

Eight semiprofessional male basketball players (age 26.3 ± 6.7 y, height 188.1 ± 6.2 cm, body mass 92.0 ± 13.8 kg) were monitored across a 10-wk period in the preparatory phase of their training plan. Player session ratings of perceived exertion (sRPE) and heart-rate (HR) responses were gathered across base, specific, and tactical/game-play training modes. Pearson correlations were used to determine the relationships between the sRPE model and 2 HR-based models: the training impulse (TRIMP) and summated HR zones (SHRZ). One-way ANOVAs were used to compare training loads between training modes for each model.

Results:

Stronger relationships between perceptual and physiological models were evident during base (sRPE-TRIMP r = .53, P < .05; sRPE-SHRZ r = .75, P < .05) and tactical/game-play conditioning (sRPE-TRIMP r = .60, P < .05; sRPE-SHRZ r = .63; P < .05) than during specific conditioning (sRPE-TRIMP r = .38, P < .05; sRPE-SHRZ r = .52; P < .05). Furthermore, the sRPE model detected greater increases (126–429 AU) in training load than the TRIMP (15–65 AU) and SHRZ models (27–170 AU) transitioning between training modes.

Conclusions:

While the training-load models were significantly correlated during each training mode, weaker relationships were observed during specific conditioning. Comparisons suggest that the HR-based models were less effective in detecting periodized increases in training load, particularly during court-based, intermittent, multidirectional drills. The practical benefits and sensitivity of the sRPE model support its use across different basketball training modes.

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

Dajo Sanders, Grant Abt, Matthijs K.C. Hesselink, Tony Myers, and 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.

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Training-Load Distribution in Endurance Runners: Objective Versus Subjective Assessment

Vincenzo Manzi, Antonio Bovenzi, Carlo Castagna, Paola Sinibaldi Salimei, Maurizio Volterrani, and Ferdinando Iellamo

Purpose:

To assess the distribution of exercise intensity in long-distance recreational athletes (LDRs) preparing for a marathon and to test the hypothesis that individual perception of effort could provide training responses similar to those provided by standardized training methodologies.

Methods:

Seven LDRs (age 36.5 ± 3.8 y) were followed during a 5-mo training period culminating with a city marathon. Heart rate at 2.0 and 4.0 mmol/L and maximal heart rate were used to establish 3 intensity training zones. Internal training load (TL) was assessed by training zones and TRIMPi methods. These were compared with the session-rating-of-perceived-exertion (RPE) method.

Results:

Total time spent in zone 1 was higher than in zones 2 and 3 (76.3% ± 6.4%, 17.3% ± 5.8%, and 6.3% ± 0.9%, respectively; P = .000 for both, ES = 0.98, ES = 0.99). TL quantified by session-RPE provided the same result. The comparison between session-RPE and training-zones-based methods showed no significant difference at the lowest intensity (P = .07, ES = 0.25). A significant correlation was observed between TL RPE and TL TRIMPi at both individual and group levels (r = .79, P < .001). There was a significant correlation between total time spent in zone 1 and the improvement at the running speed of 2 mmol/L (r = .88, P < .001). A negative correlation was found between running speed at 2 mmol/L and the time needed to complete the marathon (r = –.83, P < .001).

Conclusions:

These findings suggest that in recreational LDRs most of the training time is spent at low intensity and that this is associated with improved performances. Session-RPE is an easy-to-use training method that provides responses similar to those obtained with standardized training methodologies.