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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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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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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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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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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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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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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.

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Dajo Sanders, Tony Myers and 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.

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Sharna A. Naidu, Maurizio Fanchini, Adam Cox, Joshua Smeaton, Will G. Hopkins and Fabio R. Serpiello

measure internal TL was examined by calculating its correlations with 2 HR-based methods, Banister’s training impulse (TRIMP) 10 and Edwards’ TL. 11 HR data were collected through wearable technology devices (Team Pro; Polar Electro Oy, Kempele, Finland) and analyzed via a Microsoft Excel customized

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Mário A.M. Simim, Marco Túlio de Mello, Bruno V.C. Silva, Dayane F. Rodrigues, João Paulo P. Rosa, Bruno Pena Couto and Andressa da Silva

; TRA = training situations; TRIMP = training impulse; TRIMP_ Edw  = TRIMP Edwards; TRIMP_ Luc  = TRIMP Lucia; TRIMP_ mod  = modified TRIMP; TRIMP_ Ban  = TRIMP based on exercise duration and HR variability. We aimed to determine monitored loads for training or competition in the following sports