were no statistically significant differences in average weekly training load (in iTrimp per week), training time (in hours per week), or intensity distribution between the groups during the final 4 weeks of the competitive period. During the 3-week transition period, both groups were instructed to
Madison Taylor, Nicki Almquist, Bent Rønnestad, Arnt Erik Tjønna, Morten Kristoffersen, Matt Spencer, Øyvind Sandbakk, and Knut Skovereng
Richard J. Taylor, Dajo Sanders, Tony Myers, Grant Abt, Celia A. Taylor, and Ibrahim Akubat
individualized TRIMP (iTRIMP) method, which has shown preferential dose-response relationship with fitness in other sports, 9 , 10 , 16 – 18 has not been examined in rugby. However, 1 study that evaluated iTRIMP in rugby league using principal-component analysis revealed that iTRIMP contributed to explaining
Antonis Kesisoglou, Andrea Nicolò, Lucinda Howland, and Louis Passfield
training sessions and 1500-m TTs. 20 Participants were familiarized with all scales during their laboratory visit. The TL metrics were calculated using 3 different TRIMP formulae (bTRIMP, iTRIMP, and eTRIMP), session RPE 21 (sRPE), and a running training stress score (rTSS). The bTRIMP 22 was calculated
Ibrahim Akubat, Steve Barrett, and Grant Abt
This study aimed to assess the relationships of fitness in soccer players with a novel integration of internal and external training load (TL).
Ten amateur soccer players performed a lactate threshold (LT) test followed by a soccer simulation (Ball-Sport Endurance and Sprint Test [BEAST90mod]).
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.
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).
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.
Dajo Sanders, Grant Abt, Matthijs K.C. Hesselink, Tony Myers, and Ibrahim Akubat
To assess the dose-response relationships between different training-load methods and aerobic fitness and performance in competitive road cyclists.
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).
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).
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.
Arthur H. Bossi, Cristian Mesquida, Louis Passfield, Bent R. Rønnestad, and James G. Hopker
RPE (sRPE) was recorded. An individualized training impulse (iTRIMP), which is a training-load metric based on HR, 28 was also calculated to compare the training load between HIIT sessions. Within the iTRIMP calculation, exercise intensity is weighted according to participants’ own HR
Daniel Bok and Igor Jukić
significant relationships between v2 and total distance: individualized training impulse (iTRIMP) ratio ( r = .69) and high-intensity distance:iTRIMP ratio ( r = .58, P = .08), respectively, were recorded during soccer simulation test. 37 It seems to be that greater aerobic endurance presents a fitness
Stuart R. Graham, Stuart Cormack, Gaynor Parfitt, and Roger Eston
potentially reduces the influence of foot strikes and may provide insight into more nonlocomotor load aspects applicable to professional AF. 12 Similarly, previous model research using highly trained endurance athletes has demonstrated better fits using individualized TRIMP calculations (iTRIMPs) than using
Dajo Sanders and Teun van Erp
32 ,c 298 (33) 2 311 (53) 2 359 (80) 2 ND 401 25 ,c 392 25 ,c iTRIMP 310 (148) 14 ,a ND ND ND ND ND kJ spent 3851 (951) 20 ND ND ND 3964 (962) 15 3673 (895) 15 TSS·km −1 1.38 (0.34) 20 1.14 (0.19) 2 1.32 (0.20) 2 1.97 (0.31) 2 1.49 (0.27) 15 1.41 (0.30) 15 eTRIMP·km −1 3.37 (1.09) 20 ND ND ND 4.63 (0
Teun van Erp, Marco Hoozemans, Carl Foster, and Jos J. de Koning
. 15 , 16 Furthermore, other measurements of internal TL (eg, iTRIMP 17 and banTRIMP 4 ) have an exponential weighting factor based on the classically described exponential rise of blood lactate when exercise intensity exceeds the lactate threshold. 18 , 19 Therefore, measurements of TL are based on