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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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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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Dajo Sanders, Mathieu Heijboer, Ibrahim Akubat, Kenneth Meijer and Matthijs K. Hesselink

Purpose:

To assess if short-duration (5 to ~300 s) high-power performance can accurately be predicted using the anaerobic power reserve (APR) model in professional cyclists.

Methods:

Data from 4 professional cyclists from a World Tour cycling team were used. Using the maximal aerobic power, sprint peak power output, and an exponential constant describing the decrement in power over time, a power-duration relationship was established for each participant. To test the predictive accuracy of the model, several all-out field trials of different durations were performed by each cyclist. The power output achieved during the all-out trials was compared with the predicted power output by the APR model.

Results:

The power output predicted by the model showed very large to nearly perfect correlations to the actual power output obtained during the all-out trials for each cyclist (r = .88 ± .21, .92 ± .17, .95 ± .13, and .97 ± .09). Power output during the all-out trials remained within an average of 6.6% (53 W) of the predicted power output by the model.

Conclusions:

This preliminary pilot study presents 4 case studies on the applicability of the APR model in professional cyclists using a field-based approach. The decrement in all-out performance during high-intensity exercise seems to conform to a general relationship with a single exponential-decay model describing the decrement in power vs increasing duration. These results are in line with previous studies using the APR model to predict performance during brief all-out trials. Future research should evaluate the APR model with a larger sample size of elite cyclists.

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

Purpose: To identify the dose-response relationship between measures of training load (TL) and changes in aerobic fitness in academy rugby union players. Method: Training data from 10 academy rugby union players were collected during a 6-wk in-season period. Participants completed a lactate-threshold test that was used to assess VO2max, velocity at VO2max, velocity at 2 mmol/L (lactate threshold), and velocity at 4 mmol/L (onset of lactate accumulation; vOBLA) as measures of aerobic fitness. Internal-TL measures calculated were Banister training impulse (bTRIMP), Edwards TRIMP, Lucia TRIMP, individualized TRIMP (iTRIMP), and session RPE (sRPE). External-TL measures calculated were total distance, PlayerLoad™, high-speed distance >15 km/h, very-high-speed distance >18 km/h, and individualized high-speed distance based on each player’s vOBLA. Results: A second-order-regression (quadratic) analysis found that bTRIMP (R 2 = .78, P = .005) explained 78% of the variance and iTRIMP (R 2 = .55, P = .063) explained 55% of the variance in changes in VO2max. All other HR-based internal-TL measures and sRPE explained less than 40% of variance with fitness changes. External TL explained less than 42% of variance with fitness changes. Conclusions: In rugby players, bTRIMP and iTRIMP display a curvilinear dose-response relationship with changes in maximal aerobic fitness.