Dajo Sanders

Training-Intensity Distribution in Road Cyclists: Objective Versus Subjective Measures

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.

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

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.

Predicting High-Power Performance in Professional Cyclists

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.

Analysing a cycling grand tour: Can we monitor fatigue with intensity or load ratios?

ABSTRACT This study evaluated the changes in ratios of different intensity (rating of perceived exertion; RPE, heart rate; HR, power output; PO) and load measures (session-RPE; sRPE, individualized TRIMP; iTRIMP, Training Stress Score™; TSS) in professional cyclists. RPE, PO and HR data was collected from twelve professional cyclists (VO2max 75 ± 6 ml∙min∙kg−1) during a two-week baseline training period and during two cycling Grand Tours. Subjective:objective intensity (RPE:HR, RPE:PO) and load (sRPE:iTRIMP, sRPE:TSS) ratios and external:internal intensity (PO:HR) and load (TSS:iTRIMP) ratios were calculated for every session. Moderate to large increases in the RPE:HR, RPE:PO and sRPE:TSS ratios (d = 0.79–1.79) and small increases in the PO:HR and sRPE:iTRIMP ratio (d = 0.21–0.41) were observed during Grand Tours compared to baseline training data. Differences in the TSS:iTRIMP ratio were trivial to small (d = 0.03–0.27). Small to moderate week-to-week changes (d = 0.21–0.63) in the PO:HR, RPE:PO, RPE:HR, TSS:iTRIMP, sRPE:iTRIMP and sRPE:TSS were observed during the Grand Tour. Concluding, this study shows the value of using ratios of intensity and load measures in monitoring cyclists. Increases in ratios could reflect progressive fatigue that is not readily detected by changes in solitary intensity/load measures.

Body fluid status, plasma volume change and its relationship to physical effort during a multistage professional road cycling race

ABSTRACT Multistage cycling races are very demanding and may profoundly affect the body’s fluid homeostasis. This study aimed at monitoring body fluid volume and plasma volume (PV) changes during multi-stage cycling and to investigate whether changes are associated to work output and perceived exertion during the competition. During the Tour of the Alps cycling stage race 2017 daily hematocrit and hemoglobin evaluations, body weight, bioimpedance, RPE and power output measurements were performed on eight professional road cyclists. Results show modest to large changes in bioimpedance and blood hematocrit values during the race. Work accomplished during stage one was related to changes in vector length and body weight (n = 6, r = 0.821, p = 0.045 and r = ‒0.852, p = 0.031, respectively). Additionally, PV changes were associated with vector length changes (r = ‒0.759, p = 0.029). During the last stage, PV changes were related to reactance (Xc/H) changes (r = ‒0.716, p = 0.046). The physical work performed during the first stage seems to strongly influence fluid volumes, which on the other hand showed some associations to PV changes. These associations were not evident during the following stages but instead a relationship between Xc/h and PV changes occurred, possible indicating fluid shifts. Bioimpedance thus could be a useful tool to identify fluids shift during such competitions.