Sprint cycling rate of torque development associates with strength measurement in trained cyclists.

PURPOSE: A cyclist's rate of force/torque development (RFD/RTD) and peak force/torque can be measured during single-joint or whole-body isometric tests, or during cycling. However, there is limited understanding of the relationship between these measures, and of the mechanisms that contribute to each measure. Therefore, we examined the: (i) relationship between quadriceps central and peripheral neuromuscular function with RFD/RTD in isometric knee extension, isometric mid-thigh pull (IMTP), and sprint cycling; and (ii) relationship among RFD/RTD and peak force/torque between protocols. METHODS: Eighteen trained cyclists completed two familiarisation and two experimental sessions. Each session involved an isometric knee extension, IMTP, and sprint cycling protocol, where peak force/torque, average and peak RFD/RTD, and early (0-100 ms) and late (0-200 ms) RFD/RTD were measured. Additionally, measures of quadriceps central and peripheral neuromuscular function were assessed during the knee extension. RESULTS: Strong relationships were observed between quadriceps early EMG activity (EMG50/M) and knee extension RTD (r or ρ = 0.51-0.65) and IMTP late RFD (r = 0.51), and between cycling early or late RTD and peak twitch torque (r or ρ = 0.70-0.75). Strong-to-very strong relationships were observed between knee extension, IMTP, and sprint cycling for peak force/torque, early and late RFD/RTD, and peak RFD/RTD (r or ρ = 0.59-0.80). CONCLUSION: In trained cyclists, knee extension RTD or IMTP late RFD are related to measures of quadriceps central neuromuscular function, while cycling RTD is related to measures of quadriceps peripheral neuromuscular function. Further, the strong associations among force/torque measures between tasks indicate a level of transferability across tasks.

The delta concept does not effectively normalise exercise responses to exhaustive interval training.

OBJECTIVES: This study was designed to quantify inter- and intra-individual variability in performance, physiological, and perceptual responses to high-intensity interval training prescribed using the percentage of delta (%Δ) method, in which the gas exchange threshold and maximal oxygen uptake (V̇O2max) are taken into account to normalise relative exercise intensity. DESIGN: Repeated-measures, within-subjects design with mixed-effects modelling. METHODS: Eighteen male and four female cyclists (age: 36 ±â€¯12 years, height: 178 ±â€¯10 cm, body mass: 75.2 ±â€¯13.7 kg, V̇O2max: 51.6 ±â€¯5.3 ml·kg-1·min-1) undertook an incremental test to exhaustion to determine the gas exchange threshold and V̇O2max as prescription benchmarks. On separate occasions, participants then completed four high-intensity interval training sessions of identical intensity (70 %Δ) and format (4-min on, 2-min off); all performed to exhaustion. Acute high-intensity interval training responses were modelled with participant as a random effect to provide estimates of inter- and intra-individual variability. RESULTS: Greater variability was generally observed at the between- compared with the within-individual level, ranging from 50 % to 89 % and from 11 % to 50 % of the total variability, respectively. For the group mean time to exhaustion of 20.3 min, inter- and intra-individual standard deviations reached 9.3 min (coefficient of variation = 46 %) and 4.5 min (coefficient of variation = 22 %), respectively. CONCLUSIONS: Due to the high variability observed, the %Δ method does not effectively normalise the relative intensity of exhaustive high-intensity interval training across individuals. The generally larger inter- versus intra-individual variability suggests that day-to-day biological fluctuations and/or measurement errors cannot explain the identified shortcoming of the method.

Change in sprint cycling torque is not associated with change in isometric force following six weeks of sprint cycling and resistance training in strength-trained novice cyclists.

Strong relationships exist between sprint cycling torque and isometric mid-thigh pull (IMTP) force production at one timepoint; however, the relationships between the changes in these measures following a training period are not well understood. Accordingly, this study examined the relationships in the changes of sprint cycling torque and IMTP force following six-weeks of sprint cycling and resistance training performed by strength-trained novice cyclists (n = 14). Cycling power, cadence, torque and IMTP force (Peak force [PF]/torque, average and peak rate of force/torque development [RFD/RTD], and RFD/RTD from 0 to 100 ms and 0-200 ms) were assessed before and after training. Training consisted of three resistance and three sprint cycling sessions per week. Training resulted in improvements in IMTP PF (13.1%) and RFD measures (23.7%-32.5%), cycling absolute (10.7%) and relative (10.5%) peak power, peak torque (11.7%) and RTD measures (27.9%-56.7%). Strong-to-very strong relationships were observed between cycling torque and IMTP force measures pre- (r = 0.57-0.84; p < 0.05) and post-training (r = 0.63-0.87; p < 0.05), but no relationship (p > 0.05) existed between training-induced changes in cycling torque and IMTP force. Divergent training-induced changes in sprint cycling torque and IMTP force indicate that these measures assess distinct neuromuscular attributes. Training-induced changes in IMTP force are not indicative of training-induced changes in sprint cycling torque.

Assessing the rate of torque development in sprint cycling: a methodological study.

The present study examined (i) the magnitude of the rate of torque development (RTD) and (ii) the between-day reliability of RTD at the start of a cycling sprint when sprint resistance, sprint duration, and the pedal downstroke were altered. Nineteen well-trained cyclists completed one familiarisation and three testing sessions. Each session involved one set of 1-s sprints and one set of 5-s sprints. Each set contained one moderate (0.3 N m kg-1), one heavy (0.6 N m kg-1), and one very heavy (1.0 N m kg-1) resistance sprint. RTD measures (average and peak RTD, RTD 0-100 ms, and RTD 0-200 ms) were calculated for downstroke 1 in the 1-s sprint. For the 5-s sprints, RTD measures were calculated for each of the first three downstrokes, as an average of downstrokes 1 and 2, and as an average of downstrokes 2 and 3. Whilst RTDs were greatest in downstroke 3 at all resistances, the greatest number of reliable RTD measures were obtained using the average of downstrokes 2 and 3 with heavy or very heavy resistances, where average and peak RTD, and RTD 0-200 ms were deemed reliable (ICC ≥ 0.8, CV ≤ 10%). Since only 1-2 downstrokes can be completed within 1 s, the greatest RTD reliability cannot be achieved using a 1-s sprint; therefore, the average of downstrokes 2 and 3 during a >2-s cycling sprint (e.g. 5-s test) with heavy or very heavy resistance is recommended for the assessment of RTD in sprint cyclists.HighlightsWhilst RTD measures were greatest in pedal downstroke 3 at all resistances, the greatest number of reliable RTD measures were obtained using the average of pedal downstrokes 2 and 3 with heavy or very heavy resistances, with average and peak RTD, and RTD 0-200 ms having acceptable reliability.RTD 0-100 ms and all RTD measurements for downstroke 1 were not reliable and should not be used. As only 1-2 downstrokes can be performed in 1 s, the greatest RTD reliability cannot be achieved using a 1-s sprint. Instead, RTD may be evaluated using a 5-s sprint.

Energy Expenditure Equation Choice: Effects on Cycling Efficiency and its Reliability.

PURPOSE: There are several published equations to calculate energy expenditure (EE) from gas exchanges. The authors assessed whether using different EE equations would affect gross efficiency (GE) estimates and their reliability. METHODS: Eleven male and 3 female cyclists (age 33 [10] y; height: 178 [11] cm; body mass: 76.0 [15.1] kg; maximal oxygen uptake: 51.4 [5.1] mL·kg-1·min-1; peak power output: 4.69 [0.45] W·kg-1) completed 5 visits to the laboratory on separate occasions. In the first visit, participants completed a maximal ramp test to characterize their physiological profile. In visits 2 to 5, participants performed 4 identical submaximal exercise trials to assess GE and its reliability. Each trial included three 7-minute bouts at 60%, 70%, and 80% of the gas exchange threshold. EE was calculated with 4 equations by Péronnet and Massicotte, Lusk, Brouwer, and Garby and Astrup. RESULTS: All 4 EE equations produced GE estimates that differed from each other (all P < .001). Reliability parameters were only affected when the typical error was expressed in absolute GE units, suggesting a negligible effect-related to the magnitude of GE produced by each EE equation. The mean coefficient of variation for GE across different exercise intensities and calculation methods was 4.2%. CONCLUSIONS: Although changing the EE equation does not affect GE reliability, exercise scientists and coaches should be aware that different EE equations produce different GE estimates. Researchers are advised to share their raw data to allow for GE recalculation, enabling comparison between previous and future studies.