Analysis of a Submaximal Cycle Test to Monitor Adaptations to Training: Implications for Optimizing Training Prescription.

ABSTRACT: Capostagno, B, Lambert, MI, and Lamberts, RP. Analysis of a submaximal cycle test to monitor adaptations to training: Implications for optimizing training prescription. J Strength Cond Res 35(4): 924-930, 2021-The Lamberts and Lambert Submaximal Cycle Test (LSCT) was developed to monitor training adaptation to optimize the training prescription of cyclists. However, it is not known which of the variables within the LSCT are most closely associated with changes in training status. The aim of this study was to retrospectively analyze the LSCT data of cyclists (n = 15) who completed a 2-week high-intensity interval training intervention. The cyclists were retrospectively allocated to 1 of 2 groups based on the change in their 40-km time trial (40-km TT) performance. The "adapters" (n = 7) improved their 40-km TT performance, while the "nonadapters" (n = 8) failed to improve their 40-km TT performance. The variables measured in the LSCT were analyzed to determine which measures tracked the improvements in 40-km TT performance the best. Heart rate recovery increased significantly during the training period in the "adapters" group, but decreased in the "nonadapters" group. Mean power output in stage 2 of the LSCT tended to increase during the high-intensity interval training period in the "adapters" group and was unchanged in the "nonadapters" group. The findings of this study suggest that heart rate recovery and mean power output during stage 2 are the most sensitive markers to track changes in training status within the LSCT.

A Systematic Review of Submaximal Cycle Tests to Predict, Monitor, and Optimize Cycling Performance.

Finding the optimal balance between high training loads and recovery is a constant challenge for cyclists and their coaches. Monitoring improvements in performance and levels of fatigue is recommended to correctly adjust training to ensure optimal adaptation. However, many performance tests require a maximal or exhaustive effort, which reduces their real-world application. The purpose of this review was to investigate the development and use of submaximal cycling tests that can be used to predict and monitor cycling performance and training status. Twelve studies met the inclusion criteria, and 3 separate submaximal cycling tests were identified from within those 12. Submaximal variables including gross mechanical efficiency, oxygen uptake (VO2), heart rate, lactate, predicted time to exhaustion (pTE), rating of perceived exertion (RPE), power output, and heart-rate recovery (HRR) were the components of the 3 tests. pTE, submaximal power output, RPE, and HRR appear to have the most value for monitoring improvements in performance and indicate a state of fatigue. This literature review shows that several submaximal cycle tests have been developed over the last decade with the aim to predict, monitor, and optimize cycling performance. To be able to conduct a submaximal test on a regular basis, the test needs to be short in duration and as noninvasive as possible. In addition, a test should capture multiple variables and use multivariate analyses to interpret the submaximal outcomes correctly and alter training prescription if needed.

Standardized versus customized high-intensity training: effects on cycling performance.

PURPOSE: To determine whether a submaximal cycling test could be used to monitor and prescribe high-intensity interval training (HIT). METHODS: Two groups of male cyclists completed 4 HIT sessions over a 2-wk period. The structured-training group (SG; n = 8, VO2max = 58.4 ± 4.2 mL · min-1 · kg-1) followed a predetermined training program while the flexible-training group (FG; n = 7, VO2max = 53.9 ± 5.0 mL · min-1 · kg-1) had the timing of their HIT sessions prescribed based on the data of the Lamberts and Lambert Submaximal Cycle Test (LSCT). RESULTS: Effect-size calculations showed large differences in the improvements in 40-km time-trial performance after the HIT training between SG (8 ± 45 s) and FG (48 ± 42 s). Heart-rate recovery, monitored during the study, tended to increase in FG and remain unchanged in SG. CONCLUSIONS: The results of the current study suggest that the LSCT may be a useful tool for coaches to monitor and prescribe HIT.

Effect of resistance training regimens on treadmill running and neuromuscular performance in recreational endurance runners.

The purpose of this study was to assess the effects of heavy resistance, explosive resistance, and muscle endurance training on neuromuscular, endurance, and high-intensity running performance in recreational endurance runners. Twenty-seven male runners were divided into one of three groups: heavy resistance, explosive resistance or muscle endurance training. After 6 weeks of preparatory training, the groups underwent an 8-week resistance training programme as a supplement to endurance training. Before and after the 8-week training period, maximal strength (one-repetition maximum), electromyographic activity of the leg extensors, countermovement jump height, maximal speed in the maximal anaerobic running test, maximal endurance performance, maximal oxygen uptake ([V·]O(2max)), and running economy were assessed. Maximal strength improved in the heavy (P = 0.034, effect size ES = 0.38) and explosive resistance training groups (P = 0.003, ES = 0.67) with increases in leg muscle activation (heavy: P = 0.032, ES = 0.38; explosive: P = 0.002, ES = 0.77). Only the heavy resistance training group improved maximal running speed in the maximal anaerobic running test (P = 0.012, ES = 0.52) and jump height (P = 0.006, ES = 0.59). Maximal endurance running performance was improved in all groups (heavy: P = 0.005, ES = 0.56; explosive: P = 0.034, ES = 0.39; muscle endurance: P = 0.001, ES = 0.94), with small though not statistically significant improvements in [V·]O(2max) (heavy: ES = 0.08; explosive: ES = 0.29; muscle endurance: ES = 0.65) and running economy (ES in all groups < 0.08). All three modes of strength training used concurrently with endurance training were effective in improving treadmill running endurance performance. However, both heavy and explosive strength training were beneficial in improving neuromuscular characteristics, and heavy resistance training in particular contributed to improvements in high-intensity running characteristics. Thus, endurance runners should include heavy resistance training in their training programmes to enhance endurance performance, such as improving sprinting ability at the end of a race.

Higher fat oxidation in running than cycling at the same exercise intensities.

This study examined the differences in fat and carbohydrate oxidation during running and cycling at the same relative exercise intensities, with intensity determined in a number of ways. Specifically, exercise intensity was expressed as a percentage of maximum workload (WL(max)), maximum oxygen uptake (%VO(2max)), and maximum heart rate (%HR(max)) and as rating of perceived exertion (RPE). Ten male triathletes performed maximal running and cycling trials and subsequently exercised at 60%, 65%, 70%, 75%, and 80% of their WL(max). VO(2), HR, RPE, and plasma lactate concentrations were measured during all submaximal trials. Fat and carbohydrate oxidation were calculated from VO(2) and VCO(2) data. A 2-way ANOVA for repeated measures was used to determine any statistically significant differences between exercise modes. Fat oxidation was shown to be significantly higher in running than in cycling at the same relative intensities expressed as either %WL(max) or %VO(2max). Neither were there any significant differences in VO(2max) and HR(max) between the 2 exercise modes, nor in submaximal VO(2) or RPE between the exercise modes at the same %WL(max). However, heart rate and plasma lactate concentrations were significantly higher when cycling at 60% and 65% and 65-80%WL(max), respectively. In conclusion, fat oxidation is significantly higher during running than during cycling at the same relative intensity expressed as either %WL(max) or %VO(2max).