Effects of Body Weight vs. Lean Body Mass on Wingate Anaerobic Test Performance in Endurance Athletes.

The aim of this study was to determine the influence of body weight or lean body mass-based load on Wingate Anaerobic Test performance in male and female endurance trained individuals. Thirty-one participants (22 male cyclists and triathletes and 9 female triathletes) completed two randomized Wingate Anaerobic Test (body weight and lean body mass loads) in stationary start. There were no significant differences in power outputs variables between loads in any group. However, when comparing specific groups within the sample (e. g. cyclists vs cyclists) medium to large effect sizes were observed for Relative Mean Power Output (ES=0.53), Relative Lowest Power (ES=0.99) and Relative Power Muscle Mass (ES=0.54). Regarding gender differences, male cyclists and triathletes displayed higher relative and absolute power outputs (p<0.001) compared to female triathletes regardless of the protocol used. FI was lower in female triathletes compared to male triathletes and cyclists in body weight (p<0.001) and lean body mass (p<0.01) protocols. Body composition and anthropometric characteristics were similar in male cyclists and triathletes, but there were differences between genders. These results suggest that using either body weight-based or lean body mass-based load can be used interchangeably. However, there may be some practically relevant differences when evaluating this on an individual level.

Utility of the W´BAL model in training programme design for masters cyclists.

The present study aims to determine the utility of integrating balance model (W´BAL-INT) in designing interval training programmes as assessed by improvements in power output, critical power (CP), and W prime (W´) defined as the finite work capacity above CP. Fourteen male cyclists (age = 42 ± 7 yr, body mass = 69.6 ± 6.5 kg, height = 175 ± 5 cm, CP = 302 ± 32 W, relative CP = 4.35 ± 0.66 W·kg-1) were randomized into two training groups: Short-Medium-Long intervals (SML-INT; n = 7) or Long intervals (L-INT, n = 7) [training sessions separated by 72 h], along with 3-4 sessions of moderate intensity training per week, for 4 weeks. All sessions were designed to result in the complete depletion of the W´ as gauged by the W´BAL-INT. CP and W´ were assessed using the specified efforts (i.e. 12, 7 and 3 min) and calculated with the 2-parameter CP linear model. Training loads between the groups were compared using different metrics. CP improved in both the SML-INT and L-INT groups by 5 ± 4% and 6 ± 5% (p < 0.001) respectively, without significant changes in W´. Mean maximal power over 3, 7 and 12 min increased significantly in the SML-INT group by 5%, 4% and 9%, (p < 0.05) without significant changes in the L-INT group. There were no differences between groups in training zone distribution or training load using BikeScore and relative intensity, but there was significantly (p < 0.05) higher TRIMPS for the Long-INT group. Therefore, W´BAL model may prove to be a useful tool for coaches to construct SML-INT training programmes.HighlightsCP significantly improved with both training models during the present intervention and in power output in some of the time to exhaustion (TTE) trials, despite a difference in training load between the groups as assessed by TRIMPS.We recommend designing endurance training sessions based on the use of the W´BAL-INT model.The structured interval model can be an easy and standardized way for cyclists and coaches to monitor their potential for flat and mid-mountain stages.

Training Periodization, Intensity Distribution, and Volume in Trained Cyclists: A Systematic Review.

A well-planned periodized approach endeavors to allow road cyclists to achieve peak performance when their most important competitions are held. PURPOSE: To identify the main characteristics of periodization models and physiological parameters of trained road cyclists as described by discernable training intensity distribution (TID), volume, and periodization models. METHODS: The electronic databases Scopus, PubMed, and Web of Science were searched using a comprehensive list of relevant terms. Studies that investigated the effect of the periodization of training in cyclists and described training load (volume, TID) and periodization details were included in the systematic review. RESULTS: Seven studies met the inclusion criteria. Block periodization (characterized by employment of highly concentrated training workload phases) ranged between 1- and 8-week blocks of high-, medium-, or low-intensity training. Training volume ranged from 8.75 to 11.68 h·wk-1 and both pyramidal and polarized TID were used. Traditional periodization (characterized by a first period of high-volume/low-intensity training, before reducing volume and increasing the proportion of high-intensity training) was characterized by a cyclic progressive increase in training load, the training volume ranged from 7.5 to 10.76 h·wk-1, and pyramidal TID was used. Block periodization improved maximum oxygen uptake (VO2max), peak aerobic power, lactate, and ventilatory thresholds, while traditional periodization improved VO2max, peak aerobic power, and lactate thresholds. In addition, a day-by-day programming approach improved VO2max and ventilatory thresholds. CONCLUSIONS: No evidence is currently available favoring a specific periodization model during 8 to 12 weeks in trained road cyclists. However, few studies have examined seasonal impact of different periodization models in a systematic way.

Relative Proximity of Critical Power and Metabolic/Ventilatory Thresholds: Systematic Review and Meta-Analysis.

BACKGROUND: Critical power (CP) has been redefined as the new 'gold standard' that represents the boundary between the heavy- and severe-exercise intensity domains and hence the maximal metabolic steady state (MMSS). However, several other "thresholds", for instance, the maximal lactate steady state [MLSS], ventilatory thresholds [VT1, VT2] and respiratory compensation point [RCP]) have been considered synonymous with CP. OBJECTIVE: This study aimed to systematically review the scientific literature and perform a meta-analysis to determine the degree of correspondence/difference between CP and MLSS, VT1, VT2 and RCP. METHODS: A literature search on 2 databases (Scopus and Web of Science) was conducted on October 2, 2019. After analyzing 356 resultant articles, studies were included if they met the following inclusion criteria: (a) studies were randomized controlled trials, (b) studies included interrelations between CP and VT1, VT2, MLSS, RCP. Articles were excluded if they constituted duplicate articles or did not meet the inclusion criteria. Nine studies met the inclusion criteria and were included in this meta-analysis. This resulted in 104 participants. A random effects weighted meta-analysis with correlation coefficients was used to pool the results. RESULTS: The pooled correlation coefficient of CP and all thresholds analyzed was r = 0.73 (p > 0.00001). The subgroup analysis for each threshold with CP demonstrated significant correlation coefficients of r = 0.80 (95% CI [0.40; 1.21], Z = 3.90, p = 0.0001) for CP & RCP; r = 0.77 (CI 95% = [0.36; 1.18], Z = 3.71, p = 0.0002) for CP & MLSS; r = 0.76 (CI 95% = [0.31; 1.21], Z = 3.32, p = 0.0009) for CP & VT1. However, CP & VT2, r = 0.39 (CI 95% = [- 0.37; 1.15], Z = 1.01, p = 0.31) were not significantly correlated. Despite the significant correlations between CP and VT1, MLSS and RCP these variables and VT2 under- (VT1, 30%; MLSS, 11%) or over-estimated (RCP, 6%; VT2, 21%) CP. CONCLUSION: Regardless of the presence of significant correlations among CP and ventilatory or metabolic thresholds CP differs significantly from each. Thus, logically, if CP represents the best estimate of the heavy-severe exercise intensity transition none of the thresholds considered (i.e., VT1, VT2, MLSS, RCP), at least as determined in the studies analyzed herein, should be considered synonymous with such.