In the present study, we investigated the relation between cognitive performance and heart rate variability as a function of fitness level. We measured the effect of three cognitive tasks (the psychomotor vigilance task, a temporal orienting task, and a duration discrimination task) on the heart rate variability of two groups of participants: a high-fit group and a low-fit group. Two major novel findings emerged from this study. First, the lowest values of heart rate variability were found during performance of the duration discrimination task, compared to the other two tasks. Second, the results showed a decrement in heart rate variability as a function of the time on task, although only in the low-fit group. Moreover, the high-fit group showed overall faster reaction times than the low-fit group in the psychomotor vigilance task, while there were not significant differences in performance between the two groups of participants in the other two cognitive tasks. In sum, our results highlighted the influence of cognitive processing on heart rate variability. Importantly, both behavioral and physiological results suggested that the main benefit obtained as a result of fitness level appeared to be associated with processes involving sustained attention.

Heart rate variability (HRV) has been proposed as a useful marker that can show the performance adaptation and optimize the training process in elite athletes. The development of wearable technology permits the measurement of this marker through smartphone applications. The purpose of this study is to assess the validity and reliability of short and ultra-short HRV measurements in elite cyclists using different smartphone applications. Twenty-six professional cyclists were measured at rest in supine and in seated positions through the simultaneous use of an electrocardiogram and two different smartphone applications that implement different technologies to measure HRV: Elite HRV (with a chest strap) and Welltory (photoplethysmography). Level of significance was set at p < 0.05. Compared to an electrocardiogram, Elite HRV and Welltory showed no differences neither in supine nor in seated positions (p > 0.05) and they showed very strong to almost perfect correlation levels (r = 0.77 to 0.94). Furthermore, no differences were found between short (5 min) and ultra-short (1 min) length measurements. Intraclass correlation coefficient showed good to excellent reliability and the standard error of measurement remained lower than 6%. Both smartphone applications can be implemented to monitor HRV using short- and ultra-short length measurements in elite endurance athletes.

Background:The evaluation of performance in endurance athletes and the subsequent individualisation of training is based on the determination of individual physiological thresholds during incremental tests. Gas exchange or blood lactate analysis are usually implemented for this purpose, but these methodologies are expensive and invasive. The short‐term scaling exponent alpha 1 of detrended Fluctuation Analysis (DFA‐α1) of the Heart Rate Variability (HRV) has been proposed as a non‐invasive methodology to detect intensity thresholds.Purpose:The aim of this study is to analyse the validity of DFA‐α1 HRV analysis to determine the individual training thresholds in elite cyclists and to compare them against the lactate thresholds.Methodology:38 male elite cyclists performed a graded exercise test to determine their individual thresholds. HRV and blood lactate were monitored during the test. The first (LT1 and DFA‐α1‐0.75, for lactate and HRV, respectively) and second (LT2 and DFA‐α1‐0.5, for lactate and HRV, respectively) training intensity thresholds were calculated. Then, these points were matched to their respective power output (PO) and heart rate (HR).Results:There were no significant differences (p> 0.05) between the DFA‐α1‐0.75 and LT1 with significant positive correlations in PO (r = 0.85) and HR (r = 0.66). The DFA‐α1‐0.5 was different against LT2 in PO (p= 0.04) and HR (p= 0.02), but it showed significant positive correlation in PO (r = 0.93) and HR (r = 0.71).Conclusions:The DFA1‐a‐0.75 can be used to estimate LT1 non‐invasively in elite cyclists. Further research should explore the validity of DFA‐α1‐0.5.Highlights The power and heart rate values derived from the DFA‐α1‐0.75 threshold showed high levels of validity and agreement when they were compared against the first lactate threshold. The second lactate threshold and the DFA‐α1‐0.5 were different (p< 0.05) but showed high levels of correlation. The detrended fluctuation analysis is a valid method to estimate the first lactate threshold and more studies are needed to verify its validity with the second lactate threshold.

Durability and repeatability (i.e. the ability to sustain high power output values under fatigue and to endure repeated high‐intensity efforts, respectively) are emerging as cycling performance determinants. We aimed to analyze whether these markers differ between professional cyclists of two competition levels (WorldTour [WT] and Proteam [PT]) during a Grand Tour. We studied 8 WT and 7 PT cyclists who competed in “La Vuelta 2020”. Durability was assessed with the mean maximal power (MMP) values attained between 5 sec–30 min after different levels of mechanical work done (0–35 kJ·kg⁻¹). Repeatability was assessed as the ability to repeat efforts >95% MMP. Although no differences were found for durability during the whole race (p = 0.209), a significant interaction effect was found in separate analyses by week (p = 0.011). Thus, during the first week and in the “fresh” state (0 kJ·kg⁻¹), WT cyclists solely attained significantly higher MMP values for 30‐min efforts. However, these differences enlarged with accumulating levels of fatigue (e.g. significantly higher MMP values in WT cyclists for 30‐sec, 1‐min, 5‐min, 20‐min and 30‐min efforts after 35 kJ·kg⁻¹). On the other hand, no between‐group differences were found in repeatability for the whole race (p = 0.777) or in separate analyses by week (p = 0.808). In summary, the present results support the role of durability (but not of repeatability) as a performance indicator during professional cycling races.Highlights The present study suggests that cyclists from different professional team categories (WorldTour [WT] or ProTeam [PT]) show comparable mean maximal power (MMP) values during a Grand Tour when fatigue is not considered. However, WT cyclists seem to attain greater MMP values than PT ones as the race progresses (from the first to the third week) and with accumulating levels of work done (from 0 to 35 kJ·kg⁻¹) during a given stage. In turn, no differences were found between WT and PT cyclists in the number of high‐intensity efforts (>95% of their individual MMP) performed during the race. These findings would highlight the role of durability (the ability to attenuate fatigue‐induced decline in performance during prolonged exercise) as a determining factor of cycling performance.

Ation to the phases of a bicycle motocross (BMX) race and whether this profile is related to the variables, Difficulty of track and Techniques used. After an initial test for determining maximum pedaling power (Pmax), 9 athletes belonging to the Spanish national team completed 3 series of 3 different types of races: (a) Complete track without pedaling; (b) Track, pedaling only at the gate start; and (c) track with free pedaling. The triple test was carried out over 3 days and on different level tracks: (a) high difficulty (HD), (b) medium difficulty (MD), and (c) low difficulty (LD). Our results show that average peak power applied in the BMX race was 85.21 ± 2.15% Pmax, coming down to 73.02 ± 18.38% at the gate start and to 51.37 ± 15.84% on the first straight. On the other hand, mean power (MP) in the BMX race is 33.79 ± 8.60% MPmax, with statistically significant differences in relation to the difficulty of the track (p < 0.000; 0.009; higher in the easiest). The mean velocity developed is 34.21 ± 1.0 km·h−1 with significant differences (p < 0.000) in relation to the difficulty of the track. Acyclic efforts accounted for 86.3%, and cyclic efforts accounted for the remaining 16.7% of the overall performance in the race, with differences in relation to the difficulty of the track (p ≤ 0.003). Both power profile and performance (measured as velocity) are dependent on the phases and techniques of the race and are significantly affected by the level of difficulty of the track. The greater the technical level of the track, the lesser the possibility of developing cyclic power and vice versa....