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.

Purpose: Road cycling is a sport with extreme physiological demands. Therefore, there is a need to find new strategies to improve performance. Heart-rate variability (HRV) has been suggested as an effective alternative for prescribing training load against predefined training programs. The purpose of this study was to examine the effect of training prescription based on HRV in road cycling performance. Methods: Seventeen well-trained cyclists participated in this study. After an initial evaluation week, cyclists performed 4 baseline weeks of standardized training to establish their resting HRV. Then, cyclists were divided into 2 groups, an HRV-guided group and a traditional periodization group, and they carried out 8 training weeks. Cyclists performed 2 evaluation weeks, after and before a training week. During the evaluation weeks, cyclists performed a graded exercise test to assess maximal oxygen uptake, peak power output, and ventilatory thresholds with their corresponding power output (VT1, VT2, WVT1, and WVT2, respectively) and a 40-min simulated time trial. Results: The HRV-guided group improved peak power output (5.1% [4.5%]; P = .024), WVT2 (13.9% [8.8%]; P = .004), and 40-min all-out time trial (7.3% [4.5%]; P = .005). Maximal oxygen uptake and WVT1 remained similar. The traditional periodization group did not improve significantly after the training week. There were no differences between groups. However, magnitude-based inference analysis showed likely beneficial and possibly beneficial effects for the HRV-guided group instead of the traditional periodization group in 40-min all-out time trial and peak power output, respectively. Conclusion: Daily training prescription based on HRV could result in a better performance enhancement than a traditional periodization in well-trained cyclists.

Purpose: This systematic review with meta-analysis was conducted to establish whether heart rate variability (HRV)-guided training enhances cardiac-vagal modulation, aerobic fitness, or endurance performance to a greater extent than predefined training while accounting for methodological factors. Methods: We searched Web of Science Core Collection, Pubmed, and Embase databases up to October 2020. A random-effects model of standardized mean difference (SMD) was estimated for each outcome measure. Chi-square and the I² index were used to evaluate the degree of homogeneity. Results: Accounting for methodological factors, HRV-guided training was superior for enhancing vagal-related HRV indices (SMD₊ = 0.50 (95% confidence interval (CI) = 0.09, 0.91)), but not resting HR (SMD₊ = 0.04 (95% CI = −0.34, 0.43)). Consistently small but non-significant (p > 0.05) SMDs in favor of HRV-guided training were observed for enhancing maximal aerobic capacity (SMD₊ = 0.20 (95% CI = −0.07, 0.47)), aerobic capacity at second ventilatory threshold (SMD₊ = 0.26 (95% CI = −0.05, 0.57)), and endurance performance (SMD₊ = 0.20 (95% CI = −0.09, 0.48)), versus predefined training. No heterogeneity was found for any of the analyzed aerobic fitness and endurance performance outcomes. Conclusion: Best methodological practices pertaining to HRV index selection, recording position, and approaches for establishing baseline reference values and daily changes (i.e., fixed or rolling HRV averages) require further study. HRV-guided training may be more effective than predefined training for maintaining and improving vagal-mediated HRV, with less likelihood of negative responses. However, if HRV-guided training is superior to predefined training for producing group-level improvements in fitness and performance, current data suggest it is only by a small margin.

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.

Javaloyes, A, Sarabia, JM, Lamberts, RP, Plews, D, and Moya-Ramon, M. Training prescription guided by heart rate variability vs. block periodization in well-trained cyclists. J Strength Cond Res 34(6): 1511–1518, 2020—Predefined training programs are common place when prescribing training. Within predefined training, block periodization (BP) has emerged as a popular methodology because of its benefits. Heart rate variability (HRV) has been proposed as an effective tool for prescribing training. The aim of this study is to examine the effect of HRV-guided training against BP in road cycling. Twenty well-trained cyclists participated in this study. After a preliminary baseline period to establish their resting HRV, cyclists were divided into 2 groups: an HRV-guided group and a BP group, and they completed 8 training weeks. Cyclists completed 3 evaluations weeks, before and after each period. During the evaluation weeks, cyclists performed: (a) a graded exercise test to assess V̇o ₂max, peak power output (PPO), and ventilatory thresholds with their corresponding power output (VT1, VT2, WVT1, and WVT2, respectively) and (b) a 40-minute simulated time-trial (40 TT). The HRV-guided group improved V̇o ₂max (p = 0.03), PPO (p = 0.01), WVT2 (p = 0.02), WVT1 (p = 0.01), and 40 TT (p = 0.04). The BP group improved WVT2 (p = 0.02). Between-group fitness and performance were similar after the study. The HRV-guided training could lead to a better timing in training prescription than BP in road cycling.

The aims of this study were to report the relative age effect in different competitive levels and field positions and to analyse the differences within and between different competitive levels and field positions. Data for 203 young soccer players (14.2 ± 1.1 years) included anthropometrics and physical performance (Countermovement jump [CMJ], 30-m sprint, T-test and Yo-Yo IR1). Their competitive level and their field position were registered. The percentage of relative older players (1^stHY) was higher in the better competitive levels (L1: 80.6%, p <.001; L2: 68.2%, p <.001 and L3: 58.5%, p <.01), but it was similar between field positions (DF: 68.1%, p <.001; MF: 69.6%, p <.001 and FW: 67.2%, p <.001). Anthropometrical and physical performance differences were found between players of different competitive levels but not between relative older and younger players in each competitive level and field position. The relative age effect is higher in the better competitive levels. Anthropometrical and physical performance differences between players are not due to the relative age but to the level of competition. Relatively older players do not seem to be more likely to be selected for specific field positions. The causes of relative age effect need more research.