An Index of Non-Linear HRV as a Proxy of the Aerobic Threshold Based on Blood Lactate Concentration in Elite Triathletes.

A non-linear index of heart rate (HR) variability (HRV) known as alpha1 of Detrended Fluctuation Analysis (DFA a1) has been shown to change with increasing exercise intensity, crossing a value of 0.75 at the aerobic threshold (AT) in recreational runners defining a HRV threshold (HRVT). Since large volumes of low-intensity training below the AT is recommended for many elite endurance athletes, confirmation of this relationship in this specific group would be advantageous for the purposes of training intensity distribution monitoring. Nine elite triathletes (7 male, 2 female) attended a training camp for diagnostic purposes. Lactate testing was performed with an incremental cycling ramp test to exhaustion for the determination of the first lactate threshold based on the log-log calculation method (LT1). Concurrent measurements of cardiac beta-to-beat intervals were performed to determine the HRVT. Mean LT1 HR of all 9 participants was 155.8 bpm (±7.0) vs. HRVT HR of 153.7 bpm (±10.1) (p = 0.52). Mean LT1 cycling power was 252.3 W (±48.1) vs. HRVT power of 247.0 W (±53.6) (p = 0.17). Bland-Altman analysis showed mean differences of -1.7 bpm and -5.3 W with limits of agreement (LOA) 13.3 to -16.7 bpm and 15.1 to -25.6 W for HR and cycling power, respectively. The DFA a1-based HRVT closely agreed with the LT1 in a group of elite triathletes. Since large volumes of low-intensity exercise are recommended for successful endurance performance, the fractal correlation properties of HRV show promise as a low-cost, non-invasive option to that of lactate testing for identification of AT-related training boundaries.

Retrospective Analysis of Training Intensity Distribution Based on Race Pace Versus Physiological Benchmarks in Highly Trained Sprint Kayakers.

BACKGROUND: Research results on the training intensity distribution (TID) in endurance athletes are equivocal. This non-uniformity appears to be partially founded in the different quantification methods that are implemented. So far, TID research has solely focused on sports involving the lower-body muscles as prime movers (e.g. running). Sprint kayaking imposes high demands on the upper-body endurance capacity of the athlete. As there are structural and physiological differences between upper- and lower-body musculature, TID in kayaking should be different to lower-body dominant sports. Therefore, we aimed to compare the training intensity distribution during an 8-wk macrocycle in a group of highly trained sprint kayakers employing three different methods of training intensity quantification. METHODS: Heart rate (HR) and velocity during on-water training of nine highly trained German sprint kayakers were recorded during the final 8 weeks of a competition period leading to the national championships. The fractional analysis of TID was based on three zones (Z) derived from either HR (TIDBla-HR) or velocity (TIDBla-V) based on blood lactate (Bla) concentrations (Z1 ≤ 2.5 mmol L-1 Bla, Z2 = 2.5-4.0 mmol L-1 Bla, Z3 ≥ 4.0 mmol L-1 Bla) of an incremental test or the 1000-m race pace (TIDRace): Z1 ≤ 85% of race pace, Z2 = 86-95% and Z3 ≥ 95%. RESULTS: TIDBla-V (Z1: 68%, Z2: 14%, Z3: 18%) differed from TIDBla-HR (Z1: 91%, Z2: 6%, Z3: 3%) in each zone (all p < 0.01). TIDRace (Z1: 73%, Z2: 20%, Z3: 7%) differed to Z3 in TIDBla-V (p < 0.01) and all three TIDBla-HR zones (all p < 0.01). Individual analysis revealed ranges of Z1, Z2, Z3 fractions for TIDBla-HR of 85-98%, 2-11% and 0.1-6%. For TIDBla-V, the individual ranges were 41-82% (Z1), 6-30% (Z2) and 8-30% (Z3) and for TIDRace 64-81% (Z1), 14-29% (Z2) and 4-10% (Z3). CONCLUSION: The results show that the method of training intensity quantification substantially affects the fraction of TID in well-trained sprint kayakers. TIDRace determination shows low interindividual variation compared to the physiologically based TIDBla-HR and TIDBla-V. Depending on the aim of the analysis TIDRace, TIDBla-HR and TIDBla-V have advantages as well as drawbacks and may be implemented in conjunction to maximize adaptation.

Training Characteristics of a World Championship 5000-m Finalist and Multiple Continental Record Holder Over the Year Leading to a World Championship Final.

PURPOSE: Optimal training for endurance performance remains a debated topic. In this case study, the training of a world-class middle-/long-distance runner over a year's duration is presented. METHODS: The training is analyzed via 2 methods to define training intensity distribution (TID) (1) by physiological zones and (2) by zones based on race pace. TID was analyzed over the full season, but also over the final 6, 12, and 26 weeks to allow for consideration of periodization/phases of season. The results of both methods are compared. Other training data measured include volume and number of sessions. RESULTS: The average weekly volume for the athlete was 145.8 (24.8) km·wk-1. TID by physiological analysis was polarized for the last 6 weeks of the season but was pyramidal when analyzed over the final 12, 26, and 52 weeks of the season. TID by race-pace analysis was pyramidal across all time points. The athlete finished 12th in the final of the World Championship 5000-m and made the semifinal of the 1500-m. He was ranked in the top 16 in the world for 1500, 5000, and 10,000 m. CONCLUSION: The results of this study demonstrate a potential flaw with recent work suggesting polarized training as the most effective means to improve endurance performance. Here, different analysis methods produced 2 different types of TID. A polarized distribution was only seen when analyzed by physiological approach, and only during the last 6 weeks of a 52-week season. Longer-term prospective studies relating performance and physiological changes are suggested.

Effects of 16 weeks of pyramidal and polarized training intensity distributions in well-trained endurance runners.

The aim of this study was to investigate the effects of four different training periodizations, based on two different training intensity distributions during a 16-week training block in well-trained endurance runners. Sixty well-trained male runners were divided into four groups. Each runner completed one of the following 16-week training interventions: a pyramidal periodization (PYR); a polarized periodization (POL); a pyramidal periodization followed by a polarized periodization (PYR â†’ POL); and a polarized periodization followed by a pyramidal periodization (POL â†’ PYR). The PYR and POL groups trained with a pyramidal or polarized distribution for 16 weeks. To allow for the change in periodization for the PYR â†’ POL and POL â†’ PYR groups, the 16-week intervention was split into two 8-week phases, starting with pyramidal or polarized distribution and then switching to the other. The periodization patterns were isolated manipulations of training intensity distribution, while training load was kept constant. Participants were tested pre-, mid- and post-intervention for body mass, velocity at 2 and 4 mmol·L-1 of blood lactate concentration (vBLa2, vBLa4), absolute and relative peak oxygen consumption ( V ˙ O 2 peak ) and 5-km running time trial performance. There were significant group × time interactions for relative V ˙ O 2 peak (p < 0.0001), vBLa2 (p < 0.0001) and vBLa4 (p < 0.0001) and 5-km running time trial performance (p = 0.0001). Specifically, participants in the PYR â†’ POL group showed the largest improvement in all these variables (~3.0% for relative V ˙ O 2 peak , ~1.7% for vBLa2, ~1.5% for vBLa4, ~1.5% for 5-km running time trial performance). No significant interactions were observed for body mass, absolute V ˙ O 2 peak , peak heart rate, lactate peak and rating of perceived exertion. Each intervention effectively improved endurance surrogates and performance in well-trained endurance runners. However, the change from pyramidal to polarized distribution maximized performance improvements, with relative V ˙ O 2 peak representing the only physiological correlate.

Training intensity distribution on running time in amateur endurance runners: a scoping review / Distribución de intensidades de entrenamiento sobre el tiempo de carrera en corredores recreativos de resistencia: revisión de alcance

Problem: Intensity in endurance training is important for improving race time; its optimal handling in amateur runners has not been extensively studied. The polarized training intensity distribution (TID) model emerges as a possibility to reduce race time; however, effect of this model remains to be demonstrated compared to other TID models. Objective: The objective of this study is to explore the current state of the evidence and its the gaps, according to the effect of the polarized TID model on race time in amateur runners compared to other TID models. Method: A scoping review without date restrictions was carried out in PubMed, EBSCO, SciELO, LILACS, and Google Scholar. Randomized controlled studies, quasi-experimental studies, and case studies, which comprise polarized TID model in amateur runners on race time, were include. Results: Five studies evaluated the effect on running time using the polarized TID model compared to other models in amateur runners; four of them did not show differences between groups in the race times in two, five, and ten km. Only one study showed significant diferences in the race time at 21 km. Conclusions: The model with polarized TID did not show significant differences in race time compared to other models, except for a case report in which the polarized TID was higher by 21 km compared to the threshold TID: 1 hour. 20 min. 22 seconds and 1 hour. 26 min. 34s, respectively. The scarce evidence found, the heterogeneity in the distances in the evaluated race time, the distribution of zones in the same TID, the duration of the interventions, and the monitoring of the loads, are the main limitations found in the studies. The polarized TID could contribute to adherence, lower perception of effort, and injury prevention. However, this must be tested in future studies.

Problema: La intensidad en el entrenamiento de la resistencia es importante para mejorar el tiempo de carrera; su manipulación óptima en corredores recreativos no ha sido estudiada ampliamente. El modelo de distribución de intensidad del entrenamiento (DIE) polarizado emerge como posibilidad para reducir el tiempo de carrera. Sin embargo, falta demostrar su efecto comparado con otros modelos de DIE. Objetivo: Explorar el estado actual de la evidencia científica y sus vacíos respecto al efecto del modelo de DIE polarizado sobre el tiempo de carrera en corredores recreativos, en comparación con otros modelos de DIE. Método: Se realizó una revisión de alcance sin restricción de fechas en PubMed, EBSCO, SciELO, LILACS y Google Scholar. Se incluyeron estudios controlados aleatorios, estudios cuasiexperimentales y estudios de caso, que tuvieran como DIE el modelo polarizado en corredores recreativos sobre el tiempo de carrera. Resultados: Cinco estudios evaluaron el efecto en el tiempo de carrera usando el modelo de DIE polarizado comparado con otros modelos en corredores recreativos; cuatro de ellos no mostraron diferencias entre grupos en los tiempos de carrera en dos, cinco y diez km. Solo un estudio mostró diferencias significativas en el tiempo de carrera en 21 km. Conclusiones: El modelo con DIE polarizado no mostró diferencias significativas en el tiempo de carrera comparado con otros modelos, a excepción de un reporte de caso en el cual la DIE polarizado fue superior en 21 km comparado la DIE umbral: 1 hora. 20 min. 22 s y 1 hora. 26 min. 34 s, respectivamente. La escasa evidencia encontrada, la heterogeneidad en las distancias en el tiempo de carrera evaluado, la distribución de zonas en una misma DIE, la duración de las intervenciones y la monitorización de las cargas son las principales limitaciones encontradas en los estudios. La DIE polarizado podría contribuir a la adherencia, a una menor percepción del esfuerzo y a la prevención de lesiones. No obstante, esto debe ser probado en estudios futuros.

The Effect of Polarized Training on the Athletic Performance of Male and Female Cross-Country Skiers during the General Preparation Period.

This study aimed to analyze the effect of 12 weeks of polarized training on body composition, cardiorespiratory function, and upper-body power of male and female cross-country skiers during the general preparation period. A total of 16 national cross-country skiers (8 male and 8 female; 8 national cross-country skiers and 8 national biathlon athletes) participated. Polarization training was conducted for 12 weeks from May to July in 2019 during the general preparation period for cross-country skiers. The low-weight, high-repetition method was used for strength training. The effect of the polarized training on body composition, maximum oxygen intake (VO2max), respiratory exchange rate, all-out time, and ski ergometer exercise time was assessed. There was no change in weight, BMI, and muscle mass in male and female cross-country skiers following the 12 weeks of polarized training (p > 0.05). Male body fat percentage (pre 18.1%, post 12.7%) and female body fat percentage (pre 29.1%, post 21.4%) showed a significant decrease (p < 0.05). After training, VO2max increased by 7.72% in male athletes (pre 71.05 mL/kg/min, post 77.0 mL/kg/min) and 6.32% in female athletes (pre 60.26 mL/kg/min, post 64.33 mL/kg/min). Treadmill exercise time increased by 5.39% for male athletes (pre 1038 s, post 1064 s) and 2.23% for female athletes (pre 855 s, post 874 s). However, there was no significant difference between male and female athletes (p > 0.05). The 50% recovery time from the maximum heart rate to the target heart rate decreased by 64.52% in males (pre 168.8 s, post 102.6 s) and 6.48% in females (pre 135 s, post 129.6 s). Significant differences were found only in male athletes (p < 0.05). The double-pole 500 m exercise duration for the ski ergometer significantly decreased after the training for both sexes (p < 0.05). In this study, the 12 weeks of polarized training improved the body composition and athletic performance of all cross-country skiers. Interestingly, in this study, we confirmed that polarized training had a better effect on cardiorespiratory function in male cross-country skiers than in female cross-country skiers. Conversely, we found that the outcomes of the ski ergometer exercise factors were more effective in female athletes than in male athletes. Therefore, we insist that when applying a polarized training program to athletes, it should be planned in detail by sex, exercise amount, intensity, and type of training.

Comparison of Aerobic Capacity Changes as a Result of a Polarized or Block Training Program among Trained Mountain Bike Cyclists.

This study compared the effectiveness of a block training program and a polarized training program in developing aerobic capacity in twenty trained mountain bike cyclists. The cyclists were divided into two groups: the block training program group (BT) and the polarized training program group (PT). The experiment lasted 8 weeks. During the experiment, the BT group alternated between 17-day blocks consisting of dominant low-intensity training (LIT) and 11-day blocks consisting of sprint interval training (SIT), and high-intensity interval training (HIIT), while the PT group performed SIT, HIIT, and LIT simultaneously. Before and after the experiment, the cyclists performed incremental tests during which maximal oxygen uptake (VO2max), maximal aerobic power (Pmax), power achieved at the first ventilatory threshold (PVT1), and at the second ventilatory threshold (PVT2) were measured. VO2max increased in BT group (from 3.75 ± 0.67 to 4.00 ± 0.75 L∙min-1) and PT group (from 3.66 ± 0.73 to 4.20 ± 0.89 L∙min-1). In addition, Pmax, PVT1, and PVT2 increased in both groups to a similar extent. In conclusion, the polarized training program was more effective in developing the VO2max compared to the block program. In terms of developing other parameters characterizing the cyclists' aerobic capacity, the block and polarized program induced similar results.

The Effect of Polarized Training (SIT, HIIT, and ET) on Muscle Thickness and Anaerobic Power in Trained Cyclists.

This study was undertaken to investigate the effect of two different concepts in a training program on muscle thickness and anaerobic power in trained cyclists. Twenty-six mountain bike cyclists participated in the study and were divided into an experimental group (E), which performed polarized training, comprising sprint interval training (SIT), high-intensity interval training (HIIT), and endurance training (ET), and a control group (C), which performed HIIT and ET. The experiment was conducted over the course of 9 weeks. Laboratory tests were performed immediately before and after the conducted experiment, including an ultrasound measurement of the quadriceps femoris muscle thickness and a sprint interval testing protocol (SITP). During the SITP, the cyclists performed 4 maximal repetitions, 30 s each, with a 90-s rest period between the repetitions. SITP was performed to measure maximal and mean anaerobic power. As a result of the applied training program, the muscle thickness decreased and the mean anaerobic power increased in the experimental group. By contrast, no significant changes were observed in the control group. In conclusion, a decrease in muscle thickness with a concomitant increase in mean anaerobic power resulting from the polarized training program is beneficial in mountain bike cycling.

Real-Time Estimation of Aerobic Threshold and Exercise Intensity Distribution Using Fractal Correlation Properties of Heart Rate Variability: A Single-Case Field Application in a Former Olympic Triathlete.

A non-linear heart rate variability (HRV) index based on fractal correlation properties called alpha1 of Detrended Fluctuation Analysis (DFA-alpha1), has been shown to change with endurance exercise intensity. Its unique advantage is that it provides information about current absolute exercise intensity without prior lactate or gas exchange testing. Therefore, real-time assessment of this metric during field conditions using a wearable monitoring device could directly provide a valuable exercise intensity distribution without prior laboratory testing for different applied field settings in endurance sports. Until of late no mobile based product could display DFA-alpha1 in real-time using off the shelf consumer products. Recently an app designed for iOS and Android devices, HRV Logger, was updated to assess DFA-alpha1 in real-time. This brief research report illustrates the potential merits of real-time monitoring of this metric for the purposes of aerobic threshold (AT) estimation and exercise intensity demarcation between low (zone 1) and moderate (zone 2) in a former Olympic triathlete. In a single-case feasibility study, three practically relevant scenarios were successfully evaluated in cycling, (1) estimation of a HRV threshold (HRVT) as an adequate proxy for AT using Kubios HRV software via a typical cycling stage test, (2) estimation of the HRVT during real-time monitoring using a cycling 6 min stage test, (3) a simulated 1 h training ride with enforcement of low intensity boundaries and real-time HRVT confirmation. This single-case field evaluation illustrates the potential of an easy-to-use and low cost real-time estimation of the aerobic threshold and exercise intensity distribution using fractal correlation properties of HRV. Furthermore, this approach may enhance the translation of science into endurance sports practice for future real-world settings.

Training Distribution in 1500-m Speed Skating: A Case Study of an Olympic Gold Medalist.

At the Olympic level, optimally distributing training intensity is crucial for maximizing performance. PURPOSE: The authors evaluated the effect of training-intensity distribution on anaerobic power as a substitute for 1500-m speed-skating performance in the 4 y leading up to an Olympic gold medal. METHODS: During the preparation phase of the speed-skating season, anaerobic power was recorded periodically (n = 15) using the mean power (in watts) with a 30-s Wingate test. For each training session in the 4 wk prior to each Wingate test, the volume (in hours), training type (specific, simulation, nonspecific, and strength training), and the rating of perceived exertion (RPE; CR-10) were recorded. RESULTS: Compared with the 8 lowest, the 7 highest-scoring tests were preceded by a significantly (P < .01) higher volume of strength training. Furthermore, the RPE distribution of the number of nonspecific training sessions was significantly different (P < .01). Significant (P < .05) correlations highlighted that a larger nonspecific training volume in the lower intensities RPE 2 (r = .735) and 3 (r = .592) was associated positively and the medium intensities RPE 4 (r = -.750) and 5 (r = -.579) negatively with Wingate performance. CONCLUSION: For the subject, the best results were attained with a high volume of strength training and the bulk of nonspecific training at RPE 2 and 3, and specifically not at the adjoining RPE 4 and 5. These findings are surprising given the aerobic nature of training at RPE 2 and 3 and the importance of anaerobic capacity in this middle-distance event.

Six Weeks of Polarized Versus Moderate Intensity Distribution: A Pilot Intervention Study.

BACKGROUND: Previous research indicates that polarized training-intensity-distribution (TID) programs could enhance endurance performance. Short-distance triathletes, however, perform most of their competition-specific training around moderate-intensity intervals. There is still a lack of evidence as to which program is more beneficial during triathlete training. This pilot study examined 6 weeks of training-macrocycle using polarized intensity distribution compared to moderate TID and it's effects on sub-maximal and maximal performance indices during running and cycling. METHODS: Fifteen moderately trained triathletes were either assigned to an intervention group (INT, n = 7, 2 females/5 males, Age: 29.1 ± 7.6) or a control group (CON, n = 8, 2 females/6 males, Age: 30.3 ± 6.1). We used the minimization method (Strata: gender, age competition times, training volumes) to allocate the groups. The participants underwent incremental cycling and running testings before and after the intervention period to assess performance indices until objective exhaustion. CON employed a moderate TID with either medium-intensity (MIT) or low-intensity training (LIT). INT used polarized training intensity distribution (TID), with either LIT or high-intensity training (HIT). Average training hours and anthropometric data did not indicate any differences between CON and INT during the study period. We applied the polarization index of >2 in INT (2.1 ± 0.4) and <1 in CON (0.9 ± 0.3). RESULTS: Both groups notably improved their lactate threshold 2 (+2.8 ± 5.1 %, p = 0.026) and peak (+5.4 ± 6.2 %, p = 0.002) running performance. We did not observe statistically significant time × group interaction effects in any of the performance outcomes between both groups. CONCLUSION: Polarized TID in moderately trained triathletes did not prove to be superior compared to a more moderate TID. However, more studies in larger and more highly trained subjects are needed.

Fractal Correlation Properties of Heart Rate Variability: A New Biomarker for Intensity Distribution in Endurance Exercise and Training Prescription?

Exercise and training prescription in endurance-type sports has a strong theoretical background with various practical applications based on threshold concepts. Given the challenges and pitfalls of determining individual training zones on the basis of subsystem indicators (e.g., blood lactate concentration, respiratory parameters), the question arises whether there are alternatives for intensity distribution demarcation. Considering that training in a low intensity zone substantially contributes to the performance outcome of endurance athletes and exceeding intensity targets based on a misleading aerobic threshold can lead to negative performance and recovery effects, it would be desirable to find a parameter that could be derived via non-invasive, low cost and commonly available wearable devices. In this regard, analytics conducted from non-linear dynamics of heart rate variability (HRV) have been adapted to gain further insights into the complex cardiovascular regulation during endurance-type exercise. Considering the reciprocal antagonistic behavior and the interaction of the sympathetic and parasympathetic branch of the autonomic nervous system from low to high exercise intensities, it may be promising to use an approach that utilizes information about the regulation quality of the organismic system to determine training-intensity distribution. Detrended fluctuation analysis of HRV and its short-term scaling exponent alpha1 (DFA-alpha1) seems suitable for applied sport-specific settings including exercise from low to high intensities. DFA-alpha1 may be taken as an indicator for exercise prescription and intensity distribution monitoring in endurance-type sports. The present perspective illustrates the potential of DFA-alpha1 for diagnostic and monitoring purposes as a "global" system parameter and proxy for organismic demands.

A New Detection Method Defining the Aerobic Threshold for Endurance Exercise and Training Prescription Based on Fractal Correlation Properties of Heart Rate Variability.

The short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA a1), a nonlinear index of heart rate variability (HRV) based on fractal correlation properties, has been shown to steadily change with increasing exercise intensity. To date, no study has specifically examined using the behavior of this index as a method for defining a low intensity exercise zone. The aim of this report is to compare both oxygen intake (VO2) and heart rate (HR) reached at the first ventilatory threshold (VT1), a well-established delimiter of low intensity exercise, to those derived from a predefined DFA a1 transitional value. Gas exchange and HRV data were obtained from 15 participants during an incremental treadmill run. Comparison of both VO2 and HR reached at VT1 defined by gas exchange (VT1 GAS) was made to those parameters derived from analysis of DFA a1 reaching a value of 0.75 (HRVT). Based on Bland Altman analysis, linear regression, intraclass correlation (ICC) and t testing, there was strong agreement between VT1 GAS and HRVT as measured by both HR and VO2. Mean VT1 GAS was reached at 39.8 ml/kg/min with a HR of 152 bpm compared to mean HRVT which was reached at 40.1 ml/kg/min with a HR of 154 bpm. Strong linear relationships were seen between test modalities, with Pearson's r values of 0.99 (p < 0.001) and.97 (p < 0.001) for VO2 and HR comparisons, respectively. Intraclass correlation between VT1 GAS and HRVT was 0.99 for VO2 and 0.96 for HR. In addition, comparison of VT1 GAS and HRVT showed no differences by t testing, also supporting the method validity. In conclusion, it appears that reaching a DFA a1 value of 0.75 on an incremental treadmill test is closely associated with crossing the first ventilatory threshold. As training intensity below the first ventilatory threshold is felt to have great importance for endurance sport, utilization of DFA a1 activity may provide guidance for a valid low training zone.

Session RPE Breakpoints Corresponding to Intensity Thresholds in Elite Open Water Swimmers.

This study aims to assess the correspondence between session rating of perceived exertion (sRPE) breakpoints with both the first lactate threshold (LT1) and the second lactate threshold (LT2) in elite open water swimmers (OWS). Six elite OWS of the National Olympic Team specialized in distances between 5 and 25 km participated to the study. OWS performed a set of 6 times 500 m incremental swimming step test during which blood lactate concentration (BLC), split time (ST), stroke frequency (SF), and rating of perceived exertion (RPE) were collected. To assess the corresponding breakpoints, we considered LT1 as the highest workload not associated with rise in BLC and LT2 as the increase of 2mM above LT1. According to the LT1 and LT2, the identified zones were: Z1 ≤3, Z2 between 4 and 6, Z3 ≥ 7. In conclusion, the intensity zones determined for OWS resulted different from what previously reported for other endurance disciplines.

Effects of Different Training Intensity Distribution in Recreational Runners.

Purpose: To compare the impact of two different training intensity distributions in terms of conditional and performance parameters and spent time to training in recreational athletes. Methods: Two different training intensity distribution model were performed for 8 weeks by 38 recreational runners. Runners recruited were randomly assigned to 2 different training models based on HR intensity detected with maximal test. The percentage distribution splitted in zone 1, 2, and 3 were by 77/3/20 and 40/50/10 in polarized endurance training group (PET) and focused endurance training (FOC) group, respectively. Programs were balanced for total training impulse (TRIMP). To evaluate effects of training, before and after treatment were performed a maximal exercise test to determine Maximal Oxygen Uptake (V'O2max), Ventilatory Threshold (VT), respiratory-compensation point (RCT) Running Economy (RE), and 2 Km performance. To investigate the effects of training on muscular performance were performed one repetition maximum (1 RM), squat jump (SJ), and counter movement jump (CMJ). Results: Both groups significantly improved their velocity at V'O2max (3.2 and 4.0%), at VT (4.0 and 3.2%), RCT (5.7 and 3.4%), the average velocity in 2 Km performance (3.5 and 3.0%) and RE (-5.3 and -8.7%) for PET and FOC, respectively for each variable. No differences were found between the groups on any parameter investigated except about the total training time (PET = 29.9 ± 3.1 h and FOC = 24.8 ± 2.0 h). Conclusion: Focused Endurance Training obtains similar improvements than Polarized Endurance Training saving 17% of training time in recreational runners.

Polarized Versus High-Intensity Multimodal Training in Recreational Runners.

PURPOSE: This study longitudinally compared changes in running performance (5-km time trial) and fitness (maximal oxygen uptake [VO2max] and body composition [BC]) between polarized training and CrossFit Endurance (CFE) in recreational runners. METHODS: Participants (N = 21) completed 12 wk of CFE or polarized endurance training (POL). Both groups trained 5 d·wk-1. POL ran 5 d·wk-1, whereas CFE ran 3 d·wk-1 and performed CrossFit 3 d·wk-1 (run + CrossFit 1 d·wk-1). Intensity was classified as low, moderate, or high (zone 1, 2, or 3) according to ventilatory thresholds. POL was prescribed greater volume (295 [67] min·wk-1), distributed as 85%/5%/10% in Z1/Z2/Z3. CFE emphasized a lower volume (110 [18] min·wk-1) distribution of 48%/8%/44%. RESULTS: POL ran 283 (75.9) min·wk-1 and 47.3 (11.6) km·wk-1, both exceeding the 117 (32.2) min·wk-1 and 19.3 (7.17) km·wk-1 in CFE (P < .001). The POL distribution (74%/11%/15%) had greater total and percentage Z1 (P < .001) than CFE (46%/15%/39%), which featured higher percentage Z3 (P < .001). Time trial improved -93.8 (40.4) s (-6.21% [2.16%]) in POL (P < .001) and -84.2 (65.7) s (-5.49% [3.56%]) in CFE (P = .001). BC improved by -2.45% (2.59%) fat in POL (P = .02) and -2.62% (2.53%) in CFE (P = .04). The magnitude of improvement was not different between groups for time trial (P = .79) or BC (P = .88). Both groups increased VO2max (P ≤ .01), but with larger magnitude (P = .04, d = 0.85) in POL (4.3 [3.6] mL·kg·min-1) than CFE (1.78 [1.9] mL·kg·min-1). CONCLUSIONS: Recreational runners achieved similar improvement in 5-km performance and BC through polarized training or CFE, but POL yielded a greater increase in VO2max. Extrapolation to longer distances requires additional research.

The Effect of Periodization and Training Intensity Distribution on Middle- and Long-Distance Running Performance: A Systematic Review.

This review aimed to examine the current evidence for 3 primary training intensity distribution types: (1) pyramidal training, (2) polarized training, and (3) threshold training. Where possible, the training intensity zones relative to the goal race pace, rather than physiological or subjective variables, were calculated. Three electronic databases (PubMed, Scopus, and Web of Science) were searched in May 2017 for original research articles. After analysis of 493 resultant original articles, studies were included if they met the following criteria: (1) Their participants were middle- or long-distance runners; (2) they analyzed training intensity distribution in the form of observational reports, case studies, or interventions; (3) they were published in peer-reviewed journals; and (4) they analyzed training programs with a duration of 4 wk or longer. Sixteen studies met the inclusion criteria, which included 6 observational reports, 3 case studies, 6 interventions, and 1 review. According to the results of this analysis, pyramidal and polarized training are more effective than threshold training, although the latest is used by some of the best marathon runners in the world. Despite this apparent contradictory finding, this review presents evidence for the organization of training into zones based on a percentage of goal race pace, which allows for different periodization types to be compatible. This approach requires further development to assess whether specific percentages above and below race pace are key to inducing optimal changes.

Assessment of Differences in the Anthropometric, Physiological and Training Characteristics of Finishers and Non-finishers in a Tropical 161-km Ultra-marathon.

This study aimed to compare and determine the differences in the physiological, anthropometric and training characteristics of the finishers (FIN) and non-finishers (N-FIN) in a 161-km race. Two groups of runners (FIN; N=12 and N-FIN; N=14) completed a series of anthropometric and physiological measurements over two separate sessions at least three weeks prior to the race. Training sessions starting from six weeks prior to the race were recorded. Sum of 7 skinfolds, arm and calf girths, VO2max and peak treadmill speed (PTS) were taken during session 1 while the lactate threshold (LT) and running economy (RE) were assessed during session 2. Effect size calculations showed moderate and clear differences in the lactate concentration at LT1 (ES = 0.88, P = 0.05), velocity at LT2 (ES = 0.70, P = 0.07), longest run attempted (ES = 0.73, P = 0.07) and number of cross-training hours (ES = 0.73, P = 0.06) between the FIN and N-FIN. The results suggest that from a physiological perspective, the ability to finish a 161-km race might be differentiated by metabolic attributes via LT measurements. Runners should not neglect the importance of the long runs and should incorporate cross-training to provide additional stimuli to the body while allowing the running muscles to recover from fatigue.

Is Marathon Training Harder than the Ironman Training? An ECO-method Comparison.

Purpose: To compare the absolute and relative training load of the Marathon (42k) and the Ironman (IM) training in recreational trained athletes. Methods: Fifteen Marathoners and Fifteen Triathletes participated in the study. Their performance level was the same relative to the sex's absolute winner at the race. No differences were presented neither in age, nor in body weight, height, BMI, running VO2max max, or endurance training experience (p > 0.05). They all trained systematically for their respective event (IM or 42k). Daily training load was recorded in a training log, and the last 16 weeks were compared. Before this, gas exchange and lactate metabolic tests were conducted in order to set individual training zones. The Objective Load Scale (ECOs) training load quantification method was applied. Differences between IM and 42k athletes' outcomes were assessed using Student's test and significance level was set at p < 0.05. Results: As expected, Competition Time was significantly different (IM 11 h 45 min ± 1 h 54 min vs. 42k 3 h 6 min ± 28 min, p < 0.001). Similarly, Training Weekly Avg Time (IM 12.9 h ± 2.6 vs. 42k 5.2 ± 0.9), and Average Weekly ECOs (IM 834 ± 171 vs. 42k 526 ± 118) were significantly higher in IM (p < 0.001). However, the Ratio between Training Load and Training Time was superior for 42k runners when comparing ECOs (IM 65.8 ± 11.8 vs. 42k 99.3 ± 6.8) (p < 0.001). Finally, all ratios between training time or load vs. Competition Time were superior for 42k (p < 0.001) (Training Time/Race Time: IM 1.1 ± 0.3 vs. 42k 1.7 ± 0.5), (ECOs Training Load/Race Time: IM 1.2 ± 0.3 vs. 42k 2.9 ± 1.0). Conclusions: In spite of IM athletes' superior training time and total or weekly training load, when comparing the ratios between training load and training time, and training time or training load vs. competition time, the preparation of a 42k showed to be harder.