Training Intensity Distribution Research Articles

Training intensity distribution analysis by race pace vs. physiological approach in world-class middle- and long-distance runners

This study aimed to analyse the training intensity distribution (TID) of a group of 7 world-class middle- and long-distance runners over 50 weeks using two different approaches to organise TID zones: (1) based on individual specific race pace and; (2) based on physiological parameters. Analysed training data included training volume, intensity and frequency. The average weekly volume for the group was 135.4 ± 29.4 km·week−1. Training volumes for Z1, Z2 and Z3 were 88.5 ± 1.1%, 7.4 ± 0.8% and 4.1 ± 0.7% respectively for race-pace based approach, and 87.2 ± 1.2%, 6.1 ± 0.7% and 6.6 ± 0.9% respectively for the physiological approach. Differences were found between the approaches in Z2 (large effect, ES = 1.20) and Z3 (moderate effect, ES = 0.93). The approach based on race-pace zones produced pyramidal distributions in both middle- and long-distance runners across all phases of the season. The physiological approach produced polarised and pyramidal distributions depending of the phase of the season in the middle-distance runners, and pyramidal type TID across all phases of the season in the long-distance runners. The results of this study demonstrate that the training analysis in a world-class group of runners shows different TID when assessed relative to race pace versus to physiological zones. This highlights a potential deficiency in training analysis and prescription methods which do not make reference to specific performance. An approach which makes reference to both physiological and performance measures may allow for a more consistent and logical analysis.

Analysis of top kayakers’ training-intensity distribution and physiological adaptation based on structural modelling

High performance sport is important Analyse the effects of high intensity training on physical and functional capacities of elite kayakers by using the principal component analysis. The research analysed physical load during 1 year’s training cycle and used principal components analysis methods and Mann–Whitney Exact Test. The Principal component analysis approach revealed highly different adaptation of both well-trained athletes to the applied physical load. The coaches should pay more attention to individual skills of athletes, as well as to individual intensity and volume of the workout during the training sessions and the recovery time and quality. The Principal component algorithm suggested for monitoring and analysing the athletes’ training programs, as well as the findings of this study may be useful for planning the training programs.

In-Season Session Training Load Relative to Match Load in Professional Ice Hockey.

Allard, P, Martinez, R, Deguire, S, and Tremblay, J. In-season session training load relative to match load in professional ice hockey. J Strength Cond Res 36(2): 486-492, 2022-The aim of this study was to describe match load and intensity across player positions and match periods, the distribution of pregame training load and intensity over training days before a match, by player position, and the cumulative weekly training load over a season. Fifty professional ice hockey players, which at one time or another were part of the roster for the Laval Rocket during the 2017-18 season of the American Hockey League, participated in this study. External load was monitored in every training session and match over the season using portable inertial measurement units. Data are reported in absolute and relative values to a match. Defensemen have a lower intensity during matches than forwards (-1.70 OIL·min-1) while load is similar across position. The first period shows a higher relative load (+5.28% relative on-ice load [OIL]) while the third has a lower relative intensity (-2.91% OIL·min-1). Defensemen seem to train at a systematic higher relative intensity than wingers and centers (+8.34% relative OIL·min-1). Finally, the weekly training load remains relatively constant throughout the season (equivalent of 3.56 ± 1.69 matches played per week). Our results support the need for player monitoring in ice hockey using an individualized approach.

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.

Training intensity distribution of young elite soccer players

The current study described the training load and intensity distribution of 30 elite Under 20 soccer players (17.9 ± 0.6 years, 180.3 ± 5.7 cm, 73.7 ± 8.8kg) from a 3-time FIFA Club World Cup champion. Session-rating of perceived exertion (s-RPE), internal training load (ITL) and monotony were recorded across 40 training sessions. Mixed-effects modeling was used for data analysis. The athletes performed 33.0 ± 6.9 out of 40 planned training sessions. Most common reasons for absence from training included sickness or minor injuries. Overall, these training sessions summed 2928.7 ± 627.6 minutes. Athletes performed significantly more training sessions at low and moderate intensity zones than at high-intensity zone (p <0.0001). The current data indicate that elite young soccer players perform their training sessions predominantly at the low-intensity zone. Training monitoring is an important aspect of the sport training process evolution. In fact, previous evidencehas already shown that an appropriate intensity distribution preventsmaladaptation from sports training and may optimize athletic performance. Therefore, coaches should implement strategies to monitor training loads during pre-season and competitive periods.

Development in Adolescent Middle-Distance Athletes: A Study of Training Loadings, Physical Qualities, and Competition Performance.

Jones, TW, Shillabeer, BC, Ryu, JH, and Cardinale, M. Development in adolescent middle-distance athletes: a study of training loadings, physical qualities, and competition performance. J Strength Cond Res 35(12S): S103-S110, 2021-The purpose of this study was to examine changes in running performance and physical qualities related to middle-distance performance over a training season. The study also examined relationships between training loading and changes in physical qualities as assessed by laboratory and field measures. Relationships between laboratory and field measures were also analyzed. This was a 9-month observational study of 10 highly trained adolescent middle-distance athletes. Training intensity distribution was similar over the observational period, whereas accumulated and mean distance and training time and accumulated load varied monthly. Statistically significant (p < 0.05) and large effect sizes (Cohen's d) (>0.80) were observed for improvements in: body mass (5.6%), 600-m (4.6%), 1,200-m (8.7%), and 1,800-m (6.1%) time trial performance, critical speed (7.1%), V̇o2max (5.5%), running economy (10.1%), vertical stiffness (2.6%), reactive index (3.8%), and countermovement jump power output relative to body mass (7.9%). Improvements in 1,800 m TT performance were correlated with increases in V̇o2max (r = 0.810, p = 0.015) and critical speed (r = 0.918, p = 0.001). Increases in V̇o2max and critical speed were also correlated (r = 0.895, p = 0.003). Data presented here indicate that improvements in critical speed may be reflective of changes in aerobic capacity in adolescent middle-distance athletes.

Training intensity distribution of young elite soccer players

Training intensity distribution of young elite soccer players

Training Characteristics of Male and Female Professional Road Cyclists: A 4-Year Retrospective Analysis.

To describe the training intensity and load characteristics of professional cyclists using a 4-year retrospective analysis. Particularly, this study aimed to describe the differences in training characteristics between men and women professional cyclists. For 4 consecutive years, training data were collected from 20 male and 10 female professional cyclists. From those training sessions, heart rate, rating of perceived exertion, and power output (PO) were analyzed. Training intensity distribution as time spent in different heart rate and PO zones was quantified. Training load was calculated using different metrics such as Training Stress Score, training impulse, and session rating of perceived exertion. Standardized effect size is reported as Cohen's d. Small to large higher values were observed for distance, duration, kilojoules spent, and (relative) mean PO in men's training (d = 0.44-1.98). Furthermore, men spent more time in low-intensity zones (ie,zones 1 and 2) compared with women. Trivial differences in training load (ie,Training Stress Score and training impulse) were observed between men's and women's training (d = 0.07-0.12). However, load values expressed per kilometer were moderately (d = 0.67-0.76) higher in women compared with men's training. Substantial differences in training characteristics exist between male and female professional cyclists. Particularly, it seems that female professional cyclists compensate their lower training volume, with a higher training intensity, in comparison with male professional cyclists.

Quantifying the Training-Intensity Distribution in Middle-Distance Runners: The Influence of Different Methods of Training-Intensity Quantification.

To compare the training-intensity distribution (TID) across an 8-week training period in a group of highly trained middle-distance runners employing 3 different methods of training-intensity quantification. A total of 14 highly trained middle-distance runners performed an incremental treadmill test to exhaustion to determine the heart rate (HR) and running speed corresponding to the ventilatory thresholds (gas-exchange threshold and respiratory-compensation threshold), as well as fixed rating of perceived exertion (RPE) values, which were used to demarcate 3 training-intensity zones. During the following 8weeks, the TID (total and percentage of time spent in each training zone) of all running training sessions (N = 695) was quantified using continuous running speed, HR monitoring, and RPE. Compared with the running-speed-derived TID (zone 1, 79.9% [7.3%]; zone 2, 5.3% [4.9%]; and zone 3, 14.7% [7.3%]), HR-demarcated TID (zone 1, 79.6% [7.2%]; zone 2, 17.0% [6.3%]; and zone 3, 3.4% [2.0%]) resulted in a substantially higher training time in zone 2 (effect size ± 95% confidence interval: -1.64 ± 0.53; P < .001) and lower training time in zone 3 (-1.59 ± 0.51; P < .001). RPE-derived TID (zone 1, 39.6% [8.4%]; zone 2, 31.9% [8.7%]; and zone 3, 28.5% [11.6%]) reduced time in zone 1 compared with both HR (-5.64 ± 1.40; P < .001) and running speed (-5.69 ± 1.9; P < .001), whereas time in RPE training zones 2 and 3 was substantially higher than both HR- and running-speed-derived zones. The results show that the method of training-intensity quantification substantially affects computation of TID.

Training Progression in Recreational Cyclists: No Linear Dose-Response Relationship With Training Load.

Vermeire, KM, Vandewiele, G, Caen, K, Lievens, M, Bourgois, JG, and Boone, J. Training progression in recreational cyclists: no linear dose-response relationship with training load. J Strength Cond Res 35(12): 3500-3505, 2021-The purpose of the study was to assess the relationship between training load (TL) and performance improvement in a homogeneous group of recreational cyclists, training with a self-oriented training plan. Training data from 11 recreational cyclists were collected over a 12-week period. Before and after the training period, subjects underwent a laboratory incremental exercise test with blood lactate measurements to determine the power output associated with the aerobic threshold (PAT) and the anaerobic threshold (PANT), and the maximal power output (PMAX) was also determined. Mean weekly TL (calculated using the training impulse (TRIMP) of Banister, Edwards TRIMP, Lucia TRIMP and the individualized TRIMP) were correlated to the progression in fitness parameters using Pearson Correlation. Training intensity distribution (TID) was also determined (% in zone 1 as <AT; % in zone 2 as between AT and ANT; % in zone 3 as >ANT). No significant correlations between mean weekly TRIMP values and the improvement on PMAX (r = -0.22 to 0.08), PANT (r = -0.56 to -0.31) and PAT (r = -0.08 to 0.41) were found. The TID was significant in a multiple regression with PANT as dependent variable (y = 0.0088 + 0.1094 × Z1 - 0.2704 × Z2 + 1.0416 × Z3; p = 0.02; R2 = 0.62). In conclusion, this study shows that the commonly used TRIMP methods to quantify TL do not show a linear dose-response relationship with performance improvement in recreational cyclists. Furthermore, the study shows that TID might be a key factor to establish a relationship with performance improvement.

Response.

Dear Editor-in-Chief, We thank Boullosa et al. (1) for their interest in our study (2), and we appreciate their support in further stimulating discussion in this research area. We are aware that the nonfunctional overreaching “sword of Damocles” is often mentioned with sudden increases in workload. Therefore, all participants in our study completed the short form of the Acute Recovery and Stress Scale (3), and heart rate variability was measured frequently, although neither of these data sets were presented (2). However, we did monitor both data sets and found no impaired, long-lasting recovery–stress state indicating adverse health effects. Our data do not allow us to comment on the assumption by Boullosa et al. (1) that reduced total workload might have induced a better performance outcome in our recreational runners. However, we agree that in order to judge the responses to exercise, an all-day integrative view is required (4), including habitual exercise, which should be addressed in future studies. In rebuttal to the remark that the 3-wk training intensity distribution (TID) of our polarized training group (POL) is not an adequate reproduction of previously described typical polarized TID in recreational and subelite runners as presented by Muñoz et al. (5) and Esteve-Lanao et al. (6), our reply is as follows: We are aware of the different methodological issues surrounding TID quantification, especially comparability between different studies. One solution may be to calculate a “polarization index” (7), which in POL (2) was 1.94 a.u. and slightly higher than the studies quoted by Boullosa et al. (1.87 a.u. [5] and 1.75 a.u. [6]) (1). The difference between the TID of Muñoz et al. (5) (72.9/13.5/13.6%) and our TID (2) (69.7/13.4/16.9%) is that our mean time spent in zone 1 was slightly lower (−3.2%) and higher in zone 3 (+3.3%), but we consider these differences to be minor. Thus, our POL–TID appears somewhat more “polarized” in comparison with the quoted TID (5). The referred TID of Esteve-Lanao et al. (6) presents a rather “pyramidal” TID (80.5/11.8/8.3%), whereas a recalculation (mentioned within the discussion of [6]) revealed a more “polarized” TID (74/11/15%, polarization index = 2.0 a.u.). Therefore, depending on the TID quantification, shifts in the proportion of zones may occur. Nevertheless, both TID calculations by Esteve-Lanao et al. (6) showed more time spent in zone 1 and slightly less time in zone 2 (compared with our recreational runners), which is likely attributable to the substantially greater amount of overall training time of subelite runners. Subelite runners performing >80 km·wk−1 will not or cannot invest too much time in zones 2 and 3 because recovery between frequent demanding training sessions becomes challenging/impossible. We therefore discourage the comparison of TID between (sub)elite and recreational runners. We agree that training time in zone 1 constitutes the greatest proportion within a polarized model in (sub)elite runners. However, optimal TID, especially the proportion of zones 1 and 3, has yet to be identified in both recreational and (sub)elite runners (8). In fact, the individual responses within our three groups are quite heterogenous without conclusive identification of a best practice TID in recreational runners. Future studies with well-designed crossover designs are warranted to address this matter in detail. Christoph Zinner University of Applied Sciences for Police and Administration of Hesse Wiesbaden, GERMANY Integrative & Experimental Exercise Science & Training Institute for Sport Sciences University of Würzburg, GERMANY Daniela Schäfer Olstad Polar Electro Oy Kempele, FINLAND Billy Sperlich Integrative & Experimental Exercise Science & Training Institute for Sport Sciences University of Würzburg, GERMANY

Perspectives and Determinants for Training-Intensity Distribution in Elite Endurance Athletes.

Training-intensity distribution (TID), or the intensity of training and its distribution over time, has been considered an important determinant of the outcome of a training program in elite endurance athletes. The polarized and pyramidal TID, both characterized by a high amount of low-intensity training (below the first lactate or ventilatory threshold), but with different contributions of threshold training (between the first and second lactate or ventilatory threshold) and high-intensity training (above the second lactate or ventilatory threshold), have been reported most frequently in elite endurance athletes. However, the choice between these 2 TIDs is not straightforward. This article describes the historical, evolutionary, and physiological perspectives of the success of the polarized and pyramidal TID and proposes determinants that should be taken into account when choosing the most appropriate TID.

Effects of Group Running on the Training Intensity Distribution of Collegiate Cross-Country Runners.

Farrell III, JW, Dunn, A, Cantrell, GS, Lantis, DJ, Larson, DJ, and Larson, RD. Effects of group running on the training intensity distribution of collegiate cross-country runners. J Strength Cond Res 35(10): 2862-2869, 2021-Collegiate cross-country training is often conducted and prescribed in a group setting. This may result in the application of an inappropriate training stimulus to athletes due to potentially different physiological responses to the same training prescription. The aim of this investigation was to quantify the training intensity distribution (TID) of a collegiate cross-country team and the associated physiological adaptions. Sixteen subjects, 8 male subjects and 8 female subjects, performed a graded exercise test before and after observational period to determine peak oxygen consumption (V̇o2peak), the speed (S@), heart rate (HR@), and oxygen consumption (V̇o2@) associated with 2 and 4 mmol·L-1 of blood lactate. Training intensity distribution was quantified by assessing time spent in 3 intensity zones calculated as zone 1 (low intensity, HR values <HR@2); zone 2 (moderate intensity, HR values >HR@2 and <HR@4); and zone 3 (HR values >HR@4). No statistical differences were observed between male subjects and female subjects for percent of training time spent in zones 1, 2, and 3. No significant interactions were observed between sex and time for performance variables. Male subjects and female subjects improved V̇o2peak, S@4, and V̇o2@4 with male subjects also increasing V̇o2@2. No significant differences were observed between male subjects and female subjects when comparing percent changes for variables. Examining individual data showed that 2 female subjects experienced performance decrements and trained proportionally more in zones 2 and 3 compared with the overall group. The TID and performance decrements of the 2 highlighted subjects suggest that conducting training in a group setting may potentially be detrimental for some collegiate runners.

The Polarization-Index: A Simple Calculation to Distinguish Polarized From Non-polarized Training Intensity Distributions.

The training intensity distribution (TID) of endurance athletes has retrieved substantial scientific interest since it reflects a vital component of training prescription: (i) the intensity of exercise and its distribution over time are essential components for adaptation to endurance training and (ii) the training volume (at least for most endurance disciplines) is already near or at maximum, so optimization of training procedures including TID have become paramount for success. This paper aims to elaborate the polarization-index (PI) which is calculated as log10(Zone 1/Zone 2∗Zone 3∗100), where Zones 1–3 refer to aggregated volume (time or distance) spent with low, mid, or high intensity training. PI allows to distinguish between non-polarized and polarized TID using a cut-off > 2.00 a.U. and to quantify the level of a polarized TID. Within this hypothesis paper, examples from the literature illustrating the usefulness of PI-calculation are discussed as well as its limitations. Further it is elucidated how the PI may contribute to a more precise definition of TID descriptors.

Training Intensity Distribution In A Chinese Top-level Women's Single Sculls Rower Preparing For 2008 Olympic Games Season

PURPOSE: This study was to report seasonal training intensity distribution characteristics in a Chinese top-level Women’s Single rower during 2007-2008, while season preparing for 2008 Olympic Games. METHODS: A Chinese top-level (33 y, height 177 cm, weight 71 kg) World Rowing Cup champion Women's Single Sculls rower participated in this case study. The season’s training load included overall duration (calculated in hours and km), frequency (calculated by overall sessions), and training intensity (measured by blood lactate and estimated by heart rate), Her performances at World Cup and Olympic competitions during the seasons, and 2000m ergometer test were also collected and analyzed. RESULTS: A training program of 46 weeks (33 weeks of pre-season and 13 weeks of in-season) was analyzed. 193 training days was performed during the season. Total training hours were 737 hours. Total training time consisted of 48.9% on-water rowing (362 hours), 9.18% ergometer rowing (68 hours), 15.2% condition training (112 hours), and 18.4% warm-up and recovery programs (136 hours). All training sessions were quantified using continuous heart rate monitoring, a subset of 117 training sessions was quantified using blood lactate measurements. Intensity distribution across rowing and ergometer training hours (n=489 hours) based on heart rate analysis and blood lactate analysis was 91.12% in zone 1 (74.5% ≤2.0 mM blood lactate, 16.8% between 2.1 and 3.5 mM blood lactate), and 4.13% in zone 2 (>3.5 and 5 mM blood lactate) and 4.75% in zone 3 (over 5mM blood lactate). 2000m ergometer trials performed in weeks 2, 12 and 24 was in times 6 minutes 57 seconds, 6 minutes 55 seconds and 6 minutes 46 seconds, respectively. Maximal power test was 309 W, 313 W and 334 W, respectively. Rowing performance improved 1.29% and 6.27% during the period. She finished the season in 3rd place in the World Cup and fourth place in the 2008 Olympic Games. CONCLUSION: Training-intensity distribution with an emphasis on low-intensity polarized training model led to the success in top Chinese top-level Women's Single Sculls rower in the 2007-08 season. Possible mechanisms underlying the association between intensity distribution and performance success require further investigation.

Training and Competition Readiness in Triathlon.

Triathlon is characterized by the multidisciplinary nature of the sport where swimming, cycling, and running are completed sequentially in different events, such as the sprint, Olympic, long-distance, and Ironman formats. The large number of training sessions and overall volume undertaken by triathletes to improve fitness and performance can also increase the risk of injury, illness, or excessive fatigue. Short- and medium-term individualized training plans, periodization strategies, and work/rest balance are necessary to minimize interruptions to training due to injury, illness, or maladaptation. Even in the absence of health and wellbeing concerns, it is unclear whether cellular signals triggered by multiple training stimuli that drive training adaptations each day interfere with each other. Distribution of training intensity within and between different sessions is an important aspect of training. Both internal (perceived stress) and external loads (objective metrics) should be considered when monitoring training load. Incorporating strength training to complement the large body of endurance work in triathlon can help avoid overuse injuries. We explore emerging trends and strategies from the latest literature and evidence-based knowledge for improving training readiness and performance during competition in triathlon.

Dose-Response Relationship Between Internal Training Load and Changes in Performance During the Preseason in Youth Soccer Players.

Figueiredo, DH, Figueiredo, DH, Moreira, A, Gonçalves, HR, and Dourado, AC. Dose-response relationship between internal training load and changes in performance during the preseason in youth soccer players. J Strength Cond Res 35(8): 2294-2301, 2021-The aim of this study was to describe training intensity distribution based on the session rating of perceived exertion (sRPE) and heart rate (HR) methods and examine the dose-response relation between internal training load (ITL) and change in performance of 16 youth soccer players (mean ± SD age: 18.75 ± 0.68 years, height: 175.3 ± 5.5 cm, body mass: 68.7 ± 6.5 kg, and body fat: 10.7 ± 1.2%) belonging to a Brazilian first division team during a 3-week preseason. The sRPE and HR data were registered daily to calculate the ITL and the training intensity distribution, in 3 intensity zones (low, moderate, and high). The Yo-yo Intermittent Recovery Level 1 (Yo-yo IR1) was evaluated before and after experimental period. The total time spent in the low-intensity zone (HR method) was greater (p < 0.01) compared with the moderate- and high-intensity zones. No difference was observed between training intensity zones determined by the sRPE method (p > 0.05). Negative correlations were observed between weekly mean sRPE-TL (r = -0.69), Edward's-TL (r = -0.50), and change in Yo-yo IR1. Linear regression indicated that weekly mean sRPE-TL (F1;14 = 13.3; p < 0.01) and Edward's-TL (F1;14 = 4.8; p < 0.05) predicted 48.7 and 25.5% of the variance in performance change, respectively. Stepwise linear regression revealed that these 2-predictor variables (F2;13 = 18.9; p < 0.001) explained 74.5% of the variance in performance change. The results suggest that the sRPE and HR methods cannot be used interchangeably to determine training intensity distribution. Moreover, sRPE-TL seems to be more effective than the HR-based TL method to predict changes in performance in youth soccer players.

Effect of Training Load Distribution on Aerobic Fitness Measures in Hurling Players

Malone, S, Hughes, B, and Collins, K. Effect of training load distribution on aerobic fitness measures in hurling players. J Strength Cond Res 33(3): 825-830, 2019-The aim of the current investigation was to quantify the training intensity distribution of hurling players and their association with changes in aerobic fitness measures within hurling players. Thirty hurling players (n = 30; age 26.5 ± 3.2 years; height 178.9 ± 3.2 cm; body mass 81.5 ± 4.5 kg) were observed during the pre-championship training period (12 weeks). Aerobic fitness measures (V[Combining Dot Above]O2max), peak treadmill velocity (km·h), heart rates (HRs) at 2 and 4 mmol·L blood lactate concentrations, and Yo-Yo intermittent recovery test performance (Yo-YoIR1) were assessed before and after the training period. Training intensities were categorized using 3 individualized HR zones based on the following criteria: low intensity (≤HR 2 mmol·L), moderate intensity (between HR 2 and 4 mmol·L), and high intensity (≥HR 4 mmol·L). Analysis of 1,025 individual training sessions showed that players spent 58.1 ± 2.5%, 24.3 ± 2.9%, and 17.6 ± 1.3% of time in the low-, moderate-, and high-intensity zones, respectively. The training time spent at high intensity (Zone 3) showed moderate-to-large associations with improvements in S2 (r = 0.80; p = 0.04) and S4 (r = 0.58; p = 0.03). Similar trends were observed for time spent in high intensity and improvements in V[Combining Dot Above]O2max (r = 0.77; p = 0.001) and Yo-YoIR1 performance (r = 0.65; p = 0.05). Hurling players have been shown to spend the majority of training time in low (≤ HR 2 mmol·L) to moderate intensity (between HR 2 and 4 mmol·L) training. However, only the time spent at high intensity (≥90% of maximal HR) was related to changes in aerobic fitness. These results support the usefulness of the quantification of aerobic training load using HR. The study also supports the efficacy of spending elements of training time within the high-intensity zone to increase hurling player's aerobic fitness characteristics.

Effects of a 6-Week Period of Polarized or Threshold Training on Performance and Fatigue in Elite Swimmers.

To quantify the impact of a polarized distribution of training intensity on performance and fatigue in elite swimmers. Twenty-two elite junior swimmers (12 males, age = 17 [3]y, and 10 females, age = 17 [3]y) participated in a crossover intervention study over 28 wk involving 2- × 6-wk training periods separated by 6 wk. Swimmers were randomly assigned to a training group for the first period: polarized (81% in zone 1, blood lactate concentration, [La]b ≤ 2mmol·L-1; 4% in zone 2, 2mmol·L-1 < [La]b ≤ 4mmol·L-1; and 15% in zone 3, [La]b > 4mmol·L-1) or threshold (65%/25%/10%). Before and after each period, they performed a 100-m maximal swimming test to determine performance, maximal [La]b, and oxygen consumption and an incremental swimming test to determine speed corresponding to [La]b = 4mmol·L-1 (V4mmol·L-1). Self-reported indices of well-being were collected with a daily questionnaire. Polarized training elicited small to moderately greater improvement than threshold training on 100-m performance (within-group change ± 90% confidence interval: 0.97% ± 1.02% vs 0.09% ± 0.94%, respectively) with less fatigue and better quality of recovery. There was no substantial gender effect. No clear differences were observed in physiological adaptations between groups. In elite junior swimmers, a 6-wk period of polarized training induced small improvements in 100-m time-trial performance and, in combination with less perceived fatigue, forms a viable option for coaches preparing such cohorts of swimmers for competition.

The Road to Rio: A Brief Report of Training-Load Distribution of Open-Water Swimmers During the Olympic Season.

The 10-km open-water swimming race is an endurance event that takes place in lakes, rivers, or sea and has been an Olympic event since 2008. The aim of the present brief report is to describe training volume and intensity distribution of elite open-water swimmers during the 2016 Olympic season, verifying if, in order to maximize performance, most of the training would be performed at low intensities. Eight elite Italian open-water swimmers (3 male and 5 female; 25 [2]y, 1.74 [0.05]m, 68.26 [8.17]kg) specialized in distances between 5 and 25km participated in the study. Training load was determined using an online training diary. Training intensity was categorized according to the 3-zone model: Z1, light intensity; Z2, moderate intensity; and Z3, high intensity. Session rating of perceived exertion was used to quantify training-intensity distribution. This method assigns the entire session into a single intensity zone based on the rating of perceived exertion recorded 30min posttraining. Total yearly training volume was 3576.93 (272.390)km (3220.80-4041.97), distributed across 446 (37) (397-484) sessions monitored during the 2016 Olympic season. Training-intensity distribution in each zone was 76.83% (8.11%) in Z1, 17.70% (6.79%) in Z2, and 5.47% (5.93%) in Z3. High volumes in Z1 appear to be an important training method used by elite open-water swimmers. However, future research is necessary to study the effects of different training-intensity distribution on open-water swimming performances.