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

Training Intensity Distribution of a 7-Day HIIT Shock Microcycle: Is Time in the "Red Zone" Crucial for Maximizing Endurance Performance? A Randomized Controlled Trial

BACKGROUND

Various studies have shown that the type of intensity measure affects training intensity distribution (TID) computation. These conclusions arise from studies presenting data from meso- and macrocycles, while microcycles, e.g., high-intensity interval training shock microcycles (HIIT-SM) have been neglected so far. Previous literature has suggested that the time spent in the high-intensity zone, i.e., zone 3 (Z3) or the "red zone", during HIIT may be important to achieve improvements in endurance performance parameters. Therefore, this randomized controlled trial aimed to compare the TID based on running velocity (TIDV), running power (TIDP) and heart rate (TIDHR) during a 7-day HIIT-SM. Twenty-nine endurance-trained participant were allocated to a HIIT-SM consisting of 10 HIIT sessions without (HSM, n = 9) or with (HSM + LIT, n = 9) additional low-intensity training or a control group (n = 11). Moreover, we explored relationships between time spent in Z3 determined by running velocity (Z3V), running power (Z3P), heart rate (Z3HR), oxygen uptake ( Z 3 V ˙ O 2 ) and changes in endurance performance.

RESULTS

Both intervention groups revealed a polarized pattern for TIDV (HSM: Z1: 38 ± 17, Z2: 16 ± 17, Z3: 46 ± 2%; HSM + LIT: Z1: 59 ± 18, Z2: 14 ± 18, Z3: 27 ± 2%) and TIDP (Z1: 50 ± 8, Z2: 14 ± 11, Z3: 36 ± 7%; Z1: 62 ± 15, Z2: 12 ± 16, Z3: 26 ± 2%), while TIDHR (Z1: 48 ± 13, Z2: 26 ± 11, Z3: 26 ± 7%; Z1: 65 ± 17, Z2: 22 ± 18, Z3: 13 ± 4%) showed a pyramidal pattern. Time in Z3HR was significantly less compared to Z3V and Z3P in both intervention groups (all p < 0.01). There was a time x intensity measure interaction for time in Z3 across the 10 HIIT sessions for HSM + LIT (p < 0.001, pη2 = 0.30). Time in Z3V and Z3P within each single HIIT session remained stable over the training period for both intervention groups. Time in Z3HR declined in HSM from the first (47%) to the last (28%) session, which was more pronounced in HSM + LIT (45% to 16%). A moderate dose-response relationship was found for time in Z3V and changes in peak power output (rs = 0.52, p = 0.028) as well as time trial performance (rs = - 0.47, p = 0.049) with no such associations regarding time in Z3P, Z3HR, and Z 3 V ˙ O 2 .

CONCLUSION

The present study reveals that the type of intensity measure strongly affects TID computation during a HIIT-SM. As heart rate tends to underestimate the intensity during HIIT-SM, heart rate-based training decisions should be made cautiously. In addition, time in Z3V was most closely associated with changes in endurance performance. Thus, for evaluating a HIIT-SM, we suggest integrating a comprehensive set of intensity measures. Trial Registration Trial register: Clinicaltrials.gov, registration number: NCT05067426.

A clustering mining method for sports behavior characteristics of athletes based on the ant colony optimization

This study aims to enhance the precision of analyzing athlete behavior characteristics, thereby optimizing sports training and competitive strategies. This study introduces an innovative Ant Colony Optimization (ACO) clustering model designed to address the high-dimensional clustering issues in athlete behavior data by simulating the path selection mechanism of ants searching for food. The development process of this model includes fine-tuning ACO parameters, optimizing for features specific to sports data, and comparing it with traditional clustering algorithms, and similar research models based on the neural network, support vector machines, and deep learning. The results indicate that the ACO model significantly outperforms the comparison algorithms in terms of silhouette coefficient (0.72) and Davies-Bouldin index (1.05), demonstrating higher clustering effectiveness and model stability. Particularly noteworthy is the recall rate (0.82), a key performance indicator, where the ACO model accurately captures different behavioral characteristics of athletes, validating its effectiveness and reliability in athlete behavior analysis. The innovation lies not only in the application of the ACO algorithm to address practical issues in the field of sports but also in showcasing the advantages of the ACO algorithm in handling complex, high-dimensional sports data. However, its generality and efficiency on a larger scale or different types of sports data still need further validation. In conclusion, through the introduction and optimization of the ACO clustering model, this study provides a novel and effective approach for a deeper understanding and analysis of athlete behavior characteristics. This study holds significant importance in advancing sports science research and practical applications.

Demarcation of Intensity From 3 to 5 Zones Aids in Understanding Physiological Performance Progression in Highly Trained Under-23 Rowing Athletes

ABSTRACT

Watts, SP, Binnie, MJ, Goods, PSR, Hewlett, J, Fahey-Gilmour, J, and Peeling, P. Demarcation of intensity from 3 to 5 zones aids in understanding physiological performance progression in highly trained under-23 rowing athletes. J Strength Cond Res 37(11): e593-e600, 2023-The purpose of this investigation was to compare 2 training intensity distribution models (3 and 5 zone) in 15 highly trained rowing athletes ( n = 8 male; n = 7 female; 19.4 ± 1.1 years) to determine the impact on primary (2,000-m single-scull race) and secondary (2,000-m ergometer time trial, peak oxygen consumption [V̇O 2 peak], lactate threshold 2 [LT2 power]) performance variables. Performance was assessed before and after 4 months training, which was monitored through a smart watch (Garmin Ltd, Olathe, KS) and chest-strap heart rate (HR) monitor (Wahoo Fitness, Atlanta, GA). Two training intensity distribution models were quantified and compared: a 3-zone model (Z1: between 50% V̇O 2 peak and lactate threshold 1 (LT1); Z2: between LT1 and 95% LT2; Z3: >95% LT2) and a 5-zone model (T1-T5), where Z1 and Z3 were split into 2 additional zones. There was significant improvement in LT2 power for both male (4.08% ± 1.83, p < 0.01) and female (3.52% ± 3.38, p = 0.02) athletes, with male athletes also demonstrating significant improvement in 2,000-m ergometer time trial (2.3% ± 1.92, p = 0.01). Changes in V̇O 2 peak significantly correlated with high-quality aerobic training (percent time in T2 zone; r = 0.602, p = 0.02), whereas changes in LT2 power significantly correlated with "threshold" training (percent time in T4 zone; r = 0.529, p = 0.04). These correlations were not evident when examining intensity distribution through the 3-zone model. Accordingly, a 5-zone intensity model may aid in understanding the progression of secondary performance metrics in rowing athletes; however, primary (on-water) performance remains complex to quantify.

The proportional distribution of training by elite endurance athletes at different intensities during different phases of the season

The present review examines retrospective analyses of training intensity distribution (TID), i.e., the proportion of training at moderate (Zone 1, Z1), heavy (Z2) and severe (Z3) intensity by elite-to-world-class endurance athletes during different phases of the season. In addition, we discuss potential implications of our findings for research in this field, as well as for training by these athletes. Altogether, we included 175 TIDs, of which 120 quantified exercise intensity on the basis of heart rate and measured time-in-zone or employed variations of the session goal approach, with demarcation of zones of exercise intensity based on physiological parameters. Notably, 49% of the TIDs were single-case studies, predominantly concerning cross-country skiing and/or the biathlon. Eighty-nine TIDs were pyramidal (Z1 > Z2 > Z3), 65 polarized (Z1 > Z3 > Z2) and 8 "threshold" (Z2 > Z1 = Z3). However, these relative numbers varied between sports and the particular phases of the season. In 91% (n = 160) of the TIDs >60% of the endurance exercise was of low intensity. Regardless of the approach to quantification or phase of the season, cyclists and swimmers were found to perform a lower proportion of exercise in Z1 (<72%) and higher proportion in Z2 (>16%) than athletes involved in the triathlon, speed skating, rowing, running, cross-country skiing or biathlon (>80% in Z1 and <12% in Z2 in all these cases). For most of the athletes their proportion of heavy-to-severe exercise was higher during the period of competition than during the preparatory phase, although with considerable variability between sports. In conclusion, the existing literature in this area does not allow general conclusions to be drawn. The methods utilized for quantification vary widely and, moreover, contextual information concerning the mode of exercise, environmental conditions, and biomechanical aspects of the exercise is often lacking. Therefore, we recommend a more comprehensive approach in connection with future investigations on the TIDs of athletes involved in different endurance sports.

Drafting in long-track speed skating team pursuit on the ice rink

Drafting is distinctive for team pursuit races in long-track speed skating. This study aims to compare the impact of drafting on physical intensity (heart rate [HR]) and perceived intensity (ratings of perceived exertion [RPE]) per drafting position. Eighteen skilled male (n = 9) and female (n = 9) skaters (20.0 ± 4.8 years) skated three trials, in first, second or third position, with consistent average velocity (F_2,10 = 2.30, p = 0.15, η_p² = 0.32). Differences in HR and RPE (Borg CR-10 scale) were compared within-subjects (three positions) using a repeated-measures ANOVA (p < 0.05). Compared to the first position, HR was lower in the second (benefit 3.2%) and third (benefit 4.7%) position and lower in third compared to second position (benefit 1.5%), observed in 10 skaters (F_2,28 = 28.9, p < 0.001, η_p²= 0.67). RPE was lower when comparing second (benefit 18.5%) and third (benefit 16.8%) position to first (F_1.3,22.1 = 7.02, p < 0.05, η_p²= 0.29) and similar for third and second positions., observed in 8 skaters. Even though the physical intensity was lower when drafting in third versus second position, the perceived intensity was equal. There were large interindividual differences between skaters. Coaches are advised to adopt a multidimensional, tailored approach when selecting and training skaters for a team pursuit.

Individually guided training prescription by heart rate variability and self-reported measure of stress tolerance in recreational runners: Effects on endurance performance

The aim of this study was to determine the effect of endurance training individually guided by objective (Heart Rate Variability-HRV) or self-report measure of stress (DALDA-questionnaire) in comparison to predefined endurance training prescription for improving endurance performance in recreational runners. After a 2-week preliminary baseline period to establish resting HRV and self-reported measure of stress, thirty-six male recreational runners were randomly assigned to HRV-guided (G_HRV; n = 12), DALDA-guided (G_D; n = 12) or predefined training (G_T; n = 12) prescription groups. Before and after 5-weeks of endurance training, participants performed a track field peak velocity (V_{peak_TF}), time limit (T_lim) at 100% of V_{peak_TF} and 5 km time-trial (5 km TT) tests. G_D lead to higher improvements in V_{peak_TF} (8.4 ± 1.8%; ES = 1.41) and 5 km TT (-12.8 ± 4.2%; ES = -1.97), than G_HRV (6.6 ± 1.5% and -8.3 ± 2.8%; ES = -1.20; 1.24) and G_T (4.9 ± 1.5% and -6.0 ± 3.3%; ES = -0.82; 0.68), respectively, with no differences for T_lim. Self-report measures of stress may be used to individualize endurance training prescription on a daily basis leading to better performance enhancement, which may be used with HRV for a holistic understanding of daily training-induce adaptations.

Beneficial Mitochondrial Biogenesis in Gastrocnemius Muscle Promoted by High-Intensity Interval Training in Elderly Female Rats

OBJECTIVE

Exercise can attenuate mitochondrial dysfunction caused by aging. Our study aimed to compare 12 weeks of high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) on the expression of mitochondria proteins [e.g., AMP-activated protein kinase (AMPK), Estrogen-related receptor alpha (ERRα), p38 mitogen-activated protein kinase (P38MAPK), and Peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1-α)] in gastrocnemius muscle of old female rats.

MATERIALS AND METHODS

In this experimental study, thirty six old female Wistar rats (18-month-old and 270-310 g) were divided into three groups: i. HIIT, ii. MICT, and iii. Control group (C). The HIIT protocol was performed for 12 weeks with 16-28 minutes (2 minutes training with 85-90% VO_2max in high intensity and 2 minutes training with 45-75% VO_2max low intensity). The MICT was performed for 30-60 minutes with the intensity of 65-70% VO_2max. The gastrocnemius muscle expression of AMPK, ERRα, P38MAPK, and PGC1α proteins were determined by Western blotting.

RESULTS

The expression of AMPK (P=0.004), P38MAPK (P=0.003), PGC-1α (P=0.028), and ERRα (P=0.006) in HIIT was higher than C group. AMPK (P=0.03), P38MAPK (P=0.032), PGC-1α (P=0.015), and ERRα (P=0.028) in MICT was higher than the C group. Also expression of AMPK (P=0.008), P38MAPK (P=0.009), PGC-1α (P=0.020) and ERRα (P=0.014) in MICT was higher than MICT group.

CONCLUSION

It seems that exercise training has beneficial effects on mitochondrial biogenesis, but the HIIT training method is more effective than MICT in improving mitochondrial function in aging.

Using global navigation satellite systems for modeling athletic performances in elite football players

This study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation Satellite System (GNSS) measurements in real-world situations. Data were collected from professional players in the Superleague division during a 1.5 season period (2019–2021). A baseline modeling performance was provided by time-series forecasting methods and compared with two multivariate modeling approaches using ridge regularisation and long short term memory neural networks. The multivariate models considered commercial features and new features extracted from GNSS raw data as predictor variables. A control condition in which profiles were predicted from predictors of the same session outlined the predictability of A-V profiles. Multivariate models were fitted either per player or over the group of players. Predictor variables were pooled according to the mean or an exponential weighting function. As expected, the control condition provided lower error rates than other models on average (p = 0.001). Reference and multivariate models did not show significant differences in error rates (p = 0.124), regardless of the nature of predictors (commercial features or extracted from signal processing methods) or the pooling method used. In addition, models built over a larger population did not provide significantly more accurate predictions. In conclusion, GNSS features seemed to be of limited relevance for predicting individual A-V profiles. However, new signal processing features open up new perspectives in athletic performance or injury occurrence modeling, mainly if higher sampling rate tracking systems are considered.

Left ventricular function and mechanics in backs and forwards elite rugby union players

PURPOSE

The aim of the present study was to assess left ventricular (LV) morphological and regional functional adaptations in backs and forwards elite rugby union (RU) players.

METHODS

Thirty-nine elite male RU players and twenty sedentary controls have been examined using resting echocardiography. RU players were divided into two groups, forwards (n = 22) and backs (n = 17). Evaluations included tissue Doppler and 2D speckle tracking analysis to assess LV strains and twisting mechanics.

RESULTS

The elite RU players exhibited a LV remodeling characterized by an increase in LV mass indexed to body surface area (82.2 ± 13.2 vs. 99.9 ± 16.1 and 119.7 ± 13.4 g.m^-2, in controls, backs and forwards; P < 0.001). Compared to backs, forwards exhibited lower global longitudinal strain (19.9 ± 2.5 vs. 18.0 ± 1.6%; P < 0.05), lower early diastolic velocity (16.5 ± 1.8 vs. 15.0 ± 2.3 cm.s^-1; P < 0.05) and lower diastolic longitudinal strain rate (1.80 ± 0.34 vs. 1.54 ± 0.26 s^-1; P < 0.01), especially at the apex. LV twist and untwisting velocities were similar in RU players compared to controls, but with lower apical (-46.2 ± 22.1 vs. -28.2 ± 21.7 deg.s^-1; P < 0.01) and higher basal rotational velocities (33.9 ± 20.9 vs. 48.4 ± 20.7 deg.s^-1; P < 0.05).

CONCLUSION

RU players exhibited an increase in LV mass which was more pronounced in forwards. In forwards, LV global longitudinal strain was depressed, LV filling pressures were decreased and LV relaxation depressed at the apex.

The relationship between training characteristics and durability in professional cyclists across a competitive season

RESULTS

Absolute 5MMP_fatigue, 12MMP_fatigue and relative 12MMP_fatigue were significantly lower in late-season compared with early- and mid-season (p < 0.05). The difference in absolute 12MMP_fresh and 12MMP_fatigue was significantly greater in late than in early- and mid-season.A significant relationship was found between training time below the first ventilatory threshold (Time < VT1) and improvements in absolute and relative 2MMP_fatigue (r = 0.43 p = 0.018 and r = 0.376 p = 0.04 respectively); and between a shift towards a polarised training intensity distribution and improvements in absolute and relative 12MMP_fatigue (r = 0.414p = 0.023 for both) between subsequent periods.

CONCLUSION

There is greater variability in the fatigue power profile across a competitive season than the fresh power profile.