Effects of Increased Load of Low- Versus High-Intensity Endurance Training on Performance and Physiological Adaptations in Endurance Athletes

The Effect of Polarized Training Intensity Distribution on Maximal Oxygen Uptake and Work Economy Among Endurance Athletes: A Systematic Review

High-intensity training (HIT) has commonly been the most effective training method for improvement in maximal oxygen uptake (VO2max) and work economy, alongside a substantial volume of low-intensity training (LIT). The polarized training model combines both low- and high-intensity training into a specific training intensity distribution and has gained attention as a comprehensive approach. The objective of this review was to systematically search the literature in order to identify the effects of polarized training intensity distribution on VO2max, peak oxygen uptake (VO2peak), and work economy among endurance athletes. A literature search was performed using PubMed and SPORTDiscus. A total of 1836 articles were identified, and, after the selection process, 14 relevant studies were included in this review. The findings indicate that a polarized training approach seems to be effective for enhancing VO2max, VO2peak, and work economy over a short-term period for endurance athletes. Specifically, a training intensity distribution involving a moderate to high volume of HIT (15-20%) combined with a substantial volume of LIT (75-80%) appears to be the most beneficial for these improvements. It was concluded that polarized training is a beneficial approach for enhancing VO2max, VO2peak, and work economy in endurance athletes. However, the limited number of studies restricts the generalizability of these findings.

Training Session Models in Endurance Sports: A Norwegian Perspective on Best Practice Recommendations

BACKGROUND

Our scientific understanding of the mechanistic and practical connections between training session prescriptions, their execution by athletes, and adaptations over time in elite endurance sports remains limited. These connections are fundamental to the art and science of coaching.

OBJECTIVE

By using successful Norwegian endurance coaches as key informants, the aim of this study is to describe and compare best practice session models across different exercise intensities in Olympic endurance sports.

METHODS

Data collection was based on a four-step pragmatic qualitative study design, involving questionnaires, training logs from successful athletes, and in-depth and semi-structured interviews, followed by negotiation among researchers and coaches to assure our interpretations. Twelve successful and experienced male Norwegian coaches from biathlon, cross-country skiing, long-distance running, road cycling, rowing, speed skating, swimming, and triathlon were chosen as key informants. They had been responsible for the training of world-class endurance athletes who altogether have won > 370 medals in international championships.

RESULTS

The duration of low-intensity training (LIT) sessions ranges from 30 min to 7 h across sports, mainly due to modality-specific constraints and load tolerance considerations. Cross-training accounts for a considerable part of LIT sessions in several sports. Moderate (MIT)- and high-intensity training (HIT) sessions are mainly conducted as intervals in specific modalities, but competitions also account for a large proportion of annual HIT in most sports. Interval sessions are characterized by a high accumulated volume, a progressive increase in intensity throughout the session, and a controlled, rather than exhaustive, execution approach. A clear trend towards shorter intervals and lower work: rest ratio with increasing intensity was observed. Overall, the analyzed sports implement considerably more MIT than HIT sessions across the annual cycle.

CONCLUSIONS

This study provides novel insights on quantitative and qualitative aspects of training session models across intensities employed by successful athletes in Olympic endurance sports. The interval training sessions revealed in this study are generally more voluminous, more controlled, and less exhaustive than most previous recommendations outlined in research literature.

Changes in female football players' in-season training load, intensity and physical performance: training progression matters more than accumulated load

Introduction

This observational study investigated: (1) potential changes in female football players' in-season training load, intensity and physical performance, and (2) if in-season accumulated training load, intensity, or their progression are associated to changes in physical performance.

Methods

Thirty-five national level female players (∼21 years, n = 35) from three top-teams of the Finnish national league participated. Players performed tests at the beginning and at the end of the 27-week in-season. Tests were: 30-m sprint, countermovement jump (CMJ) and 1,200-m shuttle run, used to calculate maximal aerobic speed (MAS). Players' external and internal training load and intensity were monitored in all on-field training sessions and official matches (3,941 data samples) using Polar Team Pro system.

Results

Training load decreased towards the end of the in-season (p < 0.05), but intensity remained stable. No changes in physical performance test results occurred from before to after in-season tests at a group level. Change of CMJ correlated negatively with accumulated training load, intensity and progression of total distance (TD) and low-intensity running distance (LIRD) (r = -0.398 to -0.599, p < 0.05). Instead, development of MAS correlated positively with progression of TD and LIRD intensities (r = 0.594 and 0.503, p < 0.05). Development of both CMJ and MAS correlated positively with intensity progression of very-high-intensity running distance (VHIRD) and number of accelerations and decelerations (r = 0.454-0.588, p < 0.05).

Discussion

Reduced training load over the in-season is not detrimental for players' physical performance when training intensity progressively increases. Intensity progression of VHIRD, moderate- and high-intensity accelerations and decelerations are indicators of both MAS and CMJ development, respectively.

AI-Assisted Fatigue and Stamina Control for Performance Sports on IMU-Generated Multivariate Times Series Datasets

BACKGROUND

Optimal sports performance requires a balance between intensive training and adequate rest. IMUs provide objective, quantifiable data to analyze performance dynamics, despite the challenges in quantifying athlete training loads. The ability of AI to analyze complex datasets brings innovation to the monitoring and optimization of athlete training cycles. Traditional techniques rely on subjective assessments to prevent overtraining, which can lead to injury and underperformance. IMUs provide objective, quantitative data on athletes' physical status during action. AI and machine learning can turn these data into useful insights, enabling data-driven athlete performance management. With IMU-generated multivariate time series data, this paper uses AI to construct a robust model for predicting fatigue and stamina.

MATERIALS AND METHODS

IMUs linked to 19 athletes recorded triaxial acceleration, angular velocity, and magnetic orientation throughout repeated sessions. Standardized training included steady-pace runs and fatigue-inducing techniques. The raw time series data were used to train a supervised ML model based on frequency and time-domain characteristics. The performances of Random Forest, Gradient Boosting Machines, and LSTM networks were compared. A feedback loop adjusted the model in real time based on prediction error and bias estimation.

RESULTS

The AI model demonstrated high predictive accuracy for fatigue, showing significant correlations between predicted fatigue levels and observed declines in performance. Stamina predictions enabled individualized training adjustments that were in sync with athletes' physiological thresholds. Bias correction mechanisms proved effective in minimizing systematic prediction errors. Moreover, real-time adaptations of the model led to enhanced training periodization strategies, reducing the risk of overtraining and improving overall athletic performance.

CONCLUSIONS

In sports performance analytics, the AI-assisted model using IMU multivariate time series data is effective. Training can be tailored and constantly altered because the model accurately predicts fatigue and stamina. AI models can effectively forecast the beginning of weariness before any physical symptoms appear. This allows for timely interventions to prevent overtraining and potential accidents. The model shows an exceptional ability to customize training programs according to the physiological reactions of each athlete and enhance the overall training effectiveness. In addition, the study demonstrated the model's efficacy in real-time monitoring performance, improving the decision-making abilities of both coaches and athletes. The approach enables ongoing and thorough data analysis, supporting strategic planning for training and competition, resulting in optimized performance outcomes. These findings highlight the revolutionary capability of AI in sports science, offering a future where data-driven methods greatly enhance athlete training and performance management.

THE DIFFERENCES BETWEEN MODERN MODES OF BIATHLON TRAINING AT THE OLYMPIC WINTER GAMES

ABSTRACT Introduction The biathlon is a snow sport that combines cross-country skiing and shooting, originating in Scandinavia. It requires athletes to not only have the ability to glide quickly over long distances but also to have the ability to shoot quickly and accurately. There is little research on biathlon characteristics and analysis of influencing factors and training strategies in China. Objective This study analyzes modern biathlon athletes’ specific explosive power, endurance and training effects at the Winter Olympics. Methods Twenty biathlon athletes were selected as research volunteers. Physiological and biochemical indicators of the athletes were experimentally tested after training. Results There was a correlation between maximum speed and the height of the athletes’ double stand test (SD) (p < 0.05). The heavier athletes skied relatively faster. However, the excessive body fat rate is not conducive to maintaining high-intensity skiing in the long term. The athletes’ VO2max was closely related to their skiing performance and shot hit percentage (p < 0.05). Conclusion Maintaining gun ski training can positively improve the competitive level of world-class biathletes. The athlete's muscles have a solid ability to generate high mechanical power in a short time. It is beneficial to take advantage of a favorable position after the start of the competition. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

The Training Characteristics of World-Class Distance Runners: An Integration of Scientific Literature and Results-Proven Practice

In this review we integrate the scientific literature and results-proven practice and outline a novel framework for understanding the training and development of elite long-distance performance. Herein, we describe how fundamental training characteristics and well-known training principles are applied. World-leading track runners (i.e., 5000 and 10,000 m) and marathon specialists participate in 9 ± 3 and 6 ± 2 (mean ± SD) annual competitions, respectively. The weekly running distance in the mid-preparation period is in the range 160–220 km for marathoners and 130–190 km for track runners. These differences are mainly explained by more running kilometers on each session for marathon runners. Both groups perform 11–14 sessions per week, and ≥ 80% of the total running volume is performed at low intensity throughout the training year. The training intensity distribution vary across mesocycles and differ between marathon and track runners, but common for both groups is that volume of race-pace running increases as the main competition approaches. The tapering process starts 7–10 days prior to the main competition. While the African runners live and train at high altitude (2000–2500 m above sea level) most of the year, most lowland athletes apply relatively long altitude camps during the preparation period. Overall, this review offers unique insights into the training characteristics of world-class distance runners by integrating scientific literature and results-proven practice, providing a point of departure for future studies related to the training and development in the Olympic long-distance events.

The Effect of Rifle Carriage on the Physiological and Accelerometer Responses During Biathlon Skiing

Purpose

Investigate the effect of biathlon rifle carriage on physiological and accelerometer-derived responses during biathlon skiing.

Methods

Twenty-eight biathletes (11F, 17M) completed two XC skiing time-trials (~2,300 m), once with and once without the biathlon rifle, with concurrent measurements of HR, skiing speed and accelerations recorded from three triaxial accelerometers attached at the Upper-spine, Lower-spine and Pelvis. Exercise intensity was quantified from HR, skiing speed as well from accelerometry-derived PlayerLoad™ per minute (PL·min^-1) and average net force (AvF_Net). All metrics were analyzed during Uphill, Flat and Downhill sections of the course. Relationships between accelerometry-derived metrics and skiing speed were examined.

Results

Time-trials were faster for males compared with females (mean difference: 97 ± 73 s) and No-Rifle compared to With-Rifle (mean difference: 16 ± 9 s). HR was greatest during Downhill (183 ± 5 bpm), followed by Uphill (181 ± 5 bpm) and was lowest in the Flat sections (177 ± 6 bpm, p <0.05). For PL·min^-1 and AvF_Net there were 3-way Rifle x Gradient x Sensor-Position interactions. Typically, these metrics were greatest during Uphill and Flat sections and were lowest during Downhill sections. Rifle carriage had no impact on the AvF_Net at the Lower-Spine or Pelvis. Significant positive linear relationships were identified between skiing speed and accelerometer-derived metrics during Uphill, Flat and Downhill skiing (r = 0.12-0.61, p < 0.05).

Conclusions

The accelerometry-derived approach used in this study provides the potential of a novel method of monitoring the external demands during skiing. In particular, AvF_Net with sensors located close to the center of mass displayed greatest utility because it followed the expected response of external intensity where responses were greatest during uphill sections, followed by flats and lowest during downhills. In addition, there were significant positive relationships between AvF_Net and skiing speed ranging from small to large. Accelerometry-derived measures could provide useful estimates of the external demands in XC skiing and biathlon.