Competition Between Desired Competitive Result, Tolerable Homeostatic Disturbance, and Psychophysiological Interpretation Determines Pacing Strategy

Fractional utilization of the 10-minute treadmill test velocity in running performance

This study aimed to evaluate the applicability of the 10-minute submaximal treadmill test (T10 test), a self-paced test, in determining critical speed (CS) and predicting running performance. Specifically, we sought to identify the percentage of T10 velocity (vT10) that runners performed in official distance races, and to compare physiological and performance indicators between sexes. 60 recreational runners (n=34 males and n=26 females) underwent a maximum incremental test, the novel T10 test, and ran 1200-m and 2400-m on the track. Runners self-reported their best performance times. Generalized Linear Model was used to compare running performances between sexes. For both males and females, the %vT10 in 5 km, 10 km, and half-marathon races occurred at 107.5% and 106.5%, 99.9% and 100.8%, and 92.6% and 97.1%, respectively. There was no interaction effect (p=0.520) and no main effect of sex (p=0.443). There was a main effect of distance (p<0.001), indicating that %vT10 in the 5km race differed from that found in the 10 km race (p=0.012), as well as in the half-marathon (p<0.001). Our findings suggest that %vT10 values can be used to determine pace in recreational endurance runners for race distances regardless of sex.

Pacing Demands in Competitive Nordic Skiing

BACKGROUND AND PURPOSE

Cross-country skiing, biathlon, and Nordic combined are Winter Olympics sports that involve cross-country skiing in undulating terrain, characterized by various subtechniques and repeated intensity fluctuations. The stochastic interval profile of these sports necessitates the continuous regulation of work and energy expenditure throughout training sessions and competitions, a concept known as pacing. With the advent of technological advancements that allow for the measurement of these features during training and competitions, scientific studies have broadened our understanding of the associated racing and pacing demands. We provide the current scientific overview of pacing demands in competitive cross-country skiing, biathlon, and Nordic combined and propose guidelines for how performance can be enhanced by adjusting pacing behavior.

CONCLUSIONS AND PRACTICAL APPLICATIONS

The study of pacing in skiing has evolved from basic lap-to-lap, or segment, analyses to detailed insights into micropacing strategies. This includes analysis of speed, internal and external power, subtechnique distribution, and associated temporal patterns, combined with subjective ratings of effort. While several objective tools such as heart rate, blood lactate concentration, and speed measurements are widely used in practice, current understanding suggests that these measures should supplement, rather than replace, the use of perceived effort (eg, rating of perceived exertion) to regulate intensity during training and competition in undulating terrain. Therefore, the ability to self-regulate effort appears to be an important performance characteristic and should be developed in adolescents and systematically used to optimize and evaluate the training process and race performance throughout athletes' careers.

"Falling Behind," "Letting Go," and Being "Outsprinted" as Distinct Features of Pacing in Distance Running

INTRODUCTION

In distance running, pacing is characterized by changes in speed, leading to runners dropping off the leader's pace until a few remain to contest victory with a final sprint. Pacing behavior has been well studied over the last 30 years, but much remains unknown. It might be related to finishing position, finishing time, and dependent on critical speed (CS), a surrogate of physiologic capacity. We hypothesized a relationship between CS and the distance at which runners "fell behind" and "let go" from the leader or were "outsprinted" as contributors to performance.

METHODS

100-m split times were obtained for athletes in the men's 10,000-m at the 2008 Olympics (N = 35). Split times were individually compared with the winner at the point of "falling behind" (successive split times progressively slower than the winner), "letting go" (large increase in time for distance compared with winner), or "outsprinted" (falling behind despite active acceleration) despite being with the leader with 400 m remaining.

RESULTS

Race times ranged between 26:55 and 29:23 (world record = 26:17). There were 3 groups who fell behind at ∼1000 (n = 11), ∼6000 (n = 16), and ∼9000 m (n = 2); let go at ∼4000 (n = 10), ∼7000 (n = 14), and ∼9500 m (n = 5); or were outkicked (n = 6). There was a moderate correlation between CS and finishing position (r = .82), individual mean pace (r = .79), "fell behind" distance (r = .77), and "let go" distance (r = .79). D' balance was correlated with performance in the last 400 m (r = .87).

CONCLUSIONS

Athletes displayed distinct patterns of falling behind and letting go. CS serves as a moderate predictor of performance and final placing. Final placing during the sprint is related to preservation of D' balance.

Differences in pacing behaviour between global championship medal performances and world records in men's and women's middle- and long-distance running track events

The differences in pacing demands between track distance-running championship and meet (e.g., World Record [WR]) races have not been specified yet in the current literature. Therefore, the aim of this study was to determine pacing behaviour differences between WRs and global championship (i.e., World Championships and Olympic Games) medal performances (GCMs) in middle- and long-distance running events. Percentages of mean race section speeds were compared through analysis of variance between men's and women's 169 WRs and 189 GCMs over 800 m, 1500 m, 3000 m steeplechase, 5000 m and 10,000 m. U-shaped and negative pacing approaches are observed during men's and women's 1500 m WRs and GCMs, respectively. The first and third 400 m of men's and women's 1500 m GCMs were relatively slower and faster, respectively (p ≤ 0.05, 1.31 ≤ d ≤ 1.69). Even profiles are followed during women's 3000 m steeplechase WRs and GCMs, whereas positive approaches were adopted in men's GCMs. Finally, whereas 5000 m and 10,000 m GCMs were finished with a fast endspurt, WRs had a U-shaped profile in men, with differences between the initial and last race stages (p ≤ 0.01, 1.20 ≤ d ≤ 3.66), and an even profile in women. Coaches should consider the different pacing demands existing among meet and global championship races to specifically implement training characteristics targeting either goal type.

Evolution of 1500-m Olympic Running Performance

PURPOSE

This study determined the evolution of performance and pacing for each winner of the men's Olympic 1500-m running track final from 1924 to 2020.

METHODS

Data were obtained from publicly available sources. When official splits were unavailable, times from sources such as YouTube were included and interpolated from video records. Final times, lap splits, and position in the peloton were included. The data are presented relative to 0 to 400 m, 400 to 800 m, 800 to 1200 m, and 1200 to 1500 m. Critical speed and D' were calculated using athletes' season's best times.

RESULTS

Performance improved ∼25 seconds from 1924 to 2020, with most improvement (∼19 s) occurring in the first 10 finals. However, only 2 performances were world records, and only one runner won the event twice. Pacing evolved from a fast start-slow middle-fast finish pattern (reverse J-shaped) to a slower start with steady acceleration in the second half (J-shaped). The coefficient of variation for lap speeds ranged from 1.4% to 15.3%, consistent with a highly tactical pacing pattern. With few exceptions, the eventual winners were near the front throughout, although rarely in the leading position. There is evidence of a general increase in both critical speed and D' that parallels performance.

CONCLUSIONS

An evolution in the pacing pattern occurred across several "eras" in the history of Olympic 1500-m racing, consistent with better trained athletes and improved technology. There has been a consistent tactical approach of following opponents until the latter stages, and athletes should develop tactical flexibility, related to their critical speed and D', in planning prerace strategy.

The pacing differences in performance levels of marathon and half-marathon runners

Introduction

Many studies indicate a considerable impact of optimal pacing on long-distance running performance. Given that the amount of carbohydrates in metabolic processes increases supralinearly with the running intensity, we may observe differences between the pacing strategies of two long-distance races and different performance levels of runners. Accordingly, the present study aimed to examine the differences in pacing strategies between marathon and half-marathon races regarding the performance levels of runners.

Methods

The official results and split times from a total of 208,760 (marathon, N = 75,492; half-marathon, N = 133,268) finishers in the "Vienna City Marathon" between 2006 and 2018 were analyzed. The percentage of the average change of speed for each of the five segments (CS 1-5), as well as the absolute change of speed (ACS) were calculated. The CS 1-5 for the marathon are as follows: up to the 10th km, 10th - 20th km, 20th - 30th km, 30th - 40th km, and from the 40th km to the 42.195 km. For the half-marathon, the CS 1-5 are half of the marathon values. Four performance groups were created as quartiles of placement separately for sexes and races: high-level (HL), moderate to high-level (MHL), moderate to low-level (MLL), and low-level (LL).

Results

Positive pacing strategies (i.e., decrease of speed) were observed in all performance groups of both sex and race. Across CS 1-5, significant main effects (p < 0.001) were observed for the segment, performance level, and their interaction in both sex and race groups. All LL groups demonstrated higher ACS (men 7.9 and 6.05%, as well as women 5.83 and 5.49%, in marathon and half-marathon, respectively), while the HL performance group showed significantly lower ACS (men 4.14 and 2.97%, as well as women 3.16 and 2.77%, in marathon and half-marathon, respectively). Significant main effects (p < 0.001) for the race were observed but with a low effect size in women (ŋ2 = 0.001).

Discussion

Better runners showed more even pacing than slower runners. The half-marathoners showed more even pacing than the marathoners across all performance groups but with a trivial practical significance in women.

Highly Trained Biathletes With a Fast-Start Pacing Pattern Improve Time-Trial Skiing Performance by Pacing More Evenly

PURPOSE

In sprint biathlon, a J-shaped pacing pattern is commonly used. We investigated whether biathletes with a fast-start pacing pattern increase time-trial skiing and shooting performance by pacing more evenly.

METHODS

Thirty-eight highly trained biathletes (∼21 y, 27 men) performed an individual 7.5 (3 × 2.5 km for women) or 10-km (3 × 3.3 km for men) time trial on roller skis with a self-selected pacing strategy (day 1). Prone (after lap 1) and standing shooting (after lap 2) stages were performed using paper targets. Based on their pacing strategy in the first time trial (ratio between the initial ∼800-m segment pace on lap 1 and average ∼800-m segment pace on laps 1-3), participants were divided into an intervention group with the fastest starting pace (INT, n = 20) or a control group with a more conservative starting pace (CON, n = 18). On day 2, INT was instructed to reduce their starting pace, while CON was instructed to maintain their day 1 strategy.

RESULTS

INT increased their overall time-trial performance more than CON from day 1 to day 2  (mean ± 95% CI; 1.5% ± 0.7% vs 0.0% ± 0.9%, P = .02). From day 1 to day 2, INT reduced their starting pace (5.0% ± 1.5%, P < .01), with reduced ratings of perceived exertion during lap 1 (P < .05). For CON, no change was found for starting pace (-0.8% ± 1.2%) or ratings of perceived exertion between days. No differences were found for shooting performance for either group.

CONCLUSION

Highly trained biathletes with a pronounced fast-start pattern improve skiing performance without any change in shooting performance by pacing more evenly.

Self-regulation of effort for a better health-related quality of life: a multidimensional activity pacing model for chronic pain and fatigue management

PURPOSE

To propose a comprehensive multidimensional model of activity pacing that improves health-related quality of life and promotes sustained physical activity engagement among adults with chronic conditions.

MATERIALS AND METHODS

A narrative review was conducted to examine the existing literature on activity pacing, health-related quality of life, pain and fatigue management, and physical activity promotion in chronic conditions.

RESULTS

The literature revealed a lack of a cohesive approach towards a multidimensional model for using activity pacing to improve health-related quality of life. A comprehensive multidimensional model of activity pacing was proposed, emphasizing the importance of considering all aspects of pacing for sustained physical activity engagement and improved health-related quality of life. The model incorporates elements such as rest breaks, self-regulatory skills, environmental factors, and effective coping strategies for depression/anxiety. It takes into account physical, psychological, and environmental factors, all of which contribute significantly to the enhancement of health-related quality of life, physical function, and overall well-being, reflecting a holistic approach.

CONCLUSIONS

The model offers guidance to researchers and clinicians in effectively educating patients on activity pacing acquisition and in developing effective interventions to enhance physical activity engagement and health outcomes among adults with chronic conditions. Additionally, it serves as a tool towards facilitating discussions on sustained physical activity and a healthy lifestyle for patients, which can eventually lead to improved quality of life.

Can we predict long-duration running power output? A matter of selecting the appropriate predicting trials and empirical model

When facing a long-distance race, athletes and practitioners could develop an efficient pacing strategy and training paces if an accurate performance estimate of the target distance is achieved. Therefore, this study aims to determine the validity of different empirical models (i.e. critical power [CP], Power law and Peronnet) to predict long-duration power output (i.e. 60 min) when using two or three time trial configurations. In a 5-week training period, fifteen highly trained athletes performed nine-time trials (i.e. 1, 2, 3, 4, 5, 10, 20, 30, and 60 min) in a randomized order. Their power-duration curves were defined through the work-time (CPwork), power-1/time (CP1/time), two-parameter hyperbolic (CP2hyp), three-parameter hyperbolic (CP3hyp) CP models using different two- and three-time trial configurations. The undisclosed proprietary CP models of the Stryd (CPstryd) and Golden Cheetah training software (CPcheetah) were also computed as well as the non-asymptotic Power law and Peronnet models. These were extrapolated to the 60-min power output and compared to the actual performance. The shortest valid configuration (95% confidence interval < 12 W) for CPwork and CP1/time was 3-30 min (Bias: 8.3 [4.9 to 11.7] W), for CPstryd was 10-30 min (Bias: 4.2 [- 1.0 to 9.4] W), for CP2hyp, CP3hyp and CPcheetah was 3-5-30 min (Bias < 5.7 W), for Power law was 1-3-10 min (- 1.0 [- 11.9 to 9.9] W), and for Peronnet was 4-20 min (- 3.0 [- 10.2 to 4.3] W). All the empirical models provided valid estimates when the two or three predicting trial configurations selected attended each model fitting needs.

The Evolution of World-Class Endurance Training: The Scientist's View on Current and Future Trends

BACKGROUND

Elite sport is continuously evolving. World records keep falling and athletes from a longer list of countries are involved.

PURPOSE

This commentary was designed to provide insights into present and future trends associated with world-class endurance training based on the perspectives, experience, and knowledge of an expert panel of 25 applied sport scientists.

RESULTS

The key drivers of development observed in the past 10-15 years were related to (1) more accessible scientific knowledge for coaches and athletes combined with (2) better integration of practical and scientific exchange across multidisciplinary perspectives within professionalized elite athlete support structures, as well as (3) utilization of new technological advances. Based on these perspectives, we discerned and exemplified the main trends in the practice of endurance sports into the following categories: better understanding of sport-specific demands; improved competition execution; larger, more specific, and more precise training loads; improved training quality; and a more professional and healthier lifestyle. The main areas expected to drive future improvements were associated with more extensive use of advanced technology for monitoring and prescribing training and recovery, more precise use of environmental and nutritional interventions, better understanding of athlete-equipment interactions, and greater emphasis on preventing injuries and illnesses.

CONCLUSIONS

These expert insights can serve as a platform and inspiration to develop new hypotheses and ideas, encourage future collaboration between researchers and sport practitioners, and, perhaps most important, stimulate curiosity and further collaborative studies about the training, physiology, and performance of endurance athletes.

Short-Term Speed Variability as an Index of Pacing Stochasticity in Athletic Running Events

We aimed to compare differences in performance and pacing variability indices between 5000 m heats and finals during major championships in men and women. Data with 100 m time resolution were used to compare overall pacing variability (standard deviation of 100 m section times, SD; and coefficient of variation, CV%) and short-term pacing variability (root mean square of successive differences between 100 m section times, RMSSD). The changes in performance and pacing indices differed between races and competitions. For instance, the men's final in Beijing 2008 was quicker than the heat (p < 0.01) while the CV% was reduced (p = 0.03) and RMSSD increased (p < 0.01). For women, the heats and the final exhibited a similar mean time in London 2017 (p = 0.33) but with CV% (p < 0.001) and RMSSD (p < 0.001) showing opposite trends. Individual analyses of men's and women's champions revealed highly individual variability metrics. The use of RMSSD can complement overall variability indices for better characterization of pacing stochasticity.