Running economy and running performance

NewsBot · May 17, 2019

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Are Changes In Running Economy Associated With
Changes In Performance In Runners? A Systematic
Review and Meta Analysis
Ian J. O Sullivan et al
ACSM ANNUAL MEETING
May 28 – June 1, 2019 – Orlando, Florida

Improvements in running economy (RE) are thought to lead to improvements in
running performance (P). The identification of modifiable factors that affect RE
and by association, P has been the focus of a significant body of research in recent
years. Modifiable factors affecting RE are broadly classified as, biomechanical,
anthropometric, physiological, extrinsic and training related. Interventions have
been used to alter one or more of these factors with a view to improving RE. The
underlying assumption is that an improvement in RE will also lead to an improvement
in P. PURPOSE: The aim of this study was to assess the effect of interventions
of at least 2-weeks’ duration on RE and P and to determine whether there is a
relationship between changes in RE (ΔRE) and changes in running performance
(ΔP). METHODS: A database search was carried out in Web of Science, Scopus
and SPORTDiscus. In accordance with a PRISMA checklist 10 studies reporting
12 comparisons between interventions and controls were included in the review.
RESULTS: There was no correlation between percentage ΔRE and percentage ΔP
(r = 0.46, P = 0.936, 12 comparisons). There was a low risk of reporting bias in
relation to incomplete data sets. There was an unclear risk of selection bias associated
with random allocation to intervention and control groups and reporting of baseline
differences in RE and P between intervention and control groups. There was also
an unclear risk of performance bias relating to the monitoring of non-intervention
training, detection bias associated with differences in determination of the performance
outcome measure and attrition bias associated with reporting of participant dropout.
Meta-analyses found no statistically significant differences between interventions
and control for RE (SMD (95% CI) = -0.37 (-1.43, 0.69), 204 participants, p =
0.49) or for P (SMD (95% CI) = -0.65 (-26.02, 24.72, 204 participants, p = 0.99).
CONCLUSIONS: Methodologies for subject allocation to intervention and control
groups and the reporting of differences in baseline characteristics of control and
intervention groups and reporting of participant dropout were infrequently applied in
the included studies. Studies of greater statistical power, with standardised measures of
performance and greater control of non-intervention training are required.
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Admin2 · May 17, 2019

Related Thread:
The relationship between running economy and biomechanical variables in distance runners

NewsBot · Sep 21, 2020

Visual Classification of Running Economy by Distance Running Coaches
Robbie G. Cochrum , Jennifer L. Caputo , John M. Coons , Dana K. Fuller , Don W. Morgan , Ryan T. Conners & show all
European Journal of Sport Science : 15 Sep 2020

Objectives: This study assessed the ability of coaches to rank-order distance runners on running economy (RE) through visual assessment. Methods: Running economy was measured in five trained recreational distance runners at a speed of 3.57 m·s-1, with a minimum VO2 difference of 2-mL.kg-1.min-1 between adjacent runners. Following measurement of RE, participants were filmed from the front, side, and rear while running on a treadmill. Endurance coaches from high school to international level (N = 121) viewed each video and ranked the runners on a scale from 1 (most economical) to 5 (least economical). Coaches also completed a demographic questionnaire and listed running style biomechanical observations they used in determining each ranking. A cumulative odds ordinal logistic regression with proportional odds was run to determine the effect of coaching level, years of coaching experience, years of training experience, competition level, certification status, and education level, on the ability of distance running coaching to accurately rank RE among a group of runners. Results: No coaching characteristic was a significant predictor of ranking accuracy, χ2 = 3.566, p = .735. Conclusions: Visual assessment of effort, based on RE, is a difficult task, even for the trained eye and could be related to difficulty in understanding the interaction effect of various RE factors or the translation of scientific-based knowledge to the field of play. Practically, coaches should be cautious when recommending biomechanical adjustments without considering the interconnected factors related to such changes.
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NewsBot · Jan 24, 2022

There Is No Global Running Pattern More Economic Than Another
at Endurance Running Speeds
Aurélien Patoz, Thibault Lussiana, Bastiaan Breine, Cyrille Gindre, and Kim Hébert-Losier
International Journal of Sports Physiology and Performance, (Ahead of Print)

Purpose: The subjective Volodalen® score (V®score) and the objective duty factor metric can both assess global running
patterns. The authors aimed to investigate the relation between running economy (RE) at endurance running speeds and the
global running pattern quantified using both subjective and objective measures. Methods: RE and 3-dimensional whole-body
kinematics were acquired by indirect calorimetry and an optoelectronic system, respectively, for 52 trained runners during
treadmill runs at 10, 12, and 14 km/h. Results: Correlations between RE and V®score and RE and duty factor were negligible and
nonsignificant across speeds tested (P ≥ .20), except for a low and significant correlation between RE and V®score at 10 km/h.
Conclusions: These findings suggest there is no global running pattern more economic than another at endurance running speeds.
Therefore, there is no advantage of choosing, favoring, or prescribing one specific global running pattern along a continuum
based on V®score or duty factor metrics, and coaches should not try to modify the spontaneous running pattern of runners at
endurance running speed to improve RE.
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NewsBot · Apr 20, 2022

Effect of Different Intervention on Running Economy - A Systematic Review of the Literature
Shahin Ketabi et al
International Journal of Motor Control and Learning (IJMCL)

Context: This article systematically reviews the available literature on biomechanically motivated
interventions to improve running economy aside from conventional endurance training. It was
aimed to identify the possible mechanisms behind the potential improvements and to extract
principles to guide researchers and coaches in how to make use of this potential.
Evidence acquisition: The search strategy yielded 26 intervention papers and four reviews which
were suitable for inclusion.
Results: It was concluded that plyometric and strength training protocols were consistently
beneficial to reduce the oxygen consumption per distance traveled in steady state running showing
an average effect size of 3.8%. Footwear interventions showed smaller effects of 1.9% on average
but still may offer considerable improvements which can potentially be applied immediately.
Conclusions: It was suggested that the energy consumption savings achieved by footwear
interventions are not realizable by energy return mechanisms of the footwear alone. It is most likely
that footwear assists to improve RE by optimizing energy storage and return mechanisms within the
biological system. Future research should aim at verifying this interplay to provide more efficient
training programs as well as footwear which ameliorates the utilization of the mechanisms
embedded within the human locomotor system.
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NewsBot · Nov 17, 2022

Running economy in long-distance runners is positively affected by running experience and negatively by aging
Vinícius Ribeirodos Anjos SouzaaAldoSeffrinaRonaldo Alvesda CunhaaLavíniaVivanaClaudio Andre Barbosade LirabRodrigo LuizVancinicKatjaWeissdBeatKnechtledMarilia SantosAndradee
Physiology & Behavior; Volume 258, 1 January 2023, 114032

Introduction
The maximum oxygen uptake (max), the maximum rate of oxygen that can be sustained before the onset of blood lactate accumulation, and the metabolic cost of locomotion are the main physiological factors associated with long-distance running performance. The latter is known as the running economy. Generally, runners reach peak performance in long races between 25 and 30 years of age, with a progressive decline occurring thereafter. However, it is not known whether the running economy is affected or how it is affected by aging.

Aim
To investigate the effect of age and years of running experience on the running economy of amateur long-distance runners aged 20–80 years.

Methods
Sixty-nine recreational long-distance runners, divided into five age groups according to decade of life, participated in this study: Group 1 (n= 9) 27.2 ± 1.3 years, Group 2 (n= 18) 35.9 ± 2.2 years, Group 3 (n= 17) 43.4 ± 2.8 years, Group 4 (n= 17) 53.0 ± 2.3 years, and Group 5 (n= 8) 65.5 ± 2.9 years. For running economy assessment, oxygen cost (OC) and energy cost (EC) were measured. Furthermore, the participants were interviewed on their running experience.

Results
For EC, the two independent variables composing the regression model were age (ß = 0.703, t= 5.443, p < 0.001) and running experience (ß = −0.230, t = −1.785, p= 0.07), and 34% of the energy cost variation can be explained by these two factors. EC and OC were compared among the groups. There were no significant differences between Groups 1 and 2 (p= 0.999), Groups 1 and 3 (p= 1.000), and Groups 1 and 4 (p= 0.528). However, Group 5 had a significantly higher energy cost than Group 1 (p < 0.001), Group 2 (p < 0.001), Group 3 (p < 0.001) and Group 4 (p < 0.001).

Conclusion
The number of years of running experience has a positive effect on running economy, but it is insufficient to overcome the negative effect of the aging process. Furthermore, running economy was significantly worse in participants aged ≥60 years compared with that in younger athletes.
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NewsBot · Mar 6, 2024

Press Release:
Running performance helped by mathematical research

How to optimise running? A new mathematical model1 has shown, with great precision, the impact that physiological and psychological parameters have on running performance and provides tips for optimised training. The model grew out of research conducted by a French-British team including two CNRS researchers2, the results of which will appear on March 5th 2024 in the journal Frontiers in Sports and Active Living.

This innovative model was developed thanks to extremely precise data3 from the performances of Matthew Hudson-Smith (400m), Femke Bol (400m), and Jakob Ingebrigtsen (1500m) at the 2022 European Athletics Championships in Munich, and for Gaia Sabbatini (1500m) at the 2021 European Athletics U23 Championships in Tallinn. It led to an optimal control problem for finishing time, effort, and energy expenditure. This is the first time that such a model has also considered the variability of motor control, i.e., the role of the brain in the process of producing movement. The simulations allow the researchers to have access to the physiological parameters of the runners—especially oxygen consumption (or VO2)4, and energy expenditure during the race—as well as compute their variations. Quantifying costs and benefits in the model provides immediate access to the best strategy for achieving the runner’s optimal performance.

The study details multiple criteria, such as the importance of a quick start in the first 50 metres (due to the need for fast oxygen kinetics), or reducing the decrease in velocity in a 400m race. The scientists also demonstrated that improving the aerobic metabolism (oxygen uptake) and the ability to maintain VO2 are crucial elements to 1500m race performance.

The development of this model represents considerable progress in studying variations in physiological parameters during championship races, for which in vivo measurements are not possible.

1 For more details on the model, “Be a champion, 40 facts you didn't know about sports and science”, Amandine Aftalion, Springer, to appear May 14th 2024.

2 From the Centre for Analysis and Social Mathematics (CNRS/EHESS), in collaboration with the Jacques-Louis Lions Laboratory (CNRS/Sorbonne Université/Université Paris Cité) and the Carnegie School of Sport at Leeds Beckett University.

3 Values measured every 100 milliseconds.

4 Rate at which oxygen is transformed into energy.

Modeling the optimization of World-class 400m and 1500m running performances using high-resolution data. Antoine Le Hyaric, Amandine Aftalion, Brian Hanley. Frontiers in Sports and Active Living, March 5th 2024.

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