Abstract:The benefits of drafting for elite marathon runners are intuitive, but the quantitative energetic and time savings are still unclear due to the different methods used for converting aerodynamic drag force reductions to gross metabolic power savings. Further, we lack a mechanistic understanding of the relationship between aerodynamic drag forces and ground reaction forces (GRF) over a range of running velocities. Here, we quantified how small horizontal impeding forces affect gross metabolic power and GRF over … Show more
“…Such results show the importance of the front pacers in reducing CD and open up interesting perspectives for future marathon record attempts. Figure 16Time savings for different formations with respect to a solo runner computed with the models of [25,26]. …”
Section: Resultsmentioning
confidence: 99%
“…Time savings for different formations with respect to a solo runner computed with the models of [25,26]. …”
Section: Resultsmentioning
confidence: 99%
“…An estimate of the time saving associated with the complex configurations examined in this section was finally computed. To do this, we applied the relationship proposed by da Silva et al [25]. They found that the average change in gross metabolic power normalΔPmeta was 6.13% per 1% body weight of horizontal impeding force, here represented by the aerodynamic drag.…”
Section: Resultsmentioning
confidence: 99%
“…The new velocity U associated with the aerodynamic drag force FD (and, therefore, with the drag coefficient CD) of the drafting formation was then estimated by linearizing the relation between RE and U around the reference point U⋆ here [26] U=U⋆false(1+αnormalΔREfalse),where the coefficient α≃2/3 for the typical velocity U⋆ of an elite marathoner. The variation of the running economy was assumed as normalΔRE≃normalΔPmeta, where we used the body mass of Kipchoge (mr=52kg) to estimate the reduction in the gross metabolic power [25] normalΔPmeta≃0.0613normalΔCDFD⋆mrg,with g the acceleration of gravity. The race times over the distance of a marathon were estimated for the formations described in figures 12, 14 and 15, and the time savings were reported in table 3.…”
Drafting is a strategy in marathon running races that reduces the drag force acting on a designated runner. Drafting involves a formation of athletes, called pacers, running in front of (and sometimes behind) the designated runner. The present experiments evaluated complex formations involving up to seven pacers with the aim of enhancing the performances of an elite marathoner. Wind tunnel measurements with 1/10 scale articulated runner manikins were carried out. First, simple formations with one or two pacers were examined and the results were compared with previous investigations. Second, the runner formations of the Nike Breaking2 and INEOS 1:59 Challenge events whose goal was to break the 2 h marathon barrier were studied. The main finding was the identification of three complex formations of six and seven pacers that allow a drag-force reduction of about 60% with respect to a solo runner and an estimated time saving of 262 s. These results point to how it may be possible to run the fastest marathon ever.
“…Such results show the importance of the front pacers in reducing CD and open up interesting perspectives for future marathon record attempts. Figure 16Time savings for different formations with respect to a solo runner computed with the models of [25,26]. …”
Section: Resultsmentioning
confidence: 99%
“…Time savings for different formations with respect to a solo runner computed with the models of [25,26]. …”
Section: Resultsmentioning
confidence: 99%
“…An estimate of the time saving associated with the complex configurations examined in this section was finally computed. To do this, we applied the relationship proposed by da Silva et al [25]. They found that the average change in gross metabolic power normalΔPmeta was 6.13% per 1% body weight of horizontal impeding force, here represented by the aerodynamic drag.…”
Section: Resultsmentioning
confidence: 99%
“…The new velocity U associated with the aerodynamic drag force FD (and, therefore, with the drag coefficient CD) of the drafting formation was then estimated by linearizing the relation between RE and U around the reference point U⋆ here [26] U=U⋆false(1+αnormalΔREfalse),where the coefficient α≃2/3 for the typical velocity U⋆ of an elite marathoner. The variation of the running economy was assumed as normalΔRE≃normalΔPmeta, where we used the body mass of Kipchoge (mr=52kg) to estimate the reduction in the gross metabolic power [25] normalΔPmeta≃0.0613normalΔCDFD⋆mrg,with g the acceleration of gravity. The race times over the distance of a marathon were estimated for the formations described in figures 12, 14 and 15, and the time savings were reported in table 3.…”
Drafting is a strategy in marathon running races that reduces the drag force acting on a designated runner. Drafting involves a formation of athletes, called pacers, running in front of (and sometimes behind) the designated runner. The present experiments evaluated complex formations involving up to seven pacers with the aim of enhancing the performances of an elite marathoner. Wind tunnel measurements with 1/10 scale articulated runner manikins were carried out. First, simple formations with one or two pacers were examined and the results were compared with previous investigations. Second, the runner formations of the Nike Breaking2 and INEOS 1:59 Challenge events whose goal was to break the 2 h marathon barrier were studied. The main finding was the identification of three complex formations of six and seven pacers that allow a drag-force reduction of about 60% with respect to a solo runner and an estimated time saving of 262 s. These results point to how it may be possible to run the fastest marathon ever.
“…Aiding force fields are known to increase braking and reduce propulsive impulses, while the impeding force field increases propulsive but reduces braking impulses 11 . Recently, it has been demonstrated that an application of horizontal impeding force affects metabolic power in running gait and increases 6.13% per 1% body weight of horizontal impeding force 12 . Along with the changing mechanical demands, the application of these force fields reduced (aiding force field) and increased (impeding force field) the effort to walk and the activity of the plantar flexor muscle (medial gastrocnemius), which is necessary for propulsion 11,13 .…”
Walking with pulling force fields acting at the body center of mass (in the forward or backward directions) is compatible with inclined walking and is used in clinical practice for gait training. From the perspective of known differences in the motor strategies that underlie walking with the respective force fields, the present study elucidated whether the adaptation acquired by walking on a split-belt treadmill with either one of the force fields affects subsequent walking in a force field in the opposite directions. Walking with the force field induced an adaptive and de-adaptive behavior of the subjects, with the aspect evident in the braking and propulsive impulses of the ground reaction force (difference in the peak value between the left and right sides for each stride cycle) as parameters. In the parameters, the adaptation acquired during walking with a force field acting in one direction was transferred to that in the opposite direction only partially. Furthermore, the adaptation that occurred while walking in a force field in one direction was rarely washed out by subsequent walking in a force field in the opposite direction and thus was maintained independently of the other. These results demonstrated possible independence in the neural functional networks capable of controlling walking in each movement task with an opposing force field.
Looney, DP, Hoogkamer, W, Kram, R, Arellano, CJ, and Spiering, BA. Estimating metabolic energy expenditure during level running in healthy, military-age women and men. J Strength Cond Res 37(12): 2496–2503, 2023—Quantifying the rate of metabolic energy expenditure (Ṁ) of varied aerobic exercise modalities is important for optimizing fueling and performance and maintaining safety in military personnel operating in extreme conditions. However, although equations exist for estimating oxygen uptake during running, surprisingly, there are no general equations that estimate Ṁ. Our purpose was to generate a general equation for estimating Ṁ during level running in healthy, military-age (18–44 years) women and men. We compiled indirect calorimetry data collected during treadmill running from 3 types of sources: original individual subject data (n = 45), published individual subject data (30 studies; n = 421), and published group mean data (20 studies, n = 619). Linear and quadratic equations were fit on the aggregated data set using a mixed-effects modeling approach. A chi-squared (χ
2) difference test was conducted to determine whether the more complex quadratic equation was justified (p < 0.05). Our primary indicator of model goodness-of-fit was the root-mean-square deviation (RMSD). We also examined whether individual characteristics (age, height, body mass, and maximal oxygen uptake [V̇O2max]) could minimize prediction errors. The compiled data set exhibited considerable variability in Ṁ (14.54 ± 3.52 W·kg−1), respiratory exchange ratios (0.89 ± 0.06), and running speeds (3.50 ± 0.86 m·s−1). The quadratic regression equation had reduced residual sum of squares compared with the linear fit (χ
2, 3,484; p < 0.001), with higher combined accuracy and precision (RMSD, 1.31 vs. 1.33 W·kg−1). Age (p = 0.034), height (p = 0.026), and body mass (p = 0.019) were associated with the magnitude of under and overestimation, which was not the case for V̇O2max (p = 0.898). The newly derived running energy expenditure estimation (RE3) model accurately predicts level running Ṁ at speeds from 1.78 to 5.70 m·s−1 in healthy, military-age women and men. Users can rely on the following equations for improved predictions of running Ṁ as a function of running speed (S, m·s−1) in either watts (W·kg−1 = 4.43 + 1.51·S + 0.37·S2) or kilocalories per minute (kcal·kg−1·min−1 = 308.8 + 105.2·S + 25.58·S2).
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