Modeling the Benefits of Cooperative Drafting: Is There an Optimal Strategy to Facilitate a Sub-2-Hour Marathon Performance?

BACKGROUND: During a race, competing cyclists often cooperate by alternating between leading and drafting positions. This approach allows them to maximize velocity by using the energy saved while drafting, a technique to reduce the overall drag by exploiting the leader's slipstream. We have argued that a similar cooperative drafting approach could benefit elite marathon runners in their quest for the sub-2-hour marathon. OBJECTIVE: Our aim was to model the effects of various cooperative drafting scenarios on marathon performance by applying the critical velocity concept for intermittent high-intensity running. METHODS: We used the physiological characteristics of the world's most elite long-distance runners and mathematically simulated the depletion and recovery of their distance capacity when running above and below their critical velocity throughout a marathon. RESULTS: Our simulations showed that with four of the most elite runners in the world, a 2:00:48 (h:min:s) marathon is possible, a whopping 2 min faster than the current world record. We also explored the possibility of a sub-2-hour marathon using multiple runners with the physiological characteristics of Eliud Kipchoge, arguably the best marathon runner of our time. We found that a team of eight Kipchoge-like runners could break the sub-2-hour marathon barrier. CONCLUSION: In the context of cooperative drafting, we show that the best team strategy for improving marathon performance time can be optimized using a mathematical model that is based on the physiological characteristics of each athlete.

Cortical Spectral Activity and Connectivity during Active and Viewed Arm and Leg Movement.

Active and viewed limb movement activate many similar neural pathways, however, to date most comparison studies have focused on subjects making small, discrete movements of the hands and feet. The purpose of this study was to determine if high-density electroencephalography (EEG) could detect differences in cortical activity and connectivity during active and viewed rhythmic arm and leg movements in humans. Our primary hypothesis was that we would detect similar but weaker electrocortical spectral fluctuations and effective connectivity fluctuations during viewed limb exercise compared to active limb exercise due to the similarities in neural recruitment. A secondary hypothesis was that we would record stronger cortical spectral fluctuations for arm exercise compared to leg exercise, because rhythmic arm exercise would be more dependent on supraspinal control than rhythmic leg exercise. We recorded EEG data while ten young healthy subjects exercised on a recumbent stepper with: (1) both arms and legs, (2) just legs, and (3) just arms. Subjects also viewed video playback of themselves or another individual performing the same exercises. We performed independent component analysis, dipole fitting, spectral analysis, and effective connectivity analysis on the data. Cortical areas comprising the premotor and supplementary motor cortex, the anterior cingulate, the posterior cingulate, and the parietal cortex exhibited significant spectral fluctuations during rhythmic limb exercise. These fluctuations tended to be greater for the arms exercise conditions than for the legs only exercise condition, which suggests that human rhythmic arm movements are under stronger cortical control than rhythmic leg movements. We did not find consistent spectral fluctuations in these areas during the viewed conditions, but effective connectivity fluctuated at harmonics of the exercise frequency during both active and viewed rhythmic limb exercise. The right premotor and supplementary motor cortex drove the network. These results suggest that a similarly interconnected neural network is in operation during active and viewed human rhythmic limb movement.

Independent Component Analysis of Gait-Related Movement Artifact Recorded using EEG Electrodes during Treadmill Walking.

There has been a recent surge in the use of electroencephalography (EEG) as a tool for mobile brain imaging due to its portability and fine time resolution. When EEG is combined with independent component analysis (ICA) and source localization techniques, it can model electrocortical activity as arising from temporally independent signals located in spatially distinct cortical areas. However, for mobile tasks, it is not clear how movement artifacts influence ICA and source localization. We devised a novel method to collect pure movement artifact data (devoid of any electrophysiological signals) with a 256-channel EEG system. We first blocked true electrocortical activity using a silicone swim cap. Over the silicone layer, we placed a simulated scalp with electrical properties similar to real human scalp. We collected EEG movement artifact signals from ten healthy, young subjects wearing this setup as they walked on a treadmill at speeds from 0.4-1.6 m/s. We performed ICA and dipole fitting on the EEG movement artifact data to quantify how accurately these methods would identify the artifact signals as non-neural. ICA and dipole fitting accurately localized 99% of the independent components in non-neural locations or lacked dipolar characteristics. The remaining 1% of sources had locations within the brain volume and low residual variances, but had topographical maps, power spectra, time courses, and event related spectral perturbations typical of non-neural sources. Caution should be exercised when interpreting ICA for data that includes semi-periodic artifacts including artifact arising from human walking. Alternative methods are needed for the identification and separation of movement artifact in mobile EEG signals, especially methods that can be performed in real time. Separating true brain signals from motion artifact could clear the way for EEG brain computer interfaces for assistance during mobile activities, such as walking.

Isolating gait-related movement artifacts in electroencephalography during human walking.

OBJECTIVE: High-density electroencephelography (EEG) can provide an insight into human brain function during real-world activities with walking. Some recent studies have used EEG to characterize brain activity during walking, but the relative contributions of movement artifact and electrocortical activity have been difficult to quantify. We aimed to characterize movement artifact recorded by EEG electrodes at a range of walking speeds and to test the efficacy of artifact removal methods. We also quantified the similarity between movement artifact recorded by EEG electrodes and a head-mounted accelerometer. APPROACH: We used a novel experimental method to isolate and record movement artifact with EEG electrodes during walking. We blocked electrophysiological signals using a nonconductive layer (silicone swim cap) and simulated an electrically conductive scalp on top of the swim cap using a wig coated with conductive gel. We recorded motion artifact EEG data from nine young human subjects walking on a treadmill at speeds from 0.4 to 1.6 m s(-1). We then tested artifact removal methods including moving average and wavelet-based techniques. MAIN RESULTS: Movement artifact recorded with EEG electrodes varied considerably, across speed, subject, and electrode location. The movement artifact measured with EEG electrodes did not correlate well with head acceleration. All of the tested artifact removal methods attenuated low-frequency noise but did not completely remove movement artifact. The spectral power fluctuations in the movement artifact data resembled data from some previously published studies of EEG during walking. SIGNIFICANCE: Our results suggest that EEG data recorded during walking likely contains substantial movement artifact that: cannot be explained by head accelerations; varies across speed, subject, and channel; and cannot be removed using traditional signal processing methods. Future studies should focus on more sophisticated methods for removal of EEG movement artifact to advance the field.

The role of elastic energy storage and recovery in downhill and uphill running.

In level running, humans and other animals store and recover elastic energy during each step. What role does elastic energy play during downhill and uphill running? We measured the fluctuations of the mechanical energy of the center of mass (CoM) of 15 human participants running at 3 m s(-1) on the level, downhill and uphill on a force-measuring treadmill mounted at 3, 6 and 9 deg. In level running, nearly symmetrical decreases and increases of the combined gravitational potential and kinetic (GPE+KE) energy of the CoM indicated equal possible elastic energy storage and recovery. However, asymmetrical fluctuations during hill running indicate reduced maximum possible elastic energy storage and return. We analyzed mechanical energy generation and dissipation during level and hill running by quantifying the anatomically estimated elastic energy storage (AEEE) in the arch and Achilles' tendon using peak ground reaction forces and anatomical characteristics. AEEE did not change with grade. At shallow downhill grades, the body must generate mechanical energy, though it dissipates more than it generates. At steeper downhill grades, little to no energy generation is required and only mechanical energy dissipation must occur. The downhill grade at which mechanical energy must no longer be generated occurs at approximately -9 deg, near the metabolically optimal running grade. At shallow uphill grades, mechanical energy must be generated to raise the CoM, and at steeper grades, additional energy must be generated to offset reduced elastic energy storage and return.

Running perturbations reveal general strategies for step frequency selection.

Recent research has suggested that energy minimization in human walking involves both a fast preprogrammed process and a slow optimization process. Here, we studied human running to test whether these two processes represent control mechanisms specific to walking or a more general strategy for minimizing energetic cost in human locomotion. To accomplish this, we used free response experiments to enforce step frequency with a metronome at values above and below preferred step frequency and then determined the response times for the return to preferred steady-state step frequency when the auditory constraint was suddenly removed. In forced response experiments, we applied rapid changes in treadmill speed and examined response times for the processes involved in the consequent adjustments to step frequency. We then compared the dynamics of step frequency adjustments resulting from the two different perturbations to each other and to previous results found in walking. Despite the distinct perturbations applied in the two experiments, both responses were dominated by a fast process with a response time of 1.47 ± 0.05 s with fine-tuning provided by a slow process with a response time of 34.33 ± 0.50 s. The dynamics of the processes underlying step frequency adjustments in running match those found previously in walking, both in magnitude and relative importance. Our results suggest that the underlying mechanisms are fundamental strategies for minimizing energetic cost in human locomotion.

Energetically optimal stride frequency in running: the effects of incline and decline.

At a given running speed, humans strongly prefer to use a stride frequency near their 'optimal' stride frequency that minimizes metabolic cost. Although there is no definitive explanation for why an optimal stride frequency exists, elastic energy usage has been implicated. Because the possibility for elastic energy storage and return may be impaired on slopes, we investigated whether and how the optimal stride frequency changes during uphill and downhill running. Presuming a smaller role of elastic energy, we hypothesized that altering stride frequency would change metabolic cost less during uphill and downhill running than during level running. To test this hypothesis, we collected force and metabolic data as nine male subjects ran at 2.8 m s(-1) on the level, 3 deg uphill and 3 deg downhill. Stride frequency was systematically varied above and below preferred stride frequency (PSF ±8% and ±15%). Ground reaction force data were used to calculate potential, kinetic and total mechanical energy, and to calculate the theoretical maximum possible and estimated actual elastic energy storage and return. Contrary to our hypothesis, we found that neither the overall relationship between metabolic cost and stride frequency nor the energetically optimal stride frequency changed substantially with slope. However, estimated actual elastic energy storage as a percentage of total positive power increased with increasing stride frequency on all slopes, indicating that muscle power decreases with increasing stride frequency. Combined with the increased cost of force production and internal work with increasing stride frequency, this leads to an intermediate optimal stride frequency and overall U-shaped curve.