A worldwide comparison of long-distance running training in 2019 and 2020: associated effects of the COVID-19 pandemic.

Objective: The goal of the present study was to investigate possible effects of the COVID-19 pandemic on long-distance running training. Methods: This is a retrospective study with a within-subject design. We analyzed 10,703,690 records of running training during 2019 and 2020, from 36,412 athletes from around the world. The records were obtained through web scraping of a large social network for athletes on the internet. A potential long-distance runner was defined as a user of the social network who had a record of running at least one of the six World Marathon Majors by 2019. Results: In 2020, compared with 2019, in total there was a 3.6% decrease in the number of athletes running, a 7.5% decrease in the distance and 6.7% in the duration of running training. There were large variations in these variables throughout 2020, reaching 16% fewer athletes running weekly and 35% lower running distance (Cohen's d = 0.34, p < 0.001) and 33% lower running duration (Cohen's d = 0.30, p < 0.001) in September 2020. The beginning of the decrease in running training in the first quarter of 2020 coincides with the beginning of the adoption of measures to restrict the COVID-19 pandemic; but as of the second quarter of 2020, running training appears to have undergone variations unrelated to the preventive measures. Among the ten most represented countries in the dataset, running training in Brazil appears to have been the most affected by the COVID-19 pandemic and restriction measures. Conclusion: The wide variations in long-distance running training throughout 2020 are likely related to the COVID-19 pandemic. As for the total volume, the observed decreases of up to 7.5% in the outcome variables related to running training in 2020 could also be attributed to the COVID-19 pandemic, but other factors such as injury, illness or lack of interest, may also have contributed to these decreases.

Fast Oscillatory Commands from the Motor Cortex Can Be Decoded by the Spinal Cord for Force Control.

Oscillations in the beta and gamma bands (13-30 Hz; 35-70 Hz) have often been observed in motor cortical outputs that reach the spinal cord, acting on motoneurons and interneurons. However, the frequencies of these oscillations are above the muscle force frequency range. A current view is that the transformation of the motoneuron pool inputs into force is linear. For this reason possible roles for these oscillations are unclear, since if this transformation is linear, the high frequencies in the motoneuron inputs (e.g., 20 Hz from pyramidal tract neurons) would be filtered out by the muscle and have no effect on force control. A biologically inspired mathematical model of the neuromuscular system was used to investigate the impact of high-frequency cortical oscillatory activity on force control. The model simulation results evidenced that a typical motoneuron pool has a nonlinear behavior that enables the decoding of a high-frequency oscillatory input. An input at a single frequency (e.g., beta band) leads to an increase in the steady-state force generated by the muscle. When the input oscillation was amplitude modulated at a given low frequency, the force oscillated at this frequency. In both cases, the mechanism relies on the recruitment and derecruitment of motor units in response to the oscillatory descending drive. Therefore, the results from this study suggest a potential role in force control for cortical oscillations at frequencies at or above the beta band, despite the low-pass behavior of the muscles. SIGNIFICANCE STATEMENT: The role of cortical oscillations in motor control has been a long-standing question, one view being that they are an epiphenomenon. Fast oscillations are known to reach the spinal cord, and hence they have been thought to affect muscle behavior. However, experimental limitations have hampered further advances to explain how they could influence muscle force. An approach for such a challenge was adopted in the present research: to study the problem through computer simulations of an advanced biologically compatible mathematical model. Using such a model, we found that the well-known mechanism of recruitment and derecruitment of the spinal cord motoneurons can allow the muscle to respond to cortical oscillations, suggesting that these oscillations are not epiphenomena in motor control.

Influences of premotoneuronal command statistics on the scaling of motor output variability during isometric plantar flexion.

This study focuses on neuromuscular mechanisms behind ankle torque and EMG variability during a maintained isometric plantar flexion contraction. Experimentally obtained torque standard deviation (SD) and soleus, medial gastrocnemius, and lateral gastrocnemius EMG envelope mean and SD increased with mean torque for a wide range of torque levels. Computer simulations were performed on a biophysically-based neuromuscular model of the triceps surae consisting of premotoneuronal spike trains (the global input, GI) driving the motoneuron pools of the soleus, medial gastrocnemius, and lateral gastrocnemius muscles, which activate their respective muscle units. Two types of point processes were adopted to represent the statistics of the GI: Poisson and Gamma. Simulations showed a better agreement with experimental results when the GI was modeled by Gamma point processes having lower orders (higher variability) for higher target torques. At the same time, the simulations reproduced well the experimental data of EMG envelope mean and SD as a function of mean plantar flexion torque, for the three muscles. These results suggest that the experimentally found relations between torque-EMG variability as a function of mean plantar flexion torque level depend not only on the intrinsic properties of the motoneuron pools and the muscle units innervated, but also on the increasing variability of the premotoneuronal GI spike trains when their mean rates increase to command a higher plantar flexion torque level. The simulations also provided information on spike train statistics of several hundred motoneurons that compose the triceps surae, providing a wide picture of the associated mechanisms behind torque and EMG variability.