An analysis of performance in human locomotion

Linking neural circuits to the mechanics of animal behavior in Drosophila larval locomotion

The motions that make up animal behavior arise from the interplay between neural circuits and the mechanical parts of the body. Therefore, in order to comprehend the operational mechanisms governing behavior, it is essential to examine not only the underlying neural network but also the mechanical characteristics of the animal's body. The locomotor system of fly larvae serves as an ideal model for pursuing this integrative approach. By virtue of diverse investigation methods encompassing connectomics analysis and quantification of locomotion kinematics, research on larval locomotion has shed light on the underlying mechanisms of animal behavior. These studies have elucidated the roles of interneurons in coordinating muscle activities within and between segments, as well as the neural circuits responsible for exploration. This review aims to provide an overview of recent research on the neuromechanics of animal locomotion in fly larvae. We also briefly review interspecific diversity in fly larval locomotion and explore the latest advancements in soft robots inspired by larval locomotion. The integrative analysis of animal behavior using fly larvae could establish a practical framework for scrutinizing the behavior of other animal species.

Everesting: cycling the elevation of the tallest mountain on Earth

PURPOSE

With few cycling races on the calendar in 2020 due to COVID-19, Everesting became a popular challenge: you select one hill and cycle up and down it until you reach the accumulated elevation of Mt. Everest (8,848 m or 29,029ft). With an almost infinite number of different hills across the world, the question arises what the optimal hill for Everesting would be. Here, we address the biomechanics and energetics of up- and downhill cycling to determine the characteristics of this optimal hill.

METHODS

During uphill cycling, the mechanical power output equals the power necessary to overcome air resistance, rolling resistance, and work against gravity, and for a fast Everesting time, one should maximize this latter term. To determine the optimal section length (i.e., number of repetitions), we applied the critical power concept and assumed that the U-turn associated with an additional repetition comes with a 6 s time penalty.

RESULTS

To use most mechanical power to overcoming gravity, slopes of at least 12% are most suitable, especially since gross efficiency seems only minimally diminished on steeper slopes. Next, we found 24 repetitions to be optimal, yet this number slightly depends on the assumptions made. Finally, we discuss other factors (fueling, altitude, fatigue) not incorporated in the model but also affecting Everesting performances.

CONCLUSION

For a fast Everesting time, our model suggests to select a hill climb which preferably starts at (or close to) sea level, with a slope of 12-20% and length of 2-3 km.

Acute Effects of Aerobic Exercise on Muscle Strength and Power in Trained Male Individuals: A Systematic Review with Meta-analysis

BACKGROUND

Concurrent training can be an effective and time-efficient method to improve both muscle strength and aerobic capacity. A major challenge with concurrent training is how to adequately combine and sequence strength exercise and aerobic exercise to avoid interference effects. This is particularly relevant for athletes.

OBJECTIVE

We aimed to examine the acute effects of aerobic exercise on subsequent measures of muscle strength and power in trained male individuals.

DESIGN

We performed a systematic review with meta-analysis.

DATA SOURCES

Systematic literature searches in the electronic databases PubMed, Web of Science, and Google Scholar were conducted up to July 2021.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Studies were included that applied a within-group repeated-measures design and examined the acute effects of aerobic exercise (i.e., running, cycling exercise) on subsequent measures of lower limb muscle strength (e.g., maximal isometric force of the knee extensors) and/or proxies of lower limb muscle power (e.g., countermovement jump height) in trained individuals.

RESULTS

Fifteen studies met the inclusion criteria. Aerobic exercise resulted in moderate declines in muscle strength (standardized mean difference [SMD] = 0.79; p = 0.003). Low-intensity aerobic exercise did not moderate effects on muscle strength (SMD = 0.65; p = 0.157) while moderate-to-high intensity aerobic exercise resulted in moderate declines in muscle strength (SMD = 0.65; p = 0.020). However, the difference between subgroups was not statistically significant (p = 0.979). Regarding aerobic exercise duration, large declines in muscle strength were found after > 30 min (SMD = 1.02; p = 0.049) while ≤ 30 min of aerobic exercise induced moderate declines in muscle strength (SMD = 0.59; p = 0.013). The subgroup difference was not statistically significant (p = 0.204). Cycling exercise resulted in significantly larger decrements in muscle strength (SMD = 0.79; p = 0.002) compared with running (SMD = 0.28; p = 0.035). The difference between subgroups was statistically significant (p < 0.0001). For muscle power, aerobic exercise did not result in any statistically significant changes (SMD = 0.04; p = 0.846).

CONCLUSIONS

Aerobic exercise induced moderate declines in measures of muscle strength with no statistically significant effects on proxies of muscle power in trained male individuals. It appears that higher compared with lower intensity as well as longer compared with shorter aerobic exercise duration exacerbate acute declines in muscle strength. Our results provide evidence for acute interference effects when aerobic exercies is performed before strength exercises. These findings may help practitioners to better prescribe single training sessions, particularly if environmental and/or infrastructural reasons (e.g., availability of training facilities) do not allow the application of strength training before aerobic exercise.

Muscle Actuators, Not Springs, Drive Maximal Effort Human Locomotor Performance

The current investigation examined muscle-tendon unit kinematics and kinetics in human participants asked to perform a hopping task for maximal performance with variational preceding milieu. Twenty-four participants were allocated post-data collection into those participants with an average hop height of higher (HH) or lower (LH) than 0.1 m. Participants were placed on a customized sled at a 20º angle while standing on a force plate. Participants used their dominant ankle for all testing and their knee was immobilized and thus all movement involved only the ankle joint and corresponding propulsive unit (triceps surae muscle complex). Participants were asked to perform a maximal effort during a single dynamic countermovement hop (CMH) and drop hops from 10 cm (DH10) and 50 cm (DH50). Three-dimensional motion analysis was performed by utilizing an infrared camera VICON motion analysis system and a corresponding force plate. An ultrasound probe was placed on the triceps surae muscle complex for muscle fascicle imaging. HH hopped significantly higher in all hopping tasks in comparison to LH. In addition, the HH group concentric ankle work was significantly higher in comparison to LH during all of the hopping tasks. Active muscle work was significantly higher in HH in comparison to LH as well. Tendon work was not significantly different between HH and LH. Active muscle work was significantly correlated with hopping height (r = 0.97) across both groups and hopping tasks and contributed more than 50% of the total work. The data indicates that humans primarily use a motor-driven system and thus it is concluded that muscle actuators and not springs maximize performance in hopping locomotor tasks in humans.

Human running performance from real-world big data

Wearable exercise trackers provide data that encode information on individual running performance. These data hold great potential for enhancing our understanding of the complex interplay between training and performance. Here we demonstrate feasibility of this idea by applying a previously validated mathematical model to real-world running activities of  ≈ 14,000 individuals with ≈ 1.6 million exercise sessions containing duration and distance, with a total distance of ≈ 20 million km. Our model depends on two performance parameters: an aerobic power index and an endurance index. Inclusion of endurance, which describes the decline in sustainable power over duration, offers novel insights into performance: a highly accurate race time prediction and the identification of key parameters such as the lactate threshold, commonly used in exercise physiology. Correlations between performance indices and training volume and intensity are quantified, pointing to an optimal training. Our findings hint at new ways to quantify and predict athletic performance under real-world conditions.

Laboratory performance tests provide the gold standard for running performance but do not reflect real running conditions. Here the authors use a large, real world dataset obtained from wearable exercise trackers to extract parameters that accurately predict race times and correlate with training.

Modelling the effect of curves on distance running performance

Background

Although straight ahead running appears to be faster, distance running races are predominately contested on tracks or roads that involve curves. How much faster could world records be run on straight courses?

Methods

Here,we propose a model to explain the slower times observed for races involving curves compared to straight running. For a given running velocity, on a curve, the average axial leg force (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{F}}_{a}$\end{document}F¯a) of a runner is increased due to the need to exert centripetal force. The increased \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{F}}_{a}$\end{document}F¯a presumably requires a greater rate of metabolic energy expenditure than straight running at the same velocity. We assumed that distance runners maintain a constant metabolic rate and thus slow down on curves accordingly. We combined published equations to estimate the change in the rate of gross metabolic energy expenditure as a function of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{F}}_{a}$\end{document}F¯a, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{F}}_{a}$\end{document}F¯a depends on curve radius and velocity, with an equation for the gross rate of oxygen uptake as a function of velocity. We compared performances between straight courses and courses with different curve radii and geometries.

Results

The differences between our model predictions and the actual indoor world records, are between 0.45% in 3,000 m and 1.78% in the 1,500 m for males, and 0.59% in the 5,000 m and 1.76% in the 3,000 m for females. We estimate that a 2:01:39 marathon on a 400 m track, corresponds to 2:01:32 on a straight path and to 2:02:00 on a 200 m track.

Conclusion

Our model predicts that compared to straight racecourses, the increased time due to curves, is notable for smaller curve radii and for faster velocities. But, for larger radii and slower speeds, the time increase is negligible and the general perception of the magnitude of the effects of curves on road racing performance is not supported by our calculations.

Are We Reaching the Limits of Homo sapiens?

Echoing scientific and industrial progress, the Twentieth century was an unprecedented period of improvement for human capabilities and performances, with a significant increase in lifespan, adult height, and maximal physiological performance. Analyses of historical data show a major slow down occurring in the most recent years. This triggered large and passionate debates in the academic scene within multiple disciplines; as such an observation could be interpreted as our upper biological limits. Such a new phase of human history may be related to structural and functional limits determined by long term evolutionary constraints, and the interaction between complex systems and their environment. In this interdisciplinary approach, we call into question the validity of subsequent forecasts and projections through innovative and related biomarkers such as sport, lifespan, and height indicators. We set a theoretical framework based on biological and environmental relevance rather than using a typical single-variable forecasting approach. As demonstrated within the article, these new views will have major social, economical, and political implications.

The physiology of submaximal exercise: The steady state concept

The steady state concept implies that the oxygen flow is invariant and equal at each level along the respiratory system. The same is the case with the carbon dioxide flow. This condition has several physiological consequences, which are analysed. First, we briefly discuss the mechanical efficiency of exercise and the energy cost of human locomotion, as well as the roles played by aerodynamic work and frictional work. Then we analyse the equations describing the oxygen flow in lungs and in blood, the effects of ventilation and of the ventilation - perfusion inequality, and the interaction between diffusion and perfusion in the lungs. The cardiovascular responses sustaining gas flow increase in blood are finally presented. An equation linking ventilation, circulation and metabolism is developed, on the hypothesis of constant oxygen flow in mixed venous blood. This equation tells that, if the pulmonary respiratory quotient stays invariant, any increase in metabolic rate is matched by a proportional increase in ventilation, but by a less than proportional increase in cardiac output.

Body composition changes affect energy cost of running during 12 months of specific diet and training in amateur athletes

Considering the relation between body weight composition and energy cost of running, we tested the hypothesis that by modifying body composition by means of a combined protocol of specific diet and training, the energy cost of motion (Cr) may be reduced. Forty-five healthy and normal-weight subjects were divided into 3 groups that performed a different treatment: the first group attended a dietary protocol (D), the second group participated in a running program (R), and the third group followed both the dietary and running protocols (R&D). Each subject underwent 3 anthropometric and exercise evaluation tests during 1 year (at entry (T0), month 6 (T6), and month 12 (T12)) to assess body composition and Cradjustments. The mean fat mass (FM) values were reduced in R&D from 12.0 ± 4.0 to 10.4 ± 3.0 kg (p < 0.05 T0 vs. T12) and in the D group from 14.2 ± 5.8 to 11.6 ± 4.7 kg (p < 0.05 T0 vs. T12). Conversely, the mean fat free mass values increased in R&D (from 56.3 ± 8.8 to 58.3 ± 9.8 kg, p < 0.05 T0 vs. T12) and in the D group (from 50.6 ± 13.2 to 52.9 ± 13.6 kg, p < 0.05 T0 vs. T12). The mean Cr values of the 2 groups were significantly modified throughout the 1-year protocol (1.48 ± 0.16 and 1.40 ± 0.15 kcal·kg–b·km−1in the R&D group at T0 and T12, respectively; 1.83 ± 0.17 and 1.76 ± 0.23 kcal·kg–b·km−1in D group at T0 to T12, respectively). The R&D and D groups that underwent the diet protocol had a positive change in body composition during the year (FM/fat free mass ratio decline), which determined a Crreduction.

Maximal oxygen consumption in healthy humans: theories and facts

This article reviews the concept of maximal oxygen consumption ([Formula: see text]) from the perspective of multifactorial models of [Formula: see text] limitation. First, I discuss procedural aspects of [Formula: see text] measurement: the implications of ramp protocols are analysed within the theoretical work of Morton. Then I analyse the descriptive physiology of [Formula: see text], evidencing the path that led to the view of monofactorial cardiovascular or muscular [Formula: see text] limitation. Multifactorial models, generated by the theoretical work of di Prampero and Wagner around the oxygen conductance equation, represented a radical change of perspective. These models are presented in detail and criticized with respect to the ensuing experimental work. A synthesis between them is proposed, demonstrating how much these models coincide and converge on the same conclusions. Finally, I discuss the cases of hypoxia and bed rest, the former as an example of the pervasive effects of the shape of the oxygen equilibrium curve, the latter as a neat example of adaptive changes concerning the entire respiratory system. The conclusion is that the concept of cardiovascular [Formula: see text] limitation is reinforced by multifactorial models, since cardiovascular oxygen transport provides most of the [Formula: see text] limitation, at least in normoxia. However, the same models show that the role of peripheral resistances is significant and cannot be neglected. The role of peripheral factors is greater the smaller is the active muscle mass. In hypoxia, the intervention of lung resistances as limiting factors restricts the role played by cardiovascular and peripheral factors.

Sprint exercise performance: does metabolic power matter?

Prevailing physiological paradigms explain both sprint and endurance exercise performance in terms of the availability of metabolic energy. However, for all-out efforts of 60 s or less, the prevailing view is no longer viable. Contemporary evidence indicates that sprinting performance is determined by musculoskeletal force application, with a duration dependency explained by the intrinsically rapid rates at which skeletal muscle fatigues in vivo.

Energetics of running in top-level marathon runners from Kenya

On ten top-level Kenyan marathon runners (KA) plus nine European controls (EC, equivalent to KA), we measured maximal oxygen consumption (VO2max) and the energy cost of running (Cr) on track during training camps at moderate altitude, to better understand the KA dominance in the marathon. At each incremental running speed, steady-state oxygen consumption (VO2) was measured by telemetric metabolic cart, and lactate by electro-enzymatic method. The speed requiring VO2 = VO2max provided the maximal aerobic velocity (νmax). The energy cost of running was calculated by dividing net VO2 by the corresponding speed. The speed at lactate threshold (ν(ΘAN)) was computed from individual Lâ(b) versus speed curves. The sustainable VO2max fraction (Fd) at ν(ΘAN) (F(ΘAN)) was computed dividing nu(ΘAN) by νmax. The Fd for the marathon (Fmar) was determined as Fmar = 0.92 F(ΘAN). Overall, VO2max (64.9 ± 5.8 vs. 63.9 ± 3.7 ml kg(-1) min(-1)), νmax (5.55 ± 0.30 vs. 5.41 ± 0.29 m s(-1)) and Cr (3.64 ± 0.28 vs. 3.63 ± 0.31 J kg(-1) m(-1)) resulted the same in KA as in EC. In both groups, Cr increased linearly with the square of speed. F(ΘAN) was 0.896 ± 0.054 in KA and 0.909 ± 0.068 in EC; Fmar was 0.825 ± 0.050 in KA and 0.836 ± 0.062 in EC (NS). Accounting for altitude, running speed predictions from present data are close to actual running performances, if F(ΘAN) instead of Fmar is taken as index of Fd. In conclusion, both KA and EC did not have a very high VO2max, but had extremely high Fd, and low Cr, equal between them. The dominance of KA over EC cannot be explained on energetic grounds.

Influence of road incline and body position on power-cadence relationship in endurance cycling

In race cycling, the external power-cadence relationship at the performance level, that is sustainable for the given race distance, plays a key role. The two variables of interest from this relationship are the maximal external power output (P (max)) and the corresponding optimal cadence (C (opt)). Experimental studies and field observations of cyclists have revealed that when cycling uphill is compared to cycling on level ground, the freely chosen cadence is lower and a more upright body position seems to be advantageous. To date, no study has addressed whether P (max) or C (opt) is influenced by road incline or body position. Thus, the main aim of this study was to examine the effect of road incline (0 vs. 7%) and racing position (upright posture vs. dropped posture) on P (max) and C (opt). Eighteen experienced cyclists participated in this study. Experiment I tested the hypothesis that road incline influenced P (max) and C (opt) at the second ventilatory threshold ([Formula: see text] and [Formula: see text]). Experiment II tested the hypothesis that the racing position influenced [Formula: see text], but not [Formula: see text]. The results of experiment I showed that [Formula: see text] and [Formula: see text] were significantly lower when cycling uphill compared to cycling on level ground (P < 0.01). Experiment II revealed that [Formula: see text] was significantly greater for the upright posture than for the dropped posture (P < 0.01) and that the racing position did not affect [Formula: see text]. The main conclusions of this study were that when cycling uphill, it is reasonable to choose (1) a lower cadence and (2) a more upright body position.

Power-cadence relationship in endurance cycling

In maximal sprint cycling, the power-cadence relationship to assess the maximal power output (P (max)) and the corresponding optimal cadence (C (opt)) has been widely investigated in experimental studies. These studies have generally reported a quadratic power-cadence relationship passing through the origin. The aim of the present study was to evaluate an equivalent method to assess P (max) and C (opt) for endurance cycling. The two main hypotheses were: (1) in the range of cadences normally used by cyclists, the power-cadence relationship can be well fitted with a quadratic regression constrained to pass through the origin; (2) P (max) and C (opt) can be well estimated using this quadratic fit. We tested our hypothesis using a theoretical and an experimental approach. The power-cadence relationship simulated with the theoretical model was well fitted with a quadratic regression and the bias of the estimated P (max) and C (opt) was negligible (1.0 W and 0.6 rpm). In the experimental part, eight cyclists performed an incremental cycling test at 70, 80, 90, 100, and 110 rpm to yield power-cadence relationships at fixed blood lactate concentrations of 3, 3.5, and 4 mmol L(-1). The determined power outputs were well fitted with quadratic regressions (R (2) = 0.94-0.96, residual standard deviation = 1.7%). The 95% confidence interval for assessing individual P (max) and C (opt) was ±4.4 W and ±2.9 rpm. These theoretical and experimental results suggest that P (max), C (opt), and the power-cadence relationship around C (opt) could be well estimated with the proposed method.