Stride-to-stride variability and complexity between novice and experienced runners during a prolonged run at anaerobic threshold speed

Evaluation of the optimal number of steps to obtain reliable running spatio-temporal parameters and their variability

BACKGROUND

Spatio-temporal running parameters and their variability help to determine a runner's running style. However, determining whether a change is due to the measurement or to a specific condition such as an injury is a matter of debate, as no recommendation on the number of steps required to obtain reliable assessments exists.

RESEARCH QUESTION

What is the optimal number of steps required to measure different spatio-temporal parameters and study their variability at different running speeds?

METHODS

Twenty-five runners performed three experimental sessions of three bouts of treadmill running at 8, 10 and 12 km/h separated by 24 h. We measured cadence, stride, step, contact and flight time. We calculated the duty factor and the leg stiffness index (Kleg). Mean spatio-temporal parameters and linear (coefficient of variation, standard deviation) and non-linear (Higuchi fractal index, α1 coefficient of detrended fluctuation analysis) analyses were computed for different numbers of steps. Relative reliability was determined using the intraclass coefficient correlation. The minimal number of steps which present a good reliability level was considered as the optimal number of steps for measurement. Absolute reliability was assessed by calculating minimal detectable change.

RESULTS

To assess the mean values of spatio-temporal running parameters, between 16 and 150 steps were required. We were unable to obtain an optimal number of steps for cadence, stride and step-time variabilities for all speeds. For the linear analyses, we deduced the optimal number of steps for Kleg and the contact time (around 350 steps). Non-linear analyses measurements required between 350 and 540 steps, depending on the parameter.

SIGNIFICANCE

Researchers and clinicians should optimize experimental conditions (number of steps and running speed) depending on the parameter or the variability analysis targeted. Future studies must use absolute reliability metrics to report changes in response to a specific condition with no bias due to measurement error.

Optimizing Wearable Device and Testing Parameters to Monitor Running-Stride Long-Range Correlations for Fatigue Management in Field Settings

PURPOSE

There are important methodological considerations for translating wearable-based gait-monitoring data to field settings. This study investigated different devices' sampling rates, signal lengths, and testing frequencies for athlete monitoring using dynamical systems variables.

METHODS

Secondary analysis of previous wearables data (N = 10 runners) from a 5-week intensive training intervention investigated impacts of sampling rate (100-2000 Hz) and signal length (100-300 strides) on detection of gait changes caused by intensive training. Primary analysis of data from 13 separate runners during 1 week of field-based testing determined day-to-day stability of outcomes using single-session data and mean data from 2 sessions. Stride-interval long-range correlation coefficient α from detrended fluctuation analysis was the gait outcome variable.

RESULTS

Stride-interval α reduced at 100- and 200- versus 300- to 2000-Hz sampling rates (mean difference: -.02 to -.08; P ≤ .045) and at 100- compared to 200- to 300-stride signal lengths (mean difference: -.05 to -.07; P < .010). Effects of intensive training were detected at 100, 200, and 400 to 2000 Hz (P ≤ .043) but not 300 Hz (P = .069). Within-athlete α variability was lower using 2-session mean versus single-session data (smallest detectable change: .13 and .22, respectively).

CONCLUSIONS

Detecting altered gait following intensive training was possible using 200 to 300 strides and a 100-Hz sampling rate, although 100 and 200 Hz underestimated α compared to higher rates. Using 2-session mean data lowers smallest detectable change values by nearly half compared to single-session data. Coaches, runners, and researchers can use these findings to integrate wearable-device gait monitoring into practice using dynamic systems variables.

Stride-to-stride variability is altered when running to isochronous visual cueing but remains unaltered with fractal cueing

Running synchronised to external cueing is often implemented in both clinical and training settings, and isochronous cueing has been shown to improve running economy. However, such cueing disregards the natural stride-to-stride fluctuations present in human locomotion which is thought to reflect higher levels of adaptability. The present study aimed to investigate how alterations in the temporal structure of cueing affect stride-to-stride variability during running. We hypothesised that running using cueing with a fractal-like structure would preserve the natural stride-to-stride variability of young adults. Thirteen runners performed four 8-min trials: one uncued (UNC) trial and three cued trials presenting an isochronous (ISO), a fractal (FRC) and a random (RND) structure. Repeated measures ANOVAs were used to identify changes in the dependent variables. We have found no main effect on the cardiorespiratory parameters, whereas a significant main effect was observed in the temporal structure of stride-to-stride variability. During FRC, the participants were able to retain the fractal patterns of their natural locomotor variability observed during the UNC condition, while during the ISO and RND they exhibited more random of fluctuations (i.e., lower values of fractal scaling). Our results demonstrate that cueing based on the natural stride-to-stride fluctuations opens new avenues for training and rehabilitation.

Relationship between biomechanics and energy cost in graded treadmill running

The objective of this study was to determine whether the relationships between energy cost of running (Cr) and running mechanics during downhill (DR), level (LR) and uphill (UR) running could be related to fitness level. Nineteen athletes performed four experimental tests on an instrumented treadmill: one maximal incremental test in LR, and three randomized running bouts at constant speed (10 km h^-1) in LR, UR and DR (± 10% slope). Gas exchange, heart rate and ground reaction forces were collected during steady-state. Subjects were split into two groups using the median Cr for all participants. Contact time, duty factor, and positive external work correlated with Cr during UR (all, p < 0.05), while none of the mechanical variables correlated with Cr during LR and DR. Mechanical differences between the two groups were observed in UR only: contact time and step length were higher in the economical than in the non-economical group (both p < 0.031). This study shows that longer stance duration during UR contributes to lower energy expenditure and Cr (i.e., running economy improvement), which opens the way to optimize specific running training programs.

Indoor running temporal variability for different running speeds, treadmill inclinations, and three different estimation strategies

Inertial measurement units (IMU) constitute a light and cost-effective alternative to gold-standard measurement systems in the assessment of running temporal variables. IMU data collected on 20 runners running at different speeds (80, 90, 100, 110 and 120% of preferred running speed) and treadmill inclination (±2, ±5, and ±8%) were used here to predict the following temporal variables: stride frequency, duty factor, and two indices of running variability such as the detrended fluctuation analysis alpha (DFA-α) and the Higuchi's D (HG-D). Three different estimation methodologies were compared: 1) a gold-standard optoelectronic device (which provided the reference values), 2) IMU placed on the runner's feet, 3) a single IMU on the runner's thorax used in conjunction with a machine learning algorithm with a short 2-second or a long 120-second window as input. A two-way ANOVA was used to test the presence of significant (p<0.05) differences due to the running condition or to the estimation methodology. The findings of this study suggest that using both IMU configurations for estimating stride frequency can be effective and comparable to the gold-standard. Additionally, the results indicate that the use of a single IMU on the thorax with a machine learning algorithm can lead to more accurate estimates of duty factor than the strategy of the IMU on the feet. However, caution should be exercised when using these techniques to measure running variability indices. Estimating DFA-α from a short 2-second time window was possible only in level running but not in downhill running and it could not accurately estimate HG-D across all running conditions. By taking a long 120-second window a machine learning algorithm could improve the accuracy in the estimation of DFA-α in all running conditions. By taking these factors into account, researchers and practitioners can make informed decisions about the use of IMU technology in measuring running biomechanics.

The Application of non-linear methods to quantify changes to movement dynamics during running: A scoping review

The aim of this scoping review was to evaluate research approaches that quantify changes to non-linear movement dynamics during running in response to fatigue, different speeds, and fitness levels. PubMed and Scopus were used to identify appropriate research articles. After the selection of eligible studies, study details and participant characteristics were extracted and tabulated to identify methodologies and findings. Twenty-seven articles were included in the final analysis. To evaluate non-linearities in the time series, a range of approaches were identified including motion capture, accelerometery, and foot switches. Common methods of analysis included measures of fractal scaling, entropy, and local dynamic stability. Conflicting findings were evident when studies examined non-linear features in fatigued states when compared to non-fatigued. More pronounced alterations to movement dynamics are evident when running speed is changed markedly. Greater fitness levels resulted in more stable and predictable running patterns. The mechanisms by which these changes are underpinned require further examination. These could include the physiological demand of running, biomechanical constraints of the runner, and the attentional demands of the task. Moreover, the practical implications are yet to be elucidated. This review has identified gaps in the literature which should be addressed for further understanding of the field.

Stride-to-stride variability and fluctuations at intensities around lactate threshold in distance runners

Stride-to-stride variability and fluctuations in running have been widely investigated in relation to fatigue, injury, and other factors. However, no studies have examined the relationship of stride-to-stride variability and fluctuations with lactate threshold (LT), a well-known performance indicator for distance runners that represents the threshold at which fast-twitch muscle fibers are activated and the glycolytic system is hyperactivated. In this study, we examined a relationship between LT and stride-to-stride variability and fluctuations in trained middle- and long-distance runners (n = 33). All runners were asked to perform multistage graded exercise tests while wearing accelerometers on the upper surface of their shoes. The LT was determined by measuring blood lactate concentrations after each stage. Three gait parameters for each step were calculated based on the acceleration data: stride time (ST), ground contact time (CT), and peak acceleration (PA). The coefficient of variation (CV) and the long-range correlations (α) for each parameter were also calculated. The effects of the runner's group and the relative intensity for CV and α on gait parameters were evaluated using a two-way repeated measures analysis of variance. Although no significant effect was observed in the CV and α of ST, significant intensity main effects were observed for the CV and α of CT and PA. The lack of significant changes in ST might be the result of runners' adequate control of ST to minimize energy cost. All the parameters showing significant changes with increasing intensity decreased dramatically when they were close to LT. This might have been caused by an increase in physiological load near LT and be interpreted as a variation in motor control because of alternations in the mobilized muscle fibers and physiological changes around the LT. The α should be useful for non-invasive LT detection.

Running gait produces long range correlations: A systematic review

BACKGROUND

Walking and running are common forms of locomotion, both of which exhibit variability over many gait cycles. Many studies have investigated the patterns generated from that ebb and flow, and a large proportion suggests human gait exhibits Long Range Correlations (LRCs). LRCs refer to the observation that healthy gait characteristic, like stride times, are positively correlated to themselves over time. Literature on LRCs in walking gait is well known but less attention has been given to LRCs in running gait.

RESEARCH QUESTION

What is the state of the art concerning LRCs in running gait?

METHODS

We conducted a systematic review to identify the typical LRC patterns present in human running gait, in addition to disease, injury, and running surface effects on LRCs. Inclusion criteria were human subjects, running related experiments, computed LRCs, and experimental design. Exclusion criteria were studies on animals, non-humans, walking only, non-running, non-LRC analysis, and non-experiments.

RESULTS

The initial search returned 536 articles. After review and deliberation, our review included 26 articles. Almost every article produced strong evidence for LRCs apparent in running gait and in all running surfaces. Additionally, LRCs tended to decrease due to fatigue, past injury, increased load carriage and seem to be lowest at preferred running speed on a treadmill. No studies investigated disease effects on LRCs in running gait.

SIGNIFICANCE

LRCs seem to increase with deviations away from preferred running speed. Previously injured runners produced decreased LRCs compared to non-injured runners. LRCs also tended to decrease due to an increase in fatigue rate, which has been associated with increased injury rate. Lastly, there is a need for research on the typical LRCs in an overground environment, for which the typical LRCs found in a treadmill environment may or may not transfer.

Augmented Cooper test: Biomechanical contributions to endurance performance

Running mechanics are modifiable with training and adopting an economical running technique can improve running economy and hence performance. While field measurement of running economy is cumbersome, running mechanics can be assessed accurately and conveniently using wearable inertial measurement units (IMUs). In this work, we extended this wearables-based approach to the Cooper test, by assessing the relative contribution of running biomechanics to the endurance performance. Furthermore, we explored different methods of estimating the distance covered in the Cooper test using a wearable global navigation satellite system (GNSS) receiver. Thirty-three runners (18 highly trained and 15 recreational) performed an incremental laboratory treadmill test to measure their maximum aerobic speed (MAS) and speed at the second ventilatory threshold (sVT2). They completed a 12-minute Cooper running test with foot-worm IMUs and a chest-worn GNSS-IMU on a running track 1–2 weeks later. Using the GNSS receiver, an accurate estimation of the 12-minute distance was obtained (accuracy of 16.5 m and precision of 1.1%). Using this distance, we showed a reliable estimation [R² > 0.9, RMSE ϵ (0.07, 0.25) km/h] of the MAS and sVT2. Biomechanical metrics were extracted using validated algorithm and their association with endurance performance was estimated. Additionally, the high-/low-performance runners were compared using pairwise statistical testing. All performance variables, MAS, sVT2, and average speed during Cooper test, were predicted with an acceptable error (R² ≥ 0.65, RMSE ≤ 1.80 kmh⁻¹) using only the biomechanical metrics. The most relevant metrics were used to develop a biomechanical profile representing the running technique and its temporal evolution with acute fatigue, identifying different profiles for runners with highest and lowest endurance performance. This profile could potentially be used in standardized functional capacity measurements to improve personalization of training and rehabilitation programs.

Complexity of Running and Its Relationship with Joint Kinematics during a Prolonged Run

We investigated the effect of prolonged running on joint kinematics and its association with stride complexity between novice and elite runners. Ten elite marathoners and eleven healthy individuals took part in a 20 min submaximal prolonged running experiment at their preferred running speed (PRS). A three-dimensional motion capture system was utilized to capture and calculate the alpha exponent, stride-to-stride fluctuations (SSFs), and stride-to-stride variability (SSV) of spatiotemporal parameters and joint kinematics. In the results, the elite athletes ran at a considerably higher PRS than the novice runners, yet no significant differences were found in respiratory exchange ratio with increasing time intervals. For the spatiotemporal parameters, we observed a significant increase in the step width and length variability in novice runners with increasing time-interval (p < 0.05). However, we did not observe any differences in the alpha exponent of spatiotemporal parameters. Significant differences in SSF of joint kinematics were observed, particularly in the sagittal plane for ankle, knee, and hip at heel strike (p < 0.05). While in mid-stance, time-interval differences were observed in novices who ran with a lower knee flexion angle (p < 0.05). During toe-off, significantly higher SSV was observed, particularly in the hip and ankle for novices (p < 0.05). The correlation analysis of joint SSV revealed a distinct negative relationship with the alpha exponent of step-length and step-width for elite runners, while, for novices, a positive relation was observed only for the alpha exponent of step-width. In conclusion, our study shows that increased step-width variability seen in novices could be a compensatory mechanism to maintain performance and mitigate the loss of stability. On the other hand, elite runners showed a training-induced effective modulation of lower-limb kinematics to improve their running performance.

Decoupling of Internal and External Workload During a Marathon: An Analysis of Durability in 82,303 Recreational Runners

Aim

This study characterised the decoupling of internal-to-external workload in marathon running and investigated whether decoupling magnitude and onset could improve predictions of marathon performance.

Methods

The decoupling of internal-to-external workload was calculated in 82,303 marathon runners (13,125 female). Internal workload was determined as a percentage of maximum heart rate, and external workload as speed relative to estimated critical speed (CS). Decoupling magnitude (i.e., decoupling in the 35–40 km segment relative to the 5–10 km segment) was classified as low (< 1.1), moderate (≥ 1.1 but < 1.2) or high (≥ 1.2). Decoupling onset was calculated when decoupling exceeded 1.025.

Results

The overall internal-to-external workload decoupling experienced was 1.16 ± 0.22, first detected 25.2 ± 9.9 km into marathon running. The low decoupling group (34.5% of runners) completed the marathon at a faster relative speed (88 ± 6% CS), had better marathon performance (217.3 ± 33.1 min), and first experienced decoupling later in the marathon (33.4 ± 9.0 km) compared to those in the moderate (32.7% of runners, 86 ± 6% CS, 224.9 ± 31.7 min, and 22.6 ± 7.7 km), and high decoupling groups (32.8% runners, 82 ± 7% CS, 238.5 ± 30.7 min, and 19.1 ± 6.8 km; all p < 0.01). Compared to females, males’ decoupling magnitude was greater (1.17 ± 0.22 vs. 1.12 ± 0.16; p < 0.01) and occurred earlier (25.0 ± 9.8 vs. 26.3 ± 10.6 km; p < 0.01). Marathon performance was associated with the magnitude and onset of decoupling, and when included in marathon performance models utilising CS and the curvature constant, prediction error was reduced from 6.45 to 5.16%.

Conclusion

Durability characteristics, assessed as internal-to-external workload ratio, show considerable inter-individual variability, and both its magnitude and onset are associated with marathon performance.

Changes in spatiotemporal parameters, joint and CoM kinematics and leg stiffness in novice runners during a high-intensity fatigue protocol

Even though running enjoys growing popularity, the effects of fatigue on the running kinematics of novices have rarely been studied. This is surprising, given the risk of running-related injuries when detrimental movement patterns are adopted. Therefore, the goal of the present study was to characterize the effects of fatigue induced by a high-intensity running protocol on spatiotemporal and stiffness parameters as well as on joint kinematics and center of mass (CoM) motion in novice runners. 14 participants performed a standardized treadmill familiarization and ran at 13 km/h until voluntary exhaustion. Kinematics were captured using a 3D motion capture system. Spatiotemporal and stiffness parameters as well as the range of motion (RoM) of the joints and CoM were compared by use of paired t-tests. Time series of the joint angles and CoM motion were analyzed by the statistical parametric mapping method. The results revealed that novice runners did not change spatiotemporal or stiffness parameters, but showed adaptations in joint kinematics, e.g. decreased dorsiflexion and increased pronation in the ankle joint during the swing phase. The findings of this study underline the importance of strengthening the ankle joint to prevent excessive pronation and increase its stability in novice runners.

Effects of motor skill level and speed on movement variability during running

Variability in movement is an informative biological feature. This study aimed to examine the effects of motor skill level and running speed on movement variability. Twenty-nine male college students (fourteen athletes and fifteen non-athletes) participated in this study. All participants performed three motor tasks: 3 m/s running, 5 m/s running, and sprint running. Lower-limb kinematic data were acquired using a 16-camera infrared motion capture system. Lower-limb coordination during the stance phase was quantified using a continuous relative phase (CRP) method for interlimb (hip-hip, knee-knee, ankle-ankle) and intralimb (hip-knee, knee-ankle). The variabilities of stride length, stride cadence, joint angles, intralimb CRP, and interlimb CRP were calculated as standard deviations of each measurement. The results revealed that there were significant interaction effects between motor skill level and speed on movement variability for stride length (p = 0.047), ankle angle during propulsive phase (p = 0.001), knee-ankle CRP during propulsive phase (p = 0.007) and knee-knee CRP during propulsive phase (p = 0.009). Athletes showed greater angle variability, coordination variability and lower stride length variability during sprinting (all p < 0.05). In contrast, no between groups variability difference was observed when jogging at fixed lower speeds (all p > 0.05). Movement variability was greater for sprinting compared to jogging. Skill level was found to differentially affect the role of coordination variability in sprint performance. For athletes, hip-knee deviation phase and hip-hip deviation phase during braking phase were negatively correlated with sprinting speed (r = -0.563 and -0.642, respectively; both p < 0.05). For non-athletes, hip-knee deviation phase was positively correlated with sprinting speed (r = 0.581, p = 0.023). In conclusion, stride length become more stable, joint angle and coordination become more variable with long-term training. Results of this study also suggest that the relationship between coordination variability and performance is complicated and may depend on motor skill level. More longitudinal studies are needed to definitively determine the relationship between movement variability and performance.

Biomechanical Response of the Lower Extremity to Running-Induced Acute Fatigue: A Systematic Review

Objective: To investigate (i) typical protocols used in research on biomechanical response to running-induced fatigue, (ii) the effect of sport-induced acute fatigue on the biomechanics of running and functional tests, and (iii) the consistency of analyzed parameter trends across different protocols.

Methods: Scopus, Web of Science, Pubmed, and IEEE databases were searched using terms identified with the Population, Interest and Context (PiCo) framework. Studies were screened following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and appraised using the methodological index for non-randomized studies MINORS scale. Only experimental studies with at least 10 participants, which evaluated fatigue during and immediately after the fatiguing run were included. Each study was summarized to record information about the protocol and parameter trends. Summary trends were computed for each parameter based on the results found in individual studies.

Results: Of the 68 included studies, most were based on in-lab (77.9%) protocols, endpoint measurements (75%), stationary measurement systems (76.5%), and treadmill environment (54.4%) for running. From the 42 parameters identified in response to acute fatigue, flight time, contact time, knee flexion angle at initial contact, trunk flexion angle, peak tibial acceleration, CoP velocity during balance test showed an increasing behavior and cadence, vertical stiffness, knee extension force during MVC, maximum vertical ground reaction forces, and CMJ height showed a decreasing trend across different fatigue protocols.

Conclusion: This review presents evidence that running-induced acute fatigue influences almost all the included biomechanical parameters, with crucial influence from the exercise intensity and the testing environment. Results indicate an important gap in literature caused by the lack of field studies with continuous measurement during outdoor running activities. To address this gap, we propose recommendations for the use of wearable inertial sensors.

Perceptual-motor styles

Even for a stereotyped task, sensorimotor behavior is generally variable due to noise, redundancy, adaptability, learning or plasticity. The sources and significance of different kinds of behavioral variability have attracted considerable attention in recent years. However, the idea that part of this variability depends on unique individual strategies has been explored to a lesser extent. In particular, the notion of style recurs infrequently in the literature on sensorimotor behavior. In general use, style refers to a distinctive manner or custom of behaving oneself or of doing something, especially one that is typical of a person, group of people, place, context, or period. The application of the term to the domain of perceptual and motor phenomenology opens new perspectives on the nature of behavioral variability, perspectives that are complementary to those typically considered in the studies of sensorimotor variability. In particular, the concept of style may help toward the development of personalised physiology and medicine by providing markers of individual behaviour and response to different stimuli or treatments. Here, we cover some potential applications of the concept of perceptual-motor style to different areas of neuroscience, both in the healthy and the diseased. We prefer to be as general as possible in the types of applications we consider, even at the expense of running the risk of encompassing loosely related studies, given the relative novelty of the introduction of the term perceptual-motor style in neurosciences.

Fatigue-Related Changes in Spatiotemporal Parameters, Joint Kinematics and Leg Stiffness in Expert Runners During a Middle-Distance Run

Fatigue with its underlying mechanisms and effects is a broadly discussed topic and an important phenomenon, particularly in endurance sports. Although several studies have already shown a variety of changes in running kinematics with fatigue, few of them have analyzed competitive runners and even fewer have focused on middle-distance running. Furthermore, the studies investigating fatigue-related changes have mostly reported the results in terms of discrete parameters [e.g., range of motion (RoM)] in the frontal or sagittal plane, and therefore potentially overlooked effects occurring in subphases of the gait cycle or in the transverse plane. On this basis, the goal of the present study was to analyze the effects of exhaustive middle-distance running on expert runners by means of both discrete parameters and time series analysis in 3D. In this study, 13 runners ran on a treadmill to voluntary exhaustion at their individually determined fatigue speeds which was held constant during the measurements. Kinematic data were collected by means of a 3D motion capture system. Spatiotemporal and stiffness parameters as well as the RoM of joints and of center of mass (CoM) within the stance and flight phases were calculated. Independent t-tests were performed to investigate any changes in means and coefficients of variation (CV) of these parameters between the rested (PRE) and fatigued (POST) state. Statistical parametric mapping method was applied on the time series data of the joints and the CoM. Results from this exploratory study revealed that during a middle-distance run, expert runners change their stance time, rather than their step frequency or step length in order to maintain the constant running speed as long as possible. Increased upper body movements occurred to counteract the increased angular moment of the lower body possibly due to longer stance times. These findings provide insights into adaptation strategies of expert runners during a fatiguing middle-distance run and may serve a valuable information particularly for comparisons with other group of runners (e.g., females or non-athletes) as well with other conditions (e.g., non-constant speed or interval training), and might be useful for the definition of training goals (e.g., functional core training).

A non-linear analysis of running in the heavy and severe intensity domains

PURPOSE

Altered movement complexity, indicative of system dysfunction, has been demonstrated with increased running velocity and neuromuscular fatigue. The critical velocity (CV) denotes a metabolic and neuromuscular fatigue threshold. It remains unclear whether changes to complexity during running are coupled with the exercise intensity domain in which it is performed. The purpose of this study was to examine whether movement variability and complexity differ exclusively above the CV intensity during running.

METHODS

Ten endurance-trained participants ran at 95%, 100%, 105% and 115% CV for 20 min or to task failure, whichever occurred first. Movement at the hip, knee, and ankle were sampled throughout using 3D motion analysis. Complexity of kinematics in the first and last 30 s were quantified using sample entropy (SampEn) and detrended fluctuation analysis (DFA-α). Variability was determined using standard deviation (SD).

RESULTS

SampEn decreased during all trials in knee flexion/extension and it increased in hip internal/external rotation, whilst DFA-α increased in knee internal/external rotation. SD of ankle plantar/dorsiflexion and inversion/eversion, knee internal/external rotation, and hip flexion/extension and abduction/adduction increased during trials. Hip flexion/extension SampEn values were lowest below CV. DFA-α was lower at higher velocities compared to velocities below CV in ankle plantar/dorsiflexion, hip flexion/extension, hip adduction/abduction, hip internal/external rotation. In hip flexion/extension SD was highest at 115% CV.

CONCLUSIONS

Changes to kinematic complexity over time are consistent between heavy and severe intensity domains. The findings suggest running above CV results in increased movement complexity and variability, particularly at the hip, during treadmill running.

Classification of runners' performance levels with concurrent prediction of biomechanical parameters using data from inertial measurement units

Identification of runner's performance level is critical to coaching, performance enhancement and injury prevention. Machine learning techniques have been developed to measure biomechanical parameters with body-worn inertial measurement unit (IMU) sensors. However, a robust method to classify runners is still unavailable. In this paper, we developed two models to classify running performance and predict biomechanical parameters of 30 subjects. We named the models RunNet-CNN and RunNet-MLP based on their architectures: convolutional neural network (CNN) and multilayer perceptron (MLP), respectively. In addition, we examined two validation approaches, subject-wise (leave-one-subject-out) and record-wise. RunNet-MLP classified runner's performance levels with an overall accuracy of 97.1%. Our results also showed that RunNet-CNN outperformed RunNet-MLP and gradient boosting decision tree in predicting biomechanical parameters. RunNet-CNN showed good agreement (R² > 0.9) with the ground-truth reference on biomechanical parameters. The prediction accuracy for the record-wise method was better than the subject-wise method regardless of biomechanical parameters or models. Our findings showed the viability of using IMUs to produce reliable prediction of runners' performance levels and biomechanical parameters.

Motor style at rest and during locomotion in human

Humans exhibit various motor styles that reflect their intra- and interindividual variability when implementing sensorimotor transformations. This opens important questions, such as, At what point should they be readjusted to maintain optimal motor control? Do changes in motor style reveal the onset of a pathological process and can these changes help rehabilitation and recovery? To further investigate the concept of motor style, tests were carried out to quantify posture at rest and motor control in 18 healthy subjects under four conditions: walking at three velocities (comfortable walking, walking at 4 km/h, and race walking) and running at maximum velocity. The results suggest that motor control can be conveniently decomposed into a static component (a stable configuration of the head and column with respect to the gravitational vertical) and dynamic components (head, trunk, and limb movements) in humans, as in quadrupeds, and both at rest and during locomotion. These skeletal configurations provide static markers to quantify the motor style of individuals because they exhibit large variability among subjects. Also, using four measurements (jerk, root mean square, sample entropy, and the two-thirds power law), it was shown that the dynamics were variable at both intra- and interindividual levels during locomotion. Variability increased following a head-to -toe gradient. These findings led us to select dynamic markers that could define, together with static markers, the motor style of a subject. Finally, our results support the view that postural and motor control are subserved by different neuronal networks in frontal, sagittal, and transversal planes.NEW & NOTEWORTHY During human locomotion, motor control can be conveniently decomposed into a static and dynamic components. Variable dynamics were observed at both the intra- and interindividual levels during locomotion. Variability increased following a head-to-toe gradient. Finally, our results support the view that postural and motor control are subserved by different neuronal networks in the frontal, sagittal, and transversal planes.

Distance running stride-to-stride variability for sagittal plane joint angles

Recent research indicates that distance running stride-to-stride variability (SSV) is related to performance and injury. Previous studies have primarily focused on stride characteristics (stride length and time). We assessed SSV for sagittal plane joint angles with the primary purpose of testing for significant differences among the lower body joints. The secondary purpose was to determine if strong correlations exist among joint SSV measures. Thirty recreational adult runners participated in the study (8 females, 22 males, 39 ± 10 years; 53.1 ± 25.7 km/week). A 6-camera motion capture system (200 Hz) collected kinematic data during treadmill running at a preferred pace. A 2 by 3 repeated measures factorial ANOVA (phase-stance, swing; joint-hip, knee, ankle) was run (p = 0.05). There was a significant interaction effect (p < 0.001) and post hoc analysis revealed knee swing to be the most variable condition by far. For all three joints, there were strong correlations between stance and swing SSV (r = 0.80 to r = 0.88) and correlations among the joints were moderate to strong (r = 0.55 to 0.86). This study helps to better understand the joints/phases that contribute most to variability in the overall stride. Also, the strong correlations suggest that runners appear to have an overall SSV pattern that is similar across joints/phases.

Variability of running coordination in experts and novices: A 3D uncontrolled manifold analysis

The uncontrolled manifold (UCM) approach has been widely used in recent studies to examine variability in daily tasks; however, it has not yet been used to study running or the effects of expertise. Therefore, the aim of this study was to analyse the synergy structure stabilizing the centre of mass (CoM) trajectory in experts compared to novices during running at two different speeds using a subject-specific 3D model. A total of 25 healthy young adults (13 experts, 12 novices) participated in the study. All subjects ran at 10 and 15 km h^-1 on a treadmill. In each case, kinematics of 20 consecutive gait cycles were recorded and the effects of expertise and gait cycle phase on the synergy structure were investigated at both speeds. Specifically, the variance affecting the CoM ( U C M ⊥ ) , the variance not affecting the CoM ( U C M ∥ ) , and their ratio ( U C M R a t i o ) were analysed. Descriptively, in both groups there was a synergy stabilizing the CoM trajectory in running. However, the ANOVA showed no differences in U C M R a t i o between the two groups. In novices, U C M ⊥ and U C M ∥ were significantly higher compared to experts at the 15 km h^-1 condition. In both groups, there was more variability in the stance phase compared to the flight phase in the majority of cases. The results indicate that experts adopted a more consistent running style. The stride-to-stride variability was diminished but not abolished. This difference was only visible at the 15 km h^-1 condition. Furthermore, variability was less constrained in the stance phase compared to the flight phase.

Reliability of the fluctuations within the stride time series measured in runners during treadmill running to exhaustion

BACKGROUND

The fluctuations within stride time series (i.e., stride time variability and complexity) during running exhibit long-range correlation. Detecting the breakdown of the long-range correlation was proposed for monitoring the occurrence of running-related injuries during running. However, the stride time fluctuations were only measured from the unilateral side. In addition, the reliability of the stride time fluctuations of within-subject repeated measures remains largely unknown, particularly during exhaustive running.

PURPOSES

This study investigated between-side and between-day reliabilities of the stride time variability and complexity of right and left sides during an exhaustive running.

METHODS

The stride time variability and complexity of bilateral sides were obtained while 24 healthy participants performed a 31-minute treadmill running at their individual anaerobic threshold speed. Seven of the 24 participants performed the treadmill running test twice at two different days 5-7 days apart. Limits of agreement (LoA) and intraclass correlation coefficient (ICC) were respectively used to assess the absolute and relative between-side and between-day reliabilities.

RESULTS

The stride time variability and complexity of right and left sides were highly symmetrical (LoA: (-0.500%, 0.459%) and (-0.052, 0.051), respectively; ICC: 0.94 (0.87, 0.97) and 0.98 (0.95, 0.99), respectively). The overall stride time variability and complexity revealed good between-day reliability (LoA: (-1.044%, 0.724%) and (-0.067, 0.115), respectively; ICC: 0.78 (0.45, 0.92) and 0.81 (0.48, 0.93), respectively). However, the segmented stride time complexity showed poor between-day reliability (ICCs<0.40).

CONCLUSION

The findings demonstrated that the stride time series showed equivalent fluctuations between right and left sides and good between-day reliability in fluctuations during exhaustive running. Given the poor between-day reliability in the segmented stride time series, stride time series during exhaustive running could be collected from either right or left side and should be processed as an overall in the future.