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Quantifying and correcting for speed and stride frequency effects on running mechanics in fatiguing outdoor running
Frontiers in Sports and Active Living
Abstract
Measuring impact-related quantities in running is of interest to improve the running technique. Many quantities are typically measured in a controlled laboratory setting, even though most runners run in uncontrolled outdoor environments. While monitoring running mechanics in an uncontrolled environment, a decrease in speed or stride frequency can mask fatigue-related changes in running mechanics. Hence, this study aimed to quantify and correct the subject-specific effects of running speed and stride frequency on changes in impact-related running mechanics during a fatiguing outdoor run. Seven runners ran a competitive marathon while peak tibial acceleration and knee angles were measured with inertial measurement units. Running speed was measured through sports watches. Median values over segments of 25 strides throughout the marathon were computed and used to create subject-specific multiple linear regression models. These models predicted peak tibial acceleration, knee angles at initial contact, and maximum stance phase knee flexion based on running speed and stride frequency. Data were corrected for individual speed and stride frequency effects during the marathon. The speed and stride frequency corrected and uncorrected data were divided into ten stages to investigate the effect of marathon stage on mechanical quantities. This study showed that running speed and stride frequency explained, on average, 20%-30% of the variance in peak tibial acceleration, knee angles at initial contact, and maximum stance phase knee angles while running in an uncontrolled setting. Regression coefficients for speed and stride frequency varied strongly between subjects. Speed and stride frequency corrected peak tibial acceleration, and maximum stance phase knee flexion increased throughout the marathon. At the same time, uncorrected maximum stance phase knee angles showed no significant differences between marathon stages due to a decrease in running speed. Hence, subject-specific effects of changes in speed and stride frequency influence the interpretation of running mechanics and are relevant when monitoring, or comparing the gait pattern between runs in uncontrolled environments.
... On the other hand, runners in such data sets are typically far more experienced, and changes and adaptations in their biomechanical data are fewer and are of a smaller size. Details on the data sets used in this section can be found in Zandbergen et al. (2023a). We choose just as before in the sensor data example in Section 3.2.1. ...
Injuries to the lower extremity joints are often debilitating, particularly for professional athletes. Understanding the onset of stressful conditions on these joints is, therefore, important in order to ensure prevention of injuries as well as individualised training for enhanced athletic performance. We study the biomechanical joint angles from the hip, knee and ankle for runners who are experiencing fatigue. The data is cyclic in nature and densely collected by body-worn sensors, which makes it ideal to work with in the functional data analysis (FDA) framework.
We develop a new method for multiple change point detection for functional data, which improves the state of the art with respect to at least two novel aspects. First, the curves are compared with respect to their maximum absolute deviation, which leads to a better interpretation of local changes in the functional data compared to classical
L
2
-approaches. Second, as slight aberrations are to be often expected in a human movement data, our method will not detect arbitrarily small changes but hunts for relevant changes, where maximum absolute deviation between the curves exceeds a specified threshold, say
Δ
>
0
. We recover multiple changes in a long functional time series of biomechanical knee angle data, which are larger than the desired threshold Δ, allowing us to identify changes purely due to fatigue. In this work we analyse data from both controlled indoor as well as from an uncontrolled outdoor (marathon) setting.
... In the case of gait metrics measured by two sensors, the sensor which measured that gait metric more accurately was used-this was the chest strap for vertical oscillation, and the foot pod for stance time. Since speed, cadence, and stride length are mathematically linked, only speed and stride length were used in the representation of a runner's gait pattern, because (1) runners often vary their speed by changes in stride length as opposed to cadence [22,23], and (2) the devices quantize cadence by mapping it to an integer number of strides (foot pod) or an integer number of steps (chest strap) per minute. This quantization process introduces errors compared to using stride length, which is measured by the chest strap to the millimeter. ...
Biomechanical assessments of running typically take place inside motion capture laboratories. However, it is unclear whether data from these in-lab gait assessments are representative of gait during real-world running. This study sought to test how well real-world gait patterns are represented by in-lab gait data in two cohorts of runners equipped with consumer-grade wearable sensors measuring speed, step length, vertical oscillation, stance time, and leg stiffness. Cohort 1 (N = 49) completed an in-lab treadmill run plus five real-world runs of self-selected distances on self-selected courses. Cohort 2 (N = 19) completed a 2.4 km outdoor run on a known course plus five real-world runs of self-selected distances on self-selected courses. The degree to which in-lab gait reflected real-world gait was quantified using univariate overlap and multivariate depth overlap statistics, both for all real-world running and for real-world running on flat, straight segments only. When comparing in-lab and real-world data from the same subject, univariate overlap ranged from 65.7% (leg stiffness) to 95.2% (speed). When considering all gait metrics together, only 32.5% of real-world data were well-represented by in-lab data from the same subject. Pooling in-lab gait data across multiple subjects led to greater distributional overlap between in-lab and real-world data (depth overlap 89.3–90.3%) due to the broader variability in gait seen across (as opposed to within) subjects. Stratifying real-world running to only include flat, straight segments did not meaningfully increase the overlap between in-lab and real-world running (changes of <1%). Individual gait patterns during real-world running, as characterized by consumer-grade wearable sensors, are not well-represented by the same runner’s in-lab data. Researchers and clinicians should consider “borrowing” information from a pool of many runners to predict individual gait behavior when using biomechanical data to make clinical or sports performance decisions.
... For example, the Xsens Link system has been applied to study how running kinematics are influenced by unstable surfaces [9], a marathon [10], performance level in trail running [11] or by the cycling-to-running transition in triathletes [12]. Although wearable systems like the Xsens Link offer potentially new insights into running physiology and mechanics, the accurate IMU-based estimation of joint movement is challenging, e.g., due to integration drift in the measured accelerations and angular velocities [13,14], soft-tissue artefacts [15], anatomical calibration errors [16], and/or disagreement between the underlying linked-segment skeletal model and physiological joint articulation [8], to name a few. ...
The Xsens Link motion capture suit has become a popular tool in investigating 3D running kinematics based on wearable inertial measurement units outside of the laboratory. In this study, we investigated the reliability of Xsens-based lower extremity joint angles during unconstrained running on stable (asphalt) and unstable (woodchip) surfaces within and between five different testing days in a group of 17 recreational runners (8 female, 9 male). Specifically, we determined the within-day and between-day intraclass correlation coefficients (ICCs) and minimal detectable changes (MDCs) with respect to discrete ankle, knee, and hip joint angles. When comparing runs within the same day, the investigated Xsens-based joint angles generally showed good to excellent reliability (median ICCs > 0.9). Between-day reliability was generally lower than the within-day estimates: Initial hip, knee, and ankle angles in the sagittal plane showed good reliability (median ICCs > 0.88), while ankle and hip angles in the frontal plane showed only poor to moderate reliability (median ICCs 0.38–0.83). The results were largely unaffected by the surface. In conclusion, within-day adaptations in lower-extremity running kinematics can be captured with the Xsens Link system. Our data on between-day reliability suggest caution when trying to capture longitudinal adaptations, specifically for ankle and hip joint angles in the frontal plane.
Background
Running overuse injuries (ROIs) occur within a complex, partly injury-specific interplay between training loads and extrinsic and intrinsic risk factors. Biomechanical risk factors (BRFs) are related to the individual running style. While BRFs have been reviewed regarding general ROI risk, no systematic review has addressed BRFs for specific ROIs using a standardized methodology.
Objective
To identify and evaluate the evidence for the most relevant BRFs for ROIs determined during running and to suggest future research directions.
Design
Systematic review considering prospective and retrospective studies. (PROSPERO_ID: 236,832).
Data Sources
PubMed. Connected Papers. The search was performed in February 2021.
Eligibility Criteria
English language. Studies on participants whose primary sport is running addressing the risk for the seven most common ROIs and at least one kinematic, kinetic (including pressure measurements), or electromyographic BRF. A BRF needed to be identified in at least one prospective or two independent retrospective studies. BRFs needed to be determined during running.
Results
Sixty-six articles fulfilled our eligibility criteria. Levels of evidence for specific ROIs ranged from conflicting to moderate evidence. Running populations and methods applied varied considerably between studies. While some BRFs appeared for several ROIs, most BRFs were specific for a particular ROI. Most BRFs derived from lower-extremity joint kinematics and kinetics were located in the frontal and transverse planes of motion. Further, plantar pressure, vertical ground reaction force loading rate and free moment-related parameters were identified as kinetic BRFs.
Conclusion
This study offers a comprehensive overview of BRFs for the most common ROIs, which might serve as a starting point to develop ROI-specific risk profiles of individual runners. We identified limited evidence for most ROI-specific risk factors, highlighting the need for performing further high-quality studies in the future. However, consensus on data collection standards (including the quantification of workload and stress tolerance variables and the reporting of injuries) is warranted.
Inertial measurement units (IMUs) can be used to monitor running biomechanics in real-world settings, but IMUs are often used within a laboratory. The purpose of this scoping review was to describe how IMUs are used to record running biomechanics in both laboratory and real-world conditions. We included peer-reviewed journal articles that used IMUs to assess gait quality during running. We extracted data on running conditions (indoor/outdoor, surface, speed, and distance), device type and location, metrics, participants, and purpose and study design. A total of 231 studies were included. Most (72%) studies were conducted indoors; and in 67% of all studies, the analyzed distance was only one step or stride or <200 m. The most common device type and location combination was a triaxial accelerometer on the shank (18% of device and location combinations). The most common analyzed metric was vertical/axial magnitude, which was reported in 64% of all studies. Most studies (56%) included recreational runners. For the past 20 years, studies using IMUs to record running biomechanics have mainly been conducted indoors, on a treadmill, at prescribed speeds, and over small distances. We suggest that future studies should move out of the lab to less controlled and more real-world environments.
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.
Low reproducibility and non-optimal sample sizes are current concerns in scientific research, especially within human movement studies. Therefore, this study aimed to examine the implications of different sample sizes and number of steps on data variability and statistical outcomes from kinematic and kinetics running biomechanical variables. Forty-four participants ran overground using their preferred technique (normal) and minimizing the contact sound volume (silent). Running speed, peak vertical, braking forces, and vertical average loading rate were extracted from > 40 steps/runner. Data stability was computed using a sequential estimation technique. Statistical outcomes (p values and effect sizes) from the comparison normal vs silent running were extracted from 100,000 random samples, using various combinations of sample size (from 10 to 40 runners) and number of steps (from 5 to 40 steps). The results showed that only 35% of the study sample could reach average stability using up to 10 steps across all biomechanical variables. The loading rate was consistently significantly lower during silent running compared to normal running, with large effect sizes across all combinations. However, variables presenting small or medium effect sizes (running speed and peak braking force), required > 20 runners to reach significant differences. Therefore, varying sample sizes and number of steps are shown to influence the normal vs silent running statistical outcomes in a variable-dependent manner. Based on our results, we recommend that studies involving analysis of traditional running biomechanical variables use a minimum of 25 participants and 25 steps from each participant to provide appropriate data stability and statistical power.
This present review includes a systematic search for peer-reviewed articles published between March 2009 and March 2020 that evaluated the effects of wearable devices with biofeedback on the biomechanics of running. The included articles did not focus on physiological and metabolic metrics. Articles with patients, animals, orthoses, exoskeletons and virtual reality were not included. Following the PRISMA guidelines, 417 articles were first identified, and nineteen were selected following the removal of duplicates and articles which did not meet the inclusion criteria. Most reviewed articles reported a significant reduction in positive peak acceleration, which was found to be related to tibial stress fractures in running. Some previous studies provided biofeedback aiming to increase stride frequencies. They produced some positive effects on running, as they reduced vertical load in knee and ankle joints and vertical displacement of the body and increased knee flexion. Some other parameters, including contact ground time and speed, were fed back by wearable devices for running. Such devices reduced running time and increased swing phase time. This article reviews challenges in this area and suggests future studies can evaluate the long-term effects in running biomechanics produced by wearable devices with biofeedback.
Traditionally, running biomechanics analyses have been conducted using 3D motion capture during treadmill or indoor overground running. However, most runners complete their runs outdoors. Since changes in running terrain have been shown to influence running gait mechanics, the purpose of this study was to use a machine learning approach to objectively determine relevant accelerometer-based features to discriminate between running patterns in different environments and determine the generalizability of observed differences in running patterns. Center of mass accelerations were recorded for recreational runners in treadmill-only (n = 28) and sidewalk-only (n = 25) environments, and an independent group (n = 16) ran in both treadmill and sidewalk environments. A feature selection algorithm was used to develop a training dataset from treadmill-only and sidewalk-only running. A binary support vector machine model was trained to classify treadmill and sidewalk running. Classification accuracy was determined using 10-fold cross-validation of the training dataset and an independent testing dataset from the runners that ran in both environments. Nine features related to the consistency and variability of center of mass accelerations were selected. Specifically, there was greater ratio of vertical acceleration during treadmill running and a greater ratio of anterior-posterior acceleration during sidewalk running in both the training and testing dataset. Step and stride regularity were significantly greater in the treadmill condition for the vertical axis in both the training and testing dataset, and in the medial-lateral axis for the testing dataset. During sidewalk running, there was significantly greater variability in the magnitude of the vertical and anterior-posterior accelerations for both datasets. The classification accuracy based on 10-fold cross-validation of the training dataset (M = 93.17%, SD = 2.43%) was greater than the classification accuracy of the independent testing dataset (M = 83.81%, SD = 3.39%). This approach could be utilized in future analyses to identify relevant differences in running patterns using wearable technology.
Injuries and lack of motivation are common reasons for discontinuation of running. Real-time feedback from wearables can reduce discontinuation by reducing injury risk and improving performance and motivation. There are however several limitations and challenges with current real-time feedback approaches. We discuss these limitations and challenges and provide a framework to optimise real-time feedback for reducing injury risk and improving performance and motivation. We first discuss the reasons why individuals run and propose that feedback targeted to these reasons can improve motivation and compliance. Secondly, we review the association of running technique and running workload with injuries and performance and we elaborate how real-time feedback on running technique and workload can be applied to reduce injury risk and improve performance and motivation. We also review different feedback modalities and motor learning feedback strategies and their application to real-time feedback. Briefly, the most effective feedback modality and frequency differ between variables and individuals, but a combination of modalities and mixture of real-time and delayed feedback is most effective. Moreover, feedback promoting perceived competence, autonomy and an external focus can improve motivation, learning and performance. Although the focus is on wearables, the challenges and practical applications are also relevant for laboratory-based gait retraining.
The identification of the initial contact (IC) and toe off (TO) events are crucial components of running gait analyses. To evaluate running gait in real-world settings, robust gait event detection algorithms that are based on signals from wearable sensors are needed. In this study, algorithms for identifying gait events were developed for accelerometers that were placed on the foot and low back and validated against a gold standard force plate gait event detection method. These algorithms were automated to enable the processing of large quantities of data by accommodating variability in running patterns. An evaluation of the accuracy of the algorithms was done by comparing the magnitude and variability of the difference between the back and foot methods in different running conditions, including different speeds, foot strike patterns, and outdoor running surfaces. The results show the magnitude and variability of the back-foot difference was consistent across running conditions, suggesting that the gait event detection algorithms can be used in a variety of settings. As wearable technology allows for running gait analyses to move outside of the laboratory, the use of automated accelerometer-based gait event detection methods may be helpful in the real-time evaluation of running patterns in real world conditions.
Tibial acceleration is a surrogate measure for impact loading and might be useful for identifying lower limb fatigue injury in runners. The resultant tibial acceleration calculated from all three axes of a triaxial accelerometer provides a single metric that is independent of the sensor orientation. The purpose of this study was to investigate the relationship between resultant tibial acceleration and running velocity, and to establish a normative database of tibial acceleration profiles. Triaxial accelerometers were attached to the distal tibiae of 85 runners before they ran on a treadmill for two minutes each, at speeds of 2.7, 3.0, 3.3 and 3.7 m/s. Differences in resultant tibial acceleration were calculated using a one-way ANOVA, and the relationship between tibial acceleration and velocity was determined using a Pearson correlation coefficient and a multiple linear regression analysis. Tibial acceleration increased with higher velocities, with an average increase of 3.8 g (38%) between the slowest and fastest speeds. A moderate correlation was demonstrated between tibial acceleration and running velocity, and 19% of tibial acceleration was explained by velocity. While velocity influences tibial acceleration, individual variances to this relationship exist, highlighting the need for a personalised approach to understanding the response of each runner.
The purpose of this study was to explore the relationship between running economy
(RE) and sagittal, frontal and transverse plane kinematic parameters in long-distance runners.
A secondary purpose was to identify the kinematic predictors of RE during running at the
lowest RE value, representing an individual’s most efficient running intensity. Twenty
recreational long-distance runners ran three submaximal stages on a treadmill (65%, 75%, and
85% of velocity at maximum oxygen consumption). Respiratory data were collected using a
portable gas analysis system. Kinematics were gathered using passive retro-reflective markers,
and eight high-resolution infrared cameras to collect the respective trajectories. Hip, knee, and
ankle angles at foot strike and stance phase, as well as spatio-temporal parameters were
calculated during each gait cycle. Knee flexion/extension range of motion (ROM), knee
ab/adduction ROM and hip ab/adduction ROM during the stance phase of the gait cycle showed
positive moderate to large correlations with RE (r ± 90% CIs = 0.51 ± 0.29; 0.49 ± 0.30; 0.53
± 0.28 respectively). Knee and hip ab/adduction ROMs during the stance phase are predictors
of RE, accounting for 44% of RE variance. Therefore, sagittal and frontal plane kinematics
affect RE inducing alterations in running performance. Coaches, athletic trainers and anyone
involved in running training prescription should consider a relationship between these
parameters to ensure optimal technique, and consequently to improve RE in recreational longdistance
runners.
Background:
Impact loading in runners, assessed by the measurement of tibial acceleration, has attracted substantial research attention. Due to potential injury links, particularly tibial fatigue fractures, tibial acceleration is also used as a clinical monitoring metric. There are contributing factors and potential limitations that must be considered before widespread implementation.
Aim:
The objective of this review is to update current knowledge of the measurement of tibial acceleration in runners and to provide recommendations for those intending on using this measurement device in research or clinical practice.
Methods:
Literature relating to the measurement of tibial acceleration in steady-state running was searched. A narrative approach synthesised the information from papers written in English. A range of literature was identified documenting the selection and placement of accelerometers, the analysis of data, and the effects of intrinsic and extrinsic factors.
Results and discussion:
Tibial acceleration is a proxy measurement for the impact forces experienced at the tibia commonly used by clinicians and researchers. There is an assumption that this measure is related to bone stress and strain, however this is yet to be proven. Multi-axis devices should be secured firmly to the tibia to limit movement relative to the underlying bone and enable quantification of all components of acceleration. Additional frequency analyses could be useful to provide a more thorough characterisation of the signal.
Conclusions:
Tibial accelerations are clearly affected by running technique, running velocity, lower extremity stiffness, as well as surface and footwear compliance. The interrelationships between muscle pre-activation and fatigue, stiffness, effective mass and tibial acceleration still require further investigation, as well as how changes in these variables impact on injury risk.
Xsens MVN is an easy to use, cost efficient system that captures full-body human motion in any environment. It is based on small, unobtrusive inertial and magnetic sensors combined with advanced algorithms and biomechanical models. The newly released motion capture engine is immune to magnetic distortions and is available either as MVN Animate for the 3D character animation market, or as MVN Analyze for the human motion measurement market. This whitepaper describes key characteristics and shows an analysis of the performance of the new engine. The performance analysis includes a comparison with an optical position measurement system in combination with OpenSim for walking data, as well as a consistency analysis for running data. The analysis shows RMS differences of less than 5 degrees for the dominant joint angles during walking and consistent performance over more than 90 minutes of running data. The MVN Analyze and MVN Animate engines enable reliable and consistent tracking of any type of movement including running, jumping, squatting, crawling, cartwheeling, in any type of environment, including severe magnetically distorted environments.
Analysis of running mechanics has traditionally been limited to a gait laboratory using either force plates or an instrumented treadmill in combination with a full-body optical motion capture system. With the introduction of inertial motion capture systems, it becomes possible to measure kinematics in any environment. However, kinetic information could not be provided with such technology. Furthermore, numerous body-worn sensors are required for a full-body motion analysis. The aim of this study is to examine the validity of a method to estimate sagittal knee joint angles and vertical ground reaction forces during running using an ambulatory minimal body-worn sensor setup. Two concatenated artificial neural networks were trained (using data from eight healthy subjects) to estimate the kinematics and kinetics of the runners. The first artificial neural network maps the information (orientation and acceleration) of three inertial sensors (placed at the lower legs and pelvis) to lower-body joint angles. The estimated joint angles in combination with measured vertical accelerations are input to a second artificial neural network that estimates vertical ground reaction forces. To validate our approach, estimated joint angles were compared to both inertial and optical references, while kinetic output was compared to measured vertical ground reaction forces from an instrumented treadmill. Performance was evaluated using two scenarios: training and evaluating on a single subject and training on multiple subjects and evaluating on a different subject. The estimated kinematics and kinetics of most subjects show excellent agreement (ρ>0.99) with the reference, for single subject training. Knee flexion/extension angles are estimated with a mean RMSE <5°. Ground reaction forces are estimated with a mean RMSE < 0.27 BW. Additionaly, peak vertical ground reaction force, loading rate and maximal knee flexion during stance were compared, however, no significant differences were found. With multiple subject training the accuracy of estimating discrete and continuous outcomes decreases, however, good agreement (ρ > 0.9) is still achieved for seven of the eight different evaluated subjects. The performance of multiple subject learning depends on the diversity in the training dataset, as differences in accuracy were found for the different evaluated subjects.
There appears a linear relationship between small increases in running speed and cardiovascular health benefits. Encouraging or coaching recreational runners to increase their running speed to derive these health benefits might be more effective if their joint level kinematic and kinetic strategy was understood. The aim of this investigation was to compare the peak sagittal plane motions, moments, and powers of the hip, knee and ankle at 85%, 100%, 115% and 130% of self-selected running speed. Overground running data were collected in 12 recreational runners (6 women, 6 men) with a full body marker set using a 12-camera Vicon MX system with an AMTI force plate. Kinematics and kinetics were analyzed with Vicon Nexus software. Participants chose to run at 2.6 ± 0.5 m/s (85%); 3.0 ± 0.5 m/s (100%); 3.3 ± 0.5 m/s (115%); and 3.7 ± 0.5 m/s (130%); these four speeds approximately correspond to 6:24-, 5:33-, 5:03-, and 4:30-min kilometer running paces. Running speed had a significant effect (P < 0.05) on peak kinematic and kinetic variables of the hips, knees and ankles, with peak sagittal hip moments invariant (P > 0.54) and the peak sagittal ankle power generation (P < 0.0001) the most highly responsive variable. The timing of the peak sagittal extensor moments and powers at the hip, knee and ankle were distributed across stance in a sequential manner. This study shows that running speed affects lower limb joint kinematics and kinetics and suggests that specific intersegmental kinetic strategies might exist across the narrow range of running speeds.
Identification of errors or anomalous values, collectively considered outliers, assists in exploring data or through removing outliers improves statistical analysis. In biomechanics, outlier detection methods have explored the ‘shape’ of the entire cycles, although exploring fewer points using a ‘moving-window’ may be advantageous. Hence, the aim was to develop a moving-window method for detecting trials with outliers in intra-participant time-series data. Outliers were detected through two stages for the strides (mean 38 cycles) from treadmill running. Cycles were removed in stage 1 for one-dimensional (spatial) outliers at each time point using the median absolute deviation, and in stage 2 for two-dimensional (spatial–temporal) outliers using a moving window standard deviation. Significance levels of the t-statistic were used for scaling. Fewer cycles were removed with smaller scaling and smaller window size, requiring more stringent scaling at stage 1 (mean 3.5 cycles removed for 0.0001 scaling) than at stage 2 (mean 2.6 cycles removed for 0.01 scaling with a window size of 1). Settings in the supplied Matlab code should be customised to each data set, and outliers assessed to justify whether to retain or remove those cycles. The method is effective in identifying trials with outliers in intra-participant time series data.
Axial and resultant peak tibial acceleration might be useful for screening/monitoring runners at risk of lower limb injury. This study quantified between-session reliability and variability of axial and resultant peak tibial acceleration during running. Fourteen runners completed four running speeds at two testing sessions seven days apart with tri-axial wireless accelerometers attached to the tibia. Average mean differences between sessions across all four speeds (for the right or left side) were 4.5-5.7% (ES 0.01-0.17; ICC 0.73-0.95; CV% 7.5-17.9) for axial and 0.9-5.1% (ES 0.01-0.12; ICC 0.84-0.97; CV% 5.9-13.9)) for resultant peak tibial acceleration. While both axial and resultant peak tibial acceleration are reliable and therefore appropriate for monitoring and assessment of an intervention, resultant peak tibial acceleration should be preferentially used.
Scientific experiments on running mainly consider level running. However, the magnitude and etiology of fatigue depend on the exercise under consideration, particularly the predominant type of contraction, which differs between level, uphill and downhill running. The purpose of this review is to comprehensively summarize the neuro-physiological and biomechanical changes due to fatigue in graded running. When comparing prolonged hilly running (i.e. a combination of uphill and downhill running) to level running it is found that (1) the general shape of the neuromuscular fatigue-exercise duration curve as well as the etiology of fatigue in knee extensor and plantar flexor muscles are similar and (2) the biomechanical consequences are also relatively comparable, suggesting that duration rather than elevation changes affects neuromuscular function and running patterns. However, ‘pure’ uphill or downhill running have several fatigue-related intrinsic features compared to level running. Downhill running induces severe lower limb tissue damage, indirectly evidenced by massive increases in plasma creatine kinase/myoglobin concentration or inflammatory markers. Also low-frequency fatigue (i.e. excitation-contraction coupling failure) is systematically observed after downhill running, although it has also been found in high-intensity uphill running for different reasons. Indeed, low-frequency fatigue in downhill running is attributed to mechanical stress at the interface sarcoplasmic reticulum/T-tubule, while the inorganic phosphate accumulation probably plays a central role in intense uphill running. Other fatigue-related specificities of graded running such as strategies to minimize the deleterious effects of downhill running on muscle function, and the difference of energy cost versus heat storage or muscle activity changes in downhill, level and uphill running are also discussed.
Individuals regulate the transmission of shock to the head during running at different stride frequencies although the consequences of this on head-gaze stability remain unclear. The purpose of this study was to examine if providing individuals with visual feedback of their head-gaze orientation impacts tibial and head accelerations, shock attenuation and head-gaze motion during preferred speed running at different stride frequencies. Fifteen strides from twelve recreational runners running on a treadmill at their preferred speed were collected during five stride frequencies (preferred, ±10% and ±20% of preferred) in two visual task conditions (with and without real-time visual feedback of head-gaze orientation). The main outcome measures were tibial and head peak accelerations assessed in the time and frequency domains, shock attenuation from tibia to head, and the magnitude and velocity of head-gaze motion. Decreasing stride frequency resulted in greater vertical accelerations of the tibia (p<0.01) during early stance and at the head (p<0.01) during early and late stance; however, for the impact portion the increase in head acceleration was only observed for the slowest stride frequency condition. Visual feedback resulted in reduced head acceleration magnitude (p<0.01) and integrated power spectral density in the frequency domain (p<0.01) in late stance, as well as overall of head-gaze motion (p<0.01). When running at preferred speed individuals were able to stabilize head acceleration within a wide range of stride frequencies; only at a stride frequency 20% below preferred did head acceleration increase. Furthermore, impact accelerations of the head and tibia appear to be solely a function of stride frequency as no differences were observed between feedback conditions. Increased visual task demands through head gaze feedback resulted in reductions in head accelerations in the active portion of stance and increased head-gaze stability.
The goal of this research was to analyze the effects of fatigue on the shock waves generated by foot strike. Twenty-two subjects were instrumented with an externally attached, lightweight accelerometer placed over the tibial tuberosity. The subjects ran on treadmill for 30 min at a speed near their anaerobic threshold. Fatigue was established when the end-tidal CO2 pressure decreased. The results indicated that approximately half of the subjects reached the fatigue state toward the end of the test. Whenever fatigue occurred, the peak acceleration was found to increase. It was thus concluded that there is a clear association between fatigue and increased heel strike- induced shock waves. These results have a significant implication for the etiology of running injuries, since shock wave attenuation has been previously reported to play an important role in preventing such injuries.
In 1900, less than 20 percent of the world population lived in cities, in 2007, just more than 50 percent of the world population lived in cities. In 2050, it has been predicted that more than 70 percent of the global population (about 6.4 billion people) will be city inhabitants. There is more pressure being placed on cities through this increase in population [1]. With advent of smart cities, information and communication technology is increasingly transforming the way city municipalities and city residents organize and operate in response to urban growth. In this paper, we create a generic scheme for navigating a route through out city. A requested route is provided by using combination of A* Algorithm and Haversine formula. Haversine Formula gives minimum distance between any two points on spherical body by using latitude and longitude. This minimum distance is then provided to A* algorithm to calculate minimum distance. The process for detecting the shortest path is mention in this paper.
The effects of treadmill running on impact acceleration were examined together with the interaction between running surface and runner's fatigue state. Twenty recreational runners (11 men and 9 women) ran overground and on a treadmill (at 4.0 m/s) before and after a fatigue protocol consisting of a 30-minute run at 85% of individual maximal aerobic speed. Impact accelerations were analysed using two lightweight capacitive uniaxial accelerometers. A two-way repeated-measure analysis of variance showed that, in the pre-fatigue condition, the treadmill running decreased head and tibial peak impact accelerations and impact rates (the rate of change of acceleration), but no significant difference was observed between the two surfaces in shock attenuation. There was no significant difference in acceleration parameters between the two surfaces in the post-fatigue condition. There was a significant interaction between surface (treadmill and overground) and fatigue state (pre-fatigue and post-fatigue). In particular, fatigue when running overground decreased impact acceleration severity, but it had no such effect when running on the treadmill. The effects of treadmill running and the interaction need to be taken into account when interpreting the results of studies that use a treadmill in their experimental protocols, and when prescribing physical exercise.
A high number of recreational runners sustain a running-related injury each year. To reduce injury risk, alterations in running form have been suggested. One simple strategy for running stride frequency or length has been commonly advocated.
To characterize how running mechanics change when stride frequency and length are manipulated.
In January 2012, a comprehensive search of PubMed, CINAHL Plus, SPORTDiscus, PEDro, and Cochrane was performed independently by 2 reviewers. A second search of the databases was repeated in June 2012 to ensure that no additional studies met the criteria after the initial search.
Inclusion criteria for studies were an independent variable including manipulation of stride frequency or length at a constant speed with outcome measures of running kinematics or kinetics.
Systematic review.
Level 3.
Two reviewers independently appraised each article using a modified version of the Quality Index, designed for assessing bias of nonrandomized studies.
Ten studies met the criteria for this review. There was consistent evidence that increased stride rate resulted in decreased center of mass vertical excursion, ground reaction force, shock attenuation, and energy absorbed at the hip, knee, and ankle joints. All but 1 study had a limited number of participants, with several methodological differences existing among studies (eg, overground and treadmill running, duration of test conditions). Although speed was held constant during testing, it was individually self-selected or fixed. Most studies used only male participants.
Despite procedural differences among studies, an increased stride rate (reduced stride length) appears to reduce the magnitude of several key biomechanical factors associated with running injuries.
We analyzed gait and function of the supporting limb in participants of a marathon race at three stages: prerace, midrace (18 km), and near the end of the race (36 km). We confirmed that the most successful runners were able to maintain running speed for the duration of the race with little change in speed or gait. Speed slowed progressively during the race for those with slower race times, but stride frequency-stride length relationships remained normal for the speed they ran. These findings differ from most lab-based studies of fatigue, in which runners are forced to match a constant preset treadmill speed. Small changes in maximum ground force were seen in both slow- and fast-running participants as race end approached.
Single-subject with repeated measures.
To determine if runners can use real-time visual feedback from an accelerometer to achieve immediate reductions in tibial acceleration and vertical-force loading rates.
Stress fractures are a common injury among runners. Previous studies suggest that runners with higher than normal tibial acceleration and vertical-force loading rates are at increased risk for tibial stress fractures. If these runners can be trained to reduce the loading on their lower extremities, it may reduce their risk of stress fractures.
Five subjects participated in this study. All subjects ran on a treadmill, instrumented with force transducers, during a single 30-minute session that was divided into warm-up, feedback, no-feedback, and cool-down periods. During running, the subjects also wore an accelerometer taped to their distal right tibia. Peak positive acceleration of the tibia, vertical force impact peak, and average and instantaneous vertical-force loading rates were assessed at the end of the warm-up, feedback, and no-feedback periods.
Single-subject analysis revealed that 4 of the 5 subjects had significant reductions in their peak positive acceleration at the end of the no-feedback period compared to the warm-up. In addition, all of the subjects had significant decreases in impact peak and vertical ground reaction force loading rates at the end of the no-feedback period.
In a single session of training with real-time visual feedback, it appears that most runners can reduce the types of lower extremity loading associated with stress fractures. This may lead to training programs that reduce the risk of stress fractures for runners.
Peak tibial acceleration (PTA) is a widely used indicator of tibial bone loading. Indirect bone loading measures are of interest to reduce the risk of stress fractures during running. However, tibial compressive forces are caused by both internal muscle forces and external ground reaction forces. PTA might reflect forces from outside the body, but likely not the compressive force from muscles on the tibial bone. Hence, the strength of the relationship between PTA and maximum tibial compression forces in rearfoot-striking runners was investigated. Twelve runners ran on an instrumented treadmill while tibial acceleration was captured with accelerometers. Force plate and inertial measurement unit data were spatially aligned with a novel method based on the centre of pressure crossing a virtual toe marker. The correlation coefficient between maximum tibial compression forces and PTA was 0.04 ± 0.14 with a range of -0.15 to +0.28. This study showed a very weak and non-significant correlation between PTA and maximum tibial compression forces while running on a level treadmill at a single speed. Hence, PTA as an indicator for tibial bone loading should be reconsidered, as PTA does not provide a complete picture of both internal and external compressive forces on the tibial bone. .
Background
Runners have a high risk of acquiring a running-related injury. Understanding the mechanisms of impact force attenuation into the body when a runner fatigues might give insight into the role of running kinematics on the aetiology of overuse injuries.
Research questions
How do running kinematics change due to running-induced fatigue? And what is the influence of experience level on changes in running kinematics due to fatigue?
Methods
Three electronic databases were searched: PubMed, Web of Science, and Scopus. This resulted in 33 articles and 19 kinematic quantities being included in this review. A quality assessment was performed on all included articles and meta-analyses were performed for 18 kinematic quantities.
Results and significance
Main findings included an increase in peak acceleration at the tibia and a decrease in leg stiffness after a fatiguing protocol. Additionally, level running-induced fatigue increased knee flexion at initial contact and maximum knee flexion during swing. An increase in vertical centre of mass displacement was found in novice but not in experienced runners with fatigue. Overall, runners changed their gait pattern due to fatigue by moving to a smoother gait pattern (i.e. more knee flexion at initial contact and during swing, decreased leg stiffness). However, these changes were not sufficient to prevent an increase in peak accelerations at the tibia after a fatigue protocol. Large inter-individual differences in responses to fatigue were reported. Hence, it is recommended to investigate changes in running kinematics as a result of fatigue on a subject-specific level since group-level analysis might mask individual responses.
Background
Traditional running gait analysis is limited to artificial environments, but whether treadmill running approximates overground running is debated. This study aimed to compare treadmill gait analysis using fixed video with outdoor gait analysis using drone video capture.
Hypothesis
Measured kinematics would be similar between natural outdoor running and traditional treadmill gait analysis.
Study Design
Crossover study.
Level of Evidence
Level 2.
Methods
The study population included cross-country, track and field, and recreational athletes with current running mileage of at least 15 km per week. Participants completed segments in indoor and outdoor environments. Indoor running was completed on a treadmill with static video capture, and outdoor segments were obtained via drone on an outdoor track. Three reviewers independently performed clinical gait analysis on footage for 32 runners using kinematic measurements with published acceptable intra- and interrater reliability.
Results
Of the 8 kinematic variables measured, 2 were found to have moderate agreement indoor versus outdoor, while 6 had fair to poor agreement. Foot strike at initial contact and rearfoot position at midstance had moderate agreement indoor versus outdoor, with a kappa of 0.54 and 0.49, respectively. The remaining variables: tibial inclination at initial contact, knee flexion angle initial contact, forward trunk lean full gait cycle, knee center position midstance, knee separation midstance, and lateral pelvic drop at midstance were found to have fair to poor agreement, ranging from 0.21 to 0.36.
Conclusion
This study suggests that kinematics may differ between natural outdoor running and traditional treadmill gait analysis.
Clinical Relevance
Providing recommendations for altering gait based on treadmill gait analysis may prove to be harmful if treadmill analysis does not approximate natural running environments. Drone technology could provide advancement in clinical running recommendations by capturing runners in natural environments.
Background:
The role of biomechanical variables of running gait in the development of running related injury has not been clearly elucidated. Several systematic reviews have examined running biomechanics and its association with particular running related injuries. However, due to retrospective designs, inferences into the cause of these injuries are limited. Although prospective studies have been completed, no quantitative analysis pooling these results has been completed.
Methods:
A systematic review of MEDLINE, CINAHL, and PubMed was completed. Articles included used prospective study designs, human subjects currently completing a regular running program, and a minimum 12-week follow-up period. Excluded articles had no biomechanical data reported, participants who were beginning runners or military recruits, or had an intervention provided.
Findings:
Thirteen studies met these criteria. Pooled analyses were completed if two or more studies were available with samples that investigated the same sex and competition level. A qualitative synthesis was completed when pooled analysis was not possible. Five unique running samples were identified and allowed for pooled analyses of variables in mixed-sex collegiate runners and female recreational runners. Moderate evidence exists for increased hip adduction and reduced peak rearfoot eversion as risk factors for running related injury in female recreational runners. Variables differed in other samples of runners.
Interpretation:
A runner's sex and competition level may affect the relationship between biomechanical factors and the development of running related injury. Hip adduction and rearfoot eversion may be important factors related to running related injury in female recreational runners. Further investigation of biomechanical factors in running injury is warranted.
Tibial acceleration is frequently measured in runners and recent advances in wireless technology have led to field studies measuring tibial acceleration outside the laboratory. However, it is unknown whether lab and field measures of tibial acceleration differ within runners. Additionally, the relationship between peak axial acceleration and the more recent measure peak resultant tibial acceleration has not been determined.
Purpose:
To determine whether laboratory and field measures of tibial acceleration are comparable, and whether peak axial and peak resultant tibial acceleration are interchangeable.
Methods:
Nineteen healthy rearfoot striking runners between 18 and 45 years of age participated. A precision accelerometer was aligned with the vertical axis of the distal tibia and firmly attached. Data were collected in the following conditions during running at 3.0m/s ± 5%: traditional overground laboratory gait analysis contacting force plates, treadmill, outdoor grass, outdoor sidewalk. Acceleration data were filtered and normalized to gravity. Peaks for variables of interest were extracted from the first 40% of stride for 10 trials per condition. Differences among conditions were determined.
Results:
Peak positive acceleration was lower in laboratory and treadmill compared to grass and sidewalk conditions. However, laboratory and treadmill were similar in magnitude, as were grass and sidewalk. Peak resultant acceleration was consistently higher than peak axial acceleration, with the same pattern among conditions. Laboratory acceleration measures explained at best only half of the variance in the field conditions and did not explain the variance for grass.
Conclusion:
Tibial impact acceleration magnitude is influenced by testing procedures in runners. These findings support measuring tibial impact acceleration in the field to determine new metrics associated with injury.
Background:
Describing the response of spatiotemporal gait characteristics, and related variables such as variability and stiffness, to different stressors is important to better understand spring-mass model.
Research question:
This study aimed to examine the effect of fatigue induced by a running protocol on spatiotemporal gait parameters, step variability and vertical (Kvert) and leg stiffness (Kleg) during running on a treadmill.
Methods:
Twenty-two trained male endurance runners performed a 60-min time trial run. An analysis of spatiotemporal parameters (contact time [CT], flight time [FT], step frequency [SF] and step length [SL]), step variability (in terms of coefficient of variation [CV]) and stiffness was conducted in two different conditions: non-fatigued (before the protocol) and fatigued (after the protocol).
Results:
The pairwise comparisons (i.e., non-fatigued vs. fatigued condition) indicated that temporal parameters (i.e., CT and FT) experienced significant changes (p = 0.001 and <0.001, respectively). Step variability increased in presence of fatigue, with higher CV in CT (p = 0.039), FT (p = 0.005), SF (p = 0.046) and SL (p = 0.027) after the running protocol. The Kleg experienced a reduction in the fatigued condition (p < 0.001) whereas the Kvert remained unchanged (p = 0.602).
Significance:
The results indicate that fatigue induced by a 60-min time trial run causes some adaptations in spatiotemporal gait characteristics and stiffness in trained endurance runners. Specifically, in the presence of fatigue, the athletes showed greater CT and shorter FT, higher step variability and lower leg stiffness.
The purpose of this study was to use wearable technology data to quantify alterations in subject-specific running patterns throughout a marathon race and to determine if runners could be clustered into subgroups based on similar trends in running gait alterations throughout the marathon. Using a wearable sensor, data were collected for cadence, braking, bounce, pelvic rotation, pelvic drop, and ground contact time for 27 runners. A composite index was calculated based on the “typical” data (4–14 km) for each runner and evaluated for 14 individual 2-km sections thereafter to detect “atypical” data (ie, higher indices). A cluster analysis assigned all runners to a subgroup based on similar trends in running alterations. Results indicated that the indices became significantly higher starting at 20 to 22 km. Cluster 1 exhibited lower indices than cluster 2 throughout the marathon, and the only significant difference in characteristics between clusters was that cluster 1 had a lower age–grade performance score than cluster 2. In summary, this study presented a novel method to investigate the effects of fatigue on running biomechanics using wearable technology in a real-world setting. Recreational runners with higher age–grade performance scores had less atypical running patterns throughout the marathon compared with runners with lower age–grade performance scores.
Purpose:
Foot strike patterns (FSP) influence landing mechanics, with rearfoot strike (RFS) runners exhibiting higher impact loading than forefoot strike (FFS) runners. The few studies that included midfoot strike (MFS) runners have typically grouped them together with FFS. In addition, most running studies have been conducted in laboratories. Advances in wearable technology now allow the measurement of runners' mechanics in their natural environment. The purpose of this study was to examine the relationship between FSP and impacts across a marathon race.
Methods:
A total of 222 healthy runners (119 males, 103 females; age, 44.1 ± 10.8 yr) running a marathon race were included. A treadmill assessment was undertaken to determine FSP. An ankle-mounted accelerometer recorded tibial shock (TS) over the course of the marathon. TS was compared between RFS, MFS, and FFS. Correlations between speed and impacts were examined between FSP. TS was also compared at the 10- and 40-km race points.
Results:
RFS and MFS runners exhibited similar TS (12.24g ± 3.59g vs 11.82g ± 2.68g, P = 0.46) that was significantly higher (P < 0.001 and P < 0.01, respectively) than FFS runners (9.88g ± 2.51g). In addition, TS increased with speed for both RFS (r = 0.54, P = 0.01) and MFS (r = 0.42, P = 0.02) runners, but not FFS (r = 0.05, P = 0.83). Finally, both speed (P < 0.001) and TS (P < 0.001) were reduced between the 10- and the 40-km race points. However, when normalized for speed, TS was not different (P = 0.84).
Conclusions:
RFS and MFS exhibit higher TS than FFS. In addition, RFS and MFS increase TS with speed, whereas FFS do not. These results suggest that the impact loading of MFS is more like RFS than FFS. Finally, TS, when normalized for speed, is similar between the beginning and the end of the race.
Background: Tibial stress fractures are common running related injury and their etiology may include biomechanical factors like impact forces, shock attenuation, lower limb kinematics and how these factors are influenced by intense or prolonged running. Inertial-magnetic measurement units (IMUs) have recently emerged as an alternative to motion capture but their use to date was mostly limited to segmental and joint motion. Research question: The present study sought to examine the effects of a prolonged run on shock attenuation, peak tibial and sacral acceleration (PTA, PSA), and lower limb kinematics using IMUs. Methods: Ten trained male runners (31 +/− 5 yr, 183 +/− 3 cm, 76 +/− 9 kg) performed a twenty-minute prolonged run on an athletic track at estimated lactate threshold speed. Eight IMUs, positioned over the feet, lower and uppers legs, sacrum and sternum, were used to calculate joint kinematics, impact parameters and shock attenuation in the time domain (1-(PSA/PTA)*100). Results: PTA increased while PSA and shock attenuation did not change following the prolonged run. Hip and knee flexion at midstance decreased. Vertical lower leg angle at initial contact did not change. Conclusion: By using IMUs, it was shown that a prolonged run at estimated lactate threshold speed had significant effects on kinematics and tibial acceleration parameters. By modifying hip and knee joint kinematics during stance, the body was able to maintain sacral acceleration possibly by shifting from active shock attenuation to more passive mechanisms. Significance: The present study shows that inertial sensors can be used in outdoor running to measure joint kinematics and kinetic parameters like PTA, PSA and shock attenuation simultaneously. The results of this study show new insights into how the body copes with impact during prolonged running.
Purpose: To investigate the joint-specific contributions to the total lower extremity joint work during a prolonged fatiguing run. Methods: Recreational long-distance runners (RR; n = 13) and competitive long-distance runners (CR; n = 12) performed a 10-km treadmill run with near maximal effort. A three-dimensional motion capture system synchronized with a force instrumented treadmill was used to calculate joint kinetics and kinematics of the lower extremity in the sagittal plane during the stance phase at 13 distance points over the 10-km run. Results: A significant (P < 0.05) decrease of positive ankle joint work as well as an increase of positive knee and hip joint work was found. These findings were associated with a redistribution of the individual contributions to total lower extremity work away from the ankle towards the knee and hip joint which was more distinctive in the RR group than in the CR group. This redistribution was accomplished by significant (P < 0.05) reductions of the external ground-reaction force (GRF) lever arm and joint torque at the ankle and by the significant (P < 0.05) increase of the external GRF lever arm and joint torque at the knee and hip. Conclusion: The redistribution of joint work from the ankle to more proximal joints might be a biomechanical mechanism that could partly explain the decreased running economy in a prolonged fatiguing run. This might be
because muscle-tendon units crossing proximal joints are less equipped for energy storage and return compared to ankle plantar flexors and require greater muscle volume activation for a given force. In order to improve running performance, long-distance runners may benefit from an exercise-induced enhancement of ankle plantar flexor muscle-tendon unit capacities.
This paper postulates that overuse injury in sport is a biomechanical event resulting from the mechanical fatigue of biological tissue. A theoretical foundation and operational framework necessary to model overuse injury as a mechanical fatigue phenomenon is introduced. Adopting this framework may provide a more mechanistic understanding of overuse injury, and inform training and preventive strategies to reduce their occurrence.
Background:
The National Center for Injury Prevention and Control, noting flaws in previous running injury research, called for more rigorous prospective designs and comprehensive analyses to define the origin of running injuries.
Purpose:
To determine the risk factors that differentiate recreational runners who remain uninjured from those diagnosed with an overuse running injury during a 2-year observational period.
Study design:
Cohort study; Level of evidence, 2.
Methods:
Inclusion criteria were running a minimum of 5 miles per week and being injury free for at least the past 6 months. Data were collected at baseline on training, medical and injury histories, demographics, anthropometrics, strength, gait biomechanics, and psychosocial variables. Injuries occurring over the 2-year observation period were diagnosed by an orthopaedic surgeon on the basis of predetermined definitions.
Results:
Of the 300 runners who entered the study, 199 (66%) sustained at least 1 injury, including 73% of women and 62% of men. Of the injured runners, 111 (56%) sustained injuries more than once. In bivariate analyses, significant ( P ≤ .05) factors at baseline that predicted injury were as follows: Short Form Health Survey-12 mental component score (lower mental health-related quality of life), Positive and Negative Affect Scale negative affect score (more negative emotions), sex (higher percentage of women were injured), and knee stiffness (greater stiffness was associated with injury); subsequently, knee stiffness was the lone significant predictor of injury (odds ratio = 1.18) in a multivariable analysis. Flexibility, quadriceps angle, arch height, rearfoot motion, strength, footwear, and previous injury were not significant risk factors for injury.
Conclusion:
The results of this study indicate the following: (1) among recreational runners, women sustain injuries at a higher rate than men; (2) greater knee stiffness, more common in runners with higher body weights (≥80 kg), significantly increases the odds of sustaining an overuse running injury; and (3) contrary to several long-held beliefs, flexibility, arch height, quadriceps angle, rearfoot motion, lower extremity strength, weekly mileage, footwear, and previous injury are not significant etiologic factors across all overuse running injuries.
Fatigue, developed over the course of a run, may cause changes in running kinematics. Training status may influence the effect of fatigue on running kinematics, since well trained, competitive runners are used to running until exhaustion, whereas novice runners are not. This study aimed to determine changes in running kinematics during an exhaustive run in both novice (NOVICE) and competitive (COMP) long-distance runners. About 15 NOVICE and 15 COMP runners performed a treadmill run, until voluntary exhaustion at 3,200 m time trial pace. Joint angles and global trunk and pelvis angles were recorded at the beginning and at the end of the run. In both groups, peak pelvic anterior tilt, pelvic rotation range of motion (both during stance phase) and ankle plantar flexion during swing phase increased after the exhaustive run. There was a significant interaction effect between group and exhaustion for peak forward trunk lean, which increased only in the NOVICE group, and for hip abduction during mid-swing, which increased in NOVICE and decreased in COMP runners. In conclusion, NOVICE runners showed larger kinematic adjustments when exhausted than COMP runners. This may affect their running performance and should be taken into account when assessing a runner’s injury risk.
Recent developments in wearable and wireless sensor technology allow for a continuous three dimensional analysis of running mechanics in the sport specific setting. The present study is the first to demonstrate the possibility of analyzing three dimensional (3D) running mechanics continuously, by means of inertial magnetic measurement units, to objectify changes in mechanics over the course of a marathon.
Three well trained male distance runners ran a marathon while equipped with inertial magnetic measurement units on trunk, pelvis, upper legs, lower legs and feet to obtain a 3D view of running mechanics and to asses changes in running mechanics over the course of a marathon. Data were continuously recorded during the entire 42.2 km (26.2 Miles) of the Marathon. Data from the individual sensors were transmitted wirelessly to a receiver, mounted on the handlebar of an accompanying cyclist. Anatomical calibration was performed using both static and dynamic procedures and sensor orientations were thus converted to body segment orientations by means of transformation matrices obtained from the segment calibration. Joint angle (hip, knee and ankle) trajectories as well as center of mass (COM) trajectory and acceleration were derived from the sensor data after segment calibration.
Data were collected and repeated measures one way ANOVA׳s, with Tukey post-hoc test, were used to statistically analyze differences between the defined kinematic parameters (max hip angle, peak knee flexion at mid-stance and at mid-swing, ankle angle at initial contact and COM vertical displacement and acceleration), averaged over 100 strides, between the first and the last stages (8 and 40 km) of the marathon. Significant changes in running mechanics were witnessed between the first and the last stage of the marathon.
This study showed the possibility of performing a 3D kinematic analysis of the running technique, in the sport specific setting, by using inertial magnetic measurement units. For the three runners analyzed, significant changes were observed in running mechanics over the course of a marathon. The present measurement technique therefore allows for more in-depth study of running mechanics outside the laboratory setting.
The purpose of this study was to investigate possible footfall pattern changes in habitual forefoot runners over a prolonged, exhaustive run. A prolonged run was performed to exhaustion in 14 habitual forefoot runners. Vertical ground reaction forces (VGRFs) and kinematics were collected at the beginning and end of the run. Ankle plantar flexor torque and triceps surae electromyographic activity were measured during pre- and post-run isometric contractions. By run's end, there was an increase in VGRF loading rate and impact peak magnitude, greater dorsiflexion at foot contact and greater knee flexion angle throughout stance. Ankle plantar flexor torque decreased significantly from pre- to post-run tests. This was accompanied by a decrease in the integrated electromyographic activity (iEMG) output for the lateral and medial gastrocnemius. There were significant changes in landing mechanics for forefoot runners that indicate a transition towards more midfoot footfall patterns. A contributing factor may be ankle plantar flexor muscle fatigue that, at touchdown, is exposed to exaggerated eccentric loading. These findings suggest that a forefoot running pattern may become difficult to maintain in longer endurance events, and thus runners should pay attention to this in training to improve performance and mitigate potential injury.
Excessive and prolonged exposure to impact acceleration during running is associated with increased injury rate. Acute use of compressive garments has been speculated to improve attenuation. However, it is unknown how longer interventions of compressive garments influence attenuation in running. 40 runners trained with compressive and placebo stockings for 3 weeks. Perception of comfort, stride parameters (rate, length) and impact acceleration (head and tibial peak acceleration, magnitude, acceleration rate and attenuation) were measured every 5 min during a fatigue run (30 min at 80% of the individual's maximal aerobic speed). Compressive stockings reduced tibial peak acceleration and magnitude compared to placebo stockings at every minute (p<0.05) except for the initial measurement (p>0.05). Moreover, compressive stockings led to a lower rate of increase in tibial peak acceleration (14%, p<0.005) and magnitude (16%, p<0.001) as a result of the development of fatigue compared to placebo stockings (24% and 26% increase, p=0.014 and p=0.003, respectively). Similar perception of comfort was reported for both garments. Training with compressive stockings for 3 weeks reduced impact acceleration and the rate of increase in acceleration compared to placebo stockings. These findings suggest that compressive stockings may play a protective role by reducing impact accelerations during running.
© Georg Thieme Verlag KG Stuttgart · New York.
The treadmill is an attractive device for the investigation of human locomotion, yet the extent to which lower limb kinematics differ from overground running remains a controversial topic. This study aimed to provide an extensive three-dimensional kinematic comparison of the lower extremities during overground and treadmill running. Twelve participants ran at 4.0 m/s (+/- 5%) in both treadmill and overground conditions. Angular kinematic parameters of the lower extremities during the stance phase were collected at 250 Hz using an eight-camera motion analysis system. Hip, knee, and ankle joint kinematics were quantified in the sagittal, coronal, and transverse planes, and contrasted using paired t-tests. Of the analysed parameters hip flexion at footstrike and ankle excursion to peak angle were found to be significantly reduced during treadmill running by 12 degrees (p = 0.001) and 6.6 degrees (p = 0.010), respectively. Treadmill running was found to be associated with significantly greater peak ankle eversion (by 6.3 degrees, p = 0.006). It was concluded that the mechanics of treadmill running cannot be generalized to overground running.
To determine whether peak vertical and horizontal impact accelerations were different while running on a track or on a treadmill, 12 healthy subjects (average age 32.8 ± 9.8 y), were fitted with a novel, wireless accelerometer capable of recording triaxial acceleration over time. The accelerometer was attached to a custom-made acrylic plate and secured at the level of the L5 vertebra via a tight fitting triathlon belt. Each subject ran 4 miles on a synthetic, indoor track at a self-selected pace and accelerations were recorded on three perpendicular axes. Seven days later, the subjects ran 4 miles on a treadmill set at the individual runner's average pace on the track and the peak vertical and horizontal impact magnitudes between the track and treadmill were compared. There was no difference (P = .52) in the average peak vertical impact accelerations between the track and treadmill over the 4 mile run. However, peak horizontal impact accelerations were greater (P = .0012) on the track when compared with the treadmill. This study demonstrated the feasibility for long-term impact accelerations monitoring using a novel wireless accelerometer.
The purpose of this study was to describe kinematic changes that occur during an actual marathon. We hypothesized that (1) certain running kinematic measures would change between kilometres 8 and 40 (miles 5 and 25) of a marathon and (2) fast runners would demonstrate smaller changes than slow runners. Subjects (n = 179) were selected according to finish time (Range = 2:20:47 to 5:30:10). Two high-speed cameras were used to measure sagittal-plane kinematics at kilometres 8 and 40 of the marathon. The dependent variables were stride length, contact time, peak knee flexion during support and swing, and peak hip flexion and extension during swing. Two-tailed paired t-tests were used to compare dependent variables between kilometres 8 and 40 for all subjects, and regression analyses were used to determine whether faster runners exhibited smaller changes (between miles 5 and 25) than slower runners. For all runners, every dependent variable changed significantly between kilometres 8 and 40 (p < 0.001). Stride length increased 1.3%, contact time increased 13.1%, peak knee flexion during support decreased 3.2%, and peak hip extension, knee flexion, and hip flexion during swing decreased 27.9%, increased 4.3%, and increased 7.4%, respectively (p < 0.001). Among these significant changes, all runners generally changed the same from kilometres 8 and 40 except that fast runners decreased peak knee flexion during support less than the slow runners (p < 0.002). We believe that these changes, for all runners (fast and slow), were due to fatigue. The fact that fast runners maintained knee flexion during support more consistently might be due to their condition on the race day. Strengthening of knee extensor muscles may facilitate increased knee flexion during support throughout a marathon
A survey revealed that researchers still seem to encounter difficulties to cope with outliers. Detecting outliers by determining an interval spanning over the mean plus/minus three standard deviations remains a common practice. However, since both the mean and the standard deviation are particularly sensitive to outliers, this method is problematic. We highlight the disadvantages of this method and present the median absolute deviation, an alternative and more robust measure of dispersion that is easy to implement. We also explain the procedures for calculating this indicator in SPSS and R software.
The influence of marathon fatigue on both running kinematics and economy was investigated with 8 subjects. The measurements included a treadmill test at 3 steady submaximal speeds performed before and after the marathon. One complete left leg cycle was videotaped at 100 Hz from the left side at each speed. The analysis included contact time (braking and push-off) and flight time as well as displacements and angular velocities of the left hip and knee. This analysis was complemented by the measurements of the electromyographic (EMG) activity of the gastrocnemius muscle at each running velocity, and energy expenditure (Ė) of the last 30 s at the 2 slowest speeds. The results revealed significant increases of Ė at these 2 speeds, but the changes were not related to kinematic changes, which were greatly individual. However, the relative duration of the push-off phase was significantly increased at the 2 slowest speeds, and the gastrocnemius muscle presented higher integrated EMG values in the respective braking (NS) and push-off phases at the 2 fastest speeds. This might indicate a loss of tolerance to impact during the belt contact. The results failed, however, to demonstrate that running kinematics and running economy are interrelated when fatigue progresses. It is also possible that observed kinematic changes might reflect some adaptation to fatigue more than real failure to compensate for it.
Footstrike patterns during running can be classified discretely into a rearfoot strike, midfoot strike and forefoot strike by visual observation. However, the footstrike pattern can also be classified on a continuum, ranging from 0% to 100% (extreme rearfoot to extreme forefoot) using the strike index, a measure requiring force plate data. When force data are not available, an alternative method to quantify the strike pattern must be used. The purpose of this paper was to quantify the continuum of foot strike patterns using an easily attainable kinematic measure, and compare it to the strike index measure. Force and kinematic data from twenty subjects were collected as they ran across an embedded force plate. Strike index and the footstrike angle were identified for the four running conditions of rearfoot strike, midfoot strike and forefoot strike, as well as barefoot. The footstrike angle was calculated as the angle of the foot with respect to the ground in the sagittal plane. Results indicated that the footstrike angle was significantly correlated with strike index. The linear regression model suggested that strike index can be accurately estimated, in both barefoot and shod conditions, in the absence of force data.
tibial stress fractures, which are among the most common running related injuries, have been associated with increased lower extremity loading (i.e., peak positive acceleration of the tibia, vertical force impact peak, and average and instantaneous vertical force loading rates) during initial contact. This study was conducted to evaluate the efficacy of a gait retraining program designed to reduce this loading during running and to assess the short-term persistence of these reductions.
ten runners (six females and four males) with peak positive tibial acceleration greater than 8g, measured in an initial screening, participated in the retraining program. During the retraining sessions, subjects ran on a treadmill and received real-time visual feedback from an accelerometer attached to their distal tibias. Tibial acceleration and vertical ground reaction force data were collected from subjects during overground data collection sessions held pre-training, post-training, and at a 1-month follow-up.
peak positive acceleration of the tibia, vertical force impact peak, and average and instantaneous vertical force loading rates were all reduced immediately following the gait retraining. The decrease in tibial acceleration was nearly 50%. The reductions in vertical force loading rates and vertical force impact peak were approximately 30% and 20%, respectively. These reductions were maintained at the 1-month follow-up.
subjects were able to run with reduced tibial acceleration and vertical force loading immediately following completion of the gait retraining program and at the 1-month follow-up evaluation. This may reduce their risk of stress fractures.
the objective of this study was to characterize the biomechanical effects of step rate modification during running on the hip, knee, and ankle joints so as to evaluate a potential strategy to reduce lower extremity loading and risk for injury.
three-dimensional kinematics and kinetics were recorded from 45 healthy recreational runners during treadmill running at constant speed under various step rate conditions (preferred, ± 5%, and ± 10%). We tested our primary hypothesis that a reduction in energy absorption by the lower extremity joints during the loading response would occur, primarily at the knee, when step rate was increased.
less mechanical energy was absorbed at the knee (P < 0.01) during the +5% and +10% step rate conditions, whereas the hip (P < 0.01) absorbed less energy during the +10% condition only. All joints displayed substantially (P < 0.01) more energy absorption when preferred step rate was reduced by 10%. Step length (P < 0.01), center of mass vertical excursion (P < 0.01), braking impulse (P < 0.01), and peak knee flexion angle (P < 0.01) were observed to decrease with increasing step rate. When step rate was increased 10% above preferred, peak hip adduction angle (P < 0.01) and peak hip adduction (P < 0.01) and internal rotation (P < 0.01) moments were found to decrease.
we conclude that subtle increases in step rate can substantially reduce the loading to the hip and knee joints during running and may prove beneficial in the prevention and treatment of common running-related injuries.
Measurements of tibial acceleration during walking and running were obtained by means of a triaxial accelerometer. The accelerometer was fixed to the free end of a Steinmann pin inserted into the right tibia of one volunteer subject. The patterns of tibial acceleration showed little step-to-step variation within each experimental condition. Following foot strike and depending upon footwear, the resultant tibial acceleration reached between 2.7 and 3.7 g during walking. The tibia experienced maximal accelerations of 10.6 g during running. The high values of tibial acceleration recorded in the antero-posterior (AP) and medio-lateral (ML) directions clearly revealed the importance of measuring all three components of acceleration to quantify the magnitude of the shock experienced by the lower limbs during locomotor activities.