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

Fatigue assessment in distance runners: A scoping review of inertial sensor-based biomechanical outcomes and their relation to fatigue markers and assessment conditions

BACKGROUND

Fatigue manifests as a decline in performance during high-intensity and prolonged exercise. With technological advancements and the increasing adoption of inertial measurement units (IMUs) in sports biomechanics, there is an opportunity to enhance our understanding of running-related fatigue beyond controlled laboratory environments.

RESEARCH QUESTION

How have IMUs have been used to assess running biomechanics under fatiguing conditions?

METHODS

Following the PRISMA-ScR guidelines, our literature search covered six databases without date restrictions until September 2024. The Population, Concept, and Context criteria were used: Population (distance runners ranging from novice to competitive), Concept (fatigue induced by running a distance over 400 m), Context (assessment of fatigue using accelerometer, gyroscope, and/or magnetometer wearable devices). Biomechanical outcomes were extracted and synthesised, and interpreted in the context of three main study characteristics (cohort ability, testing environment, and the inclusion of physiological outcomes) to explore their potential role in influencing outcomes.

RESULTS

A total of 88 articles were included in the review. There was a high prevalence of treadmill-based studies (n=46, 52%), utilising only 1-2 sensors (n=69, 78%), and cohorts ranged in experience, from sedentary to elite-level runners, and were largely comprised of males (69% of all participants). The majority of biomechanical outcomes assessed showed varying responses to fatigue across studies, likely attributable to individual variability, exercise intensity, and differences in fatigue protocol settings and prescriptions. Spatiotemporal outcomes such as stride time and frequency (n=37, 42 %) and impact accelerations (n=55, 62%) were more widely assessed, with a fatigue response that appeared population and environment specific.

SIGNIFICANCE

There was notable heterogeneity in the IMU-based biomechanical outcomes and methods evaluated in this review. The review findings emphasise the need for standardisation of IMU-based outcomes and fatigue protocols to promote interpretable metrics and facilitate inter-study comparisons.

From lab to field: validity and reliability of inertial measurement unit-derived gait parameters during a standardised run

The aim was to assess concurrent validity and test-retest reliability of spatiotemporal gait parameters from a thoracic-placed inertial measurement unit (IMU) in lab- (Phase One) and field-based (Phase Two) conditions. Spatiotemporal gait parameters were compared (target speeds 3, 5 and 7.5 m·s-1) between a 100 Hz IMU and an optical measurement system (OptoJump Next, 1000 hz) in 14 trained individuals (Phase One). Additionally, 29 English Premier League football players performed weekly 3 × 60 m runs (5 m·s-1; observations = 1227; Phase Two). Mixed effects modelling assessed the effect of speed on agreement between systems (Phase One) and test-retest reliability (Phase Two). IMU step time showed strong agreement (<0.3%) regardless of individual or running speed. Direction of mean biases up to 40 ms for contact and flight time depended on the running speed and individual. Step time, length and frequency were most reliable (coefficient of variation = 1.3-1.4%) but confounded by running speed. Step time, length and frequency derived from a thoracic-placed IMU can be used confidently. Contact time could be used if bias is corrected for each individual. To optimise test-retest reliability, a minimum running distance of 40 m is needed to ensure 10 constant-speed steps is gathered.

Two sides of the same runner! The association between biomechanical and physiological markers of endurance performance in distance runners

BACKGROUND

The number of people who run to achieve competitive performance has increased, encouraging the scientific community to analyze the association of factors that can affect a runner performance.

RESEARCH QUESTION

Is there association between running spatiotemporal and angular kinematics with the physiological markers of endurance performance during a cardiorespiratory exercise test?

METHODS

This was an observational cross-sectional study with 40 distance runners simultaneously submitted to a running biomechanical analysis and cardiorespiratory exercise test on a treadmill. Mixed models were developed to verify the association between angular kinematic data obtained by the Movement Deviation Profile and the running spatiotemporal data with oxygen consumption and ventilatory thresholds.

RESULTS

Spatiotemporal variables [.e., step frequency Odds Ratio 0.09 [0.06-0.12 95 % Confidence Interval], center of mass vertical displacement Odds Ratio 0.10 [0.07-0.14 95 % Confidence Interval], and step length [Odds Ratio -0.01 [-0.01 to -0.00 95 % Confidence Interval]] were associated with VO2. Also, step frequency Odds Ratio 1.03 [1.01-1.05 95 % Confidence Interval] was associated with the first ventilatory threshold, and angular running kinematics [Movement Deviation Profile analysis] Odds Ratio 1.47 [1.13-1.91 95 % Confidence Interval] was associated with peak of exercise during the cardiorespiratory exercise test.

SIGNIFICANCE

Our findings demonstrated that: both higher step frequency and center of mass vertical displacement are associated with the increase of oxygen demand; step frequency is associated with the first ventilatory threshold, due to the entrainment mechanism and angular kinematic parameters are associated with peak aerobic speed. Future studies could also compare the biomechanical and physiological characteristics of different groups of distance runners. This could help identify the factors that contribute to oxygen demands during running and performance across different ages, genders, and levels of competition.

Adaptive Changes in Longitudinal Arch During Long-distance Running

This study investigates the biomechanical adaptations of the longitudinal arch (LA) in long-distance runners, focusing on changes in stiffness, angle, and moment during a 60-minute run. Twenty runners participated in this experiment, and were asked to run at a speed of 2.7 m·s-1 for 60 minutes. The kinematic and kinetic data collected at five-minute intervals during running were calculated, including the stiffness of LA in the loading phase (k load ) and the stiffness of LA in the unloading phase (k unload ), the maximum LA moment (M max ), the range of LA angle change (∆θ range ), and the maximum LA angle change (∆θ max ). Foot morphology was also scanned before and after running. Variations of kinematic and kinetic data were analyzed throughout the running activity, as well as variations of foot morphology pre- and post-run. Results showed that there was a significant decrease in k load (p<0.001), coupled with increases in ∆θ range (p=0.002) and ∆θ max (p<0.001), during the first 15 minutes of running, which was followed by a period of mechanical stability. No differences were found in k unload and M max throughout the running process and the foot morphology remained unchanged after running. These results highlight a critical adaptation phase that may be pivotal for improving running economy and performance.

Spatiotemporal parameters and kinematics differ between race stages in trail running-a field study

Introduction

Trail running is an emerging discipline with relatively few studies performed in ecological conditions. The aim of this work was to investigate if and how spatiotemporal parameters (STP) and kinematics differ between initial and final stage of a field trial.

Methods

Twenty trail runners (10 F, 10 M) were recruited and ran a solo 9.1 km trial. During the test, participants wore a GPS watch and an IMU-based motion capture system. Running speed, elapsed time, STP and kinematics were compared between initial and final stage, separately for uphill (UH) and downhill (DH) sections.

Results

Running speed decreased in the final stage ( p < 0.05 ). Total test time was more correlated to the time elapsed in UH sections. In the final stage and in both UH and DH sections, contact time and duty factor increased, whilst stride length and flight time decreased ( p < 0.05 ). In the final stage, ankle joint was more dorsiflexed in stance and swing phases in UH sections and stance phase only in DH sections ( p < 0.05 ). In the final stage, knee joint was less extended in swing phase in UH and DH sections, as well as less extended in stance in UH sections ( p < 0.05 ). In the final stage, hip joint was less flexed in the swing phase in UH and DH sections ( p < 0.05 ). In the final stage, forward trunk lean was higher across the entire gait cycle in in UH sections ( p < 0.05 ). Trunk contralateral axial rotation was lower, in DH sections ( p < 0.05 ).

Discussion

During the final stage, results indicate a less efficient propulsion phase, in both UH and DH sections. In UH sections, results suggest lower energy generation at the ankle joint. In DH sections, results suggest that the kinematics of swing leg may play a role in sub-optimizing propulsion phase. This study demonstrates how, in UH and DH sections, similar changes in spatiotemporal parameters can be elicited by dissimilar changes in running kinematics. To optimize performance in trail running, coaches and practitioners are advised to work on different (incline-specific) aspects of running technique.

Running 40 Minutes under Temperate or Hot Environment Does Not Affect Operating Fascicle Length

PURPOSE

Muscle mechanics is paramount in our understanding of motor performance. However, little is known regarding the sensitivity of fascicle dynamics and connective tissues stiffness to exercise duration and ambient temperature during running, both increasing muscle temperature. This study aimed to determine gastrocnemius medialis (GM) fascicle dynamics in vivo during running in temperate and hot conditions, as well as muscle-tendon unit responses.

METHODS

Using ultrafast ultrasound, 15 participants (8 men, 7 women; 26 ± 3 yr) were tested before, during (2 and 40 min), and after a running task (40 min at 10 km·h -1 ) in temperate (TEMP; ~23°C) and hot (HOT: ~38°C) conditions.

RESULTS

Although core, skin temperatures, and heart rate increased from the beginning to the end of the exercise and in a larger extent in HOT than TEMP ( P < 0.001), the physiological stress elicited did not alter running temporal parameters and GM fascicle operating lengths, with similar behavior of the fascicles on their force-length relationship, over time (2 vs 40 min) or across condition (TEMP vs HOT; P ≥ 0.248). Maximal voluntary force production did not reported statistical changes after exercise ( P = 0.060), and the connective tissues stiffness measured (i.e., passive muscle and stiffness of the series-elastic elements) did not show neither time ( P ≥ 0.281), condition ( P ≥ 0.256) nor time-condition interaction ( P ≥ 0.465) effect.

CONCLUSIONS

This study revealed that prolonged running exercise does not alter muscle-tendon unit properties and interplay, which are not influenced by ambient temperature. These findings may rule out potential detrimental effects of heat on muscle properties and encourage further investigations on longer and more intense running exercise.

Mechanical asymmetries remain low-to-moderate during 30 min of self-paced treadmill running

Introduction: We characterized the magnitude and range of gait asymmetry during self-paced treadmill running. Methods: On an instrumented treadmill, twelve trained runners (11 males, 1 female) completed a 30-min self-paced run, during which participants were instructed to cover the most distance possible. Ground reaction force recordings at a constant velocity corresponding to 70% of their maximal aerobic velocity (13.3 ± 0.8 km.h-1) allowed for the measurement of running kinetics and kinematics, as well as the calculation of spring-mass characteristics at the beginning, middle, and end of the run (minutes 1, 14, and 29, respectively). Group mean asymmetry scores were assessed using the "symmetry angle" (SA) formulae, where scores of 0% and 100% represent perfect symmetry and perfect asymmetry, respectively. Results: There was no time effect on SA scores for any of the 13 biomechanical variables (p ≥ 0.128). Mean SA scores were <2.5% for contact time (0.8% ± 0.7%), flight time (1.4% ± 0.6%), step frequency (0.7% ± 0.3%), duty factor (0.7% ± 0.3%), duration of braking (1.3% ± 0.7%) and push-off phases (0.9% ± 0.8%), as well as peak braking (2.3% ± 1.3%) and push-off forces (1.4% ± 0.9%). Mean SA scores were ≥2.5% for peak vertical loading rate (3.1% ± 1.7%), mean vertical loading rate (3.4% ± 2.1%), peak vertical forces (2.9% ± 2.2%), as well as vertical stiffness (5.2% ± 3.5%) and leg stiffness (2.5% ± 1.5%). Conclusion: Throughout a 30-min running time trial, there were consistently low-to-moderate mechanical asymmetries for spatiotemporal variables, kinetics, and spring-mass model characteristics. This suggests that trained runners maintained relatively even strides during the self-paced treadmill run, with lower extremities behaving similarly when controlling for velocity.

Vitamin D Metabolites Are Associated With Musculoskeletal Injury in Young Adults: A Prospective Cohort Study

The relationship between vitamin D metabolites and lower body (pelvis and lower limb) overuse injury is unclear. In a prospective cohort study, we investigated the association between vitamin D metabolites and incidence of lower body overuse musculoskeletal and bone stress injury in young adults undergoing initial military training during all seasons. In 1637 men and 530 women (aged 22.6 ± 7.5 years; body mass index [BMI], 24.0 ± 2.6 kg/m- 2 ; 94.3% white ethnicity), we measured serum 25-hydroxyvitamin D (25(OH)D) and 24,25-dihydroxyvitamin D (24,25(OH)2 D) by high-performance liquid chromatography tandem mass spectrometry, and 1,25-dihydroxyvitamin D (1,25(OH)2 D) by immunoassay during week 1 of training. We examined whether the relationship between 25(OH)D and 1,25(OH)2 D:24,25(OH)2 D ratio was associated with overuse injury. During 12 weeks of training, 21.0% sustained ≥1 overuse musculoskeletal injury, and 5.6% sustained ≥1 bone stress injury. After controlling for sex, BMI, 2.4 km run time, smoking, bone injury history, and Army training course (Officer, standard, or Infantry), lower body overuse musculoskeletal injury incidence was higher for participants within the second lowest versus highest quartile of 24,25(OH)2 D (odds ratio [OR] = 1.62; 95% confidence interval [CI] 1.13-2.32; p = 0.009) and lowest versus highest cluster of 25(OH)D and 1,25(OH)2 D:24,25(OH)2 D (OR = 6.30; 95% CI 1.89-21.2; p = 0.003). Lower body bone stress injury incidence was higher for participants within the lowest versus highest quartile of 24,25(OH)2 D (OR = 4.02; 95% CI 1.82-8.87; p < 0.001) and lowest versus highest cluster of 25(OH)D and 1,25(OH)2 D:24,25(OH)2 D (OR = 22.08; 95% CI 3.26-149.4; p = 0.001), after controlling for the same covariates. Greater conversion of 25(OH)D to 24,25(OH)2 D, relative to 1,25(OH)2 D (ie, low 1,25(OH)2 D:24,25(OH)2 D), and higher serum 24,25(OH)2 D were associated with a lower incidence of lower body overuse musculoskeletal and bone stress injury. Serum 24,25(OH)2 D may have a role in preventing overuse injury in young adults undertaking arduous physical training. © 2023 Crown copyright and The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR). This article is published with the permission of the Controller of HMSO and the King's Printer for Scotland.

Accelerometer-Derived Intensity Thresholds Are Equivalent to Standard Ventilatory Thresholds in Incremental Running Exercise

Accelerometer cut-points are commonly used to prescribe the amount of physical activity, but this approach includes no individual performance measures. As running kinetics change with intensity, acceleration measurements may provide more individual information. Therefore, the aim was to determine two intensity thresholds from accelerometer measures. A total of 33 participants performed a maximal incremental running test with spirometric and acceleration (Axivity AX3) measures at the left and right tibia. Ventilatory equivalents (VE/VO2, VE/VCO2) were used to determine a first and second ventilatory threshold (VT1/VT2). A first and second accelerometer threshold (ACT1/ACT2) were determined within the same regions of interest from vector magnitude (|v| = √(ax2 + ay2 + az2). Accelerometer data from the tibia presented a three-phase increase with increasing speed. Speed at VT1/VT2 (7.82 ± 0.39/10.91 ± 0.87 km/h) was slightly but significantly lower compared to the speed at ACT1/ACT2 from the left (7.71 ± 0.35/10.62 ± 0.72 km/h) and right leg (7.79 ± 0.33/10.74 ± 0.77 km/h). Correlation analysis revealed a strong relationship between speed at thresholds determined from spriometric data or accelerations (r = 0.98; p < 0.001). It is therefore possible to determine accelerometer thresholds from tibia placement during a maximal incremental running test comparable to standard ventilatory thresholds.

Estimation of horizontal running power using foot-worn inertial measurement units

Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed three machine-learning models to estimate peak horizontal power for level, uphill, and downhill running using gait spatiotemporal parameters, accelerometer, and gyroscope signals extracted from foot-worn IMUs. The prediction was compared to reference horizontal power obtained during running on a treadmill with an embedded force plate. For each model, we trained an elastic net and a neural network and validated it with a dataset of 34 active adults across a range of speeds and slopes. For the uphill and level running, the concentric phase of the gait cycle was considered, and the neural network model led to the lowest error (median ± interquartile range) of 1.7% ± 12.5% and 3.2% ± 13.4%, respectively. The eccentric phase was considered relevant for downhill running, wherein the elastic net model provided the lowest error of 1.8% ± 14.1%. Results showed a similar performance across a range of different speed/slope running conditions. The findings highlighted the potential of using interpretable biomechanical features in machine learning models for the estimating horizontal power. The simplicity of the models makes them suitable for implementation on embedded systems with limited processing and energy storage capacity. The proposed method meets the requirements for applications needing accurate near real-time feedback and complements existing gait analysis algorithms based on foot-worn IMUs.

Exercise Biomechanics for Health: Evaluating Lifelong Activities for Well-Being

Biomechanics is a multidisciplinary study of the mechanical laws and principles that govern human movement and the functioning of biological systems [...].

Quantifying and correcting for speed and stride frequency effects on running mechanics in fatiguing outdoor running

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.

Effects of level running-induced fatigue on running kinematics: A systematic review and meta-analysis

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.

Healthy Running Habits for the Distance Runner: Clinical Utility of the American College of Sports Medicine Infographic

ABSTRACT

Healthy running form is characterized by motion that minimizes mechanical musculoskeletal injury risks and improves coactivation of muscles that can buffer impact loading and reduce stresses related to chronic musculoskeletal pain. The American College of Sports Medicine Consumer Outreach Committee recently launched an infographic that describes several healthy habits for the general distance runner. This review provides the supporting evidence, expected acute motion changes with use, and practical considerations for clinical use in patient cases. Healthy habits include: taking short, quick, and soft steps; abdominal bracing; elevating cadence; linearizing arm swing; controlling forward trunk lean, and; avoiding running through fatigue. Introduction of these habits can be done sequentially one at a time to build on form, or more than one over time. Adoption can be supported by various feedback forms and cueing. These habits are most successful against injury when coupled with regular dynamic strengthening of the kinetic chain, adequate recovery with training, and appropriate shoe wear.

Cadence in youth long-distance runners is predicted by leg length and running speed

BACKGROUND

Lower cadence has been previously associated with injury in long-distance runners. Variations in cadence may be related to experience, speed, and anthropometric variables. It is unknown what factors, if any, predict cadence in healthy youth long-distance runners.

RESEARCH QUESTION

Are demographic, anthropometric and/or biomechanical variables able to predict cadence in healthy youth long-distance runners.

METHODS

A cohort of 138 uninjured youth long-distance runners (M = 62, F = 76; Mean ± SD; age = 13.7 ± 2.7; mass = 47.9 ± 13.6 kg; height = 157.9 ± 14.5 cm; running volume = 19.2 ± 20.6 km/wk; running experience: males = 3.5 ± 2.1 yrs, females = 3.3 ± 2.0 yrs) were recruited for the study. Multiple linear regression (MLR) models were developed for total sample and for each sex independently that only included variables that were significantly correlated to self-selected cadence. A variance inflation factor (VIF) assessed multicollinearity of variables. If VIF≥ 5, variable(s) were removed and the MLR analysis was conducted again.

RESULTS

For all models, VIF was > 5 between speed and normalized stride length, therefore we removed normalized stride length from all models. Only leg length and speed were significantly correlated (p < .001) with cadence in the regression models for total sample (R² = 51.9 %) and females (R² = 48.2 %). The regression model for all participants was Cadence = -1.251 *Leg Length + 3.665 *Speed + 254.858. The regression model for females was Cadence = -1.190 *Leg Length + 3.705 *Speed + 249.688. For males, leg length, cadence, and running experience were significantly predictive (p < .001) of cadence in the model (R² = 54.7 %). The regression model for males was Cadence = -1.268 *Leg Length + 3.471 *Speed - 1.087 *Running Experience + 261.378.

SIGNIFICANCE

Approximately 50 % of the variance in cadence was explained by the individual's leg length and running speed. Shorter leg lengths and faster running speeds were associated with higher cadence. For males, fewer years of running experience was associated with a higher cadence.

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.

Estimation of Temporal Parameters During Running with a Wrist-worn Inertial Sensor: an In-field Validation

The aim of this study was to estimate the temporal gait parameters using a wrist-worn Inertial Measurement Unit (IMU) during an outdoor run. While it is easier to compute running gait parameters using foot IMUs, a wrist IMU is more convenient and less obtrusive when it comes to data acquisition. During a track run of 12 minutes, we equipped 14 highly-trained male runners with one IMU on the wrist and one on each foot. We trained machine learning models based on CNN, GPR, and Lasso regression using wrist IMU signals and validated them with a foot-worn IMU reference system. Lasso model performed the best, with the accuracy for cycle time, swing time, flight time, and contact time being 0.27 % ±0.1 %, 2.6 %±1.7 %, 7.3 % ±4.9 %, and 10.6 % ±5.5 %, respectively.

Running-Induced Fatigue Changes the Structure of Motor Variability in Novice Runners

Understanding the effects of fatigue is a central issue in the context of endurance sports. Given the popularity of running, there are numerous novices among runners. Therefore, understanding the effects of fatigue in novice runners is an important issue. Various studies have drawn conclusions about the control of certain variables by analyzing motor variability. One variable that plays a crucial role during running is the center of mass (CoM), as it reflects the movement of the whole body in a simplified way. Therefore, the aim of this study was to analyze the effects of fatigue on the motor variability structure that stabilizes the CoM trajectory in novice runners. To do so, the uncontrolled manifold approach was applied to a 3D whole-body model using the CoM as the result variable. It was found that motor variability increased with fatigue (UCMꓕ). However, the UCMRatio did not change. This indicates that the control of the CoM decreased, whereas the stability was not affected. The decreases in control were correlated with the degree of exhaustion, as indicated by the Borg scale (during breaking and flight phase). It can be summarized that running-induced fatigue increases the step-to-step variability in novice runners and affects the control of their CoM.

Bone stress injuries

Bone stress injuries, including stress fractures, are overuse injuries that lead to substantial morbidity in active individuals. These injuries occur when excessive repetitive loads are introduced to a generally normal skeleton. Although the precise mechanisms for bone stress injuries are not completely understood, the prevailing theory is that an imbalance in bone metabolism favours microdamage accumulation over its removal and replacement with new bone via targeted remodelling. Diagnosis is achieved by a combination of patient history and physical examination, with imaging used for confirmation. Management of bone stress injuries is guided by their location and consequent risk of healing complications. Bone stress injuries at low-risk sites typically heal with activity modification followed by progressive loading and return to activity. Additional treatment approaches include non-weight-bearing immobilization, medications or surgery, but these approaches are usually limited to managing bone stress injuries that occur at high-risk sites. A comprehensive strategy that integrates anatomical, biomechanical and biological risk factors has the potential to improve the understanding of these injuries and aid in their prevention and management.

Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review

Physical exercise (PE) is beneficial for both physical and psychological health aspects. However, excessive training can lead to physical fatigue and an increased risk of lower limb injuries. In order to tailor training loads and durations to the needs and capacities of an individual, physical fatigue must be estimated. Different measurement devices and techniques (i.e., ergospirometers, electromyography, and motion capture systems) can be used to identify physical fatigue. The field of biomechanics has succeeded in capturing changes in human movement with optical systems, as well as with accelerometers or inertial measurement units (IMUs), the latter being more user-friendly and adaptable to real-world scenarios due to its wearable nature. There is, however, still a lack of consensus regarding the possibility of using biomechanical parameters measured with accelerometers to identify physical fatigue states in PE. Nowadays, the field of biomechanics is beginning to open towards the possibility of identifying fatigue state using machine learning algorithms. Here, we selected and summarized accelerometer-based articles that either (a) performed analyses of biomechanical parameters that change due to fatigue in the lower limbs or (b) performed fatigue identification based on features including biomechanical parameters. We performed a systematic literature search and analysed 39 articles on running, jumping, walking, stair climbing, and other gym exercises. Peak tibial and sacral acceleration were the most common measured variables and were found to significantly increase with fatigue (respectively, in 6/13 running articles and 2/4 jumping articles). Fatigue classification was performed with an accuracy between 78% and 96% and Pearson's correlation with an RPE (rate of perceived exertion) between r = 0.79 and r = 0.95. We recommend future effort toward the standardization of fatigue protocols and methods across articles in order to generalize fatigue identification results and increase the use of accelerometers to quantify physical fatigue in PE.

Concurrent Evolution of Biomechanical and Physiological Parameters With Running-Induced Acute Fatigue

Understanding the influence of running-induced acute fatigue on the homeostasis of the body is essential to mitigate the adverse effects and optimize positive adaptations to training. Fatigue is a multifactorial phenomenon, which influences biomechanical, physiological, and psychological facets. This work aimed to assess the evolution of these three facets with acute fatigue during a half-marathon. 13 recreational runners were equipped with one inertial measurement unit (IMU) on each foot, one combined global navigation satellite system-IMU-electrocardiogram sensor on the chest, and an Android smartphone equipped with an audio recording application. Spatio-temporal parameters for the running gait, along with the heart rate, its variability and complexity were computed using validated algorithms. Perceived fatigability was assessed using the rating-of-fatigue (ROF) scale at every 10 min of the race. The data was split into eight equal segments, corresponding to at least one ROF value per segment, and only level running parts were retained for analysis. During the race, contact time, duty factor, and trunk anteroposterior acceleration increased, and the foot strike angle and vertical stiffness decreased significantly. Heart rate showed a progressive increase, while the metrics for heart rate variability and complexity decreased during the race. The biomechanical parameters showed a significant alteration even with a small change in perceived fatigue, whereas the heart rate dynamics altered at higher changes. When divided into two groups, the slower runners presented a higher change in heart rate dynamics throughout the race than the faster runners; they both showed similar trends for the gait parameters. When tested for linear and non-linear correlations, heart rate had the highest association with biomechanical parameters, while the trunk anteroposterior acceleration had the lowest association with heart rate dynamics. These results indicate the ability of faster runners to better judge their physiological limits and hint toward a higher sensitivity of perceived fatigue to neuromuscular changes in the running gait. This study highlights measurable influences of acute fatigue, which can be studied only through concurrent measurement of biomechanical, physiological, and psychological facets of running in real-world conditions.