The effect of three surface conditions, speed and running experience on vertical acceleration of the tibia during running

Criteria and Guidelines for Returning to Running Following a Tibial Bone Stress Injury: A Scoping Review

Tibial bone stress injuries (BSIs) are common among long-distance runners. They have a high recurrence rate, and complexity emerges in the wider management and successful return to running. Following a tibial BSI, a critical component of complete rehabilitation is the successful return to running, and there is a lack of consistency or strong evidence to guide this process. The objectives of this review were to outline the criteria used in clinical decision-making prior to resuming running, and to establish evidence-based guidelines for the return to running process following a tibial BSI. Electronic databases including MEDLINE, CINAHL, Scopus, SPORTDiscus and AMED were searched for studies that stated criteria or provided guidelines on the objectives above. Fifty studies met the inclusion criteria and were included. Thirty-nine were reviews or clinical commentaries, three were retrospective cohort studies, two were randomised controlled trials, two were pilot studies, one was a prospective observational study, and three were case studies. Therefore, the recommendations that have been surmised are based on level IV evidence. Decisions on when an athlete should return to running should be shared between clinicians, coaches and the athlete. There are five important components to address prior to introducing running, which are: the resolution of bony tenderness, pain-free walking, evidence of radiological healing in high-risk BSIs, strength, functional and loading tests, and the identification of contributing factors. Effective return to running planning should address the athlete's risk profile and manage the risk by balancing the athlete's interests and reinjury prevention. An individualised graduated return to running programme should be initiated, often starting with walk-run intervals, progressing running distance ahead of speed and intensity, with symptom provocation a key consideration. Contributing factors to the initial injury should be addressed throughout the return to run process.

The effects of non-Newtonian fluid material midsole footwear on tibial shock acceleration and attenuation

Introduction: Given the possibility of higher ground temperatures in the future, the pursuit of a cushioning material that can effectively reduce sports injuries during exercise, particularly one that retains its properties at elevated temperatures, has emerged as a serious concern. Methods: A total of 18 man recreational runners were recruited from Ningbo University and local clubs for participation in this study. Frequency analysis was employed to investigate whether there is a distinction between non-Newtonian (NN) shoes and ethylene vinyl acetate (EVA) shoes. Results: The outcomes indicated that the utilization of NN shoes furnished participants with superior cushioning when engaging in a 90° cutting maneuver subsequent to an outdoor exercise, as opposed to the EVA material. Specifically, participants wearing NN shoes exhibited significantly lower peak resultant acceleration (p = 0.022) and power spectral density (p = 0.010) values at the distal tibia compared to those wearing EVA shoes. Moreover, shock attenuation was significantly greater in subjects wearing NN shoes (p = 0.023) in comparison to EVA shoes. Performing 90° cutting maneuver in NN shoes resulted in significantly lower peak ground reaction force (p = 0.010), vertical average loading rate (p < 0.010), and vertical instantaneous loading rate (p = 0.030) values compared to performing the same maneuvers in EVA shoes. Conclusion: The study found that the PRA and PSD of the distal tibia in NN footwear were significantly lower compared to EVA footwear. Additionally, participants exhibited more positive SA while using NN footwear compared to EVA. Furthermore, during the 90° CM, participants wearing NN shoes showed lower PGRF, VAIL, and VILR compared to those in EVA shoes. All these promising results support the capability of NN footwear to offer additional reductions in potential injury risk to runners, especially in high-temperature conditions.

An Analysis of Running Impact on Different Surfaces for Injury Prevention

The impact that occurs on the runner's foot when it lands on the ground depends on numerous factors: footwear, running technique, foot strike and landing pattern, among others. However, the surface is a decisive factor that can be selected by the runner to improve their sports practice, thereby avoiding injuries. This study aimed to assess the number and magnitude of accelerations in impact (produced by the runner when their foot strikes the ground) on three different surfaces (grass, synthetic track, and concrete) in order to know how to prevent injuries. Thirty amateur runners (age 22.6 ± 2.43 years) participated in the study. They had to run consecutively on three different surfaces at the same speed, with a three axis-accelerometer placed on the sacrum and wearing their own shoes. The results showed that the running impacts differed based on the type of surface. Higher mean acceleration (MA) and mean peak acceleration (PA) in the impacts were observed on concrete compared to the other two surfaces. There were small differences for MA: 1.35 ± 0.1 g (concrete) vs. 1.30 ± 0.1 g (synthetic track) SD: 0.43 (0.33, 0.54) and 1.30 ± 0.1 g (grass) SD: 0.36 (0.25, 0.46), and small differences for PA: 3.90 ± 0.55 g (concrete) vs. 3.68 ± 0.45 g (synthetic track) SD 0.42 (0.21, 0.64) and 3.76 ± 0.48 g (grass) SD 0.27 (0.05, 0.48), implying that greater impacts were produced on concrete compared to synthetic track and grass. The number of peaks of 4 to 5 g of total acceleration was greater for concrete, showing small differences from synthetic track: SD 0.23 (-0.45, 0.9). Additionally, the number of steps was higher on synthetic track (34.90 ± 2.67), and small differences were shown compared with concrete (33.37 ± 2.95) SD 0.30 (-0.25, 0.85) and with grass (35.60 ± 3.94) SD 0.36 (-0.19, 0.91). These results may indicate a change in technique based on the terrain. Given the increasing popularity of running, participants must be trained to withstand the accelerations in impact that occur on different surfaces in order to prevent injuries.

Evaluation of a Restoration Algorithm Applied to Clipped Tibial Acceleration Signals

Wireless accelerometers with various operating ranges have been used to measure tibial acceleration. Accelerometers with a low operating range output distorted signals and have been found to result in inaccurate measurements of peaks. A restoration algorithm using spline interpolation has been proposed to restore the distorted signal. This algorithm has been validated for axial peaks within the range of 15.0-15.9 g. However, the accuracy of peaks of higher magnitude and the resultant peaks have not been reported. The purpose of the present study is to evaluate the measurement agreement of the restored peaks using a low-range accelerometer (±16 g) against peaks sampled using a high-range accelerometer (±200 g). The measurement agreement of both the axial and resultant peaks were examined. In total, 24 runners were equipped with 2 tri-axial accelerometers at their tibia and completed an outdoor running assessment. The accelerometer with an operating range of ±200 g was used as reference. The results of this study showed an average difference of -1.40 ± 4.52 g and -1.23 ± 5.48 g for axial and resultant peaks. Based on our findings, the restoration algorithm could skew data and potentially lead to incorrect conclusions if used without caution.

Effects of Trail Running versus Road Running-Effects on Neuromuscular and Endurance Performance-A Two Arm Randomized Controlled Study

Running on less predictable terrain has the potential to increase the stimulation of the neuromuscular system and can boost aerobic performance. Hence, the purpose of this study was to analyze the effects of trail versus road running on neuromuscular and endurance performance parameters in running novices. Twenty sedentary participants were randomly assigned to either a trail (TRAIL; n = 10) or road running (ROAD; n = 10) group. A supervised and progressive, moderate intensity, and work-load-matched 8 wk endurance running program on TRAIL or ROAD was prescribed (i.e., randomized). Static balance (BESS test), dynamic balance (Y-balance test), gait analysis (RehaGait test, with regard to stride time single task, stride length dual task, velocity single task), agility performance (t-test), isokinetic leg strength (BIODEX), and predicted VO2max were assessed in pre- and post-tests. rANOVA analysis revealed no significant time-group interactions. Large effect sizes (Cohen's d) for pairwise comparison were found for TRAIL in the BESS test (d = 1.2) and predicted (pred) VO2max (d = 0.95). Moderate effects were evident for ROAD in BESS (d = 0.5), stride time single task (d = 0.52), and VO2max predicted (d = 0.53). Possible moderate to large effect sizes for stride length dual task (72%), velocity single task (64%), BESS test (60%), and the Y-balance test left stance (51%) in favor of TRAIL occurred. Collectively, the results suggested slightly more beneficial tendencies in favor of TRAIL. Additional research is needed to clearly elucidate differences between TRAIL and ROAD, not only in novices but also in experienced exercisers.

Surface effects on kinematics, kinetics and stiffness of habitual rearfoot strikers during running

The surface effects on running biomechanics have been greatly investigated. However, the effects on rearfoot strike runners remain unknown. The purpose of this study was to investigate the effects of surfaces on the running kinematics, kinetics, and lower-limb stiffness of habitual rearfoot strikers. Thirty healthy male runners were recruited to run at 3.3 ± 0.2 m/s on a customized runway covered with three different surfaces (artificial grass, synthetic rubber, or concrete), and their running kinematics, kinetics, and lower-limb stiffness were compared. Differences among the three surfaces were examined using statistical parametric mapping and one-way repeated-measure analysis of variance. There were no statistical differences in the lower-limb joint motion, vertical ground reaction force (GRF), loading rates, and lower-limb stiffness when running on the three surfaces. The braking force (17%-36% of the stance phase) and mediolateral GRF were decreased when running on concrete surface compared with running on the other two surfaces. The moments of ankle joint in all three plane movement and frontal plane hip and knee joints were increased when running on concrete surface. Therefore, habitual rearfoot strikers may expose to a higher risk of running-related overuse injuries when running on a harder surface.

Wearables for Running Gait Analysis: A Systematic Review

BACKGROUND

Running gait assessment has traditionally been performed using subjective observation or expensive laboratory-based objective technologies, such as three-dimensional motion capture or force plates. However, recent developments in wearable devices allow for continuous monitoring and analysis of running mechanics in any environment. Objective measurement of running gait is an important (clinical) tool for injury assessment and provides measures that can be used to enhance performance.

OBJECTIVES

We aimed to systematically review the available literature investigating how wearable technology is being used for running gait analysis in adults.

METHODS

A systematic search of the literature was conducted in the following scientific databases: PubMed, Scopus, Web of Science and SPORTDiscus. Information was extracted from each included article regarding the type of study, participants, protocol, wearable device(s), main outcomes/measures, analysis and key findings.

RESULTS

A total of 131 articles were reviewed: 56 investigated the validity of wearable technology, 22 examined the reliability and 77 focused on applied use. Most studies used inertial measurement units (n = 62) [i.e. a combination of accelerometers, gyroscopes and magnetometers in a single unit] or solely accelerometers (n = 40), with one using gyroscopes alone and 31 using pressure sensors. On average, studies used one wearable device to examine running gait. Wearable locations were distributed among the shank, shoe and waist. The mean number of participants was 26 (± 27), with an average age of 28.3 (± 7.0) years. Most studies took place indoors (n = 93), using a treadmill (n = 62), with the main aims seeking to identify running gait outcomes or investigate the effects of injury, fatigue, intrinsic factors (e.g. age, sex, morphology) or footwear on running gait outcomes. Generally, wearables were found to be valid and reliable tools for assessing running gait compared to reference standards.

CONCLUSIONS

This comprehensive review highlighted that most studies that have examined running gait using wearable sensors have done so with young adult recreational runners, using one inertial measurement unit sensor, with participants running on a treadmill and reporting outcomes of ground contact time, stride length, stride frequency and tibial acceleration. Future studies are required to obtain consensus regarding terminology, protocols for testing validity and the reliability of devices and suitability of gait outcomes.

CLINICAL TRIAL REGISTRATION

CRD42021235527.

The influence of surface and speed on biomechanical external loads obtained from wearable devices in rearfoot strike runners

External load variables such as peak tibial acceleration (PTA), peak vertical ground reaction forces (GRF) and its instantaneous vertical loading rate (IVLR) may contribute to running injuries although evidence is conflicting given the influence of training load and tissue health on injuries. These variables are influenced by footwear, speed, surface and foot strike pattern during running. The purpose of this study was to assess the influence of four surfaces and two running speeds on external load variables in rearfoot strike (RFS) runners. Twelve RFS runners (confirmed with sagittal foot contact angle) completed a 2-min running bout on a treadmill and 50-m running bouts over the three surfaces (pavement, rubber track and grass) in standardised shoes at their preferred speed and 20% faster. PTA and vertical GRFs were collected using inertial measurement units and in-shoe force insoles. No interaction or surface effects were observed (p > 0.017). The faster speed produced greater axial PTA (+19.2%; p < 0.001), resultant PTA (+20.7%; p < 0.001), peak vertical GRF (+6.6%; p = 0.002) and IVLR (+16.5%; p < 0.001). These findings suggest that surface type does not influence PTA, peak vertical GRF and IVLR but that running faster increases the magnitude of these external loads regardless of surface type in RFS runners.

Reliability of wearable sensors-based parameters for the assessment of knee stability

Anterior cruciate ligament (ACL) rupture represents one of the most recurrent knee injuries in soccer players. To allow a safe return to sport after ACL reconstruction, standardised and reliable procedures/criteria are needed. In this context, wearable sensors are gaining momentum as they allow obtaining objective information during sport-specific and in-the-field tasks. This paper aims at proposing a sensor-based protocol for the assessment of knee stability and at quantifying its reliability. Seventeen soccer players performed a single leg squat and a cross over hop test. Each participant was equipped with two magnetic-inertial measurement units located on the tibia and foot. Parameters related to the knee stability were obtained from linear acceleration and angular velocity signals. The intraclass correlation coefficient (ICC) and minimum detectable change (MDC) were calculated to evaluate each parameter reliability. The ICC ranged from 0.29 to 0.84 according to the considered parameter. Specifically, angular velocity-based parameters proved to be more reliable than acceleration-based counterparts, particularly in the cross over hop test (average ICC values of 0.46 and 0.63 for acceleration- and angular velocity-based parameters, respectively). An exception was represented, in the single leg squat, by parameters extracted from the acceleration trajectory on the tibial transverse plane (0.60≤ICC≤0.76), which can be considered as promising candidates for ACL injury risk assessment. Overall, greater ICC values were found for the dominant limb, with respect to the non-dominant one (average ICC: 0.64 and 0.53, respectively). Interestingly, this between-limb difference in variability was not always mirrored by LSI results. MDC values provide useful information in the perspective of applying the proposed protocol on athletes with ACL reconstruction. Thus, The outcome of this study sets the basis for the definition of reliable and objective criteria for return to sport clearance after ACL injury.

Acute Effects of Gait Interventions on Tibial Loads During Running: A Systematic Review and Meta-analysis

Introduction

Changing running technique or equipment can alter tibial loads. The efficacy of interventions to modify tibial loads during running is yet to be synthesised and evaluated. This article reviewed the effect of running technique and footwear interventions on tibial loading during running.

Methods

Electronic databases were searched using terms relevant to tibial load and running. Interventions were categorised according to their approach (i.e., footwear; barefoot running; speed; surface; overground versus treadmill; orthotics, insoles and taping; and technique); if necessary, further subgrouping was applied to these categories. Standardised mean differences (SMDs) with 95% confidence intervals (CIs) for changes in tibial loading were calculated and meta-analyses performed where possible.

Results

Database searches yielded 1617 articles, with 36 meeting the inclusion criteria. Tibial loading increased with (1) barefoot running (SMD 1.16; 95% CI 0.50, 1.82); (2) minimalist shoe use by non-habitual users (SMD 0.89; 95% CI 0.40, 1.39); (3) motion control shoe use (SMD 0.46; 95% CI 0.07, 0.84); (4) increased stride length (SMD 0.86; 95% CI 0.18, 1.55); and (5) increased running speed (SMD 1.03; 95% CI 0.74, 1.32). Tibial loading decreased when (1) individuals ran on a treadmill versus overground (SMD − 0.83; 95% CI − 1.53, − 0.12); and (2) targeted biofeedback was used (SMD − 0.93; 95% CI − 1.46, − 0.41).

Conclusions

Running barefoot, in motion control shoes or in unfamiliar minimalist shoes, and with an increased stride length increases tibial loads and may increase the risk of a tibial stress injury during periods of high training load. Adopting interventions such as running on a treadmill versus overground, and using targeted biofeedback during periods of high loads could reduce tibial stress injury.

The Use of Wearable Sensors for Preventing, Assessing, and Informing Recovery from Sport-Related Musculoskeletal Injuries: A Systematic Scoping Review

Wearable technologies are often indicated as tools that can enable the in-field collection of quantitative biomechanical data, unobtrusively, for extended periods of time, and with few spatial limitations. Despite many claims about their potential for impact in the area of injury prevention and management, there seems to be little attention to grounding this potential in biomechanical research linking quantities from wearables to musculoskeletal injuries, and to assessing the readiness of these biomechanical approaches for being implemented in real practice. We performed a systematic scoping review to characterise and critically analyse the state of the art of research using wearable technologies to study musculoskeletal injuries in sport from a biomechanical perspective. A total of 4952 articles were retrieved from the Web of Science, Scopus, and PubMed databases; 165 were included. Multiple study features-such as research design, scope, experimental settings, and applied context-were summarised and assessed. We also proposed an injury-research readiness classification tool to gauge the maturity of biomechanical approaches using wearables. Five main conclusions emerged from this review, which we used as a springboard to propose guidelines and good practices for future research and dissemination in the field.

Differences in Peak Impact Accelerations Among Foot Strike Patterns in Recreational Runners

Introduction

Running-related injuries (RRIs) occur from a combination of training load errors and aberrant biomechanics. Impact loading, measured by peak acceleration, is an important measure of running biomechanics that is related to RRI. Foot strike patterns may moderate the magnitude of impact load in runners. The effect of foot strike pattern on peak acceleration has been measured using tibia-mounted inertial measurement units (IMUs), but not commercially available insole-embedded IMUs. The aim of this study was to compare the peak acceleration signal associated with rearfoot (RFS), midfoot (MFS), and forefoot (FFS) strike patterns when measured with an insole-embedded IMU.

Materials and Methods

Healthy runners ran on a treadmill for 1 min at three different speeds with their habitual foot strike pattern. An insole-embedded IMU was placed inside standardized neutral cushioned shoes to measure the peak resultant, vertical, and anteroposterior accelerations at impact. The Foot strike pattern was determined by two experienced observers and evaluated using high-speed video. Linear effect mixed-effect models were used to quantify the relationship between foot strike pattern and peak resultant, vertical, and anteroposterior acceleration.

Results

A total of 81% of the 187 participants exhibited an RFS pattern. An RFS pattern was associated with a higher peak resultant (0.29 SDs; p = 0.029) and vertical (1.19 SD; p < 0.001) acceleration when compared with an FFS running pattern, when controlling for speed and limb, respectively. However, an MFS was associated with the highest peak accelerations in the resultant direction (0.91 SD vs. FFS; p = 0.002 and 0.17 SD vs. RFS; p = 0.091). An FFS pattern was associated with the lowest peak accelerations in both the resultant and vertical directions. An RFS was also associated with a significantly greater peak acceleration in the anteroposterior direction (0.28 SD; p = 0.033) than an FFS pattern, while there was no difference between MFS and FFS patterns.

Conclusion

Our findings indicate that runners should be grouped by RFS, MFS, and FFS when comparing peak acceleration, rather than the common practice of grouping MFS and FFS together as non-RFS runners. Future studies should aim to determine the risk of RRI associated with peak accelerations from an insole-embedded IMU to understand whether the small observed differences in this study are clinically meaningful.

Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis

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.

A support vector machine algorithm can successfully classify running ability when trained with wearable sensor data from anatomical locations typical of consumer technology

Greater understanding of differences in technique between runners may allow more beneficial feedback related to improving performance and decreasing injury risk. The purpose of this study was to develop and test a support vector machine classifier, which could automatically differentiate running technique between experienced and novice participants using only wearable sensor data. Three-dimensional linear accelerations and angular velocities were collected from six wearable sensors secured to current common smart device locations. Cross-validation was used to test the classification accuracy of models trained with a variety of combinations of sensor locations, with participants running at different speeds. Average classification accuracies ranged from 71.3% to 98.4% across the sensor combinations and running speeds tested. Models trained with only a single sensor location still showed effective classification. With the models trained with only upper arm data achieving an average accuracy of 96.4% across all tested running speeds. A post-hoc comparison of biomechanical variables between the two subgroups showed significant differences in upper body biomechanics throughout the stride. Both the methodology used to perform the classifications and the biomechanical differences identified could prove useful when aiming to shift a novice runner's technique towards movement patterns more akin to those with greater experience.

What Are the Main Risk Factors for Lower Extremity Running-Related Injuries? A Retrospective Survey Based on 3669 Respondents

Background:

Despite the many studies on running-related injuries (RRIs), risk factors for injury remain unclear in the literature.

Purpose:

To investigate the risk factors of RRIs.

Study Design:

Case-control study; Level of evidence, 3.

Methods:

An online survey was conducted among 3669 injured and noninjured runners. Injury was defined as pain of various kinds, without attention to its consequences on running practice. The survey included 41 questions on 5 main categories—personal characteristics, daily lifestyle, training and running characteristics, practice of other sporting activities, and prevention habits—as well as information about the occurrence of RRI over the previous 12 months. Continuous and qualitative variables were analyzed by Student t test and chi-square test, respectively. Sixteen variables were selected for multivariate binary logistic analysis.

Results:

Among the 3669 runners, 1852 (50.5%) reported at least 1 injury over the previous 12 months. Overuse injuries were largely represented (60.6%). The variables associated with RRIs that remained significant in the fully adjusted model were previous injury (odds ratio [OR], 1.62; 95% CI, 1.42-1.86), higher weight (OR, 1.006; 95% CI, 1.00-1.012), competitive running (OR, 1.53; 95% CI, 1.19-1.98), running >2 h/wk (OR, 1.28; 95% CI, 1.01-1.62), running >20 km/wk (OR, 1.25; 95% CI, 1.001-1.55), and stretching before running (OR, 1.46; 95% CI, 1.25-1.71).

Conclusion:

Previous injury remains the most relevant risk factor for RRIs according to the current study and previous data. Many training characteristics seem to be involved but still have to be confirmed in view of conflicting data in the literature. Further research would help clinicians better understand RRIs and how to prevent them.

The influence of playing surface on external demands and physiological responses during a soccer match simulation

We investigated the effects of playing surfaces with different impact absorption characteristics on external demand and physiological responses. Fifteen participants completed a soccer match simulation on natural grass, synthetic turf and concrete surfaces. Accelerometry-derived PlayerLoad^TM per minute (PL·min^-1) and average net force (AvF_Net) were used to quantify external demands at the centre of mass (CoM), upper-back, mid-back and hip. Heart rate, oxygen uptake, energy expenditure and RPE quantified physiological responses. The concrete surface exhibited the least impact absorption, with peak decelerations ~3.5x synthetic turf and ~10x natural grass (p < 0.001). Despite this, there was no differences in external demand between surfaces (surface: p ≥ 0.194; η²_p≤0.092). Both AvF_Net and PL·min^-1 (location: p < 0.001; η²_p≥0.859) were higher at the hip (613(91)N; 12.5(1.2)arb.u), reduced at the mid-back (521(67)N; 8.8(0.7)arb.u) and upper-back (502(60)N; 8.8(0.7)arb.u) when compared to CoM (576(78)N; 10.7(1.0)arb.u). Although playing surface did not influence the external demands, heart rate or oxygen uptake (p > 0.05), energy expenditure was highest on natural grass compared to synthetic turf (P = 0.034) and RPE was highest on synthetic turf compared to concrete (p = 0.026). Different playing surfaces can alter physiological responses to soccer-specific activity even when the external demands are similar.

Acute Effects on Impact Accelerations Running with Objects in the Hand

Amateur runners usually run carrying implements in their hands (keys, a mobile phone, or a bottle of water). However, there is a lack of literature about the effects of different handloads on impact accelerations. Thus, this study aimed to analyse the effects of carrying different objects in the hand on impact accelerations during running. Nineteen male recreational runners (age 24.3 ± 6.8 years, training volume of 25 ± 7.38 km/week) performed twenty minutes of running on a treadmill at 2.78 m/s with four different conditions: no extra weight, with keys, with a mobile phone, and with a bottle of water. Impact acceleration and spatio-temporal parameters were analysed through a wireless triaxial accelerometry system composed of three accelerometers: two placed in each tibia and one placed on the forehead. A higher tibia acceleration rate in the dominant leg was observed when participants ran holding both a mobile phone (p = 0.027; ES = 0.359) and a bottle of water (p = 0.027; ES = 0.359), compared to no extra weight. No changes were observed in peak acceleration, acceleration magnitude, and shock attenuation in any other conditions. Likewise, neither stride frequency nor step length was modified. Our results suggest that recreational runners should not worry about carrying objects in their hands, like a mobile phone or a bottle of water, in short races because their effect seems minimal.

Treadmill and Running Speed Effects on Acceleration Impacts: Curved Non-Motorized Treadmill vs. Conventional Motorized Treadmill

An increase in the popularity of running can be seen over the last decades, with a large number of injuries on it. Most of the running injuries are related to impact accelerations and are due to overuse. In order to reduce the risk of injury or to improve performance and health new treadmill designs have been created, as it can be the curved non-motorized treadmill. The aim of this study was to analyse impact accelerations, spatio-temporal parameters and perceptual differences while running on curved non-motorized treadmill (cNMT) compared to motorized treadmill (MT) at different speeds. Therefore, 27 recreational runners completed two tests consisting of 10 min warm-up and three bouts of 8 min running at 2.77 m/s, 3.33 m/s and self-selected speed on cNMT and MT, previously randomised. Although the surface did not influence spatio-temporal parameters, a reduction in impact accelerations, head acceleration rate (mean effect size [ES] = 0.86), tibia peak (mean ES = 0.45) and tibia magnitude (mean ES = 0.55), was observed while running on cNMT in comparison with running on MT. Moreover, higher heart rate (HR) (mean ES = 0.51) and rating of perceived effort (RPE) (mean ES = 0.34) were found while running on cNMT. These findings demonstrated that higher intensity training and lower impact accelerations are experimented on cNMT, what can be used by trainers and athletes while planning training sessions.

The Effect of Footwear, Running Speed, and Location on the Validity of Two Commercially Available Inertial Measurement Units During Running

Introduction: Most running-related injuries are believed to be caused by abrupt changes in training load, compounded by biomechanical movement patterns. Wearable technology has made it possible for runners to quantify biomechanical loads (e.g., peak positive acceleration; PPA) using commercially available inertial measurement units (IMUs). However, few devices have established criterion validity. The aim of this study was to assess the validity of two commercially available IMUs during running. Secondary aims were to determine the effect of footwear, running speed, and IMU location on PPA.

Materials and Methods: Healthy runners underwent a biomechanical running analysis on an instrumented treadmill. Participants ran at their preferred speed in three footwear conditions (neutral, minimalist, and maximalist), and at three speeds (preferred, +10%, −10%) in the neutral running shoes. Four IMUs were affixed at the distal tibia (IMeasureU-Tibia), shoelaces (RunScribe and IMeasureU-Shoe), and insole (Plantiga) of the right shoe. Pearson correlations were calculated for average vertical loading rate (AVLR) and PPA at each IMU location.

Results: The AVLR had a high positive association with PPA (IMeasureU-Tibia) in the neutral and maximalist (r = 0.70–0.72; p ≤ 0.001) shoes and in all running speed conditions (r = 0.71–0.83; p ≤ 0.001), but low positive association in the minimalist (r = 0.47; p < 0.05) footwear condition. Conversely, the relationship between AVLR and PPA (Plantiga) was high in the minimalist (r = 0.75; p ≤ 0.001) condition and moderate in the neutral (r = 0.50; p < 0.05) and maximalist (r = 0.57; p < 0.01) footwear. The RunScribe metrics demonstrated low to moderate positive associations (r = 0.40–0.62; p < 0.05) with AVLR across most footwear and speed conditions.

Discussion: Our findings indicate that the commercially available Plantiga IMU is comparable to a tibia-mounted IMU when acting as a surrogate for AVLR. However, these results vary between different levels of footwear and running speeds. The shoe-mounted RunScribe IMU exhibited slightly lower positive associations with AVLR. In general, the relationship with AVLR improved for the RunScribe sensor at slower speeds and improved for the Plantiga and tibia-mounted IMeasureU sensors at faster speeds.

Tibial acceleration and shock attenuation while running over different surfaces in a trail environment

OBJECTIVES

Increased tibial axial acceleration and reduced shock attenuation are associated with running injuries and are believed to be influenced by surface type. Trail running has increased in popularity and is thought to have softer surface properties than paved surface, but it is unclear if trail surfaces influence tibial acceleration and shock attenuation. The purpose of this study was to investigate peak triaxial and resultant tibial acceleration as well as axial and resultant shock attenuation among dirt, gravel, and paved surfaces.

DESIGN

Fifteen recreational runners (12 females, 3 males, age=27.7±9.1 years) ran over dirt, gravel, and paved surfaces in a trail environment while instrumented with triaxial tibial and head accelerometers.

METHODS

Differences between tri-planar peak tibial accelerations (braking, propulsion, axial, medial, lateral, and resultant) and shock attenuations (axial and resultant) among surface types were assessed with one-way ANOVAs with Bonferroni post-hoc tests.

RESULTS

No significant differences were found for tibial accelerations or shock attenuations among surface types (p>0.05).

CONCLUSIONS

Dirt and gravel trail running surfaces do not have lower tibial accelerations or greater shock attenuation than paved surfaces. While runners are encouraged to enjoy the psychological benefits of trail running, trail surfaces do not appear to reduce loading forces associated with running-related injuries.

Muscle Tone and Body Weight Predict Uphill Race Time in Amateur Trail Runners

Background: Vertical kilometer is an emerging sport where athletes continuously run uphill. The aims of this study were to assess changes in vertical impacts caused by uphill running (UR) and the relation between the anthropometric and lower limb muscular characteristics with speed. Methods: Ten male experienced runners (35 ± 7 years old) participated in this study. In the racetrack (4.2 km long, 565 m high), seven sections were stablished. Mean speed and impact value of sections with similar slope (≈21%) were calculated. The gastrocnemius stiffness (GS) and tone (GT); and the vastus lateralis stiffness (VS) and tone (VT) were assessed before the race. Results: Pearson’s correlation showed a linear relationship between vs. and VT (r = 0.829; p = 0.000), GT and GS (r = 0.792; p = 0.001). Mean speed is correlated with weight (r = −0.619; p = 0.024) and GT (r = 0.739; p = 0.004). Multiple linear regressions showed a model with weight and GT as dependent variables of mean speed. Mean impacts decreased significantly between sections along the race. Conclusions: The vertical impacts during UR were attenuated during the race. Moreover, body weight and GT were associated with the time-to-finish, which supports that low weight alone could not be enough to be faster, and strength training of plantar flexors may be a determinant in UR.

Running behaviors, motivations, and injury risk during the COVID-19 pandemic: A survey of 1147 runners

The COVID-19 pandemic has influenced activity behaviors worldwide. Given the accessibility of running as exercise, gaining information on running behaviors, motivations, and running-related injury (RRI) risk during the pandemic is warranted. The purpose of this study was to assess the influence of the COVID-19 pandemic on running volume, behaviors, motives, and RRI changes from the year prior to the pandemic to the timeframe during social isolation restrictions. Runners of all abilities were recruited via social media to complete a custom Qualtrics survey. Demographics, running volume, behaviors, motivations, and injury status were assessed for the year prior to the pandemic, and during social isolation measures. Descriptive statistics and Student’s t-tests were used to assess changes in running outcomes during the pandemic. Logistic regressions were used to assess the influence of demographics on running behaviors and injury. Adjusted RRI risk ratios were calculated to determine the odds of sustaining an injury during the pandemic. Alpha was set to.05 for all analyses. A total of 1147 runners (66% females, median age: 35 years) across 15 countries (96% United States) completed the survey. Runners reported increased runs per week (Mean Difference with Standard Error [MD]: 0.30 [0.05], p < .001), sustained runs (MD: 0.44 [0.05], p < .001), mileage (MD: 0.87 [0.33], p = .01), and running times of day (MD: 0.11 [0.03], p < .001) during the pandemic, yet reported less workouts (i.e. sprint intervals; MD: -0.33 [0.06], p < .001), and less motives (MD [SE]: -0.41 [0.04], p < .001). Behavior changes were influenced by running experience and age. There was 1.40 (CI: 1.18,1.61) times the RRI risk during the pandemic compared to prior to the social isolation period. The COVID-19 pandemic influenced runners’ behaviors with increased training volume, decreased intensity and motivation, and heightened injury risk. These results provide insights into how physical activity patterns were influenced by large-scale social isolation directives associated with the pandemic.

Effectiveness and Reliability of Foot Orthoses on Impact Loading and Lower Limb Kinematics When Running at Preferred and Nonpreferred Speeds

This study examined the effect of foot orthoses used on ground reaction forces, ankle, and knee kinematics when running at preferred and nonpreferred speeds. Sixteen runners ran on instrumented treadmills at various speeds (90%, 100%, and 110% of preferred speed) when wearing arch-support and flat-control orthoses. Two-way repeated analysis of variance (ANOVA) was performed on the mean and coefficient of variation of all variables. Results indicated that arch-support orthoses experienced larger maximum loading rates than flat-control orthoses (P = .017, 95% CI, 2.22 to 19.53). Slower speed was related to smaller loading rates (preferred: P = .002, 95% CI, -17.02 to -4.20; faster: P = .003, 95% CI, -29.78 to -6.17), shorter stride length (preferred: P < .001, 95% CI, -0.204 to -0.090; faster: P < .001, 95% CI, -0.382 to -0.237), and longer contact time (preferred: P < .001, 95% CI, 0.006-0.021; faster: 95% CI, 0.012-0.042). In arch-support condition, preferred speed induced higher stride length coefficient of variation (P = .046, 95% CI, 0.035-1.117) than faster speed, while displaying no differences in flat-control condition. These findings suggest that the use of arch-support orthoses would influence impact loading, but not spatial-temporal and joint kinematics in recreational runners.

Effect of Grade and Surface Type on Peak Tibial Acceleration in Trained Distance Runners

Runners experience repeated impact forces during training, and the culmination of these forces can contribute to overuse injuries. The purpose of this study was to compare peak vertical tibial acceleration (TA) in trained distance runners on 3 surface types (grass, asphalt, and concrete) and 3 grades (incline, decline, and level). During visit 1, subjects completed a 1-mile time trial to determine their pace for all running trials: 80% (5%) of the average time trial velocity. During visit 2, subjects were outfitted with a skin-mounted accelerometer and performed 18 separate running trials during which peak TA was assessed during the stance phase. Each subject ran 2 trials for each condition with 2 minutes of rest between trials. Peak TA was different between decline (8.04 [0.12] g) and incline running (7.31 [0.35] g; P = .020). On the level grade, peak TA was greater during grass (8.22 [1.22] g) compared with concrete (7.47 [1.65] g; P = .017). On the incline grade, grass (7.68 [1.44] g) resulted in higher peak TA than asphalt (6.99 [1.69] g; P = .030). These results suggest that under certain grade conditions grass may result in higher TA compared with either concrete or asphalt.

Tibial Acceleration-Based Prediction of Maximal Vertical Loading Rate During Overground Running: A Machine Learning Approach

Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical risk-factors of running related injuries or athletic performance during a running analysis. An interesting ground reaction force-derived variable to track is the maximal vertical instantaneous loading rate (VILR). This impact characteristic is traditionally derived from a fixed force platform, but wearable inertial sensors nowadays might approximate its magnitude while running outside the lab. The time-discrete axial peak tibial acceleration (APTA) has been proposed as a good surrogate that can be measured using wearable accelerometers in the field. This paper explores the hypothesis that applying machine learning to time continuous data (generated from bilateral tri-axial shin mounted accelerometers) would result in a more accurate estimation of the VILR. Therefore, the purpose of this study was to evaluate the performance of accelerometer-based predictions of the VILR with various machine learning models trained on data of 93 rearfoot runners. A subject-dependent gradient boosted regression trees (XGB) model provided the most accurate estimates (mean absolute error: 5.39 ± 2.04 BW⋅s^–1, mean absolute percentage error: 6.08%). A similar subject-independent model had a mean absolute error of 12.41 ± 7.90 BW⋅s^–1 (mean absolute percentage error: 11.09%). All of our models had a stronger correlation with the VILR than the APTA (p < 0.01), indicating that multiple 3D acceleration features in a learning setting showed the highest accuracy in predicting the lab-based impact loading compared to APTA.

Use of Wearables: Tracking and Retraining in Endurance Runners

Wearable devices are ubiquitous among runners, coaches, and clinicians with an ever-increasing number of devices coming on the market. In place of gold standard measures in the laboratory, these devices attempt to provide a surrogate means to track running biomechanics outdoors. This review provides an update on recent literature in the field of wearable devices in runners, with an emphasis on criterion validity and usefulness in the coaching and rehabilitation of runners. Our review suggests that while enthusiasm should be tempered, there is still much for runners to gain with wearables. Overall, our review finds evidence supporting the use of wearables to improve running performance, track global training loads applied to the runner, and provide real-time feedback on running speed and run cadence. Case studies illustrate the use of wearables for the purposes of performance and rehabilitation.

Machine learning algorithms can classify outdoor terrain types during running using accelerometry data

BACKGROUND

Running is a popular physical activity that benefits health; however, running surface characteristics may influence loading impact and injury risk. Machine learning algorithms could automatically identify running surface from wearable motion sensors to quantify running exposures, and perhaps loading and injury risk for a runner.

RESEARCH QUESTION

(1) How accurately can machine learning algorithms identify surface type from three-dimensional accelerometer sensors? (2) Does the sensor count (single or two-sensor setup) affect model accuracy?

METHODS

Twenty-nine healthy adults (23.3 ± 3.6 years, 1.8 ± 0.1 m, and 63.6 ± 8.5 kg) participated in this study. Participants ran on three different surfaces (concrete, synthetic, woodchip) while fit with two three-dimensional accelerometers (lower-back and right tibia). Summary features (n = 208) were extracted from the accelerometer signals. Feature-based Gradient Boosting (GB) and signal-based deep learning Convolutional Neural Network (CNN) models were developed. Models were trained on 90% of the data and tested on the remaining 10%. The process was repeated five times, with data randomly shuffled between train-test splits, to quantify model performance variability.

RESULTS

All models and configurations achieved greater than 90% average accuracy. The highest performing models were the two-sensor GB and tibia-sensor CNN (average accuracy of 97.0 ± 0.7 and 96.1 ± 2.6%, respectively).

SIGNIFICANCE

Machine learning algorithms trained on running data from a single- or dual-sensor accelerometer setup can accurately distinguish between surfaces types. Automatic identification of surfaces encountered during running activities could help runners and coaches better monitor training load, improve performance, and reduce injury rates.

A narrative review of potential measures of dynamic stability to be used during outdoor locomotion on different surfaces

Dynamic stability of locomotion plays an important role in running injuries, particularly during trail running where ankle injuries occur frequently. Several studies have investigated dynamic stability of locomotion using wearable accelerometer measurements. However, no study has reviewed how dynamic stability of locomotion is quantified using accelerometry. Therefore, the present review aims to synthetise the methods and findings of studies investigating stability related parameters measured by accelerometry, during locomotion on various surfaces, and among asymptomatic participants. A systematic search of studies associated with locomotion was conducted. Only studies including assessment of dynamic stability parameters based on accelerometry, including at least one group of asymptomatic participants, and conditions that occur during trail running were considered relevant for this review. Consequently, all retrieved studies used a non-obstructive portable accelerometer or an inertial measurement unit. Fifteen studies used a single tri-axial accelerometer placed above the lumbar region allowing outdoor recordings. From trunk accelerations, a combination of index of cycle repeatability and signal dispersion can adequately be used to assess dynamic stability. However, as most studies included indoor conditions, studies addressing the biomechanics of trail running in outdoor conditions are warranted.

The relationship between static and dynamic foot posture and running biomechanics: A systematic review and meta-analysis

BACKGROUND

Medial longitudinal arch characteristics are thought to be a contributing factor to lower limb running injuries. Running biomechanics associated with different foot types have been proposed as one of the potential underlying mechanisms. However, no systematic review has investigated this relationship.

RESEARCH QUESTION

The aim of this study was to conduct a systematic literature search and synthesize the evidence about the relationship between foot posture and running biomechanics.

METHODS

For this systematic review and meta-analysis different electronic databases (Pubmed, Web of Science, Cochrane, SportDiscus) were searched to identify studies investigating the relationship between medial longitudinal arch characteristics and running biomechanics. After identification of relevant articles, two independent researchers determined the risk of bias of included studies. For homogenous outcomes, data pooling and meta-analysis (random effects model) was performed, and levels of evidence determined.

RESULTS

Of the 4088 studies initially identified, a total of 25 studies were included in the qualitative review and seven in the quantitative analysis. Most studies had moderate and three studies a low risk of bias. Moderate evidence was found for a relationship between foot posture and subtalar joint kinematics (small pooled effects: -0.59; 95%CI -1.14 to - 0.003) and leg stiffness (small pooled effect: 0.59; 95%CI 0.18 to 0.99). Limited or very limited evidence was found for a relationship with forefoot kinematics, tibial/leg rotation, tibial acceleration/shock, plantar pressure distribution, plantar fascia tension and ankle kinetics as well as an interaction of foot type and footwear regarding tibial rotation.

SIGNIFICANCE

While there is evidence for an association between foot posture and subtalar joint kinematics and leg stiffness, no clear relationship was found for other biomechanical outcomes. Since a comprehensive meta-analysis was limited by the heterogeneity of included studies future research would benefit from consensus in foot assessment and more homogenous study designs.

Effect of running speed on temporal and frequency indicators from wearable MEMS accelerometers

Amplified by the development of new technologies, the interest in personal performance has been growing over the last years. Acceleration has proved to be an easy variable to collect, and was addressed in several works. However, few of them evaluate the effect of running speed on relevant indicators. The influence of the sensors location on the measurement is rarely studied as well. This study is dedicated to investigating the effect of running speed on acceleration measured at three different positions on 18 volunteers. All participants were equipped with three inertial measurement units: on the dorsal surface of the right foot (Fo), at the centre of gravity of the tibia (Ti), at the L4-L5 lumbar (Lu). The test was performed on a treadmill at nine randomised speeds between 8 and 18 km/h. Ten accelerometric variables were calculated. Linear regressions were used to calculate speed from the indicators calculated on (Lu), (Ti), (Fo). Indicators associated to signal energy were highly correlated with speed (r2>0.90). Median frequency appears to be affected by the frequency resolution. Finally, the measurement points closest to the impact zone result in the most correlated indicators.

The measurement of tibial acceleration in runners-A review of the factors that can affect tibial acceleration during running and evidence-based guidelines for its use

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.

The influence of running velocity on resultant tibial acceleration in runners

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 2 min 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.

Non-motorized Treadmill Running Is Associated with Higher Cardiometabolic Demands Compared with Overground and Motorized Treadmill Running

The aim of this study was to compare the cardiometabolic demands of running on a curved non-motorized treadmill (cNMT) with overground (OVR) and motorized treadmill (MOT) running. Fourteen trained male (n = 7) and female (n = 7) runners (V·O2peak 56.6 ± 4.0 mL.kg⁻¹.min⁻¹) participated in the study. Each experimental session consisted of 5 × 6-min bouts of running at progressively higher speeds, separated by 6-min rest (females 9–15 km.h⁻¹; males 10.5–16.5 km.h⁻¹). Oxygen consumption (V·O2) and heart rate (HR) during the last 2 min of each bout were measured using a portable metabolic cart. Running at a set speed on the cNMT required a higher percentage of V·O2peak than OVR (mean ± 90% CI, 22 ± 6%; ES ± 90% CI, 1.87 ± 0.15) and MOT (16 ± 6%; ES 1.50 ± 0.15) running. Similarly, HR during the cNMT was higher compared to OVR (25 ± 9 beats.min⁻¹, ES 1.23 ± 0.14) and MOT (22 ± 9 beats.min⁻¹, ES 1.35 ± 0.13) trials. The decline in running economy observed during the cNMT trial was negatively related to body mass (R² 0.493, P = 0.01), indicating lighter runners were required to work at a higher relative intensity to overcome treadmill belt resistance. These data demonstrate the higher cardiometabolic demand associated with running at a given speed on the cNMT. It is critical these differences are taken into account when prescribing training intensities on the cNMT or translating data from the laboratory to an athletic setting.