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1.
J Athl Train ; 2021 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-34478518

RESUMO

OBJECTIVE: To synthesise the current evidence on the incidence of running-related injuries (RRI) and their association with training parameters (distance, duration, frequency, intensity), as well as recent changes in training parameters. DATA SOURCES: Searches were conducted in Medline/Ovid, CINAHL, Embase and SportDiscus up to July 7, 2020. STUDY SELECTION: Included articles had to report prospective data on RRIs and training parameters, or any changes in parameters, and be published in English or French. Two reviewers independently screened titles, abstracts and full-texts. DATA EXTRACTION: Data extraction and quality assessment (QualSyst) were performed by two independent raters. DATA SYNTHESIS: Thirty-six articles totaling 23,047 runners were included. Overall, 6,043 runners (26.2%) sustained an RRI (incidence range: 8.8% to 91.3%). The incidence of RRI was 14.9% in novice runners (range: 9.4 to 94.9%), 26.1% in recreational runners (range: 17.9 to 79.3%) and 62.6% in competitive runners (range: 52.6 to 91.3%). The three most frequently injured body parts were the knee (25.8%), foot/ankle (24.4%) and lower leg (24.4%). Overall, there was conflicting evidence about the association between weekly running distance, duration, frequency, intensity or specific changes in training parameters and the onset of RRIs. CONCLUSIONS: Despite high rates of RRIs, current evidence does not consistently link RRIs with specific training parameters or recent changes in training parameters. Therefore, caution should be taken when recommending optimal parameters or progressions. Given the multifactorial nature of RRIs, future studies also need to consider the interaction between training parameters, as well as psychosocial, hormonal, lifestyle and recovery outcomes to better understand the onset of RRIs.

3.
Front Sports Act Living ; 3: 665683, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34124660

RESUMO

Bone stress injuries (BSIs) are a common orthopedic injury with short-term, and potentially long-term, effects. Training load capacity, influenced by risk factors, plays a critical role in the occurrence of BSIs. Many factors determine how one's body responds to repetitive loads that have the potential to increase the risk of a BSI. As a scientific community, we have identified numerous isolated BSI risk factors. However, we have not adequately analyzed the integrative, holistic, and cumulative nature of the risk factors, which is essential to determine an individual's specific capacity. In this narrative review, we advocate for a personalized approach to monitor training load so that individuals can optimize their health and performance. We define "cumulative risk profile" as a subjective clinical determination of the number of risk factors with thoughtful consideration of their interaction and propose that athletes have their own cumulative risk profile that influences their capacity to withstand specific training loads. In our narrative review, we outline BSI risk factors, discuss the relationship between BSIs and training load, highlight the importance of individualizing training load, and emphasize the use of a holistic assessment as a training load guide.

4.
Front Sports Act Living ; 3: 643385, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33981991

RESUMO

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.

9.
Br J Sports Med ; 55(9): 512-513, 2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-33106249
10.
Sensors (Basel) ; 20(19)2020 Sep 29.
Artigo em Inglês | MEDLINE | ID: mdl-33003316

RESUMO

Fatigue is a multifunctional and complex phenomenon that affects how individuals perform an activity. Fatigue during running causes changes in normal gait parameters and increases the risk of injury. To address this problem, wearable sensors have been proposed as an unobtrusive and portable system to measure changes in human movement as a result of fatigue. Recently, a category of wearable devices that has gained attention is flexible textile strain sensors because of their ability to be woven into garments to measure kinematics. This study uses flexible textile strain sensors to continuously monitor the kinematics during running and uses a machine learning approach to estimate the level of fatigue during running. Five female participants used the sensor-instrumented garment while running to a state of fatigue. In addition to the kinematic data from the flexible textile strain sensors, the perceived level of exertion was monitored for each participant as an indication of their actual fatigue level. A stacked random forest machine learning model was used to estimate the perceived exertion levels from the kinematic data. The machine learning algorithm obtained a root mean squared value of 0.06 and a coefficient of determination of 0.96 in participant-specific scenarios. This study highlights the potential of flexible textile strain sensors to objectively estimate the level of fatigue during running by detecting slight perturbations in lower extremity kinematics. Future iterations of this technology may lead to real-time biofeedback applications that could reduce the risk of running-related overuse injuries.


Assuntos
Fadiga/diagnóstico , Têxteis , Dispositivos Eletrônicos Vestíveis , Fenômenos Biomecânicos , Feminino , Humanos , Aprendizado de Máquina , Movimento
11.
J Athl Train ; 55(12): 1285-1291, 2020 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-33064812

RESUMO

CONTEXT: Historically, methods of monitoring training loads in runners have used simple and convenient metrics, including the duration or distance run. Changes in these values are assessed on a week-to-week basis to induce training adaptations and manage injury risk. To date, whether different measures of external loads, including biomechanical measures, provide better information regarding week-to-week changes in external loads experienced by a runner is unclear. In addition, the importance of combining internal-load measures, such as session rating of perceived exertion (sRPE), with different external-load measures to monitor week-to-week changes in training load in runners is unknown. OBJECTIVE: To compare week-to-week changes in the training loads of recreational runners using different quantification methods. DESIGN: Case series. SETTING: Community based. PATIENTS OR OTHER PARTICIPANTS: Recreational runners in Vancouver, British Columbia. MAIN OUTCOME MEASURE(S): Week-to-week changes in running time, steps, and cumulative shock, in addition to the product of each of these variables and the corresponding sRPE scores for each run. RESULTS: Sixty-eight participants were included in the final analysis. Differences were present in week-to-week changes for running time compared with timeRPE (d = 0.24), stepsRPE (d = 0.24), and shockRPE (d = 0.31). The differences between week-to-week changes in running time and cumulative shock were also significant at the overall group level (d = 0.10). CONCLUSIONS: We found that the use of an internal training-load measure (sRPE) in combination with external load (training duration) provided a more individualized estimate of week-to-week changes in overall training stress. A better estimation of training stress has significant implications for monitoring training adaptations, resulting performance, and possibly injury risk reduction. We therefore recommend the regular use of sRPE and training duration to monitor training load in runners. The use of cumulative shock as a measure of external load in some runners may also be more valid than duration alone.


Assuntos
Esforço Físico/fisiologia , Corrida/psicologia , Adaptação Fisiológica , Humanos , Corrida/fisiologia
12.
Sensors (Basel) ; 20(15)2020 Aug 04.
Artigo em Inglês | MEDLINE | ID: mdl-32759831

RESUMO

The vertical ground reaction force (vGRF) and its passive and active peaks are important gait parameters and of great relevance for musculoskeletal injury analysis and prevention, the detection of gait abnormities, and the evaluation of lower-extremity prostheses. Most currently available methods to estimate the vGRF require a force plate. However, in real-world scenarios, gait monitoring would not be limited to a laboratory setting. This paper reports a novel solution using machine learning algorithms to estimate the vGRF and the timing and magnitude of its peaks from data collected by a single inertial measurement unit (IMU) on one of the lower limb locations. Nine volunteers participated in this study, walking on a force plate-instrumented treadmill at various speeds. Four IMUs were worn on the foot, shank, distal thigh, and proximal thigh, respectively. A random forest model was employed to estimate the vGRF from data collected by each of the IMUs. We evaluated the performance of the models against the gold standard measurement of the vGRF generated by the treadmill. The developed model achieved a high accuracy with a correlation coefficient, root mean square error, and normalized root mean square error of 1.00, 0.02 body weight (BW), and 1.7% in intra-participant testing, and 0.97, 0.10 BW, and 7.15% in inter-participant testing, respectively, for the shank location. The difference between the reference and estimated passive force peak values was 0.02 BW and 0.14 BW with a delay of -0.14% and 0.57% of stance duration for the intra- and inter-participant testing, respectively; the difference between the reference and estimated active force peak values was 0.02 BW and 0.08 BW with a delay of 0.45% and 1.66% of stance duration for the intra- and inter-participant evaluation, respectively. We concluded that vertical ground reaction force can be estimated using only a single IMU via machine learning algorithms. This research sheds light on the development of a portable wearable gait monitoring system reporting the real-time vGRF in real-life scenarios.

13.
J Orthop Sports Phys Ther ; 50(10): 564-569, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32741325

RESUMO

BACKGROUND: Quantifying total running distance is valuable, as it comprises some aspects of the mechanical/neuromuscular, cardiovascular, and perceptual/psychological loads that contribute to training stress and is partially predictive of distance-running success. However, running distance is only one aspect contributing to training stress. CLINICAL QUESTION: The purpose of this commentary is to highlight (1) problems with only using running distance to quantify running training and training stress, (2) the importance of alternative approaches to quantify and monitor training stress, (3) moderating factors (effect-measure modifiers) of training loads, and (4) the challenges of monitoring training stress to assess injury risks. KEY RESULTS: Training stress is influenced by external (ie, application of mechanical load) and internal (ie, physiological/psychological effort) training load factors. In running, some commonly used external load factors include volume and pace, while physiological internal load factors include session rating of perceived exertion, heart rate, or blood lactate level. Running distance alone might vastly obscure the cumulative training stress on different training days and, ultimately, misrepresent overall training stress. With emerging and novel wearable technology that quantifies external load metrics beyond volume or pace, the future of training monitoring should have an ever-increasing emphasis on biomechanical external load metrics, coupled with internal (ie, physiological/psychological) load metrics. CLINICAL APPLICATION: It may be difficult to change the running culture's obsession with weekly distance, but advanced and emerging methods to quantify running training discussed in this commentary will, with research confirmation, improve training monitoring and injury risk stratification. J Orthop Sports Phys Ther 2020;50(10):564-569. Epub 1 Aug 2020. doi:10.2519/jospt.2020.9533.


Assuntos
Condicionamento Físico Humano/métodos , Corrida/fisiologia , Monitores de Aptidão Física , Frequência Cardíaca , Humanos , Ácido Láctico/sangue , Percepção/fisiologia , Condicionamento Físico Humano/efeitos adversos , Esforço Físico/fisiologia , Fatores de Risco , Corrida/lesões , Estresse Mecânico , Dispositivos Eletrônicos Vestíveis
14.
J Biomech ; 108: 109886, 2020 07 17.
Artigo em Inglês | MEDLINE | ID: mdl-32636000

RESUMO

The three-dimensional trajectory of the body's centre of mass (COM) is useful to determine a number of biomechanical outcomes in running research. Previous studies have used the COM to calculate measures such as overstriding, vertical stiffness, and vertical oscillation. The COM is traditionally computed using the segmental analysis method, though this is expensive and time-consuming owing to the need for a full-body marker set. The purpose of this study was to determine whether the COM trajectory can be approximated by a single sacral marker during running. Seventy-one participants underwent a biomechanical running analysis on a treadmill utilizing a full-body marker set. Marker trajectories from the sacral marker and from the COM calculated using the segmental analysis method were compared over the entire gait cycle by computing intraclass correlation coefficient (ICC) and root-mean-square error. Paired t-tests were used to determine if the positions differed in mediolateral, anteroposterior, and vertical directions at three gait events (initial contact, midstance, and toe-off). The trajectories from the two methods exhibited a similar pattern in vertical and anteroposterior directions throughout the gait cycle, displaying strong correlations in these directions (ICC = 0.98 ±â€¯0.01 and 0.83 ±â€¯0.07). Our results suggest that a single sacral marker is a valid proxy for COM trajectory in vertical and anteroposterior directions at key events during the stance phase of running in a female recreational population. Researchers can therefore use a single sacral marker to estimate COM trajectory, rather than a full-body marker set, saving on both time and supplies.


Assuntos
Corrida , Fenômenos Biomecânicos , Teste de Esforço , Feminino , Marcha , Humanos , Sacro
15.
Sensors (Basel) ; 20(10)2020 May 22.
Artigo em Inglês | MEDLINE | ID: mdl-32455927

RESUMO

Abnormal running kinematics are associated with an increased incidence of lower extremity injuries among runners. Accurate and unobtrusive running kinematic measurement plays an important role in the detection of gait abnormalities and the prevention of injuries among runners. Inertial-based methods have been proposed to address this need. However, previous methods require cumbersome sensor setup or participant-specific calibration. This study aims to validate a shoe-mounted accelerometer for sagittal plane lower extremity angle measurement during running based on a deep learning approach. A convolutional neural network (CNN) architecture was selected as the regression model to generalize in inter-participant scenarios and to minimize poorly estimated joints. Motion and accelerometer data were recorded from ten participants while running on a treadmill at five different speeds. The reference joint angles were measured by an optical motion capture system. The CNN model predictions deviated from the reference angles with a root mean squared error (RMSE) of less than 3.5° and 6.5° in intra- and inter-participant scenarios, respectively. Moreover, we provide an estimation of six important gait events with a mean absolute error of less than 2.5° and 6.5° in intra- and inter-participants scenarios, respectively. This study highlights an appealing minimal sensor setup approach for gait analysis purposes.


Assuntos
Acelerometria , Aprendizado Profundo , Análise da Marcha , Extremidade Inferior/fisiologia , Corrida , Fenômenos Biomecânicos , Humanos
16.
Sensors (Basel) ; 19(23)2019 Dec 03.
Artigo em Inglês | MEDLINE | ID: mdl-31816931

RESUMO

Continuous kinematic monitoring of runners is crucial to inform runners of inappropriate running habits. Motion capture systems are the gold standard for gait analysis, but they are spatially limited to laboratories. Recently, wearable sensors have gained attention as an unobtrusive method to analyze performance metrics and the health conditions of runners. In this study, we developed a system capable of estimating joint angles in sagittal, frontal, and transverse planes during running. A prototype with fiber strain sensors was fabricated. The positions of the sensors on the pelvis were optimized using a genetic algorithm. A cohort of ten people completed 15 min of running at five different speeds for gait analysis by our prototype device. The joint angles were estimated by a deep convolutional neural network in inter- and intra-participant scenarios. In intra-participant tests, root mean square error (RMSE) and normalized root mean square error (NRMSE) of less than 2.2° and 5.3%, respectively, were obtained for hip, knee, and ankle joints in sagittal, frontal, and transverse planes. The RMSE and NRMSE in inter-participant tests were less than 6.4° and 10%, respectively, in the sagittal plane. The accuracy of this device and methodology could yield potential applications as a soft wearable device for gait monitoring of runners.


Assuntos
Monitorização Ambulatorial/instrumentação , Redes Neurais de Computação , Têxteis , Dispositivos Eletrônicos Vestíveis , Adulto , Algoritmos , Articulação do Tornozelo/patologia , Fenômenos Biomecânicos , Vestuário , Desenho de Equipamento , Marcha , Articulação do Quadril/patologia , Humanos , Articulação do Joelho/patologia , Aprendizado de Máquina , Masculino , Monitorização Ambulatorial/métodos , Movimento (Física) , Reprodutibilidade dos Testes , Adulto Jovem
17.
J Appl Biomech ; 35(2): 123-130, 2019 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-30421631

RESUMO

High magnitudes and rates of loading have been implicated in the etiology of running-related injuries. Knowledge of kinematic variables that are predictive of kinetic outcomes could inform clinic-based gait retraining programs. Healthy novice female runners ran on a treadmill while 3-dimensional biomechanical data were collected. Kinetic outcomes consisted of vertical impact transient, average vertical loading rate, instantaneous vertical loading rate, and peak braking force. Kinematic outcomes included step length), hip flexion angle at initial contact, horizontal distance from heel to center of mass at initial contact, shank angle at initial contact, and foot strike angle. Stepwise multiple linear regression was used to evaluate the amount of variance in kinetic outcomes explained by kinematic outcomes. A moderate amount of variance in kinetic outcomes (vertical impact transient = 46%, average vertical loading rate = 37%, instantaneous vertical loading rate = 49%, peak braking force = 54%) was explained by several discrete kinematic variables-predominantly speed, horizontal distance from heel to center of mass, foot strike angle, and step length. Hip flexion angle and shank angle did not contribute to any models. Decreasing step length and transitioning from a rearfoot strike may reduce kinetic risk factors for running-related injuries. In contrast, clinical strategies such as modifying shank angle and hip flexion angle would not appear to contribute significantly to the variance of kinetic outcomes after accounting for other variables.


Assuntos
Traumatismos em Atletas/prevenção & controle , Marcha , Corrida/lesões , Adulto , Traumatismos em Atletas/fisiopatologia , Fenômenos Biomecânicos , Feminino , , Humanos , Amplitude de Movimento Articular
18.
J Orthop Sports Phys Ther ; 49(3): 136-144, 2019 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-30526232

RESUMO

BACKGROUND: The high rate of running-related injury may be associated with increased peak braking forces (PBFs) and vertical loading rates. Gait retraining has been suggested by some experts to be an effective method to reduce loading parameters. OBJECTIVES: To investigate whether PBF could be decreased following an 8-session gait retraining program among a group of female recreational runners and which self-selected kinematic strategies could achieve this decrease. METHODS: In this exploratory study, 12 female recreational runners with high PBFs (greater than 0.27 body weight) completed an 8-session gait retraining program with real-time biofeedback of braking forces over the course of a half-marathon training program. Baseline and follow-up kinetics and kinematics were analyzed with a repeated-measures analysis of variance. RESULTS: There was an average reduction of 15% in PBF (-0.04 body weight; 95% confidence interval [CI]: -0.07, -0.02 body weight; P = .001; effect size, 0.62), accompanied by a 7% increase in step frequency (11.3 steps per minute; 95% CI: 1.8, 20.9 steps per minute; P = .024; effect size, 0.38) and a 6% decrease in step length (-5.5 cm; 95% CI: -9.9, -1.0 cm; P = .020; effect size, 0.40), from baseline to follow-up. CONCLUSION: The gait retraining program significantly reduced the PBF among a group of female recreational runners. This was achieved through a combination of increased step frequency and decreased step length. Furthermore, the modified gait pattern was incorporated into the runners' natural gait pattern by the completion of the program. Based on these results, the outlined gait retraining program should be further investigated to assess whether it may be an effective injury prevention strategy for recreational runners. This study was registered with ClinicalTrials.gov (NCT03302975). LEVEL OF EVIDENCE: Prevention, level 4. J Orthop Sports Phys Ther 2019;49(3):136-144. Epub 7 Dec 2018. doi:10.2519/jospt.2019.8587.


Assuntos
Traumatismos em Atletas/prevenção & controle , Biorretroalimentação Psicológica , Condicionamento Físico Humano/métodos , Corrida/lesões , Adulto , Traumatismos em Atletas/fisiopatologia , Fenômenos Biomecânicos , Desaceleração , Feminino , Marcha/fisiologia , Análise da Marcha , Humanos , Pessoa de Meia-Idade , Condicionamento Físico Humano/efeitos adversos
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