scholarly journals Sacral acceleration can predict whole-body kinetics and stride kinematics across running speeds

2021 ◽  
Author(s):  
Ryan Alcantara ◽  
Evan Day ◽  
Michael Hahn ◽  
Alena Grabowski
Keyword(s):  
Risk Factors ◽  
Contact Time ◽  
Injury Risk ◽  
Stress Fractures ◽  
Whole Body ◽  
Accelerometer Data ◽  
Ground Contact ◽  
Wearable Sensor ◽  
Ground Contact Time ◽  
Injury Risk Factors

Background. Stress fractures are injuries caused by repetitive loading during activities such as running. The application of advanced analytical methods such as machine learning to data from multiple wearable sensors has allowed for predictions of biomechanical variables associated with running-related injuries like stress fractures. However, it is unclear if data from a single wearable sensor can accurately estimate variables that characterize external loading during running such as peak vertical ground reaction force (vGRF), vertical impulse, and ground contact time. Predicting these biomechanical variables with a single wearable sensor could allow researchers, clinicians, and coaches to longitudinally monitor biomechanical running-related injury risk factors without expensive force-measuring equipment.Purpose. We quantified the accuracy of applying quantile regression forest (QRF) and linear regression (LR) models to sacral-mounted accelerometer data to predict peak vGRF, vertical impulse, and ground contact time across a range of running speeds.Methods. Thirty-seven collegiate cross country runners (24 females, 13 males) ran on a force-measuring treadmill at 3.8 – 5.4 m/s while wearing an accelerometer clipped posteriorly to the waistband of their running shorts. We cross-validated QRF and LR models by training them on acceleration data, running speed, step frequency, and body mass as predictor variables. Trained models were then used to predict peak vGRF, vertical impulse, and contact time. We compared predicted values to those calculated from a force-measuring treadmill on a subset of data (n = 9) withheld during model training. We quantified prediction accuracy by calculating the root mean square error (RMSE) and mean absolute percentage error (MAPE).Results. The QRF model predicted peak vGRF with a RMSE of 0.150 body weights (BW) and MAPE ± SD of 4.27 ± 2.85%, predicted vertical impulse with a RMSE of 0.004 BW*s and MAPE of 0.80 ± 0.91%, and predicted contact time with a RMSE of 0.011 s and MAPE of 4.68 ± 3.00%. The LR model predicted peak vGRF with a RMSE of 0.139 BW and MAPE of 4.04 ± 2.57%, predicted vertical impulse with a RMSE of 0.002 BW*s and MAPE of 0.50 ± 0.42%, and predicted contact time with a RMSE of 0.008 s and MAPE of 3.50 ± 2.27%. There were no statistically significant differences between QRF and LR model prediction MAPE for peak vGRF (p = 0.549) or vertical impulse (p = 0.073), but the LR model’s MAPE for contact time was significantly lower than the QRF model’s MAPE (p = 0.0497).Conclusions. Our findings indicate that the QRF and LR models can accurately predict peak vGRF, vertical impulse, and contact time (MAPE < 5%) from a single sacral-mounted accelerometer across a range of running speeds. These findings may be beneficial for researchers, clinicians, or coaches seeking to monitor running-related injury risk factors without force-measuring equipment.

scholarly journals Sacral acceleration can predict whole-body kinetics and stride kinematics across running speeds

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11199
Author(s):  
Ryan S. Alcantara ◽  
Evan M. Day ◽  
Michael E. Hahn ◽  
Alena M. Grabowski
Keyword(s):  
Risk Factors ◽  
Contact Time ◽  
Measuring Equipment ◽  
Injury Risk ◽  
Stress Fractures ◽  
Whole Body ◽  
Ground Contact ◽  
Wearable Sensor ◽  
Ground Contact Time ◽  
Injury Risk Factors

Background Stress fractures are injuries caused by repetitive loading during activities such as running. The application of advanced analytical methods such as machine learning to data from multiple wearable sensors has allowed for predictions of biomechanical variables associated with running-related injuries like stress fractures. However, it is unclear if data from a single wearable sensor can accurately estimate variables that characterize external loading during running such as peak vertical ground reaction force (vGRF), vertical impulse, and ground contact time. Predicting these biomechanical variables with a single wearable sensor could allow researchers, clinicians, and coaches to longitudinally monitor biomechanical running-related injury risk factors without expensive force-measuring equipment. Purpose We quantified the accuracy of applying quantile regression forest (QRF) and linear regression (LR) models to sacral-mounted accelerometer data to predict peak vGRF, vertical impulse, and ground contact time across a range of running speeds. Methods Thirty-seven collegiate cross country runners (24 females, 13 males) ran on a force-measuring treadmill at 3.8–5.4 m/s while wearing an accelerometer clipped posteriorly to the waistband of their running shorts. We cross-validated QRF and LR models by training them on acceleration data, running speed, step frequency, and body mass as predictor variables. Trained models were then used to predict peak vGRF, vertical impulse, and contact time. We compared predicted values to those calculated from a force-measuring treadmill on a subset of data (n = 9) withheld during model training. We quantified prediction accuracy by calculating the root mean square error (RMSE) and mean absolute percentage error (MAPE). Results The QRF model predicted peak vGRF with a RMSE of 0.150 body weights (BW) and MAPE of 4.27  ±  2.85%, predicted vertical impulse with a RMSE of 0.004 BW*s and MAPE of 0.80  ±  0.91%, and predicted contact time with a RMSE of 0.011 s and MAPE of 4.68  ±  3.00%. The LR model predicted peak vGRF with a RMSE of 0.139 BW and MAPE of 4.04  ±  2.57%, predicted vertical impulse with a RMSE of 0.002 BW*s and MAPE of 0.50  ±  0.42%, and predicted contact time with a RMSE of 0.008 s and MAPE of 3.50  ±  2.27%. There were no statistically significant differences between QRF and LR model prediction MAPE for peak vGRF (p = 0.549) or vertical impulse (p = 0.073), but the LR model’s MAPE for contact time was significantly lower than the QRF model’s MAPE (p = 0.0497). Conclusions Our findings indicate that the QRF and LR models can accurately predict peak vGRF, vertical impulse, and contact time (MAPE < 5%) from a single sacral-mounted accelerometer across a range of running speeds. These findings may be beneficial for researchers, clinicians, or coaches seeking to monitor running-related injury risk factors without force-measuring equipment.

Anterior cruciate ligament injury risk factors in football

2019 ◽  
Vol 59 (10) ◽  
Author(s):  
Gian Nicola Bisciotti ◽  
Karim Chamari ◽  
Emanuele Cena ◽  
Andrea Bisciotti ◽  
Alessandro Bisciotti ◽  
...  
Keyword(s):  
Risk Factors ◽  
Anterior Cruciate Ligament ◽  
Anterior Cruciate Ligament Injury ◽  
Injury Risk ◽  
Cruciate Ligament ◽  
Ligament Injury ◽  
Injury Risk Factors ◽  
Anterior Cruciate

scholarly journals Associations between Well-Being State and Match External and Internal Load in Amateur Referees

2021 ◽  
Vol 18 (6) ◽  
pp. 3322
Author(s):  
Eñaut Ozaeta ◽  
Javier Yanci ◽  
Carlo Castagna ◽  
Estibaliz Romaratezabala ◽  
Daniel Castillo
Keyword(s):  
External Load ◽  
Contact Time ◽  
Well Being ◽  
Ground Contact ◽  
Internal Load ◽  
Match Play ◽  
Power Meter ◽  
Ground Contact Time ◽  
Training Impulse ◽  
External Loads

The main aim of this paper was to examine the association between prematch well-being status with match internal and external load in field (FR) and assistant (AR) soccer referees. Twenty-three FR and 46 AR participated in this study. The well-being state was assessed using the Hooper Scale and the match external and internal loads were monitored with Stryd Power Meter and heart monitors. While no significant differences were found in Hooper indices between match officials, FR registered higher external loads (p < 0.01; ES: 0.75 to 5.78), spent more time in zone 4 and zone 5, and recorded a greater training impulse (TRIMP) value (p < 0.01; ES: 1.35 to 1.62) than AR. Generally, no associations were found between the well-being variables and external loads for FR and AR. Additionally, no associations were found between the Hooper indices and internal loads for FR and AR. However, several relationships with different magnitudes were found between internal and external match loads, for FR, between power and speed with time spent in zone 2 (p < 0.05; r = −0.43), ground contact time with zone 2 and zone 3 (p < 0.05; r = 0.50 to 0.60) and power, speed, cadence and ground contact time correlated with time spent in zone 5 and TRIMP (p < 0.05 to 0.01; r = 0.42 to 0.64). Additionally, for AR, a relationship between speed and time in zone 1 was found (p < 0.05; r = −0.30; CL = 0.22). These results suggest that initial well-being state is not related to match officials’ performances during match play. In addition, the Stryd Power Meter can be a useful device to calculate the external load on soccer match officials.

Progressive Workload Periodization Maximizes Effects of Nordic Hamstring Exercise on Muscle Injury Risk Factors

2018 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Lucas Severo-Silveira ◽  
Maurício P. Dornelles ◽  
Felipe X. Lima-e-Silva ◽  
César L. Marchiori ◽  
Thales M. Medeiros ◽  
...  
Keyword(s):  
Risk Factors ◽  
Muscle Injury ◽  
Injury Risk ◽  
Injury Risk Factors

Biomechanics Differ for Individuals With Similar Self-Reported Characteristics of Patellofemoral Pain During a High-Demand Multiplanar Movement Task

2020 ◽  
pp. 1-10
Author(s):  
Matthew K. Seeley ◽  
Seong Jun Son ◽  
Hyunsoo Kim ◽  
J. Ty Hopkins
Keyword(s):  
Ground Reaction Force ◽  
Injury Risk ◽  
Reaction Force ◽  
Patellofemoral Pain ◽  
Joint Torque ◽  
Whole Body ◽  
Ground Contact ◽  
High Demand ◽  
Contact Phase ◽  
Hip Extension

Context: Patellofemoral pain (PFP) is often categorized by researchers and clinicians using subjective self-reported PFP characteristics; however, this practice might mask important differences in movement biomechanics between PFP patients. Objective: To determine whether biomechanical differences exist during a high-demand multiplanar movement task for PFP patients with similar self-reported PFP characteristics but different quadriceps activation levels. Design: Cross-sectional design. Setting: Biomechanics laboratory. Participants: A total of 15 quadriceps deficient and 15 quadriceps functional (QF) PFP patients with similar self-reported PFP characteristics. Intervention: In total, 5 trials of a high-demand multiplanar land, cut, and jump movement task were performed. Main Outcome Measures: Biomechanics were compared at each percentile of the ground contact phase of the movement task (α = .05) between the quadriceps deficient and QF groups. Biomechanical variables included (1) whole-body center of mass, trunk, hip, knee, and ankle kinematics; (2) hip, knee, and ankle kinetics; and (3) ground reaction forces. Results: The QF patients exhibited increased ground reaction force, joint torque, and movement, relative to the quadriceps deficient patients. The QF patients exhibited: (1) up to 90, 60, and 35 N more vertical, posterior, and medial ground reaction force at various times of the ground contact phase; (2) up to 4° more knee flexion during ground contact and up to 4° more plantarflexion and hip extension during the latter parts of ground contact; and (3) up to 26, 21, and 48 N·m more plantarflexion, knee extension, and hip extension torque, respectively, at various times of ground contact. Conclusions: PFP patients with similar self-reported PFP characteristics exhibit different movement biomechanics, and these differences depend upon quadriceps activation levels. These differences are important because movement biomechanics affect injury risk and athletic performance. In addition, these biomechanical differences indicate that different therapeutic interventions may be needed for PFP patients with similar self-reported PFP characteristics.

Trail running injury risk factors: a living systematic review

2022 ◽  
pp. bjsports-2021-104858
Author(s):  
Carel Viljoen ◽  
Dina C (Christa) Janse van Rensburg ◽  
Willem van Mechelen ◽  
Evert Verhagen ◽  
Bruno Silva ◽  
...  
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Injury Risk ◽  
Previous History ◽  
Future Research ◽  
Injury Incidence ◽  
Extrinsic Factors ◽  
Factors Associated ◽  
Injury Risk Factors ◽  
Trail Running

ObjectiveTo review and frequently update the available evidence on injury risk factors and epidemiology of injury in trail running.DesignLiving systematic review. Updated searches will be done every 6 months for a minimum period of 5 years.Data sourcesEight electronic databases were searched from inception to 18 March 2021.Eligibility criteriaStudies that investigated injury risk factors and/or reported the epidemiology of injury in trail running.ResultsNineteen eligible studies were included, of which 10 studies investigated injury risk factors among 2 785 participants. Significant intrinsic factors associated with injury are: more running experience, level A runner and higher total propensity to sports accident questionnaire (PAD-22) score. Previous history of cramping and postrace biomarkers of muscle damage is associated with cramping. Younger age and low skin phototypes are associated with sunburn. Significant extrinsic factors associated with injury are neglecting warm-up, no specialised running plan, training on asphalt, double training sessions per day and physical labour occupations. A slower race finishing time is associated with cramping, while more than 3 hours of training per day, shade as the primary mode of sun protection and being single are associated with sunburn. An injury incidence range 0.7–61.2 injuries/1000 hours of running and prevalence range 1.3% to 90% were reported. The lower limb was the most reported region of injury, specifically involving blisters of the foot/toe.ConclusionLimited studies investigated injury risk factors in trail running. Our review found eight intrinsic and nine extrinsic injury risk factors. This review highlighted areas for future research that may aid in designing injury risk management strategies for safer trail running participation.PROSPERO registration numberCRD42021240832.

scholarly journals Longitudinal Assessment of Noncontact Anterior Cruciate Ligament Injury Risk Factors During Maturation in a Female Athlete: A Case Report

2009 ◽  
Vol 44 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Gregory D. Myer ◽  
Kevin R. Ford ◽  
Jon G. Divine ◽  
Eric J. Wall ◽  
Leamor Kahanov ◽  
...  
Keyword(s):  
Risk Factors ◽  
Anterior Cruciate Ligament ◽  
Body Mass ◽  
Female Athlete ◽  
Injury Risk ◽  
Cruciate Ligament ◽  
Acl Injury ◽  
Knee Abduction ◽  
Injury Risk Factors ◽  
Anterior Cruciate

Abstract Objective: To present a unique case of a young pubertal female athlete who was prospectively monitored for previously identified anterior cruciate ligament (ACL) injury risk factors for 3 years before sustaining an ACL injury. Background: In prospective studies, previous investigators have examined cross-sectional measures of anatomic, hormonal, and biomechanical risk factors for ACL injury in young female athletes. In this report, we offer a longitudinal example of measured risk factors as the participant matured. Differential Diagnosis: Partial or complete tear of the ACL. Measurements: The participant was identified from a cohort monitored from 2002 until 2007. No injury prevention training or intervention was included during this time in the study cohort. Findings: The injury occurred in the year after the third assessment during the athlete's club basketball season. Knee examination, magnetic resonance imaging findings, and arthroscopic evaluation confirmed a complete ACL rupture. The athlete was early pubertal in year 1 of the study and pubertal during the next 2 years; menarche occurred at age 12 years. At the time of injury, she was 14.25 years old and postpubertal, with closing femoral and tibial physes. For each of the 3 years before injury, she demonstrated incremental increases in height, body mass index, and anterior knee laxity. She also displayed decreased hip abduction and knee flexor strength, concomitant with increased knee abduction loads, after each year of growth. Conclusions: During puberty, the participant increased body mass and height of the center of mass without matching increases in hip and knee strength. The lack of strength and neuromuscular adaptation to match the increased demands of her pubertal stature may underlie the increased knee abduction loads measured at each annual visit and may have predisposed her to increased risk of ACL injury.

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