Comparison of different measures to monitor week-to-week changes in training load in high school runners

Ryan, Megan R.; Napier, Christopher; Greenwood, Daniel; Paquette, Max R.

doi:10.1177/1747954120970305

Cited by 15 publications

(10 citation statements)

References 35 publications

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“…However, it is unknown if peak axial tibial compression can be used to prospectively identify stress fracture risk despite the association between a bone’s load and risk of mechanical failure. If bone loading metrics can be identified as biomechanical risk factors, data collected by wearable devices could improve our understanding of stress fracture development in long distance runners as these data could be collected over the course of a run, competitive season, or several years in the environment experienced by runners daily and not only within a laboratory ( Backes et al, 2020 ; Edwards, 2018 ; Ryan et al, 2020 ). Biomechanical risk factors could then be considered alongside other metrics such as bone mineral density or nutritional deficiencies when determining an individual’s stress fracture risk ( Wright et al, 2015 ).…”

Section: Discussionmentioning

confidence: 99%

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

Alcantara

Day

Hahn

et al. 2021

PeerJ

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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.

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Section: Discussionmentioning

confidence: 99%

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

Alcantara

Day

Hahn

et al. 2021

PeerJ

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“…However, it is unknown if peak axial tibial compression can be used to prospectively identify stress fracture risk despite the association between a bone's load and risk of mechanical failure. If bone loading metrics can be identified as biomechanical risk factors, data collected by wearable devices could improve our understanding of stress fracture development in long distance runners as these data could be collected over the course of a run, competitive season, or several years in the environment experienced by runners daily and not only within a laboratory (Backes et al, 2020;Edwards, 2018;Ryan et al, 2020). Biomechanical risk factors could then be considered alongside other metrics such as bone mineral density or nutritional deficiencies when determining an individual's stress fracture risk (Wright et al, 2015).…”

Section: Preprintmentioning

confidence: 99%

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

Alcantara¹,

Day²,

Hahn³

et al. 2021

Preprint

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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.

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“…External cumulative loading is generally calculated as the product of a given external load's magnitude (e.g. peak GRF) and the number of steps taken over a given duration of running (Firminger and Edwards, 2016;Kiernan et al, 2018;Backes et al, 2020;Ryan et al, 2020) and has been associated with runningrelated overuse injuries (Colby et al, 2014;Bertelsen et al, 2017;Kiernan et al, 2018). Previous studies have predicted the peak vertical GRF from pelvis accelerometer data with a MAPE of 4.0 -8.3% during level-ground running (Neugebauer, Hawkins and Beckett, 2012;Alcantara et al, 2021), but a personalized LSTM network could be used to measure an athlete's external cumulative load with improved accuracy (MAPE = 2.7 ± 2.0%) across a range of running speeds and slopes.…”

Section: Discrete Variable Accuracymentioning

confidence: 99%

Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution

Alcantara

Edwards

Millet

et al. 2021

Preprint

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BackgroundGround reaction forces (GRFs) are important for understanding the biomechanics of human movement but the measurement of GRFs is generally limited to a laboratory environment. Wearable devices like accelerometers have been used to measure biomechanical variables outside the laboratory environment, but they cannot directly measure GRFs. Previous studies have used neural networks to predict the entire GRF waveform during the stance phase from wearable device data, but these networks require normalization of GRFs to the duration of a step or stance phase, resulting in a loss of the GRF waveform’s temporal component. Additionally, previous studies have predicted GRF waveforms during level-ground, but not uphill or downhill running. A method of predicting the normal (perpendicular to running surface) GRF waveform using wearable devices across a range of running speeds and slopes, while maintaining the GRF waveform’s temporal component, could allow researchers and clinicians to predict kinetic and kinematic variables outside the laboratory environment.PurposeWe sought to develop a recurrent neural network capable of predicting normal GRF waveforms across a range of running speeds and slopes using data from accelerometers located on the sacrum and shoe.Methods19 subjects completed 30-s running trials on a force-measuring treadmill at five slopes (0°, ±5°, ±10°) and three speeds (2.5, 3.33, and 4.17 m/s) per slope. One biaxial accelerometer was adhered to the sacrum and two uniaxial accelerometers were adhered to the right shoe during all trials. Accelerometers on the shoe were used to classify foot strike patterns as rearfoot, midfoot, or forefoot, and sacral acceleration data were divided into overlapping 12-ms windows, allowing the neural network to iteratively predict the experimentally-measured normal GRF waveform frame-by-frame. The mean, SD, and range of accelerometer data for each 12-ms window were included as neural network input features, along with the subject’s body mass, height, running speed, slope, and percentage of a trial’s steps classified as a rearfoot, midfoot, or forefoot strike. We assessed the accuracy and generalizability of the neural network using leave-one-subject-out cross validation, which provided an ensemble of Root Mean Square Error (RMSE) and relative RMSE (rRMSE) values comparing the normal GRF waveform predicted by the neural network to the normal GRF waveform measured by the force-measuring treadmill. Additionally, we calculated the mean absolute percent error (MAPE) of step frequency, contact time, normal impulse, normal GRF active peak, and loading rate between the predicted and measured GRF waveforms.ResultsThe average ± SD RMSE was 0.16 ± 0.04 BW and rRMSE was 6.4 ± 1.5% for neural network predictions of each subject’s normal GRF waveform compared to measured GRF waveforms across all conditions. RMSE values were lower during slow uphill running (2.5 m/s, +10°; 0.13 ± 0.07 BW) compared to fast downhill running (4.17 m/s, −10°; 0.20 ± 0.05 BW). The MAPE ± SD for step frequency was 0.1 ± 0.1%, contact time was 4.9 ± 4.0%, normal impulse was 6.4 ± 6.9%, normal GRF active peak was 8.5 ± 8.2%, and loading rate was 27.6 ± 36.1%.ConclusionsWe developed a recurrent neural network that uses accelerometer data to predict the continuous normal GRF waveform across a range of running speeds and slopes. The neural network does not require preliminary identification of the stance phase, maintains the temporal component of the GRF waveform, can be applied to up- and downhill running, and facilitates the prediction of kinetic and kinematic variables outside the laboratory environment. This represents a substantial step towards accurately quantifying and monitoring the external loads experienced by the body when running outdoors.

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Comparison of different measures to monitor week-to-week changes in training load in high school runners

Cited by 15 publications

References 35 publications

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

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

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

Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution

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