A Simplified Prediction Model for Lower Extremity Long Bone Stress Injuries in Male Endurance Running Athletes

Preseason Vertical Center of Mass Displacement During Running and Bone Mineral Density Z-Score Are Risk Factors for Bone Stress Injury Risk in Collegiate Cross-country Runners

OBJECTIVES: To (1) assess relationships between running biomechanics, bone health, and bone stress injuries (BSIs), and (2) determine which variables constitute the most parsimonious BSI risk model among collegiate cross-country runners. DESIGN: Prospective, observational cohort study. METHODS: Running gait and bone mineral density (BMD) data from healthy collegiate cross-country runners were collected at preseason over 6 seasons. A generalized estimating equation model with backward selection was used to develop the most parsimonious model for estimating BSI risk, controlling for sex, running speed, and prior BSI. The variables assessed were spatiotemporal, ground reaction force, and joint kinematics, based on previous literature. Quasi-likelihood under the independence model criterion values and R2 values were used to select the best-fitting model. RESULTS: Data from 103 runners were included in the analysis. The best-fitting model included vertical center of mass (COM) displacement and BMD z-score. Injury risk increased with greater vertical COM displacement (unit = 0.5 cm; relative risk [RR] = 1.14; 95% confidence interval [CI]: 1.01, 1.29; P = .04) and decreased with greater BMD z-score (unit = 0.5; RR = 0.83; 95% CI: 0.72, 0.95; P = .007). The model performed similarly when step rate was included instead of vertical COM displacement. CONCLUSION: Vertical COM displacement and BMD z-score contributed to the best model for estimating risk the risk of bone stress injury in cross-country runners. Step rate was also an important variable for assessing injury risk. J Orthop Sports Phys Ther 2023;53(12):1-8. Epub 20 October 2023. doi:10.2519/jospt.2023.11860.

Just How Confident Can We Be in Predicting Sports Injuries? A Systematic Review of the Methodological Conduct and Performance of Existing Musculoskeletal Injury Prediction Models in Sport

BACKGROUND

An increasing number of musculoskeletal injury prediction models are being developed and implemented in sports medicine. Prediction model quality needs to be evaluated so clinicians can be informed of their potential usefulness.

OBJECTIVE

To evaluate the methodological conduct and completeness of reporting of musculoskeletal injury prediction models in sport.

METHODS

A systematic review was performed from inception to June 2021. Studies were included if they: (1) predicted sport injury; (2) used regression, machine learning, or deep learning models; (3) were written in English; (4) were peer reviewed.

RESULTS

Thirty studies (204 models) were included; 60% of studies utilized only regression methods, 13% only machine learning, and 27% both regression and machine learning approaches. All studies developed a prediction model and no studies externally validated a prediction model. Two percent of models (7% of studies) were low risk of bias and 98% of models (93% of studies) were high or unclear risk of bias. Three studies (10%) performed an a priori sample size calculation; 14 (47%) performed internal validation. Nineteen studies (63%) reported discrimination and two (7%) reported calibration. Four studies (13%) reported model equations for statistical predictions and no machine learning studies reported code or hyperparameters.

CONCLUSION

Existing sport musculoskeletal injury prediction models were poorly developed and have a high risk of bias. No models could be recommended for use in practice. The majority of models were developed with small sample sizes, had inadequate assessment of model performance, and were poorly reported. To create clinically useful sports musculoskeletal injury prediction models, considerable improvements in methodology and reporting are urgently required.

Blood sample profile helps to injury forecasting in elite soccer players

Purpose

By analyzing external workloads with machine learning models (ML), it is now possible to predict injuries, but with a moderate accuracy. The increment of the prediction ability is nowadays mandatory to reduce the high number of false positives. The aim of this study was to investigate if players’ blood sample profiles could increase the predictive ability of the models trained only on external training workloads.

Method

Eighteen elite soccer players competing in Italian league (Serie B) during the seasons 2017/2018 and 2018/2019 took part in this study. Players’ blood samples parameters (i.e., Hematocrit, Hemoglobin, number of red blood cells, ferritin, and sideremia) were recorded through the two soccer seasons to group them into two main groups using a non-supervised ML algorithm (k-means). Additionally to external workloads data recorded every training or match day using a GPS device (K-GPS 10 Hz, K-Sport International, Italy), this grouping was used as a predictor for injury risk. The goodness of ML models trained were tested to assess the influence of blood sample profile to injury prediction.

Results

Hematocrit, Hemoglobin, number of red blood cells, testosterone, and ferritin were the most important features that allowed to profile players and to analyze the response to external workloads for each type of player profile. Players’ blood samples’ characteristics permitted to personalize the decision-making rules of the ML models based on external workloads reaching an accuracy of 63%. This approach increased the injury prediction ability of about 15% compared to models that take into consideration only training workloads’ features. The influence of each external workload varied in accordance with the players’ blood sample characteristics and the physiological demands of a specific period of the season.

Conclusion

Field experts should hence not only monitor the external workloads to assess the status of the players, but additional information derived from individuals’ characteristics permits to have a more complete overview of the players well-being. In this way, coaches could better personalize the training program maximizing the training effect and minimizing the injury risk.

Lower step rate is associated with a higher risk of bone stress injury: a prospective study of collegiate cross country runners

OBJECTIVES

To determine if running biomechanics and bone mineral density (BMD) were independently associated with bone stress injury (BSI) in a cohort of National Collegiate Athletic Association Division I cross country runners.

METHODS

This was a prospective, observational study of 54 healthy collegiate cross country runners over three consecutive seasons. Whole body kinematics, ground reaction forces (GRFs) and BMD measures were collected during the preseason over 3 years via motion capture on an instrumented treadmill and total body densitometer scans. All medically diagnosed BSIs up to 12 months following preseason data collection were recorded. Generalised estimating equations were used to identify independent risk factors of BSI.

RESULTS

Univariably, step rate, centre of mass vertical excursion, peak vertical GRF and vertical GRF impulse were associated with BSI incidence. After adjusting for history of BSI and sex in a multivariable model, a higher step rate was independently associated with a decreased risk of BSI. BSI risk decreased by 5% (relative risk (RR): 0.95; 95% CI 0.91 to 0.98) with each one step/min increase in step rate. BMD z-score was not a statistically significant risk predictor in the final multivariable model (RR: 0.93, 95% CI 0.85 to 1.03). No other biomechanical variables were found to be associated with BSI risk.

CONCLUSION

Low step rate is an important risk factor for BSI among collegiate cross country runners and should be considered when developing comprehensive programmes to mitigate BSI risk in distance runners.