Distribution and Risk Factors for Stress Fractures in Competitive Figure Skaters and Association with Acute Fractures

Author(s):  
Thomas Naylor ◽  
Samuel Naylor
2020 ◽  
Author(s):  
Pauline Barbeau ◽  
Alan Michaud ◽  
Candyce Hamel ◽  
Danielle Rice ◽  
Becky Skidmore ◽  
...  

ABSTRACT Introduction Musculoskeletal injuries (MSKi) are a common challenge for those in military careers. Compared to their male peers, reports indicate that female military members and recruits are at greater risk of suffering MSKi during training and deployment. The objectives of this study were to identify the types and causes of MSKi among female military personnel and to explore the various risk factors associated with MSKi. Materials and Methods A scoping review was conducted over a 4-month time frame of English language, peer-reviewed studies published from 1946 to 2019. Search strategies for major biomedical databases (e.g., MEDLINE; Embase Classic + Embase; and the following EBM Reviews—Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Health Technology Assessment, and the NHS Economic Evaluation Database) were developed by a senior medical information specialist and included 2,891 titles/abstracts. Study selection and data collection were designed according to the Population, Concept, and Context framework. Studies were included if the study population provided stratified data for females in a military context. Results From a total of 2,287 citations captured from the literature searches, 168 peer-reviewed publications (144 unique studies) were eligible for inclusion. Studies were identified from across 10 countries and published between 1977 and 2019. Study designs were primarily prospective and retrospective cohorts. Most studies assessed both prevalence/incidence and risk factors for MSKi (62.50%), with few studies assessing cause (13.69%). For MSKi of female recruits compared to active female members, the prevalence was higher (19.7%-58.3% vs. 5.5%-56.6%), but the incidence (0.02%-57.7% vs. 13.5%-71.9%) was lower. The incidence of stress fractures was found to be much higher in female recruits than in active members (1.6%-23.9% vs. 2.7%). For anthropometric risk factors, increased body fat was a predictor of MSKi, but not stress fractures. For physiological risk factors for both female military groups, being less physically fit, later menarche, and having no/irregular menses were predictors of MSKi and stress fractures. For biomechanical risk factors, among female recruits, longer tibial length and femoral neck diameter increased the risk of stress fractures, and low foot arch increased risk of an ankle sprain. For female active military members, differences in shoulder rotation and bone strength were associated with risk of MSKi. For biological sex, being female compared to male was associated with an increased risk of MSKi, stress fractures, and general injuries. The consequences of experiencing MSKi for active military included limited duties, time off, and discharge. For recruits, these included missed training days, limited duty days, and release. Conclusions This scoping review provides insight into the current state of the evidence regarding the types and causes of MSKi, as well as the factors that influence MSKi among females in the military. Future research endeavors should focus on randomized controlled trials examining training paradigms to see if women are more susceptible. The data presented in the scoping review could potentially be used to develop training strategies to mitigate some of the identified barriers that negatively impact women from pursuing careers in the military.


2004 ◽  
Vol 14 (6) ◽  
pp. 375 ◽  
Author(s):  
N. Constantini ◽  
G. Mann ◽  
M. Nyska ◽  
O. Mei-dan ◽  
A. Even ◽  
...  

Author(s):  
Leanne Saxon

Sports participation has numerous positive health benefits; however, it is also associated with an increased risk of injury. While bone injuries in sport are less frequent than ligament tears, contusions, or surface wounds, they can be debilitating for an athlete because of the time needed for recovery. In this chapter I describe the incidence and cost of bone injuries in sport, fundamentals of bone biology and repair, risk factors associated with fractures, stress fractures, and periostitis, and review both current and possible future recommendations for the treatment of bone-related injuries....


2019 ◽  
Vol 29 (10) ◽  
pp. 1501-1510 ◽  
Author(s):  
Sayaka Nose‐Ogura ◽  
Osamu Yoshino ◽  
Michiko Dohi ◽  
Mika Kigawa ◽  
Miyuki Harada ◽  
...  

2013 ◽  
Vol 45 (10) ◽  
pp. 1843-1851 ◽  
Author(s):  
ADAM S. TENFORDE ◽  
LAUREN C. SAYRES ◽  
MARY LIZ McCURDY ◽  
KRISTIN L. SAINANI ◽  
MICHAEL FREDERICSON

2005 ◽  
Vol 37 (Supplement) ◽  
pp. S206
Author(s):  
Bruce Jones

Bone ◽  
2005 ◽  
Vol 37 (2) ◽  
pp. 267-273 ◽  
Author(s):  
Ville-Valtteri Välimäki ◽  
Henrik Alfthan ◽  
Eero Lehmuskallio ◽  
Eliisa Löyttyniemi ◽  
Timo Sahi ◽  
...  

2021 ◽  
Author(s):  
Ryan Alcantara ◽  
Evan Day ◽  
Michael Hahn ◽  
Alena Grabowski

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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