Literature DB >> 28804727

Development of a Prediction Model for Stress Fracture During an Intensive Physical Training Program: The Royal Marines Commandos.

Maria T Sanchez-Santos1,2, Trish Davey3, Kirsten M Leyland1,2, Adrian J Allsopp3, Susan A Lanham-New4, Andrew Judge1,5, Nigel K Arden1,2,5, Joanne L Fallowfield3.   

Abstract

BACKGROUND: Stress fractures (SFs) are one of the more severe overuse injuries in military training, and therefore, knowledge of potential risk factors is needed to assist in developing mitigating strategies.
PURPOSE: To develop a prediction model for risk of SF in Royal Marines (RM) recruits during an arduous military training program. STUDY
DESIGN: Case-control study; Level of evidence, 3.
METHODS: RM recruits (N = 1082; age range, 16-33 years) who enrolled between September 2009 and July 2010 were prospectively followed through the 32-week RM training program. SF diagnosis was confirmed from a positive radiograph or magnetic resonance imaging scan. Potential risk factors assessed at week 1 included recruit characteristics, anthropometric assessment, dietary supplement use, lifestyle habits, fitness assessment, blood samples, 25(OH)D, bone strength as measured by heel broadband ultrasound attention, history of physical activity, and previous and current food intake. A logistic least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was used to select potential predictors among 47 candidate variables. Model performance was assessed using measures of discrimination (c-index) and calibration. Bootstrapping was used for internal validation of the developed model and to quantify optimism.
RESULTS: A total of 86 (8%) volunteer recruits presented at least 1 SF during training. Twelve variables were identified as the most important risk factors of SF. Variables strongly associated with SF were age, body weight, pretraining weightbearing exercise, pretraining cycling, and childhood intake of milk and milk products. The c-index for the prediction model, which represents the model performance in future volunteers, was 0.73 (optimism-corrected c-index, 0.68). Although 25(OH)D and VO2max had only a borderline statistically significant association with SF, the inclusion of these factors improved the performance of the model.
CONCLUSION: These findings will assist in identifying recruits at greater risk of SF during training and will support interventions to mitigate this injury risk. However, external validation of the model is still required.

Entities:  

Keywords:  Royal Marines; military; prediction; risk factors; stress fracture

Year:  2017        PMID: 28804727      PMCID: PMC5533266          DOI: 10.1177/2325967117716381

Source DB:  PubMed          Journal:  Orthop J Sports Med        ISSN: 2325-9671


  50 in total

1.  Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets.

Authors:  E W Steyerberg; M J Eijkemans; F E Harrell; J D Habbema
Journal:  Stat Med       Date:  2000-04-30       Impact factor: 2.373

2.  Risk factors for clinical stress fractures in male military recruits: a prospective cohort study.

Authors:  Ville-Valtteri Välimäki; Henrik Alfthan; Eero Lehmuskallio; Eliisa Löyttyniemi; Timo Sahi; Harri Suominen; Matti J Välimäki
Journal:  Bone       Date:  2005-08       Impact factor: 4.398

Review 3.  Stress fractures: diagnosis, treatment, and prevention.

Authors:  Deepak S Patel; Matt Roth; Neha Kapil
Journal:  Am Fam Physician       Date:  2011-01-01       Impact factor: 3.292

4.  Nutritional influences on bone mineral density: a cross-sectional study in premenopausal women.

Authors:  S A New; C Bolton-Smith; D A Grubb; D M Reid
Journal:  Am J Clin Nutr       Date:  1997-06       Impact factor: 7.045

5.  The impact of lifestyle factors on stress fractures in female Army recruits.

Authors:  J M Lappe; M R Stegman; R R Recker
Journal:  Osteoporos Int       Date:  2001       Impact factor: 4.507

Review 6.  Stress fractures: pathophysiology, epidemiology, and risk factors.

Authors:  Stuart J Warden; David B Burr; Peter D Brukner
Journal:  Curr Osteoporos Rep       Date:  2006-09       Impact factor: 5.096

7.  Regularization Paths for Generalized Linear Models via Coordinate Descent.

Authors:  Jerome Friedman; Trevor Hastie; Rob Tibshirani
Journal:  J Stat Softw       Date:  2010       Impact factor: 6.440

8.  Use of simple measures of physical activity to predict stress fractures in young men undergoing a rigorous physical training program.

Authors:  R A Shaffer; S K Brodine; S A Almeida; K M Williams; S Ronaghy
Journal:  Am J Epidemiol       Date:  1999-02-01       Impact factor: 4.897

9.  Factors associated with discharge during marine corps basic training.

Authors:  Jared P Reis; Daniel W Trone; Caroline A Macera; Mitchell J Rauh
Journal:  Mil Med       Date:  2007-09       Impact factor: 1.437

10.  How to develop a more accurate risk prediction model when there are few events.

Authors:  Menelaos Pavlou; Gareth Ambler; Shaun R Seaman; Oliver Guttmann; Perry Elliott; Michael King; Rumana Z Omar
Journal:  BMJ       Date:  2015-08-11
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  2 in total

Review 1.  Non-Modifiable Risk Factors for Stress Fractures in Military Personnel Undergoing Training: A Systematic Review.

Authors:  Grace M Lennox; Patrick M Wood; Ben Schram; Elisa F D Canetti; Vini Simas; Rodney Pope; Robin Orr
Journal:  Int J Environ Res Public Health       Date:  2021-12-31       Impact factor: 3.390

2.  Risk factors for musculoskeletal injuries in the military: a qualitative systematic review of the literature from the past two decades and a new prioritizing injury model.

Authors:  Stefan Sammito; Vedran Hadzic; Thomas Karakolis; Karen R Kelly; Susan P Proctor; Ainars Stepens; Graham White; Wes O Zimmermann
Journal:  Mil Med Res       Date:  2021-12-10
  2 in total

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