Models to predict injury, physical fitness failure and attrition in recruit training: a retrospective cohort study

Robin M. Orr; Bruce S. Cohen; Stephen C. Allison; Lakmini Bulathsinhala; Edward J. Zambraski; Mark Jaffrey

doi:10.1186/s40779-020-00260-w

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Models to predict injury, physical fitness failure and attrition in recruit training: a retrospective cohort study

RESEARCH | Updated：2023-06-05

- Models to predict injury, physical fitness failure and attrition in recruit training: a retrospective cohort study
- Models to predict injury, physical fitness failure and attrition in recruit training: a retrospective cohort study
- MMR 2020年7卷第4期页码：391-400
- Affiliations：
  
  1.Tactical Research Unit, Bond University, 2 Promethean Way, Gold Coast, Robina, QLD 4226, Australia.
  2.United States Army Research Institute for Environmental Medicine, Natick, MA 01760, USA.
  3.Defence Science and Technology Group, Port Melbourne, VIC 3207, Australia.
- Author bio：
  
  * rorr@bond.edu.au
- Funds：
- DOI：10.1186/s40779-020-00260-w
  中图分类号：
- 纸质出版：2020-12
- Accepted：
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Models to predict injury, physical fitness failure and attrition in recruit training: a retrospective cohort study[J]. MMR, 2020,7(4):391-400.

Orr et al.: Models to predict injury, physical fitness failure and attrition in recruit training: a retrospective cohort study. Mil Med Res, 2020, 7: 26.
Models to predict injury, physical fitness failure and attrition in recruit training: a retrospective cohort study[J]. MMR, 2020,7(4):391-400. DOI： 10.1186/s40779-020-00260-w.

Orr et al.: Models to predict injury, physical fitness failure and attrition in recruit training: a retrospective cohort study. Mil Med Res, 2020, 7: 26. DOI： 10.1186/s40779-020-00260-w.

摘要

Abstract

Background:

Attrition rate in new army recruits is higher than in incumbent troops. In the current study

we identified the risk factors for attrition due to injuries and physical fitness failure in recruit training. A variety of predictive models were attempted.

Methods:

This retrospective cohort included 19

769 Army soldiers of the Australian Defence Force receiving recruit training during a period from 2006 to 2011. Among them

7692 reserve soldiers received a 28-day training course

and the remaining 12

077 full-time soldiers received an 80-day training course. Retrieved data included anthropometric measures

course-specific variables

injury

and physical fitness failure. Multivariate regression was used to develop a variety of models to predict the rate of attrition due to injuries and physical fitness failure. The area under the receiver operating characteristic curve was used to compare the performance of the models.

Results:

In the overall analysis that included both the 28-day and 80-day courses

the incidence of injury of any type was 27.8%. The 80-day course had a higher rate of injury if calculated per course (34.3%

. 17.6% in the 28-day course)

but lower number of injuries per person-year (1.56

. 2.29). Fitness test failure rate was significantly higher in the 28-day course (30.0%

. 12.1%). The overall attrition rate was 5.2% and 5.0% in the 28-day and 80-day courses

respectively. Stress fracture was common in the 80-day course (

=44) and rare in the 28-day course (

=1). The areas under the receiver operating characteristic curves for the course-specific predictive models were relatively low (ranging from 0.51 to 0.69)

consistent with "failed" to "poor" predictive accuracy. The course-combined models performed somewhat better than the course-specific models

with two models having AUC of 0.70 and 0.78

which are considered "fair" predictive accuracy.

Conclusion:

Attrition rate was similar between 28-day and 80-day courses. In comparison to the 80-day full course

the 28-day course had a lower rate of injury but a higher number of injuries per person-year and of fitness test failure. These findings suggest fitness level at the commencement of training is a critically important factor to consider when designing the course curriculum

particularly short courses.

关键词

Keywords

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