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Profiling the Typical Training Load of a Law Enforcement Recruit Class.

Danny MaupinBenjamin SchramElisa F D CanettiJoseph M DullaJ Jay DawesRobert George LockieRobin Marc Orr
Published in: International journal of environmental research and public health (2022)
Law enforcement academies, designed to prepare recruits for their prospective career, represent periods of high physical and mental stress, potentially contributing to recruits' injuries. Managing stress via monitoring training loads may mitigate injuries while ensuring adequate preparation. However, it is vital to first understand an academy's typical training load. The aim of this study was to profile the typical training load of law enforcement recruits over the course of 22 weeks. Data were prospectively collected using global positioning system (GPS) units placed on recruits during a portion of the academy training, while a desktop analysis was retrospectively applied to six other classes. A Bland-Altman plot was conducted to assess the agreement between the two methods. A linear mixed model was conducted to analyse the difference in distances covered per week, while other variables were presented graphically. Adequate agreement between the desktop analysis and GPS units was observed. Significant differences ( p -value < 0.01) in distance covered (9.64 to 11.65 km) exist between weeks during early academy stages, which coincide with increases (~6 h) in physical training. Significant decreases in distances were experienced during the last five weeks of academy training. Most acute:chronic workload ratios stayed between the proposed 0.8 to 1.3 optimal range. Results from this study indicate that large increases in training occur early in the academy, potentially influencing injuries. Utilizing a desktop analysis is a pragmatic and reliable approach for instructors to measure load.
Keyphrases
  • virtual reality
  • mental health
  • randomized controlled trial
  • intensive care unit
  • high resolution
  • mass spectrometry
  • stress induced
  • drug induced
  • respiratory failure
  • preterm birth
  • neural network
  • data analysis