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Modelling the relationships between volume, intensity and injury-risk in professional rugby league players

  • Cloe Cummins
  • , Mitchell Welch
  • , Brendan Inkster
  • , Balin Cupples
  • , Dan Weaving
  • , Ben Jones
  • , Doug King
  • , Aron Murphy

Research output: Contribution to journal › Article › peer-review

35 Citations (Scopus)

Abstract

Objective This study aimed to: (a) identify the association between external-workloads and injury-risk in the subsequent week; and (b) understand the effectiveness of workload variables in establishing injury-risk. Design Retrospective cohort study. Methods Workload and injury data (soft-tissue) were collected from forty-eight professional male rugby league players. Load variables included duration (min), total distance (m), relative distance (m min−1), high speed distance ([m]>20 km h−1), very-high speed distance ([m]>25 km h−1), acceleration and deceleration efforts (count) and PlayerLoad (Arbitrary Unit: AU). Cumulative two-, three- and four-weekly loads; Acute:Chronic Workload Ratio (ACWR); Mean-Standard Deviation Workload Ratio (MSWR) and strain values were calculated and divided into three equally-sized bins (low, moderate and high). Generalised Estimating Equations analysed relationships between workload variables and injury probability in the subsequent week. Results Injury-risk increased alongside increases in the ACWR for duration, total distance and PlayerLoad. Conversely, injury-risk decreased (Area Under Curve: 0.569–0.585) with increases in the four-weekly duration, total distance, accelerations, decelerations and PlayerLoad. For relative distance, high four-weekly workloads (high: >60 m min−1) demonstrated a positive association with injury-risk, whilst high two-weekly loads (high: >82 m min−1) were negatively associated. Conclusions A range of external workload metrics and summary statistics demonstrate either positive or negative associations with injury-risk status. Such findings provide the framework for the development of decision-support systems in which external workload metrics (e.g. total or high speed distance) can be uniquely and routinely monitored across a range of summary statistics (i.e. cumulative weekly loads and ACWR) in order to optimise player performance and welfare.
Original languageEnglish
Pages (from-to)653-660
JournalJournal of Science and Medicine in Sport
Volume22
Early online date18 Dec 2018
DOIs
Publication statusPublished - 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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