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

doi:10.1016/j.jsams.2018.11.028

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

J Sci Med Sport. 2019 Jun;22(6):653-660. doi: 10.1016/j.jsams.2018.11.028. Epub 2018 Dec 18.

Authors

Cloe Cummins¹, Mitchell Welch², Brendan Inkster³, Balin Cupples⁴, Dan Weaving⁵, Ben Jones⁶, Doug King⁷, Aron Murphy²

Affiliations

¹ School of Science and Technology, University of New England, Australia; Institute for Sport Physical Activity and Leisure, Leeds Beckett University, United Kingdom. Electronic address: ccummin5@une.edu.au.
² School of Science and Technology, University of New England, Australia.
³ Vodafone Warriors, New Zealand.
⁴ Vodafone Warriors, New Zealand; Sydney School of Education and Social Work, The University of Sydney, Australia.
⁵ Institute for Sport Physical Activity and Leisure, Leeds Beckett University, United Kingdom; Leeds Rhinos Rugby League club, United Kingdom.
⁶ School of Science and Technology, University of New England, Australia; Institute for Sport Physical Activity and Leisure, Leeds Beckett University, United Kingdom; Leeds Rhinos Rugby League club, United Kingdom; Yorkshire Carnegie Rugby Union club, United Kingdom; The Rugby Football League, United Kingdom.
⁷ School of Science and Technology, University of New England, Australia; Sports Performance Research Institute New Zealand (SPRINZ) at AUT Millennium, Faculty of Health and Environment Sciences, Auckland University of Technology, New Zealand.

PMID: 30651223
DOI: 10.1016/j.jsams.2018.11.028

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 (mmin^-1), high speed distance ([m]>20kmh^-1), very-high speed distance ([m]>25kmh^-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: >60mmin^-1) demonstrated a positive association with injury-risk, whilst high two-weekly loads (high: >82 mmin^-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.

Keywords: Injury prevention; Microtechnology; Team sport; Training load.

MeSH terms

Acceleration
Athletic Injuries / etiology*
Deceleration
Football / injuries*
Humans
Male
Microtechnology / instrumentation
Models, Statistical
Retrospective Studies
Risk Factors
Workload*