Cloe Cummins1, Mitchell Welch2, Brendan Inkster3, Balin Cupples4, Dan Weaving5, Ben Jones6, Doug King7, Aron Murphy2. 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.
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.
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.
Authors: Ashleigh V Morrice-West; Peta L Hitchens; Elizabeth A Walmsley; Kate Tasker; Ser Lin Lim; Ariel D Smith; R Chris Whitton Journal: Sci Rep Date: 2022-07-07 Impact factor: 4.996