Modelling the relationships between volume, intensity and injury-risk in professional rugby league players.
J Sci Med Sport 2018;
22:653-660. [PMID:
30651223 DOI:
10.1016/j.jsams.2018.11.028]
[Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 11/06/2018] [Accepted: 11/29/2018] [Indexed: 11/15/2022]
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.
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