TY - JOUR
T1 - Modelling the relationships between volume, intensity and injury-risk in professional rugby league players
AU - Cummins, Chloe
AU - Welch, Mitchell
AU - Inkster, Brendan
AU - Cupples, Balin
AU - Weaving, Daniel
AU - Jones, Ben
AU - King, Doug
AU - Murphy, Aron
PY - 2019/6/30
Y1 - 2019/6/30
N2 - 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.
AB - 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.
KW - Injury prevention
KW - Microtechnology
KW - Team sport
KW - Training load
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85059816954&partnerID=MN8TOARS
UR - http://www.scopus.com/inward/record.url?scp=85059816954&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059816954&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/402a856e-89ab-368b-b451-6bd8e18c0b1a/
U2 - 10.1016/j.jsams.2018.11.028
DO - 10.1016/j.jsams.2018.11.028
M3 - Article (journal)
SN - 1440-2440
VL - 22
SP - 653
EP - 660
JO - Journal of Science and Medicine in Sport
JF - Journal of Science and Medicine in Sport
IS - 6
ER -