Conclusions Our results demonstrate that the existence of a relationship between training load and injury continues to be well supported in the literature and is strongest for subjective internal training load .
The directionality of this relationship appears to depend on the type and timeframe of load measured
Background The relationship between training load and musculoskeletal injury is a rapidly advancing area of research in need of an up date d systematic review .
Multivariate modelling of subjective and objective monitoring data improve the detection of non-contact injury risk in elite Australian footballers.
OBJECTIVES To assess the association between workload , subjective wellness , musculoskeletal screening measures  and non-contact injury risk in elite Australian footballers . DESIGN Prospect i ve cohort study . METHODS Across 4 seasons in 70 players from one club , cumulative weekly workloads ( acute ; 1 week , chronic ; 2- , 3- , 4-week ) and acute : chronic workload ratio 's ( ACWR : 1-week load/average 4-weekly load ) for session-Rating of Perceived Exertion ( sRPE ) and GPS-derived distance and sprint distance were calculated . Wellness , screening and non-contact injury data were also documented . Univariate and multivariate regression models determined injury incidence rate ratios ( IRR ) while accounting for interaction/moderating effects . Receiver operating characteristics determined model predictive accuracy ( area under curve : AUC ) . RESULTS Very low cumulative chronic ( 2- , 3- , 4- week ) workloads were associated with the greatest injury risk ( univariate IRR=1.71 - 2.16 , 95 % CI=1.10 - 4.52 ) in the subsequent week . In multivariate analysis , the interaction between a low chronic load and a very high distance ( adj-IRR=2.60 , 95 % CI=1.07 - 6.34 ) or low sRPE ACWR ( adj-IRR=2.52 , 95 % CI=1.01 - 6.29 ) was associated with increased injury risk . Subjectively reporting " yes " ( vs. " no " ) for old lower limb pain and heavy non-football activity in the previous 7 days ( multivariate adj-IRR=2.01 - 2.25 , 95 % CI=1.02 - 4.95 ) and playing experience ( > 9 years ) ( multivariate adj-IRR=2.05 , 95 % CI=1.03 - 4.06 ) was also associated with increased injury risk , but screening data were not . Predictive capacity of multivariate models was significantly better than univariate ( AUCmultivariate=0.70 , 95 % CI 0.64 - 0.75 ; AUCunivariate range=0.51 - 0.60 ) . CONCLUSIONS Chronic load is an important moderating factor in the workload-injury relationship . Low chronic loads coupled with low or very high ACWR are associated with increased injury risk
Conclusions Our results demonstrate that the existence of a relationship between training load and injury continues to be well supported in the literature and is strongest for subjective internal training load .
The directionality of this relationship appears to depend on the type and timeframe of load measured
Background The relationship between training load and musculoskeletal injury is a rapidly advancing area of research in need of an up date d systematic review .