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 .
Importance of Various Training-Load Measures in Injury Incidence of Professional Rugby League Athletes.
PURPOSE To investigate the ability of various internal and external training-load ( TL ) monitoring measures to predict injury incidence among positional groups in professional rugby league athletes . METHODS TL and injury data were collected across 3 seasons ( 2013 - 2015 ) from 25 players competing in National Rugby League competition . Daily TL data were included in the analysis , including  session rating of perceived exertion ( sRPE-TL ) , total distance ( TD ) , high-speed-running distance ( > 5 m/s ) , and high-metabolic-power distance ( HPD ; > 20 W/kg ) . Rolling sums were calculated , nontraining days were removed , and athletes ' corresponding injury status was marked as " available " or " unavailable . " Linear ( generalized estimating equations ) and nonlinear ( r and om forest ; RF ) statistical methods were adopted . RESULTS Injury risk factors varied according to positional group . For adjustables , the TL variables associated most highly with injury were 7-d TD and 7-d HPD , whereas for hit-up forwards they were sRPE-TL ratio and 14-d TD . For outside backs , 21- and 28-d sRPE-TL were identified , and for wide-running forwards , sRPE-TL ratio . The individual RF models showed that the importance of the TL variables in injury incidence varied between athletes . CONCLUSIONS Differences in risk factors were recognized between positional groups and individual athletes , likely due to varied physiological capacities and physical dem and s. Furthermore , these results suggest that robust machine-learning techniques can appropriately monitor injury risk in professional team-sport athletes
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 .