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The importance of contributors that can result in negative player outcomes in sport and the feasibility and barriers to modifying these to optimise player health and well-being have yet to be established. Within rugby codes (rugby league, rugby union and rugby sevens), within male and female cohorts across playing levels (full-time senior, part-time senior, age grade), this project aims to develop a consensus on contributors to negative biopsychosocial outcomes in rugby players (known as the CoNBO study) and establish stakeholder perceived importance of the identified contributors and barriers to their management. This project will consist of three parts; part 1: a systematic review, part 2: a three-round expert Delphi study and part 3: stakeholder rating of feasibility and barriers to management. Within part 1, systematic searches of electronic databases (PubMed, Scopus, MEDLINE, SPORTDiscus, CINAHL) will be performed. The systematic review protocol is registered with PROSPERO. Studies will be searched to identify physical, psychological and/or social factors resulting in negative player outcomes in rugby. Part 2 will consist of a three-round expert Delphi consensus study to establish additional physical, psychological and/or social factors that result in negative player outcomes in rugby and their importance. In part 3, stakeholders (eg, coaches, chief executive officers and players) will provide perceptions of the feasibility and barriers to modifying the identified factors within their setting. On completion, several manuscripts will be submitted for publication in peer-reviewed journals. The findings of this project have worldwide relevance for stakeholders in the rugby codes.PROSPERO registration numberCRD42022346751.

This study investigated differences in external training load between microcycle lengths and its variation between microcycles, players, and head coaches. Commonly used external training load variables including total-, high-speed- (5-7 m∙s-1), and sprint-distance (> 7 m∙s-1) alongside combined high acceleration and deceleration distance (> 2 m∙s-2). Which were also expressed relative to time were collected using microtechnology within a repeated measures design from 54 male rugby league players from one Super League team over four seasons. 4337 individual observations across ninety-one separate microcycles and six individual microcycle lengths (5 to 10 day) were included. Linear mixed effects models established the differences in training load between microcycle-length and the variation between-microcycles, players and head coaches. The largest magnitude of difference in training load was seen when comparing 5-day with 9-day (ES = 0.31 to 0.53) and 10-day (ES = 0.19 to 0.66) microcycles. The greatest number of differences between microcycles were observed in high- (ES = 0.3 to 0.53) and sprint-speed (ES = 0.2 to 0.42) variables. Between-microcycle variability ranged between 11% to 35% dependent on training load variable. Training load also varied between players (5-65%) and head coaches (6-20%) with most variability existing within high-speed (19-43%) and sprinting (19-65%). Overall, differences in training load between microcycle lengths exist, likely due to manipulation of session duration. Furthermore, training load varies between microcycle, player and head coach.

This study investigated differences in external training load between microcycle lengths and its variation between microcycles, players, and head coaches. Commonly used external training load variables including total-, high-speed- (5–7 m∙s-1), and sprint-distance (> 7 m∙s-1) alongside combined high acceleration and deceleration distance (> 2 m∙s-2). Which were also expressed relative to time were collected using microtechnology within a repeated measures design from 54 male rugby league players from one Super League team over four seasons. 4337 individual observations across ninety-one separate microcycles and six individual microcycle lengths (5 to 10 day) were included. Linear mixed effects models established the differences in training load between microcycle-length and the variation between-microcycles, players and head coaches. The largest magnitude of difference in training load was seen when comparing 5-day with 9-day (ES = 0.31 to 0.53) and 10-day (ES = 0.19 to 0.66) microcycles. The greatest number of differences between microcycles were observed in high- (ES = 0.3 to 0.53) and sprint-speed (ES = 0.2 to 0.42) variables. Between-microcycle variability ranged between 11% to 35% dependent on training load variable. Training load also varied between players (5–65%) and head coaches (6–20%) with most variability existing within high-speed (19–43%) and sprinting (19–65%). Overall, differences in training load between microcycle lengths exist, likely due to manipulation of session duration. Furthermore, training load varies between microcycle, player and head coach.