Understanding the Complexity of Athlete Sleep: A Systems Approach to Modelling the Athlete Sleep Environment

Background Athlete sleep can be disrupted by sport- and non-sport-related factors. However, the interactions of these sleep-related factors are not well understood. Systems science approaches, such as causal loop diagramming (CLD), provide an opportunity to map these interactions. This project aimed to (1) develop a CLD of the athlete sleep system describing the interacting variables, (2) identify feedback loops and potential leverage points to optimise athlete sleep, and (3) demonstrate the applicability of a CLD to guide the development of a sleep monitoring framework. Methods A systems-based model of athlete sleep was developed using CLD, informed by existing literature and refined through a consensus process involving ten subject matter experts (nine from Australia, one from the USA). The model was then interrogated using network analysis to identify key variables based on their position and connectivity within the system. These findings informed the development of a generic sleep monitoring framework. Results The CLD comprised 48 variables and 120 relationships across four domains: travel demands, behavioural and lifestyle factors, athlete condition and readiness, and resourcing and scheduling constraints. Overall, 12 key variables were identified, with hours of travel, number of time zones crossed, dose of caffeine, and amount of resources or expenditure available being the most connected upstream variables. Level of pre-sleep arousal, sleep opportunity, travel fatigue, and level of performance received the greatest number of incoming relationships, representing points at which multiple pathways converge. Physical condition, level of performance, quality of diet, and importance of competition most frequently linked different quadrants of the system. These variables informed a three-tier monitoring framework capturing upstream determinants and downstream consequences of sleep. Conclusions According to the CLD, sleep health in athletes extends beyond an individual behaviour issue and is influenced by the structure and functioning of sport systems. Interactions among travel demands, physical readiness, behavioural and lifestyle factors, and organisational constraints sustain athlete sleep health over time. Consequently, meaningful improvements in sleep health are likely to arise from both system-level interventions and individual behaviour change strategies.