Understanding the Complexity of Athlete Sleep: A Systems Approach to Modelling the Athlete Sleep Environment
Study Details
This systems-modelling study developed a causal loop diagram of athlete sleep using published evidence and consensus from 10 experts, then applied network analysis to identify highly connected variables 🔍
Here are the key findings ⬇️
Key Findings
🧩 The final model contained 48 variables, 120 relationships and 290 feedback loops
🏟️ Variables spanned four domains: travel demands, behavioural/lifestyle factors, athlete condition and readiness, and scheduling/resource constraints
✈️ Highly connected upstream variables included hours of travel, number of time zones crossed, caffeine dose and available resources
😵 Variables receiving the most incoming influences included pre-sleep arousal, sleep opportunity, travel fatigue and performance
🔗 Physical condition, performance, diet quality and competition importance frequently linked different parts of the system
☕ Caffeine was an important modifiable factor, with both dose and proximity to sleep potentially influencing sleep health
📅 Early-morning training and evening competition may reduce sleep opportunity or increase arousal, while travel and congested schedules can amplify these effects
🔄 Poor sleep may also reinforce other problems: fatigue can increase napping or caffeine use, which may then influence subsequent sleep
📊 The authors proposed a three-tier framework: monitor upstream exposures, identify emerging sleep/performance strain, then consider behavioural and system-level responses
⚠️ This is a hypothesis-generating model rather than a predictive one, and network connectivity does not prove which factors are most causally important
Conclusion
Improving athlete sleep may require more than sleep hygiene; monitoring travel, caffeine, scheduling, arousal and recovery together may identify causes of poor sleep that individual behaviour change alone cannot fix ✅
Reference
https://link.springer.com/article/10.1007/s40279-026-02568-4
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