Research shows that athlete sleep is shaped by a connected system of travel demands, caffeine use, pre-sleep stress, and team scheduling, not just individual habits. A 2026 Gram Research analysis of a sports sleep model built with ten experts identified 12 key factors, with travel hours and time zones crossed among the most influential.
According to Gram Research analysis, a 2026 study in Sports Medicine found that poor sleep in athletes comes from a web of connected factors, not just personal habits. Researchers worked with ten sports experts to map how travel, caffeine, pre-sleep stress, training demands, and team schedules affect sleep. The resulting map contained 48 factors and 120 links between them. Four factors, including travel fatigue and pre-sleep stress, had the most incoming connections, making them points where problems tend to build up. The team turned these findings into a three-tier sleep monitoring framework. The authors concluded that better athlete sleep will likely require both changes to how sports are organized and individual habit changes. Because the model was built from expert opinion, it shows how factors may interact rather than proving cause and effect.
Key Statistics
A 2026 study published in Sports Medicine built a systems map of athlete sleep with ten subject matter experts and found 48 connected variables and 120 relationships.
A 2026 sports sleep model identified 12 key variables, with hours of travel and the number of time zones crossed among the most connected upstream factors affecting athlete sleep.
A 2026 expert-based systems analysis found that pre-sleep arousal, sleep opportunity, travel fatigue, and performance level received the greatest number of incoming relationships among athlete sleep factors.
The Quick Take
- What they studied: What factors affect how well athletes sleep, and how do those factors influence each other?
- Who participated: Ten subject matter experts on athlete sleep, nine from Australia and one from the USA, who reviewed and refined a model built from existing research. No individual athletes were studied directly.
- Key finding: The model contained 48 factors and 120 links, and 12 key factors stood out. Hours of travel, the number of time zones crossed, caffeine intake, and available resources were among the most influential starting points, while pre-sleep stress, sleep opportunity, and travel fatigue had the most connections pointing into them.
- What it means for you: Athletes and coaches may find that sleep problems are linked to travel schedules, caffeine timing, and training loads, not only to bedtime habits. Tracking these connected factors together may help people spot patterns, though this study does not prove that changing any one of them will fix sleep.
The Research Details
The researchers used a method called causal loop diagramming, which is a way of drawing maps of how different things influence one another. They started with findings from existing scientific literature on athlete sleep. They then used this information to draw a first version of the map showing which factors affect which others.
Next, ten experts reviewed and adjusted the map until they reached agreement. The experts came mostly from Australia, with one from the USA. Their input helped shape the final map, which included four main areas: travel demands, behaviors and lifestyle, athlete health and readiness, and the resources and schedules that organizations provide.
Finally, the team used network analysis, a method that counts how many connections each factor has, to find the factors that play the biggest roles. They then used these results to build a simple monitoring framework that athletes or teams could use to track sleep.
Most sleep research looks at one factor at a time, such as caffeine or screen use. A map that shows how many factors work together gives a clearer picture of real life, where travel, training, and team pressures all happen at once. This approach can reveal where small changes may have the biggest ripple effects.
This study is a modeling project based on expert consensus and published literature, not a test on real athletes. The experts were all from a small group in Australia and the USA, so their views may not match every sport or country. Readers should treat the map as a useful thinking tool that suggests possible relationships, not as proof that those relationships exist in every athlete. The sample size is not clearly stated beyond the ten experts.
What the Results Show
The final map contained 48 factors linked by 120 relationships, grouped into four areas: travel demands, behavioral and lifestyle factors, athlete condition and readiness, and resources and scheduling constraints. This shows that sleep is affected by many connected parts rather than a single cause.
The analysis identified 12 key factors. Hours of travel, number of time zones crossed, caffeine dose, and the amount of resources or spending available were the most connected starting points. In plain terms, these are factors that tend to influence many other parts of the system.
Level of pre-sleep arousal, which means how wired or stressed someone feels before bed, along with sleep opportunity, travel fatigue, and performance level received the most incoming links. These are places where several pathways meet, so problems in one area may show up in these factors. Physical condition, diet quality, and how important a competition is also acted as bridges linking different parts of the system.
The authors used the map to build a three-tier monitoring framework. The first tier tracks upstream factors that can drive sleep, such as travel and caffeine. The second tier tracks sleep itself, and the third tier tracks downstream effects, such as performance and recovery. This structure may help teams notice when a problem starts upstream before it shows up as poor sleep or reduced performance.
The model also suggests that sleep health depends on how sport systems are run. Scheduling, travel planning, and resource decisions appear to shape athletes’ sleep just as much as personal choices do.
Earlier sleep research in athletes has often focused on individual actions, such as limiting screens, keeping a regular bedtime, or reducing caffeine late in the day. This study builds on that work by showing how those actions connect to travel, training loads, and organizational decisions. It fits with a growing view that athlete health is shaped by the environment around the athlete, not only by personal discipline.
The map was built mainly from expert opinion and existing literature, so it reflects what the experts believed and what had already been published. It has not been tested by measuring sleep in a group of athletes over time. The number of experts was small, and most were from Australia, which may limit how well the findings apply to other countries or sports. The map also simplifies real life, since it cannot show how strong each link is or how factors change from day to day. Future studies would need to test these relationships directly.
The Bottom Line
Based on this model, sleep planning for athletes may work best when it covers both personal habits and the wider schedule. Teams may consider tracking travel, caffeine timing, pre-sleep stress, and training load together, since these appear to interact. Confidence in these specific connections is moderate, because they come from expert consensus rather than direct testing. Confidence that sleep is influenced by more than personal habits is stronger, since this idea is consistent with the broader research the authors reviewed.
Athletes, coaches, sports scientists, team staff, and sports organizations planning travel and training schedules may find this useful. Recreational athletes who travel for events or train at irregular hours may also benefit from thinking about the connected factors. Anyone with a diagnosed sleep disorder should work with a healthcare provider rather than relying on this model.
This study is a modeling project, so it does not provide a timeline for results. Any changes based on the framework would likely take several weeks of consistent tracking before patterns become clear. Improvements in sleep from changing habits or schedules may take a few weeks to show, and results will vary widely between individuals.
Frequently Asked Questions
What affects how well athletes sleep?
Athlete sleep is influenced by travel hours, time zones crossed, caffeine intake, pre-sleep stress, and training demands. A 2026 systems model in Sports Medicine mapped 48 connected factors and identified 12 key ones, showing that sleep problems often have several overlapping causes.
Does travel affect an athlete’s sleep?
Travel appears to be one of the strongest influences on athlete sleep. In a 2026 expert-based model, hours of travel and the number of time zones crossed were among the most connected starting factors, linking to travel fatigue and sleep opportunity.
Is sleep only an individual habit issue for athletes?
Not entirely. A 2026 Sports Medicine study suggests athlete sleep is also shaped by sport system structure, including scheduling and resource decisions. The authors concluded that improvements likely need both system-level changes and individual behavior changes.
How can athletes track factors that affect their sleep?
One approach is to log travel hours, caffeine intake, and a pre-sleep stress rating each day, alongside nightly sleep and morning sleep quality. A 2026 model proposed a three-tier framework that tracks upstream causes, sleep itself, and downstream effects like performance.
Want to Apply This Research?
- Log each day’s hours of travel, number of time zones crossed, total caffeine in milligrams, and a 1 to 10 rating of how stressed or wired you feel in the hour before bed. Record hours of sleep and a 1 to 10 sleep quality rating each morning. Review these numbers weekly to spot links between inputs and sleep.
- Use the app to set a caffeine cutoff time, such as no caffeine within 8 hours of bedtime, and track whether sleep quality improves over two weeks. When traveling, log the time zones crossed and plan a short wind-down routine for the first two nights after arrival.
- Follow the three tiers in the app: upstream factors such as travel and caffeine, the sleep measures themselves, and downstream outcomes such as training performance and how rested you feel. Check trends monthly, and share summaries with a coach or healthcare provider if sleep problems persist for more than a few weeks.
This article summarizes a modeling study based on expert opinion and existing research, not a direct test of treatments in athletes. It is for general information only and is not medical advice. If you have ongoing sleep problems, talk with a doctor or sleep specialist before making changes to caffeine, medications, or training.
This research translation is published by Gram Research, the science division of Gram, an AI-powered nutrition tracking app.