Author(s): CROSBIE, F., LÉGER, D., NEDELEC, M., Institution: INSEP, Country: FRANCE, Abstract-ID: 2727

INTRODUCTION:
Elite adolescent athletes in dual-career settings face a battle between demanding schedules and biological sleep needs. While sleep has been studied in athletes, it has often been done in isolated periods, such as training camps, school weeks or competitions. However, we lack longitudinal data tracking the same athletes across a part of the season, to observe how they adjust to the different constraints. This study investigates the interaction between shifting schedules and sleep-wake patterns to find the real drivers of sleep debt.
METHODS:
Twenty-five elite male rugby union players (16.5 ± 0.7 years) were continuously monitored over 10 weeks using wrist actigraphy and daily sleep diaries. Their academic and athletic schedules were collected, in order to create a day-to-day schedule. The observation period spanned five distinct phases, including academic terms, a training camp, and holidays. Linear mixed models and correlation analyses were used to assess the relationships between daily scheduling constraints and sleep parameters.
RESULTS:
Athletes faced chronic sleep restriction during Academic (7.2–7.5 h) and Training Camp (7.6 h) phases compared to Holidays (8.1 h). Subjective sleep quality (PSQI) and daytime sleepiness (Epworth Sleepiness Scale) deteriorated significantly (p < 0.05) throughout the academic phase, highlighting the cumulative effect of sleep debt.

Activity start time was the primary structural constraint, strongly correlated with wake time (r=0.82), whereas later activity end times had a negligible impact on bedtime (r=−0.12). Despite this, bedtimes were consistently delayed. While curfew was set at 22:00 during the week, average bedtime was 23:23. This phase delay was further accentuated on days with later starts or during rest periods, as bedtime was significantly tied to the opportunity for a delayed wake time (r=0.61). Indeed, bedtimes were pushed to 01:00 on Saturday nights and 01:05 during holidays, suggesting a strong circadian and behavioural shift.

Interestingly, athletes demonstrated the capacity to maximise recovery on pre-match nights (8.65 h). On these Friday nights, they maintained consistency with a minimal bedtime delay (+26 min), while delaying wake time a (+2.6 h) to prioritise sleep when the performance context was immediate.
CONCLUSION:
These results demonstrate that sleep debt is primarily driven by morning constraints rather than late-night training. Beyond these structural anchors, the significant shift in bedtime suggests circadian and behavioral factors that further prevent athletes from utilizing their available sleep window, leading to cumulative health and performance impairments.
Interestingly, athletes successfully maximised sleep on pre-match nights, proving they can extend recovery when the stakes are high. To fix this chronic sleep debt, we must (1) delay morning starts to remove the structural constraint and (2) improve bedtime hygiene to advance bedtime.