## 4 Current Off-Training Wellbeing/Wellness Profiling in Elite Sports
### Sleep and Circadian System  
Sleep quality has also been brought to the center of the athletic recovery problem and health. Sleep is regulated by the circadian system and is classically viewed as an important part of the recovery process [47]. Deprivation of sleep leads to impaired brain function, impacting decision making in performance [48]. Furthermore, it is associated with metabolic problems [49], impairment of the immune system function, muscle repair, and might also stimulate overtraining symptoms [50], suggesting a broader impact on physiological health.  
The relationship between sleep, recovery and performance is held by the sleep phase (circadian timing), duration, and quality [51]. A recent review has pointed out that both objective (sleep efficiency, latency, wake episodes and total wake episode duration usingtigraphy) and subjective (Pittsburgh Sleep Quality Index, Likert scale, Liverpool Jet-Lag Questionnaire, and RESTQ) parameters are effective for monitoring sleep quality in sport [52]. Objective measures involve the use of wearable trackers that are accelerometer- and heart rate photoplethysmography-based. However, as highlighted in recent discussions [17], there remain significant challenges with the use of inertial measurement units and the algorithms embedded within them for accurately detecting critical sleep timepoints. Additionally, the restricted access to raw data from many of the leading sensor brands further complicates the depth of analysis and control over sleep patterns, posing a hurdle to optimizing sleep for athletic performance and recovery.  
Despite the recognized importance of sleep, elite athletes often experience less total sleep than non-athlete counterparts, regardless of the type of sport [53]. Recent findings suggest that high to medium off-training PA, combined with medium to high training responses, may be associated with decreased sleep quality [54]. An interesting study proposed a statistical package incorporating unsupervised clustering algorithms to analyze athletes' sleep quality data from questionnaires. This approach allows for grouping athletes based on different characteristics, creating thresholds, and enabling a personalized approach to recommendations [55]. In this proposed method, athletes exhibiting poor overall sleep quality and concerning sleeping behavior are classified in the priority group. Recommendations for this group may include objective monitoring and the use of cognitive behavior therapy to alleviate pre-sleep anxiety. Athletes experiencing sleep disturbances linked to sleep routine and environmental factors would be suggested to undergo monitoring with diaries and interviews to identify the reasons behind bedtime and wake time inconsistencies. Athletes with low-priority scores would be advised to maintain their sleep habits, with periodic assessments. It would be noteworthy to explore the effectiveness of an intervention program based on this assessment method in transitioning athletes between high- and medium-priority groups to low priority. This could provide valuable insights into the impact of targeted interventions on improving sleep quality and overall well-being among elite athletes.