## 4 Current Off-Training Wellbeing/Wellness Profiling in Elite Sports
### Nutrition and Eating Behaviors  
Nutrition stands as an additional important factor influencing athletes' performance, wellness, and well-being. Ingested nutrients can play a crucial role in cellular signaling pathways that modulate muscle adaptations to both endurance and resistance training. For instance, the consumption of carbohydrates and ergogenic acids, such as caffeine, creatine, sodium bicarbonate, and beta-alanine, is recognized as integral to performance enhancement [27]. Additionally, dietary supplements are commonly used for various reasons, ranging from health maintenance to performance enhancement [56]. The concept of periodized nutrition is frequently emphasized as an optimization strategy to maximize performance. It involves a planned and purposeful use of essential micro and macronutrients tailored to enhance adaptations targeted by individual exercise sessions or periodic training plans [27; 57]. Nutrition is a field rich in descriptive, prescriptive, and predictive knowledge, allowing for the individualization of responses to enhance sports performance. The literature offersa lot of recommendations considering, for example, endurance [50] or strength-dependent sports [58; 59; 60], or training session intensities and conditions [57], and the impacts of omnivorous and vegetarian nutrition in physical performance [61]. While there is a wealth of knowledge in the literature, the integration of big data analytics and pervasive computing for facilitating access and monitoring in the field of sports nutrition is yet to find its appeal in sports. Some of the few studies in this area have employed machine learning algorithms for image detection and recognition. For instance, the NutriNet network architecture was introduced as a solution for food and drink image detection and recognition, employing deep convolutional neural networks. Initially designed for the dietary assessment of Parkinson's disease patients [62], this technology showcases the potential for leveraging machine learning in nutritional monitoring. In addition, the automation of remapping over 4000 unique food items onto food composition tables has been explored using fuzzy matching and machine learning approaches. This method, achieving a precision of 88.75%, demonstrates the potential of data-driven approaches to streamline dietary intake quantification and analysis [63]. As wearables and data analytics continue to advance, there is significant potential for innovative solutions to further optimize athletes' performance, wellness, and well-being through personalized and data-driven nutritional strategies. One critical aspect that can benefit from these advancements is the control for doping on ingested substances. Athletes must remain vigilant about the safety and allowance for drugs and supplement use in accordance with Anti-Doping Agency policies. Data-driven approaches to monitor dietary intake can play a role in compliance with anti-doping policies by analyzing nutritional patterns, ensuring adherence to regulations. This not only supports the integrity of athletes but also serves as a proactive measure to safeguard their health and well-being. Low energy availability (LEA), an inadequate balance between an athlete's nutritional energy intake and the energy expended in exercise [64], is a relevant issue to diet and eating habits. It can give rise to a syndrome known as relative energy deficiency in sport (RED-S), resulting in impaired physiological functioning of the body. RED-S manifests as metabolic rate disturbances, menstrual irregularities, compromised bone health, weakened immunity, impaired protein synthesis, and cardiovascular issues [65]. Recent studies showed that the prevalence of RED-S ranges from 22% to 58% across various sports (i.e., athletics, combat, horse racing, rowing, cycling), impacting both male and female athletes [66; 67].