# The Relationship between Training Load Measures and Next-Day Well-Being in Rugby Union Players
## 1 Introduction  
The general aim of athletic training is to induce a physiological stress which stimulates adaptation [1]. Athlete monitoring of the training process plays an important role in the decisions that are taken surrounding manipulations to training. One of the purposes of athlete monitoring is to better understand the relationship between what is done and how players may respond [2]. This has commonly been termed the dose-response (D-R) relationship [3]. [PERSON] et al. [4] suggested that training can produce two responses (fitness and fatigue) and in its simplest form, an athlete's current fitness minus accrued fatigue will determine a players' performance potential at any given time. Thus, to better understand the performance potential of a player, coaches need to be able to quantify aspects of each training session with measures that are related to changes in measures of player's fitness and fatigue [2]. The aim being for coaches to determine an appropriate dose-response relationship enabling them to take a proactive rather than reactive role. Elite rugby union has seen an increase in match-play activity profiles in combination with shorter recovery times [3]. Forwards are involved in more contacts/collisions, alongside an increase in repeated high-speed efforts and less with the ball being out of play for all playerpositions [5, 6]. Thus, increasing the need for coaches to provide players with sufficient recovery. Questionnaires have been used in team sports to manage well-being although much of this is done in a reactive manner [7, 8]. However better understanding of how training load (TL) affects well-being could provide coaches and practitioners with the tools to monitor and manipulate training to manage players' well-being better and thereby reducing the potential risk of injury or excessive fatigue [7]. Whilst previous dose-response studies have identified a range of internal load measures which can be applied to monitor and assess fitness in rugby union [9, 10], there is limited research on the relationship between TL metrics and the resulting exercise-induced fatigue, perceived or otherwise. Currently coaches typically use well-being questionnaires to quantify subjective fatigue and recovery. In addition, various sprint performance, jump performance and biochemical/hormonal/immunological measures are used to assess objective fatigue [11]. Current research is descriptive in nature, focusing on identifying trends in fatigue markers during and post periods of high-intensity training during pre-season [12, 13] or short micro-cycles which included match-play [7, 14, 15]. Current evidence has identified that multi-item self-report measures such as Recovery-Stress questionnaire (REST-Q) and Profile of Mood State (POMS) questionnaire demonstrate the strongest validity and reliability as measures of well-being [4]. Despite the validity and reliability of REST-Q and POMS questionnaires, the most commonly used measures of well-being in an applied setting are more brief, custom self-report measures [1, 5, 6, 7]. These custom self-report measures typically report player's perceptions of muscle soreness, fatigue, sleep quality, stress and mood. The relationships between these custom self-report measures have identified that summed wellness (weekly score relationships with phases of training; pre-season and in-season) and training load measures typically demonstrate negative relationships [8, 9, 10]. Alternatively, mixed linear models have demonstrated weak relationships between post-match or match loads and next-day well-being scores [11, 12]. An additional weakness of the current research is the statistical analysis applied. The strength of relationships between self-report measures and training loads are stronger in studies that have applied correlation measures [1]. In contrast, those studies that have used mixed models to account for within repeated measures of each athlete have identified smaller relationships [1, 7].