Blood test data were traditionally confined to the clinic for diagnostic purposes, but are now becoming more routinely used in many professional and elite high-performance settings as a physiological profiling and monitoring tool. A wealth of information based on robust research evidence can be gleaned from blood tests, including: the identification of iron, vitamin or energy deficiency; the identification of oxidative stress and inflammation; and the status of red blood cell populations. Serial blood test data can be used to monitor athletes and make inferences about the efficacy of training interventions, nutritional strategies or indeed the capacity to tolerate training load. Via a profiling and monitoring approach, blood biomarker measurement combined with contextual data has the potential to help athletes avoid injury and illness via adjustments to diet, training load and recovery strategies. Since wide inter-individual variability exists in many biomarkers, clinical populationbased reference data can be of limited value in athletes, and statistical methods for longitudinal data are required to identify meaningful changes within an athlete. Data quality is often compromised by poor pre-analytic controls in sport settings. The biotechnology industry is rapidly evolving, providing new technologies and methods, some of which may be well suited to athlete applications in the future. This review provides current perspectives, limitations and recommendations for sports science and sports medicine practitioners using blood profiling and monitoring for nutrition and performance purposes.

The reported biochemical changes around ARH in elite athletes suggest that it may be of value to monitor biomarkers of ARH at rest, pre- and post-simulated performance tests, and before and after training micro- and meso-cycles, and altitude camps, to identify individual tolerance to training loads, potentially allowing the prevention of non-functionally over-reached states and optimisation of the individual training taper and training programme.

ObjectivesThe menstrual cycle can affect sports participation and exercise performance. There are very few data on specific menstrual cycle symptoms (symptoms during various phases of the cycle, not only during menstruation) experienced by exercising women. We aimed to characterise the most common symptoms, as well as the number and frequency of symptoms, and evaluate whether menstrual cycle symptoms are associated with sporting outcomes.Methods6812 adult women of reproductive age (mean age: 38.3 (8.7) years) who were not using combined hormonal contraception were recruited via the Strava exercise app user database and completed a 39-part survey. Respondents were from seven geographical areas, and the questions were translated and localised to each region (Brazil, n=892; France, n=1355; Germany, n=839; Spain, n=834; UK and Ireland, n=1350; and USA, n=1542). The survey captured exercise behaviours, current menstrual status, presence and frequency of menstrual cycle symptoms, medication use for symptoms, perceived effects of the menstrual cycle on exercise and work behaviours, and history of hormonal contraception use. We propose a novel Menstrual Symptom index (MSi) based on the presence and frequency of 18 commonly reported symptoms (range 0–54, where 54 would correspond to all 18 symptoms each occurring very frequently).ResultsThe most prevalent menstrual cycle symptoms were mood changes/anxiety (90.6%), tiredness/fatigue (86.2%), stomach cramps (84.2%) and breast pain/tenderness (83.1%). After controlling for body mass index, training volume and age, the MSi was associated with a greater likelihood of missing or changing training (OR=1.09 (CI 1.08 to 1.10); p≤0.05), missing a sporting event/competition (OR=1.07 (CI 1.06 to 1.08); p≤0.05), absenteeism from work/academia (OR=1.08 (CI 1.07 to 1.09); p≤0.05) and use of pain medication (OR=1.09 (CI 1.08 to 1.09); p≤0.05).ConclusionMenstrual cycle symptoms are very common in exercising women, and women report that these symptoms compromise their exercise participation and work capacity. The MSi needs to be formally validated (psychometrics); at present, it provides an easy way to quantify the frequency of menstrual cycle symptoms.