Objectives: To investigate the effect of menstrual cycle (MC) and hormonal contraception (HC) phases in elite rowers training, performance and wellness monitoring.Methods: Twelve French elite rowers were follow-up for 4,2 cycles on average in their final preparation for the Olympics and Paralympics Games in Tokyo 2021 through an on-site longitudinal study based on repeated measures. Daily self-reported evaluation using Likert rating scales of wellness (sleep quality, fitness, mood, injuries’ pain), menstrual symptoms and training parameters (perceived exertion and self-assessment of performance) were collected (n = 1,281) in parallel to a coach evaluation of rowers’ performance (n = 136), blinded to theirs MC and HC phases. Salivary samples of estradiol and progesterone were collected in each cycle to help to classify the MC into 6 phases and HC into 2–3 phases depending on the pills’ hormone concentration. A chi-square test normalized by each rower was used to compare the upper quintile scores of each studied variable across phases. A Bayesian ordinal logistic regression was applied to model the rowers’ self-reported performance.Results: Rowers with a natural cycle, n = 6 ( + 1 amenorrhea) evaluate their performance and wellness with significant higher score indices at the middle of their cycle. Top assessments are rarer at the premenstrual and menses phases, when they more frequently experience menstrual symptoms which are negatively correlated with their performance. The HC rowers, n = 5, also better evaluate their performance when taking the pills and more frequently experience menstrual symptoms during the pill withdrawal. The athletes self-reported performance is correlated with their coach’s evaluation.Conclusion: It seems important to integrate MC and HC data in the wellness and training monitoring of female athletes since these parameters vary across hormonal phases affecting training perception of both athlete and coach.
Combining information both within and across trajectories, we propose a simple estimator for the local regularity of the trajectories of a stochastic process. Independent trajectories are measured with errors at randomly sampled time points. The proposed approach is model-free and applies to a large class of stochastic processes. Non-asymptotic bounds for the concentration of the estimator are derived. Given the estimate of the local regularity, we build a nearly optimal local polynomial smoother from the curves from a new, possibly very large sample of noisy trajectories. We derive non-asymptotic pointwise risk bounds uniformly over the new set of curves. Our estimates perform well in simulations, in both cases of differentiable or non-differentiable trajectories. Real data sets illustrate the effectiveness of the new approaches.
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