Exploring the effect of the menstrual cycle or oral contraception on elite athletes' training responses when workload is not objectively quantifiable: the MILS approach and findings from female Olympians.

Objectives: Develop the Markov Index Load State (MILS) model, based on hidden Markov chains, to assess athletes' workload responses and investigate the effects of menstrual cycle (MC)/oral contraception (OC), sex steroids hormones and wellness on elite athletes' training. Methods: On a 7-month longitudinal follow-up, daily training (volume and perceived effort, n=2200) and wellness (reported sleep quality and quantity, fitness, mood, menstrual symptoms, n=2509) data were collected from 24 female rowers and skiers preparing for the Olympics. 51 MC and 54 OC full cycles relying on 214 salivary hormone samples were analysed. MC/OC cycles were normalised, converted in % from 0% (first bleeding/pill withdrawal day) to 100% (end). Results: MILS identified three chronic workload response states: 'easy', 'moderate' and 'hard'. A cyclic training response linked to MC or OC (95% CI) was observed, primarily related to progesterone level (p=8.23e-03 and 5.72e-03 for the easy and hard state, respectively). MC athletes predominantly exhibited the 'easy' state during the cycle's first half (8%-53%), transitioning to the 'hard' state post-estimated ovulation (63%-96%). OC users had an increased 'hard' state (4%-32%) during pill withdrawal, transitioning to 'easy' (50%-60%) when on the pill. Wellness metrics influenced the training load response: better sleep quality (p=5.20e-04), mood (p=8.94e-06) and fitness (p=6.29e-03) increased the likelihood of the 'easy' state. Menstrual symptoms increased the 'hard' state probability (p=5.92e-02). Conclusion: The MILS model, leveraging hidden Markov chains, effectively analyses cumulative training load responses. The model identified cyclic training responses linked to MC/OC in elite female athletes.

Impact of menstrual cycle or combined oral contraception on elite female cyclists' training responses through a clustering analysis of training sessions.

Objectives: (i) To classify training sessions of elite female cyclists according to an intensity index based on a longitudinal follow-up using multiparametric data collected in situ (ii) to measure the effect of estimated menstrual cycle (MC) phases and oral contraceptive pills (OC) phases on the athletes' training responses on each type of training identified. Method: Thirteen elite French cyclists were followed up over 30 months and 5,190 training sessions were collected and 81 MC/OCs full cycles analyzed. Power sensors and position devices captured training data in situ, which was summarized into 14 external load variables. Principal Component Analysis and K-means clustering were used to identify cycling sessions according to an intensity load index. The clusters were then verified and categorized through the analysis of heart rate and rate of perceived effort. A calendar method was used to estimate 3 phases of the MC: menstruation, mid-cycle phase (MP) and late-cycle phase (LP). Two phases were defined among monophasic OC users: pills' taking/withdrawal. Results: Four main types of training effort were identified: Intensive, Long, Medium and Light. In the MC group (n = 7; 52 cycles), the intensity index is 8% higher during the mid-cycle (vs. menstrual phase, p = 0.032) in the Intensive effort sessions. No differences were observed in Long, Medium or Light effort, nor between the phases of pills' taking/withdrawal among OC users. Conclusion: The clustering analyses developed allows a training classification and a robust method to investigate the influence of the MC/OC in situ. A better training response during the mid-cycle when the sessions are the most intense suggest an impact of the MC when the athletes approach their maximal capacity.