A Randomized Crossover Pilot Trial on the Influence of a Daily Mindfulness Yoga Practice on Menstrual Cycle Symptoms, Measures of Well-Being, and Training Perception in Athletic Women.

Abstract

The menstrual cycle (MC) is a series of hormonal fluctuations that occur approximately every 22-35 days in reproductive-age females, often resulting in a range of physical and psychological symptoms at different points of the cycle. MC symptoms range from mild discomfort to debilitating effects. One intervention that may be able to address these symptoms is yoga, a mind-body practice that incorporates physical postures with breathwork to foster a mindful connection to the body. In the present randomized crossover trial, participants were enrolled for three consecutive MCs and completed a 10-minute daily yoga protocol, which included ten different yoga poses, for one full MC while answering daily questions about MC symptoms, well-being, and training performance; they answered the same questions for the other cycles without the yoga intervention. Twenty women who participated in resistance training sports were recruited. A series of generalized linear mixed models were used to determine whether the association between each outcome variable and current day of the MC varied by cycle type (intervention cycle or control cycle). There was a statistically significant Day × Cycle interaction for bloating, low-back pain, menstrual cramps, and stress, indicating a lower probability of experiencing these symptoms across the MC during the intervention than the control cycle. Daily yoga was also significantly associated with changes in perceived athletic performance across the MC, as shown in the linear mixed-model figures. A 10-minute daily yoga practice was significantly associated with changes in MC symptoms and has the potential to mitigate some MC symptoms and stress perception in an athletic cohort. Furthermore, daily yoga was significantly associated with changes in perceived performance and may stabilize perceived performance metrics across the MC.