Clinical application of monitoring indicators of female dancer health, including application of artificial intelligence in female hormone networks

Abstract

AbstractObjectivesThe purpose of this study was to assess the effectiveness of monitoring professional female dancer health with a variety of subjective and objective monitoring methods, including application of artificial intelligence (AI) techniques to modelling menstrual cycle hormones and delivering swift personalised clinical advice.MethodsFemale dancers from a ballet company completed a published online dance-specific health questionnaire. Over the study period, dancers recorded wellbeing and training metrics, with menstrual cycle tracking and blood tests. For menstrual cycle hormones AI-based techniques modelled hormone variation over a cycle, based on capillary blood samples taken at two time points. At regular, virtual, clinical interviews with each dancer, findings were discussed, and personalised advice given.Results14 female dancers (mean age 25.5 years, SD 3.7) participated in the study. 10 dancers recorded positive scores on the dance health questionnaire, suggesting a low risk of relative energy deficiency in sport (RED-S). 2 dancers were taking hormonal contraception. Apart from 1 dancer, those not on hormonal contraception reported current eumenorrhoeic status. The initiative of monitoring menstrual cycles and application of AI to model menstrual cycle hormones found that subclinical hormone disruption was occurring in 6 of the 10 dancers reporting regular cycles. 4 of the 6 dancers who received personalised advice, showed improved menstrual hormone function, including one dancer who had planned pregnancy.ConclusionsMultimodal monitoring facilitated delivery of prompt personalised clinical medical feedback specific for dance. This strategy enabled the early identification and swift management of emergent clinical issues. These innovations received positive feedback from the dancers.Summary boxesWhat are the new findings?Monitoring female dancers with a variety of interactive methods – dance specific questionnaire, online tracking and blood testing – together with individual clinical discussion, facilitates comprehensive, personalised support for dancer health.The clinical application of artificial intelligence (AI) techniques to endocrine function provides the finer detail of female hormone network function.This novel approach to monitoring dynamic hormone function enabled the detection of subtle female hormone dysfunction as a result of changes in training and nutrition patterns, which occurred before change in menstruation pattern from menstrual tracking.This multifaceted clinical approach was also effective and helpful in supporting dancers restore full hormone network function through personalised training and nutritional strategies.How might this study impact on clinical practice in the future?Personalised, dance specific health advice based on subjective and objective measures can support sustainable individual dancer health.Clinical application of artificial intelligence (AI) to menstrual cycle hormones can provide a dynamic and complete picture of hormone network function, without the need to do daily blood tests to measure all four key menstrual cycle hormones.This AI approach to modelling hormones enables early detection of subtle, subclinical endocrine dysfunction due to low energy availability in female exercisers. This clinical tool can also facilitate the close clinical monitoring of the restoration of full hormone network function in recovery from low energy availability.Using AI to model female hormones can be an important clinical tool for female athletes, including those athletes where it is difficult to distinguish between perimenopause symptoms and those associated with low energy availability.