An Overarching Conceptual Framework for Menstrual Health in Sport Research: Theory, Causality and Validation

Menstrual health in sport is a complex domain shaped by biological, psychological and contextual factors. Central to these interactions is the menstrual cycle, a fundamental physiological process that affects female athletes across multiple dimensions. While research in this area is growing, it often lacks a unifying sport-specific framework to guide theory development, data interpretation and practical application. This article addresses this gap by proposing an overarching conceptual framework (i.e. nomological network) to integrate diverse constructs related to menstrual health in sport to support a more coherent theory-driven approach across both laboratory and field settings. The framework brings together key elements, with the construct of menstrual-related effects representing the primary mechanisms through which menstrual-related phenomena are theorised to causally influence sport-related outcomes such as performance, health, participation and behaviour. When constructs are linked to outcomes through clearly identified mechanistic pathways, it enhances the biological and theoretical plausibility of any proposed relationship, reinforces its justification within a broader system of theory, and strengthens the evidential basis for validation. However, while useful for organising constructs, shaping research questions and hypotheses, and stimulating theory-driven inquiry, the proposed framework is largely informal and therefore offers primarily heuristic value. It is insufficient on its own for formalised empirical testing. For this reason, the adoption of causal directed acyclic graphs is advocated for investigating specific research questions through robust statistical analysis, causal modelling and validation. Directed acyclic graphs are mathematical models that explicitly encode variables, hypothesised causal pathways, mechanisms and confounders in a formal causal structure that enables systematic and testable estimation of causal effects, including from observational data. This approach enhances transparency and interpretability, facilitates refinement of model specifications and supports more rigorous validation processes. Ultimately, the integration of a heuristic conceptual framework with the formal methodology of causal directed acyclic graphs provides both a structured and theory-driven foundation for organising knowledge and the formal modelling approach required to address specific research questions and strengthen empirical inquiry in menstrual health in sport research.