Prevalence of physiological and perceptual markers of low energy availability in male academy football players: a study protocol for a cross-sectional study

Ashby, Jamie, Mullen, Thomas, Smith, Philip and Dobbin, Nicholas ORCID: https://orcid.org/0000-0001-7508-1683 (2024) Prevalence of physiological and perceptual markers of low energy availability in male academy football players: a study protocol for a cross-sectional study. BMJ Open Sport & Exercise Medicine. ISSN 2055-7647 (In Press)

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Abstract

Background Low energy availability (LEA) is a core feature of the female athlete triad and relative energy deficiency in sport (REDs). LEA underpins multiple adverse health and performance outcomes in various athletic populations, including weight category, endurance, and aesthetic sports. Recent reports suggest LEA is highly prevalent in team sports such as female football, volleyball and netball. Therefore, the study aims to identify the prevalence of LEA among male academy football players (16-23 years), using surrogate markers that align with the International Olympic Committee REDs Clinical Assessment Tool-Version 2. A cross-sectional study design will be used with physiological and perceptual markers of LEA measured. The study will seek to recruit 355 players to complete several online questionnaires believed to be associated with LEA, measured using a 24-hour food and activity diary. Of the 355 players, a sub-sample of 110 participants will complete an additional 3-day food and activity diary, a venous blood sample to measure levels of total testosterone and free triiodothyronine (T3) and a resting metabolic rate (RMR) measurement to determine RMRratio. The prevalence of LEA will be determined using the low (<30 kcal·kgFFM-1·day-1) domain of energy availability and divided by the total number of participants (n = 355). Descriptive statistics will be used to summarise each variable and presented as a whole group, low (<30 kcal·kgFFM-1·day-1) or moderate (>30 kcal·kgFFM-1·day-1) energy availability. A univariable and multivariable binary logistic regression analysis will be modelled to assess the association of various surrogate markers with the presence of LEA.