Glycaemic Responses to a Food Intake Sequence Intervention Under Free-living Conditions in Elite Female Athletes
Glycaemic Responses to a Food Intake Sequence Intervention Under Free-living Conditions in Elite Female Athletes
The goal of this clinical trial is to investigate the impact of a food intake sequence intervention under free-living conditions on glycaemic responses in elite female athletes. Specifically, the main questions it aims to answer are:
To address these questions, researchers will compare eating the dietary sources of rapidly absorbed carbohydrate (CHO) at the end (CHO-last meal pattern) or at the start (CHO-first meal pattern) of standardised mixed meals, at different times of day, in a randomised, counterbalanced, crossover design.
Participants will wear a blinded continuous glucose monitor (CGM) for 6 consecutive days during a training camp. Throughout the study, they will be provided with buffet meals, at the same time and location each day. Dietary intake will be ad libitum, except for breakfast and supper, for which participants will select a preferred composition (ingredients, preparation methods, portion sizes) to replicate across study days. In all ad libitum meals (i.e., lunch, snacks, and dinner), they will be asked to maintain their assigned food intake sequence.
Concurrent data on potential confounding factors (e.g., dietary intake, physical activity, internal and external load during training sessions/competition, sleep quantity and quality, menstrual cycle phase/status) will be collected.
Due to the short camp duration, implementing a one-day washout period will not be feasible. Therefore, repeated measurements over two consecutive days per condition will be obtained to minimise carryover effects of the food intake sequence from prior meals on end-of-intervention data (the final 24 hours per condition), and to assess intraindividual consistency of outcomes at matched-times and standardised settings.
Glycaemic responses will be compared within-participant between food intake sequences using linear mixed models with random intercepts, to account for repeated measures, interindividual variability, and potential missing data.
Inclusion Criteria:
Exclusion Criteria: