Beyond Prediction: Advancing Resting Metabolic Rate Measurement and Its Role in Detecting Energy Deficiency in Athletes

Understanding and accurately estimating an athlete’s resting metabolic rate (RMR) is pivotal for optimising performance, supporting recovery, and mitigating the health risks associated with low energy availability (LEA) and Relative Energy Deficiency in Sport (REDs). This thesis addresses key gaps in RMR measurement and prediction by examining: 1) the accuracy and precision of RMR prediction equations, 2) the reliability and agreement of a new, portable indirect calorimeter (IDC), 3) the impact of moderate-intensity endurance exercise on next-day RMR, and 4) the utility of RMR-based metrics as indicators of LEA and REDs using the IOC REDs CAT2 framework. Firstly, a systematic review and meta-analysis was conducted to assess over 100 RMR prediction equations in athletic populations. Equations derived for general or older populations (e.g., Harris-Benedict, Mifflin-St Jeor) frequently misestimated athlete RMR. In contrast, the Ten-Haaf (2014) equation, developed in physically active populations, emerged as the most accurate and precise, with the highest proportion of predictions falling within ±10% of measured RMR. These findings emphasise the importance of using population-specific equations when estimating RMR in athletes. Secondly, the thesis examined the reliability and agreement of the portable Cosmed Q-NRG IDC compared to the laboratory-based Quark CPET. The Q-NRG showed a small mean underestimation (~148 kcal/24h) but demonstrated superior test-retest reliability. This enhanced reproducibility supports the Q-NRG’s suitability for longitudinal RMR monitoring, which is key for identifying metabolic changes that may signal LEA or REDs risk. Thirdly, an experimental study assessed whether self-regulated, moderate-intensity endurance exercise performed 24 hours prior affects next-day RMR in well-trained male endurance athletes. Results showed no significant difference between rest and exercise conditions, suggesting such exercise does not acutely alter RMR in this group. These findings extend earlier lab-based research and suggest that pre-test guidelines for moderate training could be reconsidered. However, higher-intensity or prolonged exercise may have a delayed effect, warranting further study. Finally, the thesis explored whether RMR metrics, measured-to-predicted RMR ratio and kcal/kg FFM/24h, can indicate LEA and REDs when used alongside the IOC REDs CAT2 tool. While neither metric correlated with short-term (7-day) EA estimates, both were significantly associated with physiological indicators such as low FT3. Diagnostic utility was greater in females, highlighting possible sex-specific responses. These findings support the use of RMR only as part of a broader assessment alongside hormonal, blood, and questionnaire data. In summary, this thesis advances the application of RMR in athlete health monitoring by showing that: 1) equations derived from athletic populations (e.g., Ten-Haaf) improve prediction accuracy; 2) portable devices like the Q-NRG offer reliable longitudinal monitoring; 3) moderate exercise does not compromise next-day RMR values in trained athletes; and 4) RMR metrics alone should not be used diagnostically but may contribute as indicators within a multimodal REDs risk assessment. These findings inform evidence-based practice in athlete energy monitoring, nutritional planning, and health protection.