Accuracy of Resting Metabolic Rate Prediction Equations in Athletes: A Systematic Review with Meta-analysis

Area of Science:

• Sports Science
• Human Physiology
• Nutritional Science

Background:

• Resting metabolic rate (RMR) prediction equations are widely used for athletes.
• The accuracy and precision of these equations can vary significantly.
• A systematic review and meta-analysis was conducted to identify the most reliable RMR prediction equations for athletes.

Approach:

• A comprehensive search of major scientific databases (PubMed, CINAHL, SPORTDiscus, Embase, Web of Science) was performed up to November 2021.
• Included studies involved adult athletes where RMR was measured by indirect calorimetry and compared to predicted values.
• Narrative synthesis and random-effects meta-analysis were employed, with subgroup analyses to explore heterogeneity.

Key Points:

• Eleven RMR prediction equations were analyzed for accuracy, with five showing no significant difference from measured values.
• The Ten-Haaf equation demonstrated the lowest heterogeneity (I²=0%) among accurate equations.
• Nine equations were assessed for precision; Ten-Haaf was the most precise, with 80.2% of participants within ±10% of measured RMR.

Conclusions:

• The Ten-Haaf equation (utilizing age, weight, and height) is recommended as the most accurate and precise for predicting RMR in most athletic populations.
• Certain equations consistently underperform and should be avoided.
• Selecting an equation that matches the specific characteristics of the athlete population (e.g., sex, sport, status) is crucial for optimal results.