Predictive formulas have been used for years because an actual measurement through Indirect Calorimetry has not been practical. But the truth is, while predictions might hold their ground statistically, when applied to individuals, they are woefully inadequate. In an examination of published articles examining the validity of various predictive equations, Frankenfield, Roth-Yousey and Compher found that even the best equation (Miffin-St Jeor) was only within 10% of measured results. While 10% may seem statistically acceptable, for the individual desiring to lose weight, an RMR estimate that is 10% higher (2200 kCals) than an actual measurement of 2000 kCals a day would be significant. That additional caloric intake would result in 21 pounds gained in one year! Additionally, these same researchers noted that errors and limitations with equations exist when applied to individuals, and that “RMR estimation errors would be eliminated by valid measurement of RMR with indirect calorimetry.”
Often after a significant weight loss, RMR is depressed even lower than expected relative to the change in body composition. Most researchers point to this as a key factor in the high rate of weight regain among the formerly obese. Identifying this post-diet RMR is a vital step that is key to long term weight loss success. It gives the information necessary to set an appropriate caloric goal for maintenance and teach a patient to eat within the constraints of their new metabolic requirements.
Each individual will react differently to dietary changes which is why a true measurement of RMR is so valuable. But if a patient reduces calories, don’t be surprised if their RMR also goes down. This may or may not be in conjunction with weight loss. It is critical to measure those changes periodically to make the necessary adjustments in diet and exercise to keep patients on track and avoid the dreaded “plateau.”