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A discussion with a registered dietitian typically begins with an intake meeting where you are asked to provide basic biometrics—your age, weight, height, and biological sex. The primary reason for this is to establish a baseline for your caloric needs.

Historically, there are two main methods for calculating this baseline:

Indirect Calorimetry

This is the gold standard for accuracy. However, it requires a specialized calorimeter machine that is inaccessible to the general public and rarely found outside of clinical research settings.

Predictive Equations

Because calorimetry isn’t practical for everyday use, equations serve as a simple, generalized solution that works reasonably well in practice.

While equations provide an excellent starting point, they are fundamentally just estimates. They were established by taking true biometric data from a sample of subjects and using regression models to fit that data with minimal error. But when you generalize across a population, error is inevitable. In nutritional practice, recognizing and accounting for that error is the difference between a stalled metabolism and successful weight management.

How Large is the Error? A Look at Mifflin-St. Jeor

To understand the scope of this error, we can look at the Mifflin-St. Jeor (MSJ) equation, one of the most widely respected predictive formulas used by dietitians today.

If we take published calorimeter data and map it against the estimated Total Daily Energy Expenditure (TDEE) generated by the MSJ equation—coloring the data points by the corresponding subject’s BMI—a striking reality emerges.

The data shows that MSJ estimates your caloric needs with the following margins of error:

• ±200–250 kcal for ~68% of the population
• ±400–500 kcal for ~95% of the population

In other words, there is a 95% chance that the MSJ equation predicts your true caloric needs with an error of up to 500 calories in either direction.

The error in these equations primarily stems from how they are developed. They are derived from available datasets that are inherently limited to specific populations, cultural contexts, and environmental conditions. Additionally, individual biological variability—often described as differences in metabolic rate (e.g., “fast” vs. “slow” metabolism)—further contributes to their inaccuracy.

The Reality of “Fast” and “Slow” Metabolism

While population averages are great for textbooks, in practice, we care about the individual.

An error margin of ±400 to 500 calories means that two individuals with the exact same age, height, and weight could have a true caloric requirement that differs by up to 800–1000 calories per day. Recent studies have confirmed exactly this phenomenon, which is often what people are experiencing when they claim to have a “fast” or “slow” metabolism.

A 400-calorie discrepancy is a massive overestimation in practice. For an overweight subject trying to lean down, 400 calories could represent half—or even the entirety—of their recommended daily caloric deficit. If the equation overestimates your needs by 400 calories and you follow its output perfectly, your weight loss process will be completely blunted.

Science provides us with a great generalized infrastructure, but it is our responsibility to personalize it.

The Solution: Salute’s Dynamic Algorithm

Standard equations give you a static number, but your body is dynamic.

During onboarding, Salute asks for your anthropometrics and nutritional goals to generate your initial estimated caloric needs. However, it doesn’t stop at the equation. By integrating your daily weight trends and food logs as active parameters, Salute’s algorithm continuously adjusts.

Instead of leaving you stuck with a user-specific offset (that ±200–250 calorie error), the algorithm actively ensures your calculated TDEE converges with your true caloric burn over time.

A Note on Accuracy

The power of this dynamic adjustment relies entirely on the quality of the data it receives.

For Salute to accurately determine your true metabolic rate, it is critical to provide high-quality, honest food logging and to carefully follow the app’s instructions for proper calibration.

If you input the right data, Salute will do the math—eliminating the guesswork of standardized equations.