Where the number on your screen came from.

BMR and TDEE

BMR, basal metabolic rate, is the energy your body uses at complete rest simply to keep functioning. Circulation, breathing, temperature, cell maintenance. It is the largest single component of most people’s expenditure, commonly put at 60 to 70% of the daily total. That is the figure Scientific Reports (2024) gives for resting metabolic rate, the closely related number studies actually measure, which runs a little above true BMR.

TDEE, total daily energy expenditure, is everything: BMR, plus the energy used digesting food, plus deliberate exercise, plus all the incidental movement of an ordinary day. Walking, fidgeting, standing, carrying things. That last category varies between individuals far more than people expect.

How the equations work

Nobody is measuring your metabolism. Apps use published equations, and Mifflin-St Jeor and Harris-Benedict are the common ones. Both were derived by measuring groups of people and fitting a formula to the results. Mifflin and colleagues published theirs in the American Journal of Clinical Nutrition (1990); the Academy of Nutrition and Dietetics later compared the four equations most used in practice, and Frankenfield and colleagues reported in the Journal of the American Dietetic Association (2005) that Mifflin-St Jeor was the most reliable, predicting resting metabolic rate within 10% of the measured value in more people than any other, while noting that "noteworthy errors and limitations exist when it is applied to individuals".

You supply height, weight, age and sex; the equation returns an estimated BMR. That figure is then multiplied by an activity factor you select to produce an estimated TDEE.

Both steps introduce error. The equation gives a population average for people matching your inputs, and real individuals scatter around it. The activity multiplier is a self-assessment from a handful of coarse categories, which is the larger source of error for most people.

Why body composition matters

Lean tissue is more metabolically active at rest than fat tissue. Wang and colleagues, validating the standard organ-level figures in the American Journal of Clinical Nutrition (2010), put skeletal muscle at about 13 kcal per kilogram per day against about 4.5 for adipose tissue, roughly three times as much, though the organs, not the muscle, do most of the work. Two people at identical height, weight, age and sex but different body composition genuinely differ in resting expenditure, and standard equations, which take only the inputs above, cannot see that difference.

Equations built on fat-free mass exist, Cunningham proposed one in the American Journal of Clinical Nutrition (1991), and in lean and athletic groups they can fit better: Jagim and colleagues, testing five equations against indirect calorimetry in the Journal of Strength and Conditioning Research (2018), found every one underestimated resting metabolic rate, with Cunningham the closest overall and the best fit for the women, though Harris-Benedict was the most accurate for the men. That advantage is population-dependent rather than general, and it depends on a reliable body-composition figure. Most people do not have one, and estimated body fat percentages carry their own error.

Treat it as a hypothesis

The right way to use the number is as a starting point to be tested, not a fact to be obeyed.

Log consistently for two to three weeks, watch what the trend actually does, and compare that with what the estimate predicted. If they disagree, the estimate was wrong, which is the expected outcome for a meaningful share of people, not a personal failing or a sign you logged badly.

Two to three weeks is the minimum because shorter periods are dominated by water and digestive shifts rather than by energy balance. Bhutani and colleagues measured that directly in Physiological Reports (2017): across two weeks, about 84% of the weight change was fat-free mass, and short-term swings of one to two kilograms are ordinary.

Why expenditure moves

TDEE is not fixed. It falls as you lose mass, and by more than the smaller body alone accounts for. Leibel, Rosenbaum and Hirsch, in the New England Journal of Medicine (1995), measured total energy expenditure falling by 6 to 8 kcal per kilogram of fat-free mass per day after a 10% weight loss, with resting and non-resting expenditure each contributing 3 to 4, so the incidental movement of an ordinary day drops as well. Fothergill and colleagues, following participants for six years in Obesity (2016), found the resting half of that persisting long after the weight loss itself.

Together these mean a target that was accurate at the start becomes less so over time, which is a large part of why progress slows in ways a straight-line projection never predicts. A forecast built on your own rate of change accounts for it.

How Loopa handles this

Loopa’s onboarding calculates your starting targets from your own inputs and explains what it used, free. The weight forecast, a Premium feature, then decelerates as you lean out rather than projecting a straight line, shows a plateau when the data says plateau, and refuses to promise a goal date it cannot reach safely. What If lets you change an input and watch the projected date move before you commit.