The Number Looked Precise. That Didn’t Make It True.

MyFitnessPal gave me a number: about 3,500 calories. Based on the information I had entered, that was roughly how much I could eat each day and maintain my current weight.

My bariatric clinic gave me another number: 2,600 calories. That was the target they wanted me to use to stay in a deficit.

There was nothing surprising about the second number being lower than the first. A deficit requires eating less energy than you use. The difference was large, but the math was easy enough to understand.

What bothered me was a different question.

How much energy was I actually using?

3,500 sounded remarkably specific. So did 2,600. Put a comma in a number, display it on a screen, and it starts to look less like an estimate and more like a fact.

I had made that mistake without realizing it.

3,500 calories was not a measurement

MyFitnessPal did not observe my metabolism. It did not place me in a metabolic chamber or measure the gases I inhaled and exhaled. It did not watch every movement I made during the day and calculate exactly how much energy each one required.

It took information about me and made an estimate. According to MyFitnessPal, its initial calorie calculations use age, height, weight, sex, and the activity level a user selects to determine the calories required to maintain that person's current weight, then add or subtract calories based on the weight-loss or weight-gain goal the user entered.

That distinction seems obvious once I write it down. It did not measure 3,500 calories. It calculated 3,500 calories from a model. Those are not the same thing.

That does not make the number useless. Most of health and fitness would become impractical very quickly if every useful decision required laboratory-grade measurement. We use equations, averages, questionnaires, consumer devices, and other approximations because they can give us a reasonable place to begin.

The problem begins when I forget that beginning is what they gave me.

An estimate can be good without being right

There are well-established ways to estimate how much energy a person uses, but estimating energy expenditure in an individual is messier than the clean number on a screen suggests.

Researchers have compared predictive equations against more direct methods of assessing energy expenditure. A 2022 systematic review in Nutrition Reviews examined 61 studies of adults with overweight or obesity and found considerable variation in how accurately different equations estimated resting and total energy expenditure. Even the better-performing equations were not perfectly precise for individuals, and the authors concluded that more research was needed, particularly on predicting total energy expenditure.

That matters in my case because I live with severe obesity. An equation can perform reasonably well across a population while still being meaningfully wrong for one person inside that population.

If the model says two people each require 3,500 calories to maintain their weight, that does not mean their bodies are secretly running identical energy budgets. Their body composition can differ. Their movement can differ. Their routines can differ. The activity category they selected in an app can be wrong. Their actual activity can change from one day to the next.

And even before getting into physiology, the inputs themselves contain uncertainty. What exactly counts as "active"? How much of my gym routine is already represented in that category? Does a workout that feels difficult necessarily burn as much energy as I imagine? How much do I move during the other 23 hours of the day?

Those questions do not make calorie estimates meaningless. They explain why a number generated from them should not be mistaken for direct observation.

Then there was 2,600

When I asked my bariatric clinic what my calorie target should actually be, they gave me 2,600 calories per day as the intake intended to keep me in a deficit.

My first instinct was to give that number more authority. It came from the clinic. It was specific to me. It was connected to people involved in my medical care rather than an app asking me to choose an activity level. All of those things matter.

But I also have to be careful about what conclusion I draw from them. The clinic was answering a practical treatment question: how much should I be eating right now? MyFitnessPal's maintenance estimate was answering a different one: given these inputs and assumptions, approximately how much energy might someone like me need to hold his weight steady?

Those numbers are related, but they are not competing laboratory results. The 2,600-calorie target does not reveal my exact daily energy expenditure either. It gives me a plan.

That may actually be more useful.

I was looking for a fact when I needed a working number

I like data. Part of rebuilding my health has involved tracking more of it: body weight, food intake, protein, exercise, blood pressure, lab results, body composition. Data gives me something concrete to work with, which is usually a strength.

It can also become a trap. The more precise a number looks, the easier it is for me to assume the uncertainty has already been removed.

3,500. 2,600. 470.9 pounds. 150 grams of protein. 30 minutes.

Numbers feel settled in a way that probably, approximately, and it depends do not. But precision on a display is not the same thing as precision in the underlying information.

A bathroom scale can show my weight to one-tenth of a pound. That does not mean every tenth of a pound represents a meaningful change in body tissue. A body-composition device can report body-fat percentage with decimal places without eliminating the limitations of the method. A fitness tracker can assign an exact calorie number to a workout it never directly measured. And a nutrition app can give me an exact calorie budget even though the calculation began with assumptions.

I do not think the answer is to stop measuring things. For me, that would throw away something genuinely useful. The better answer is to become more disciplined about what I believe the numbers mean.

A calorie target does not have to be perfect to be useful

The National Institute of Diabetes and Digestive and Kidney Diseases has its own Body Weight Planner. It asks for weight, sex, age, height, and physical activity level before calculating personalized calorie levels for reaching and maintaining a goal weight. Even a sophisticated government-developed model still begins with inputs and assumptions about the person using it.

That is not a flaw unique to calorie calculators. It is the nature of trying to model a complicated human system.

The useful question for me, then, is probably not:

What is my true calorie number?

At least not with the expectation that an app can reveal it once and settle the matter.

A better question is:

Is this calorie target helping the larger system work?

That one can be examined over time. I can look at what I am eating, and at my weight trend rather than one day's weight. I can look at hunger, training, recovery, protein intake, and how consistently I can actually follow the plan. I can bring those observations back to the clinicians helping manage my care.

If the results consistently differ from what the original estimate predicted, that is information too. The estimate does not get to overrule reality simply because it came first.

This changed how I think about being "wrong"

When I treat an estimate as a fact, any disagreement between the number and reality starts to look like a failure somewhere else. If an app says I should lose weight at a certain intake and I do not, then either I tracked incorrectly, failed to follow the plan, or somehow broke the rules.

Those explanations are possible. They are not the only possibilities. The original estimate may have been wrong. My activity level may have been miscategorized. My intake tracking may contain ordinary errors. Short-term changes in body weight may be obscuring the longer trend. Several things can be true at once.

That does not remove personal responsibility from the equation. I still have to track honestly enough for the data to mean something. I still have to follow the treatment plan. I still have to make decisions about food and activity. But responsibility does not require pretending our tools are more accurate than they are.

Sometimes the responsible response to data is not obedience. It is interpretation.

What kind of number is this?

I used to think better data would eventually remove uncertainty. If I tracked enough things carefully enough, I would know exactly what was happening. I am beginning to think that is the wrong expectation.

Better data reduces some uncertainty. It can expose patterns my memory misses. It can tell me whether something has changed. It can give my clinicians better information. It can catch assumptions that do not survive contact with what actually happened. What it cannot always do is give me a single clean explanation.

3,500 was useful because it gave me an estimate of maintenance. 2,600 was useful because it gave me a treatment target. Neither number needed to become a statement about what my body must be doing.

I still want numbers. I still want calorie targets, weigh-ins, lab results, and body-composition scans. I want as much useful information as I can reasonably collect. I just want to ask one more question when I look at them now.

Is this a measurement? An estimate? A target? A trend? A calculation built from several other assumptions?

Those categories matter, because a number can be useful without being perfectly true.