Meal and photo estimates
Learn what a description or food image can reveal, what remains invisible and which details make an approximate meal record more useful.
Read the meal method →Methods
Know what goes into an estimate.
Some entries in your personal wellness journal come directly from what you report. Others include AI, formula-based or connected-source estimates. These pages show you which is which.
Each method names the inputs, source order, missing data and limits that can affect the result.
Methods overview · About 3 min read
Inputs, source order, missing data and limits
We explain the information that can materially change a visible estimate, so you can decide how much weight to give it.
Current explanations
Two common estimates, two different problems.
Meal nutrition begins with uncertain food and portion information. Daily expenditure begins with a wearable-reported estimate or a population equation. Both require careful labels and review.
Daily energy expenditure
Review WHOOP source priority, the Mifflin–St Jeor fallback, activity factors, a worked example and period coverage rules.
Read the energy method →Method changes
Each detailed method includes a version and review date, making meaningful changes to a visible calculation easier to find.
Shared rules
What transparent estimates should tell you.
The details differ by feature, but the questions stay consistent.
01What went in?
User details, an image, a profile value or connected data.
02What came first?
The source-priority rule used when several values exist.
03What was missing?
The method states whether missing values are excluded, unavailable or folded into a displayed total.
04What can go wrong?
Individual error, incomplete input and unsupported use cases.
A deliberate boundary
A formula is not a measurement.
Published equations can be useful for general context while still being materially wrong for an individual. Connected wearable values are model-based estimates too.
User review
Correction belongs in the workflow.
AI can misunderstand food, quantities and ordinary language. When a value matters, check the source, add more detail and correct the saved record.
Use estimates with their limits attached.
Read the method, check the coverage and treat the output as context—not certainty.