Calorie Burn Estimation

Updated February 28, 2026

Calorie Burn Estimation splits estimated energy into fixed and variable parts, then tests assumptions against trend data.

Component model

Component	What it estimates	Typical source	Common assumption
Basal baseline	resting energy need	body model or profile	stable tissue and recovery
Thermic effect	digestion cost of meals	intake profile	food form and fiber alter cost
Structured activity	sport-specific movement	training plan and logs	technique and consistency match plan
NEAT and posture	spontaneous steps and fidgeting	wearables and motion patterns	non-exercise activity is stable

Each method estimates different pieces of the same picture.

Method	Inputs	Strong use case	Primary assumption
Wearable HR plus motion	optical heart rate and acceleration	general daily trends	heart-rate quality and zone fit
MET tables	activity type and duration	short planning loops	average physiology across users
Power-based data	power output and cadence	cycling and rowing blocks	calibrated device and stable form
GPS pace	speed, distance, slope, mass	running sessions	stable form and incline handling

When estimate and weight trend disagree for 14 days or more, use a calm recalibration workflow.

Condition	Likely driver	Adjustment
Burn rises but scale trend unchanged	activity overcounting or wear drift	reduce activity multiplier and audit sensor fit
Burn steady but weight increases unexpectedly	undercounted intake or low NEAT	check food logging and late-snacking
Burn stable, weight loss too fast	possible low recovery or water loss confound	hold rate changes and inspect sleep
Burn unstable day-to-day	missing sessions, stress, travel	widen moving average and reduce short changes

This process protects against mistaking sensor noise for true biological change.

For end-to-end integration, pair this logic with active calories, wearable metrics, and TDEE.