Glossary
Adaptive Calorie Goals
Updated February 28, 2026
Adaptive Calorie Goals adjust daily intake targets from a rolling evidence loop. The model changes slowly, protects recovery, and avoids overfitting any single data point.
What the engine adjusts
In nutrition planning, adaptive goals sit between intention and execution. Targets move with objective, adherence, and recovery, but only after predefined evidence thresholds are met.
Stepwise rule engine
Use a 14-day adaptive window unless you are in a known high-variance block.
- Read the 14-day trend signal from morning weight moving average.
- Compare it with the phase target and round it into three zones:
- Too slow
- On target
- Too fast
- Score adherence from logging and meal completion.
- Score recovery from sleep and training quality.
- Apply one controlled adjustment only after at least two of these signals align.
Concrete adjustment bands by phase
For fat-loss targets, begin with a weekly loss aim of 0.25% to 0.8% of body weight.
| State | Trigger condition | Targeted action |
|---|---|---|
| Fat-loss too slow | loss under 0.25% with good adherence | reduce 75 to 150 kcal and hold for two weeks |
| Fat-loss on target | trend inside 0.25% to 0.8% | hold calories 14 days and audit training quality |
| Fat-loss too fast | loss above 0.8% for two windows | add 100 to 175 kcal and remove one high-intensity session pressure |
| Maintenance up drift | trend above +0.2% of body weight | trim 75 to 150 kcal for 7 to 10 days |
| Maintenance down drift | trend below −0.2% of body weight | add 75 to 125 kcal only after adherence check |
For reconditioning phases, apply changes only in the same direction for two cycles: one nutrition block and one recovery block.
Deficit example
An 80 kg user targets fat loss. 14-day trend loss is 1.0%, adherence is 92%, sleep is mixed, and training quality is stable.
- Baseline weekly target is 0.25 to 0.8%.
- Actual loss is above band, so the rule engine flags fast loss.
- Step one adjustment is +125 kcal/day for 14 days, then trend is rechecked.
- Recovery is protected by keeping protein fixed and using carbohydrate timing around training.
Maintenance example
A 68 kg user sits in maintenance mode with 14-day trend around +0.35%, adherence 87%, and a recovery score that dipped on two travel-heavy weeks.
- Actual trend is above the maintenance band, and adherence is sufficient.
- The model applies a 100 kcal/day reduction.
- No additional changes are made for a second 10-day block.
- If trend remains above band, a second 75 kcal/day reduction follows, but only if recovery and mood stay normal.
Hold and rollback conditions for recovery stress
Hold is mandatory when two conditions are true.
| Hold condition | Evidence check |
|---|---|
| Recovery decline | poor sleep, repeated high soreness, rising fatigue |
| Low data confidence | logging gaps and unstable context signals |
Hold actions are:
| Action | Default response |
|---|---|
| Freeze calorie changes | hold for 3 to 5 days |
| Protect intake scaffolding | keep protein and hydration steady |
| Confirm inputs | recheck weight, recovery, and adherence before moving |
Rollback triggers are stronger and require escalation.
| Rollback trigger | Recovery-preserving response |
|---|---|
| Loss faster than 1.0% weekly for two checks | return to prior calorie band by +150 kcal/day |
| Mood and sleep decline with worsening training quality for seven days | reduce adjustment pressure and widen flexibility |
| Appetite collapse or dizziness | pause aggressive adaptation and request recovery action |
Rollback rule is to return to the prior calorie band by +150 kcal/day or equivalent activity reduction, then restore to normal cadence.
Adaptive goals remain active because they are not a punishment system. They are a control loop that protects performance while you pursue your body-composition target.
Fuel applies this rule set with wearable metrics, calorie burn estimation, and calorie targets, then updates macro targets only after the recalibration conditions are met.
For slower shifts and plateau handling, review adaptive learning and maintenance calories alongside your trend data.