How Kinra learns your metabolism

Most calorie apps hand you a number on day one and never revisit it. The number came from a formula that has never met you — it doesn't know how much you move, how you sleep, or what your body does with the food you eat. So the plan is wrong in some small way from the start, and it stays wrong.

Kinra works the other way around. It begins with the same kind of formula as a first guess, then quietly learns from two things it can actually observe: how your weight trend moves, and what you log. Over a few weeks it forms its own estimate of what you burn, and it adjusts your plan a little at a time. This page is the whole method — first in plain language, then with the math, cited to the research it's built on.

The plain-language version

A first estimate. When you start, Kinra needs somewhere to begin. It uses a standard resting-energy equation and an activity factor to make an opening guess at what you burn in a day.¹ Treat it as a prior, not a verdict — it's the starting line, not the finish.

Trend weight, not the scale. Your weight on any single morning is noisy: water, sodium, a late dinner, the time you weighed in. Kinra smooths your check-ins into a trend so one heavy morning doesn't move your plan and a gap between weigh-ins doesn't distort it.

Learned expenditure. Once you've logged enough, Kinra estimates what you actually burn from the energy balance between what you ate and how your trend moved — not a fixed formula.² If you log around 2,000 calories a day while your trend falls about 0.5 kg a week, your real burn is in the neighborhood of 2,550 a day.

Confidence, always. The engine reports high, medium, or low confidence based on how many complete days and weigh-ins it has to work with. When the data is thin or noisy, it says so, and holds your target steady rather than guessing.

Gentle weekly updates. Each week Kinra compares your planned pace to your observed pace, damps the correction by half, caps it, and rounds it off. Your plan nudges; it never lurches.

Guardrails. Targets stay above sensible floors and never sit too far below your estimated burn. After a long stretch in a deficit, or a stall, Kinra suggests a maintenance break instead of cutting lower.³

The math, for the curious

You never need to see any of this to use Kinra. But the engine is deterministic — the same inputs always produce the same output — so if you want to audit it, here it is.

The resting-energy prior

Kinra's opening guess uses the Mifflin–St Jeor equation for resting energy, multiplied by an activity factor.¹ It's a sensible, well-validated start — and only a start.

REE = 10·kg + 6.25·cm − 5·age + sex
sex = +5 (male) / −161 (otherwise)
prior_TDEE = REE × activity_factor

Activity factors run from about 1.2 for a sedentary week to 1.9 for a very active one.

Trend weight

Daily weight gets smoothed into a trend with a time-aware version of exponential smoothing — the same idea behind The Hacker's Diet,⁴ but adjusted so a gap between weigh-ins doesn't throw it off. The 10-day constant means the trend follows real change without chasing daily noise.

alpha = 1 − exp(−days_since_last_weight / 10)
trend = prev_trend + alpha·(weight − prev_trend)

Observed expenditure

Over rolling 14-to-28-day windows, Kinra reads your real intake against how your trend moved, using roughly 7,700 calories per kilogram of tissue as a short-window approximation.⁵ The result is an observed estimate of your burn, which it blends with the formula prior — leaning on the learned number only when the data earns it.

tissue_energy = Δtrend_kg × 7700 / days
observed_TDEE = avg_logged_kcal − tissue_energy

The weekly adjustment

When Kinra changes your target, it compares your desired pace to your observed pace, halves the correction so it never overreacts, caps the change, and rounds it. Small differences sit inside a deadband and are left alone.

rate_error = desired_rate − observed_rate   (kg/week)
delta = round10(rate_error × 7700 / 7 × 0.5)
delta = clamp(delta, −150, +150)

What Kinra deliberately does not claim

Honesty is part of the method, so here is what the engine will not pretend to do. It does not claim a lab-precise measurement of your metabolism — the observed estimate is a good working number, not a metabolic-chamber reading.⁶ It does not predict your long-term weight from a fixed rule, because bodies adapt as you lose.³ And it does not infer body-fat percentage or diagnose anything medical. When your logs and your trend disagree, it flags it gently, as a question about the data — never as an accusation about you.

This is general wellness support for healthy adults, not medical advice. Your targets are coaching estimates. For medical questions, pregnancy, or any history of disordered eating, talk with a qualified clinician.