snapkin
The targets · Decision 3 of 12
How we calculate your calorie target
The calculator can estimate an energy budget. It cannot promise how much muscle you will build. This audit separates the arithmetic currently shipped from the evidence and the resulting product decisions.
Written and maintained by Mattias Geniar. Last updated .
- What the app does
- Subtracts calories for loss, adds them for gain, and uses estimated maintenance for “Maintain weight”. Protein also changes with the objective.
- How sure we are Thin , One small study, or an engineering choice.
- The resting equation is established. Activity factors, precise pace limits and muscle-gain timelines are much less certain. Correct arithmetic does not make an individual forecast accurate.
Start with total expenditure, not resting metabolism
Snapkin uses the simplified Mifflin–St Jeor equation: 10 × weight in kilograms + 6.25 × height in centimetres − 5 × age, then +5 for male or −161 for female. Mifflin et al. (1990) derived it from 498 adults aged 19–78. Our implementation matches that published equation. It predicts resting energy expenditure; the API calls this “bmr”. The unstated-sex constant, −78, is our midpoint convention, not another validated equation.
We multiply that estimate by the activity choice: 1.35, 1.50, 1.65, 1.80 or 2.00. These are calculator assumptions, not individual measurements. The objective adjustment is applied to this total daily energy expenditure (TDEE). A deficit therefore need not put intake below resting metabolism. Steps and watch calories are not added on top.
What the three options currently do
Loss defaults to 0.5% of current weight per week, within a 0.25–0.8% band. Gain defaults to 0.2%, within a 0.125–0.25% band. An explicit pace replaces the default, subject to those bounds. These are backend fallback defaults: the current loss onboarding preselects its rounded “Steady” choice when pace is unanswered, which can be lower. Both directions use 7,700 kcal per kilogram divided by seven days: a requested 0.1 kg per week changes intake by 110 kcal per day.
“Maintain weight” (stored as “both”) sets that adjustment to zero. It does not model fat loss and muscle gain separately. Loss and maintenance ask for 2.0 g of protein per kilogram of reference weight; Gain asks for 1.6 g. Reference weight is current weight capped at the weight corresponding to BMI 27.5. Protein is limited to 45% of target calories before rounding to 5 g. That BMI cap and percentage are product heuristics, not a measurement of lean mass.
Remaining calories become fat and carbohydrate: fat gets the larger of 0.6 g per kilogram of reference weight or 45% of energy remaining after protein; carbohydrate gets the rest. This split is another default, not an optimized muscle-growth prescription.
A small surplus can support training; it does not buy a known amount of muscle
Helms et al. (2023) compared maintenance, 5% and 15% intended surpluses over eight weeks of resistance training. There were 21 participants and 17 completers. Faster weight gain tracked increased skinfold thickness more clearly than muscle thickness. However, the largest-surplus group improved bench press more, and the authors could not exclude some extra biceps growth. This small, short trial supports caution about large surpluses; it does not establish one optimal surplus.
Our gain setting therefore does more than change protein: it increases the calorie budget. But converting that surplus into kilograms with the fat-based 7,700 rule cannot tell us kilograms of muscle. Training stimulus and the mixture of fat, lean tissue and water are missing from the calculation. The gain route now skips pace selection, uses a modest default surplus, and returns no target date. Existing stored pace values remain compatible; no gain rate is presented as a muscle-growth forecast.
Can someone lose fat and gain muscle together?
Yes, body recomposition is possible; losing and gaining total body weight simultaneously is not what it means. In Garthe et al. (2011), 24 athletes trained with weights four times weekly while dieting. The slower group increased lean body mass by 2.1% ± 0.4%; the faster group was statistically unchanged at −0.2% ± 0.7%. Lean mass is not identical to muscle, and this trial does not validate Snapkin’s maintenance-calorie prescription.
The counter-evidence matters. Murphy and Koehler (2022) found impaired lean-mass gains with energy restriction in their seven-study direct comparison (effect size −0.57, p = 0.02). Their broader meta-regression associated a deficit around 500 kcal/day with no lean-mass gain. That is a pooled association, not a universal threshold. Recomposition is a possible training outcome, not something a calorie setting can guarantee.
Morton et al. (2018) pooled 49 resistance-training studies with 1,863 participants and estimated a protein breakpoint around 1.62 g/kg/day. This supports our 1.6 g/kg starting point for gaining, not an exact requirement for every person. Our 2.0 g/kg maintenance choice is a practical higher target; it is not evidence that maintenance calories uniquely cause recomposition. Eating protein does not replace progressive resistance training.
Limits the audit found
The deficit is capped at 35% of estimated expenditure before rounding, then a calorie minimum applies: 1,500 for male and 1,200 for female or unstated. Previously, nearest-50 rounding could put the final deficit slightly past the percentage cap, and protein rounding could exceed its cap. The audit identified these rounding problems. The implementation now rounds the calorie lower bound upwards to a multiple of 50 and keeps rounded protein below its cap.
The calorie minimum can prevent a loss deficit in a small, older, inactive person. It can also put a maintenance target above estimated expenditure. The app must explain that conflict rather than promise the selected direction. Neither these minima nor staying above resting metabolism proves that a diet is adequate for an individual.
Hall et al. (2011) modelled the changes in expenditure that accompany weight change. The fixed 7,700 rule omits those dynamics and cannot produce a reliable long-term timeline. Snapkin displays a weight trend, but that is not an implemented feedback algorithm that learns maintenance calories from intake and weight. A chart alone does not correct the starting target.
The wider research-page audit rechecked these displayed examples against the calculator. The short-term protein and appetite findings are not extra calorie deductions: we do not subtract the 441 kcal/day from the feeding study, or add watch calories, to these targets. Gamification and logging study averages do not enter the equation either.
What changed after the audit
The choices now read “Lose fat”, “Build muscle” and “Maintain weight”. Maintenance aims to keep weight broadly stable, with protein to support training. Recomposition remains a possible outcome, not a promise. The stored value stays “both” for compatibility with existing accounts.
New gain setups use the modest default surplus and explicitly mention resistance training. Only fat loss asks for pace and offers a rough target-date illustration. The backend also suppresses gain and maintenance dates, including requests from older clients. Plans constrained by the calorie minimum explain that the selected pace or direction may not be achievable.
Guided follow-up is deferred until after onboarding is finished. A future review should compare the user's goal with weight trends, logging consistency and training progress, explain whether an adjustment is warranted, and offer a change the user can confirm. The current app does not offer this guided review or automatically recalibrate calorie targets. Manual target editing remains available in Settings.