ZeroKnots TechnologiesMuscleTiger

MuscleTiger research notes · No. 2

How a daily calorie target should be set: what is supported, what is convention

Every calorie calculator ends with one number. Behind that number are several separate decisions: how much you burn, how fast you should lose weight, how low is too low, and how to split the calories into protein, fat and carbohydrate. Some of these decisions are well supported by research. Others are somebody's convention.

TL;DR

A calorie calculator is several separate decisions of very different quality. The resting-energy equation and the protein advice are well supported, while activity multipliers, the pace of loss and the lowest calorie levels are ranges or conventions. Treat the number as a starting point and adjust it from your own weigh-ins; if you lift, lose slowly, and if you are a woman who trains, keep intake above your energy availability.

Looking at the pieces one by one is useful, because their quality is very different. The equation for resting energy is solid. The multipliers for activity are ranges, not exact values. The recommended pace of weight loss comes mostly from studies of lean athletes. The lowest "safe" calorie levels are a tradition. Protein is well studied, while fat and carbohydrate are given as wide ranges.

In this note we go through these decisions in the order a calculator makes them, say what the evidence supports and where we made a choice ourselves, and explain why macros need limits at low calorie goals. A table near the end summarises how well each decision is supported. This is the second note in the series; the first one, on weight forecasts, explains why a calorie target should be treated as a starting point rather than a promise. Weights are in kilograms with pounds in brackets.

1. How much you burn

Resting energy

Most calculators start with the Mifflin–St Jeor equation, derived from measurements on 498 healthy adults aged 19 to 78 [1]. In its simplified form, resting energy expenditure is

BMR  =  10 m+6.25 h−5 a+s,s={+5men−161women(1)\mathrm{BMR} \;=\; 10\,m + 6.25\,h - 5\,a + s, \qquad s = \begin{cases} +5 & \text{men} \\ -161 & \text{women} \end{cases} \tag{1}

where mm is body weight in kg, hh is height in cm, aa is age in years, and ss is a constant that depends on sex. The authors found that this simplification did not reduce the accuracy of the full fit [1].

A systematic review by Frankenfield et al. [2] compared the common equations and found that Mifflin–St Jeor predicted resting metabolic rate within 10% of the measured value in more non-obese and obese people than any other equation. The same review warns that errors for individual people can still be noticeable. In the first note we put such errors in perspective: a few per cent of daily expenditure is about a slice or two of bread.

From resting to total energy

Total daily expenditure is resting energy multiplied by a physical activity level (PAL):

TDEE  =  BMR⋅PAL(2)\mathrm{TDEE} \;=\; \mathrm{BMR} \cdot \mathrm{PAL} \tag{2}

The FAO/WHO/UNU expert consultation [3] gives ranges for typical lifestyles: roughly 1.40 to 1.69 for sedentary or light activity, 1.70 to 1.99 for active, and 2.00 to 2.40 for vigorous, noting that values above 2.40 are difficult to maintain over a long period. Our activity levels lie inside these bands; the exact value within each band is our own choice, and in the app it is derived from your step history when Apple Health is connected.

A small correction while dieting

Expenditure measured during a diet tends to come out lower than a formula predicts. Leibel et al. [8] found that after people kept their weight at least 10% below their usual level, total expenditure fell by about 6 kcal per kilogram of fat-free mass per day in people who had never been obese, more than the loss of mass alone explains. While the goal is weight loss we therefore reduce the estimate by a few per cent. The size of the correction comes from a commercial tracker's analysis of its own users, not from a controlled study, and the sources disagree on how such adaptation develops. Roberts et al. [4], citing Leibel, report that non-exercise activity falls by about 262 kcal/day after a 10% loss and by about 500 kcal/day after 20%, which depends on how much you have lost rather than how fast. Breit et al. [9], using the gold-standard method, measured only about 4% lower expenditure in the first month, partly recovering later. Our constant correction is closer to the second picture.

2. How fast to lose weight

The common clinical advice is a deficit of 500 to 1,000 kcal per day, which corresponds to roughly 0.45 to 0.9 kg (1 to 2 lb) per week [10]. Athletes who want to keep muscle are told to go slower. Roberts et al. [4] review the evidence for physique athletes in contest preparation and conclude that rates of 0.5% of body mass per week or less are preferable for limiting the loss of fat-free mass.

The best-known supporting study is Garthe et al. [5]: 24 elite athletes who trained with weights four times a week were put on either a slower or a faster diet. The slower group planned to lose 0.7% of body mass per week (achieved 0.7 ± 0.8%) and gained about 2.1% in lean mass. The faster group planned 1.4% per week (achieved 1.0 ± 0.4%) and did not gain lean mass (−0.2%). Both groups lost about the same amount of weight, around 5.5% of body mass; the slower diet simply took longer (8.5 weeks against 5.3). Slower is not less effective, it is a trade of time for muscle.

We express the pace as a share of body mass per week, between about 0.5% and 1%, with a faster pace allowed when body mass index is higher and there is more fat to lose. The evidence behind this is from lean athletes, so applying it to the general population, and tying the rate to body mass index, is our extrapolation. The daily deficit follows directly from the rate and the energy density of weight change from the first note:

D  =  ρloss  m  ε7(3)D \;=\; \frac{\rho_{\text{loss}}\; m\; \varepsilon}{7} \tag{3}

Here ρloss\rho_{\text{loss}} is the target weekly loss as a share of body mass, mm is body weight (kg), and ε=9,100\varepsilon = 9{,}100 kcal/kg (4,100 kcal/lb) is the effective energy density of a change in body weight; dividing by 7 converts a weekly amount into a daily one. For example, a man weighing 80 kg (176 lb) aiming at 0.5% per week loses 0.4 kg (0.9 lb) a week, which needs a deficit of about 0.4 × 9,100 / 7 ≈ 520 kcal per day.

That example is worth a remark. Murphy and Koehler [6], in a meta-analysis of people doing resistance training, advise avoiding deficits larger than about 500 kcal per day if the aim is to preserve lean mass. At the lower end of our pace range we are right at that edge, and the slower you go, the better for muscle. If you lift weights and want to protect muscle, the lower end of the range, or below it, is the sensible choice.

The same logic runs in the other direction for people who want to gain weight. We add a modest surplus of a few hundred kcal above maintenance (roughly a tenth of it). This is our choice, not a measured optimum: the evidence for the best surplus is limited, and a larger one mostly adds fat rather than speeding up muscle gain.

3. How low is too low

Two limits keep a calorie target from becoming extreme. The first is a cap on the daily deficit, which in our case is 1,000 kcal. It matches the upper end of the general clinical advice [10], and it is twice the limit that the meta-analysis above suggests for preserving lean mass [6]. We regard it as a safety limit, not as an optimum.

The second is an absolute floor on the daily calorie goal. The clinical guideline reproduced in [10] lists 1,000 to 1,200 kcal per day for women and 1,200 to 1,500 kcal per day for men as acceptable low-calorie diets. We use 1,200 and 1,500 kcal, the upper end of these ranges, and in addition never go below the resting energy of a person of normal weight for your height. These floors are a convention and may not suit every body size.

A fixed floor has one weakness: it ignores how big you are and how much you train. Researchers use a measure called energy availability: the calories you eat minus the calories you spend on training, divided by your fat-free mass (everything in the body except fat). Roberts et al. [4] note that for female athletes it should stay at about 30 kcal per kilogram of fat-free mass per day (14 kcal per lb) or more, to avoid menstrual disturbances. As an illustration, a woman who weighs 60 kg (132 lb) and carries about 45 kg (99 lb) of fat-free mass needs 45 × 30 = 1,350 kcal a day left over after training. If training burns 400 kcal, she needs to eat about 1,750 kcal, well above a 1,200 kcal floor.

What are menstrual disturbances, and why do they matter? They mean irregular or missing periods. They are the visible sign that the body, short of energy, has switched off functions it can do without for now, including the reproductive hormones. Roberts et al. [4] report that such disturbances are common in female physique athletes during contest preparation. For someone who is dieting, a cycle that becomes irregular or stops is therefore a warning, not a harmless side effect, and the sensible response is to eat more and to talk to a doctor.

We have built this check into the calorie goal. For women, the goal cannot fall below

Gmin⁡  =  30⋅FFM+Etraining(4)G_{\min} \;=\; 30 \cdot \mathrm{FFM} + E_{\text{training}} \tag{4}

where FFM\mathrm{FFM} is fat-free mass in kg and EtrainingE_{\text{training}} is the average daily energy of your training. We estimate fat-free mass from height and weight with the Boer equation [11], because the app does not measure body composition, and we estimate training energy from your onboarding answers (sessions per week and their length). The goal is never pushed above maintenance, so the floor can only remove a deficit, never create a surplus. For most people the floor does not bind, because their goal is already above it. It matters for women who are tall or heavy, who train a lot, or whose pace of loss is high: for them it reduces the deficit. We apply it to women because the 30 kcal/kg figure comes from studies in women.

4. Protein, fat and carbohydrate

Protein

Protein has the strongest evidence of the three. Roberts et al. [4] support 1.8 to 2.7 g per kilogram per day (0.8 to 1.2 g per lb) during contest preparation, and describe a converging range of 1.6 to 2.7 g/kg/day across research groups. Morton et al. [7] pooled 49 trials with 1,863 participants and found that intakes above about 1.6 g/kg/day did not add further to gains in fat-free mass from resistance training. During a deficit a higher intake helps to retain lean mass, which is why the recommendations for dieting athletes sit higher. In a four-week study with a 40% energy deficit, 2.4 g/kg preserved more lean mass than 1.2 g/kg (Longland et al., as reported in [4]).

We use about 2 g/kg (0.9 g/lb), slightly lower when the goal is maintenance or gaining weight. For people far above their target weight we base it on the target weight rather than the current one, and we limit protein to a share of total calories, so that at low calorie goals it cannot crowd everything else out.

Fat and carbohydrate

For fat and carbohydrate the evidence gives ranges rather than numbers. For contest preparation Roberts et al. [4] suggest fat at 10 to 25% of calories (athletes in general are observed at 20 to 35%) and carbohydrate at a realistic 2 to 5 g/kg (0.9 to 2.3 g/lb), noting that very low carbohydrate during a deficit can impair training. They also caution against very low fat intakes. The calories left after protein and fat go to carbohydrate:

C  =  G−4P−9F4(5)C \;=\; \frac{G - 4P - 9F}{4} \tag{5}

where GG is the calorie goal, PP and FF are protein and fat in grams, and the factors 4 and 9 are the calories per gram of protein or carbohydrate and of fat.

Why macros need limits

Rules in grams per kilogram work well at normal calorie levels and break at low ones. At a low goal, fixed amounts of protein and fat can use up almost the whole budget and leave nothing for carbohydrate, or even add up to more than the goal. So it is safer to set protein in grams per kilogram, limit protein and fat to a share of the calories, and keep a floor under carbohydrate of about 2 g per kilogram (0.9 g/lb), taking the calories from fat when needed. The sum of the macros then never exceeds the calorie goal. The exact limits are our choice within the published ranges.

Results

DecisionWhat we doHow well it is supported
Resting energyMifflin–St Jeor [1], [2]Well supported; individual error of a few per cent
Activity multiplierLevels inside the FAO/WHO/UNU bands [3]Ranges are supported; values within them are our choice
Correction while dietingA few per cent below the formulaThe direction is supported [8]; the size is not from a controlled study
Pace of lossA share of body mass per week, scaled with BMIBased on studies of lean athletes [4], [5]; scaling with BMI is our extrapolation
Surplus for gaining weightA modest surplus of a few hundred kcalOur choice; the evidence for the best surplus is limited
Daily deficitCapped at 1,000 kcalMatches general guidance [10]; above the limit advised for keeping muscle [6]
Lowest calorie goal1,200 kcal (women) and 1,500 kcal (men)A common convention [10]
Energy availability (women)Goal not below 30 kcal per kg (14 per lb) of fat-free mass plus training energySupported for women [4]; fat-free mass and training energy are estimates
ProteinAbout 2 g/kg (0.9 g/lb), capped as a share of caloriesWell supported [4], [7]
Fat and carbohydrateCapped shares; carbohydrate at least 2 g/kg (0.9 g/lb)Ranges from [4]; our caps lie within them

How we use this in MuscleTiger

During onboarding the app asks for your goal, age, height, weight, target weight and activity (it can read activity from Apple Health) and calculates your calorie goal and macros in this order: resting energy, activity, pace, limits, then protein, fat and carbohydrate. The weight forecast you see is built on the same pace and the same energy density, so the target and the forecast agree.

For women the goal also respects energy availability, so a deficit is reduced rather than letting the goal drop below the level the research links to menstrual disturbances. The goal is a starting point: once you have logged enough weigh-ins and meals, the app corrects it with your own data.

See MuscleTiger

Conclusions

Limits of this note. Most of the evidence on pace and macros comes from lean athletes and from short studies, and was applied to a general population by us. Individual needs differ. This is general information, not medical advice; if you have a medical condition, are pregnant, or have a history of disordered eating, talk to a doctor before restricting calories.

Sources

  1. Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr 1990;51:241–7. doi:10.1093/ajcn/51.2.241
  2. Frankenfield D, Roth-Yousey L, Compher C. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J Am Diet Assoc 2005. doi:10.1016/j.jada.2005.02.005
  3. FAO/WHO/UNU. Human energy requirements: report of a joint expert consultation. FAO Food and Nutrition Technical Report Series 1, 2004; chapter 5, Table 5.3. fao.org
  4. Roberts BM, Helms ER, Trexler ET, Fitschen PJ. Nutritional recommendations for physique athletes. J Hum Kinet 2020;71:79–108. free full text (PMC) · doi:10.2478/hukin-2019-0096
  5. Garthe I, Raastad T, Refsnes PE, Koivisto A, Sundgot-Borgen J. Effect of two different weight-loss rates on body composition and strength and power-related performance in elite athletes. Int J Sport Nutr Exerc Metab 2011;21:97–104. doi:10.1123/ijsnem.21.2.97
  6. Murphy C, Koehler K. Energy deficiency impairs resistance training gains in lean mass but not strength: a meta-analysis and meta-regression. Scand J Med Sci Sports 2022. doi:10.1111/sms.14075
  7. Morton RW, Murphy KT, McKellar SR, et al. A systematic review, meta-analysis and meta-regression of the effect of protein supplementation on resistance training-induced gains in muscle mass and strength in healthy adults. Br J Sports Med 2018. doi:10.1136/bjsports-2017-097608
  8. Leibel RL, Rosenbaum M, Hirsch J. Changes in energy expenditure resulting from altered body weight. N Engl J Med 1995;332:621–8. doi:10.1056/NEJM199503093321001
  9. Breit MJ, Pan Z, et al. Using time-weighted averages of total daily energy expenditure to estimate energy intake during a weight loss intervention. Obesity 2025;33:2093–102. doi:10.1002/oby.70008
  10. Poston WSC 2nd, Foreyt JP. Successful management of the obese patient. Am Fam Physician 2000;61(12):3615–22; a review that reproduces the calorie ranges of the NHLBI obesity guideline (1998). aafp.org
  11. Boer P. Estimated lean body mass as an index for normalization of body fluid volumes in humans. Am J Physiol 1984;247:F632–6. doi:10.1152/ajprenal.1984.247.4.f632