How many calories should I eat in a day? This is one of the most common questions that I’m asked, and it’s also one of my least favourite. It’s extremely hard to determine energy requirements because calorie needs are influenced by numerous factors. Furthermore, when someone wants to know their calorie needs, it’s usually because they want to try to eat this number of calories each day. This however has its own issues. Below I explain the components of calorie requirements, what makes it so difficult to accurately determine, and issues with calorie counting.
Determining calorie needs:
We’ll start with determining calorie requirements. Daily calorie needs are made up of three components: 1) Basal metabolic rate 2) Diet induced thermogenesis 3) Activity induced energy expenditure.
Basal metabolic rate:
Basal metabolic rate reflects the calories that the body needs at complete rest. That’s right, even if you were to lie in bed all day, you still need a substantial amount of calories. The best way to determine basal metabolic rate is through something called “indirect calorimetry.” However, indirect calorimetry requires specialized equipment, which most athletes will not have access to. Instead, there are various equations available that can estimate basal metabolic rate. Depending on the equation used, varying degrees of information are required, such as age, body mass, muscle mass, and height. These equations are best estimates, but particularly for athletes, can be inaccurate. This inaccuracy may be due to the higher muscle mass in athletes, and these equations being developed in non-athletes . As a result, for many athletes, these equations underestimate basal metabolic rate. Beyond differences in body composition, there are also numerous conditions that may impact basal metabolic rate. For instance, long-term under-fuelling can lead to a suppressed basal metabolic rate [2–4]. With a suppressed basal metabolic rate, the body requires less calories at rest than what would be estimated from these equations. Another example is changes across the menstrual the cycle with some studies showing an increase in basal metabolic rate during the luteal phase of the menstrual cycle .
Diet Induced Thermogenesis:
Diet induced thermogenesis reflects the calories that are required to digest food. This component of our calorie requirements will be influenced by the type of foods consumed. The calories to digest protein is higher than that to digest carbohydrate or fat, and the calories to digest carbohydrate is higher than that of fat . As a result, diet induced thermogenesis can change depending on the macronutrient content of the diet. For instance, diet induced thermogenesis will be higher when eating a diet that is high in protein and low in fat compared to eating a diet high in fat and low in protein. The calories needed to breakdown food will also depend if that food is “whole” or “processed” as whole foods require more calories to break down food .
Activity Induced Energy Expenditure:
Activity induced energy expenditure is the most variable component of calorie requirements. Activity induced energy expenditure includes not only planned physical activity, like going for a run or to the gym, but also non-exercise activity thermogenesis (NEAT). NEAT includes movements such as walking to the car, fidgeting, shopping, and talking. Athletes often fail to consider NEAT when they are determining their energy requirements. However, a substantial amount of calories will be needed for movement outside of planned physical activity. NEAT will likely differ throughout the week or during different seasons. For instance, NEAT will be higher on a day when doing house chores compared to a day that is spent sitting doing computer work or watching TV. NEAT can also be affected by energy balance such that calorie restriction leads to a reduction in NEAT by impacting spontaneous physical activity .
In terms of calculating the calorie expenditure of planned physical activity, most devices will give you estimated caloric expenditure based on heart rate data for exercise such as running, cycling or swimming. Again, this is an estimate and there is also inaccuracies with these estimates . I’ll also point out that for other types of exercise it can be more difficult to estimate caloric energy expenditure, such as weight lifting or yoga.
I decided to do a small self-experiment in estimations of calorie needs. I tried out 2 different devices over 3 days and then also calculated what I would have estimated my calorie needs to be each day. The devices included my Garmin watch and an ActivPal accelerometer. My estimates were based on my estimated basal metabolic rate from the Harris Benedict Equation that I then multiple by an activity factor.
|Day 1||2,350 calories||2,465 calories||2,800 calories|
|Day 2||2,315 calories||2,425 calories||2,800 calories|
|Day 3||1,690 calories||2,290 calories||2,400 calories|
As can be seen, there is variability across these 2 devices and in comparison to my estimation. Personally, I think that the Garmin and ActivPal underestimated my calories needs. This is supported by a study of 12 devices that found all devices underestimated calorie requirements . In particular, I think that the Garmin device didn’t pick up small movements, which is probably why it had the lowest calorie estimation.
Calculating calorie intake:
I further speculate that these devices underestimated my calorie needs based on the next side of my experiment- trying to estimate my caloric intake. My estimated caloric intake was above what the Garmin and ActivPal estimated my calorie needs to be. However, I’m also skeptical as calorie counting is notoriously inaccurate. For instance, a meta-analysis found that athletes underestimated their calorie intake by 667 calories/day . Below I highlight some issues that I noted.
Not properly measuring food.
Food portions can be entered into different calorie counting apps, but the food portion has to first be measured accurately. This is fairly easy if you are consuming pre-portioned food, such as a granola bar or yogurt containers. However, pretty much everything else needs to be measured, and even if measuring everything out, errors can still be made. For instance, I measured ½ cup of oats using a measuring cup, and then weighed it using a kitchen scale. In a calorie counting app, I entered both the ½ cup of oats and the weighed portion of oats. The ½ cup was 153 calories versus the weighed portion was 189 calories. While this may seem like a small difference (which it is), these errors can add up especially when you consider all the other potential sources of error with calorie counting.
This is where I ran into some serious issue. Many meals that I consumed were part of a larger dish. For instance, I had fried rice one night for dinner. This was made up of rice, chicken, shredded carrots, green peppers, onions, and soya sauce that were all mixed together in one pot. I did my best guess, but it was next to impossible to know how much of each component that I actually ate.
Foods that aren’t in databases.
Many foods aren’t in databases. You can select the food that appear closest to what you’re eating, but again there is going to be errors with this. For instance, I ate a homemade muffin. There really was no easy way to accurately enter this in a calorie counting app. The only thing I could think of was adding all the ingredients for the recipe divided by the number of muffins made. However, this would not only be time consuming, but also not completely accurate because it would assume that all muffins that I made were of equal size. There were also numerous foods that I ate that weren’t in the database, such as particular brands of granola bars and breads. You could probably choose something that is closest to the label of what you’re eating, but again I ran into issues with homemade foods like sourdough bread or homemade pizza.
Individual differences in ability to extract calories from food.
Even though this isn’t a component of energy needs, I did also want to point out individual differences in calorie extractions, and that this too can make estimations of calorie needs and consumption misleading. Not all the calories in the foods consumed can be used by the body as some of these calories are lost in poop and urine . There can be induvial variability in the ability to absorb the calories from food consumed. For instance, in one study, calories lost in poop from one person was 80 calories/day versus 500 calories/day in another person . I point this out as even if we are able to count calorie meticulously, we are all unique in our ability to extract calories from the food that we eat.
Pros and cons:
All this being said, I do think it’s important to look at the pros and cons of calorie counting. One pro with calorie counting is that it can be a good tool for learning about nutrition and food. It could also likely lead to weight loss, at least in the short term. On this flip side, it could also be misleading and prevent weight loss due to the inaccuracies that I have covered in this blog. My biggest concern with calorie counting is that it can increase the risk of disordered eating development [13,14]. Calorie counting can also cause an individual to lose touch with hunger and fullness cues and to only look at the calorie component of food rather than considering other important factors. This includes macronutrient and micronutrient consumption and the timing of food intake. Calorie counting is also extremely time consuming. Personally, when I’m hungry, I just want to eat my meal rather than worrying about weighing out food and writing down portions.
Alternatives to calorie counting:
-Follow overall good nutrition practices, such as hitting carbohydrate targets pre-exercise, eating protein with every meal and eating something ever 3-5 hours.
-Listen to your hunger and fullness cues. Your body does an excellent job of keeping track of its calorie needs. It will let you know when you need more fuel and when you’ve had enough.
-Learn from your hunger and fullness cues. If you eat a meal and then you’re hungry 1 hr later that probably means that meal wasn’t big enough. You’ll know for next time that you need to eat more to stay full.
-Look for signs of under-fuelling or over-fuelling. For instance, if you’re constantly thinking about food or find that your body isn’t recovering well this could mean that you’re not fuelling properly.
In summary, calorie prescriptions are not simple. The idea that you need a specific number of calories each day is an oversimplification. Calorie needs will be different each day. On some days you will overeat and other days you will undereat. This is completely normal. The energy balance equation doesn’t magically restart each day, but rather it’s the overall picture that matters.
1. Schofield KL, Thorpe H, Sims ST. Resting metabolic rate prediction equations and the validity to assess energy deficiency in the athlete population. Exp Physiol. 2019;104:469–75.
2. Koehler K, Williams NI, Mallinson RJ, Southmayd EA, Allaway HCM, De Souza MJ. Low resting metabolic rate in exercise-associated amenorrhea is not due to a reduced proportion of highly active metabolic tissue compartments. Am J Physiol – Endocrinol Metab. 2016;311:E480–7.
3. Strock NCA, Koltun KJ, Southmayd EA, Williams NI, Souza MJ De. Indices of resting metabolic rate accurately reflect energy deficiency in exercising women. Int J Sport Nutr Exerc Metab. 2020;30:14–24.
4. Torstveit MK, Fahrenholtz IL, Stenqvist TB, Sylta O, Melin A. Within-day energy deficiency and metabolic perturbation in male endurance athletes. Int J Sport Nutr Exerc Metab. 2018;28:419–27.
5. Benton MJ, Hutchins AM, Dawes JJ. Effect of menstrual cycle on resting metabolism: A systematic review and metaanalysis. PLoS One [Internet]. 2020;15:1–21. Available from: http://dx.doi.org/10.1371/journal.pone.0236025
6. Westerterp KR. Diet induced thermogenesis. Nutr Metab. 2004;1:1–5.
7. Barr SB, Wright JC. Postprandial energy expenditure in whole-food and processed-food meals: Implications for daily energy expenditure. Food Nutr Res. 2010;54:1–9.
8. Levine JA. Non-exercise activity thermogenesis. Proc Nutr Soc. 2003;62:667–79.
9. Dooley EE, Golaszewski NM, Bartholomew JB. Estimating accuracy at exercise intensities: A comparative study of self-monitoring heart rate and physical activity wearable devices. JMIR mHealth uHealth. 2017;5:1–13.
10. Murakami H, Kawakami R, Nakae S, Nakata Y, Ishikawa-Takata K, Tanaka S, et al. Accuracy of wearable devices for estimating total energy expenditure: Comparisonwith metabolic chamber and doubly labeledwater method. JAMA Intern Med. 2016;176:702–3.
11. Capling L, Beck KL, Gifford JA, Slater G, Flood VM, O’Connor H. Validity of dietary assessment in athletes: A systematic review. Nutrients. 2017;9.
12. Lund J, Gerhart-Hines Z, Clemmensen C. Role of Energy Excretion in Human Body Weight Regulation. Trends Endocrinol Metab [Internet]. The Author(s); 2020;31:705–8. Available from: https://doi.org/10.1016/j.tem.2020.06.002
13. Simpson CC, Mazzeo SE. Calorie counting and fitness tracking technology: Associations with eating disorder symptomatology. Eat Behav [Internet]. Elsevier Ltd; 2017;26:89–92. Available from: http://dx.doi.org/10.1016/j.eatbeh.2017.02.002
14. Levinson CA, Fewell L, Brosof LC. My Fitness Pal calorie tracker usage in the eating disorders. Eat Behav. 2017;27:14–6.