“How many calories should I eat?”
This is up there with “Is this food bad for me?”, as one of the most asked questions I seem to get asked. Although, such a process has numerous limitations, there is a way you can go about this.
Quantifying required energy intake can be achieved via the utilisation of energy expenditure prediction equations. However, it is important to stress the word ‘PREDICTION’. These equations provide an ‘ESTIMATE’ of energy expended at rest – Basal Metabolic Rate (BMR) or Resting Metabolic Rate (RMR). Note: To gain an accurate measurement of energy expended at rest a laboratory test would be required (this can be quite expensive and unnecessary for your average fitness trainee).
Three popular equations for estimating BMR/RMR include:
(1) Harris and Benedict – incorporates height (cm), weight (kg) and age (years)
(2) Cunningham – incorporates fat-free mass (kg)
(3) Schofield – incorporates weight (kg) and age (years)
Next step – As mentioned above to calculate estimated total energy expenditure you must first utilise a prediction equation to estimate the energy you expend at rest. This is then multiplied by PAL value that corresponds to your self-estimated activity demands. Below is a table outlining the range of PAL values from which to base your estimation.
Although useful, the above can be confusing for individuals when trying to estimate both their expenditure during the day (e.g. job) and training. We are not all professional athletes and therefore have to consider expenditure at work/school etc. The below table outlines the estimated PAL for differing levels of activity.
A PAL of 1.6 represents the average activity level of a normally active individual, but sedentary for periods. 2.0-2.5 is considered the PAL for athletes engaging in normal training, whereas a PAL of 2.5-4.0 for athletes engaged in rigorous training/competition
I have created a spreadsheet which can predict energy intake based upon the three prediction equations above (click here to download).
Final Step – Implement a kcal surplus or deficit if this is required by for your body composition goal.
The purpose of such equations has been to predict energy expenditure within the general population, but they have also been used for active and athletic individuals. However, such equations may not be well suited to certain athletic individuals. Research amongst professional rugby league players found that measured RMR was 16.5% (approximately 310kcal) lower than that predicted via the Cunningham equation. Furthermore, Cunningham, Harris-Benedict and Schofield equations typically underestimated total energy expenditure for u16 to u24 rugby league and union players, although an over prediction was seen in some instances, highlighting the great individual variability in predictions.
Energy expenditure prediction equations are not perfect and have their limitations. However, having said that they can provide a rough initial figure for calorie intake, from which you can then re-evaluate progress (e.g. body mass) and make the necessary changes.