Skip to main content

Reliability of energy expenditure prediction equations in the weight management clinic

Buy Article:

$51.00 plus tax (Refund Policy)


Abstract Background: 

Few weight management clinics have access to indirect calorimetry with which to measure energy expenditure. Instead, they use energy expenditure prediction equations, which were not designed for use in obesity. We aimed to establish the extent to which such equations overestimate and underestimate resting energy expenditure (REE) in overweight and obese individuals. Methods: 

We compared the Schofield, Harris & Benedict, James & Lean and World Health Organisation (WHO) REE prediction equations with the clinical gold standard of indirect calorimetry in 28 males and 168 females, with a mean (SD) age of 28.9 (6.4) years and body mass index (BMI) of 19–67 kg m−2. Results: 

The mean REE estimated by indirect calorimetry, and the Schofield, Harris & Benedict, James & Lean and WHO equations were 8.09, 8.30, 8.09, 8.37 and 8.23 MJ day−1 (1934, 1983, 1933, 2001 and 1966 kcal day−1), respectively. Although rising BMI exerted only a small effect on the mean differences between indirect calorimetry and the predicted REE [Schofield: +272 kJ (+65 kcal)/10 units BMI, P = 0.02; Harris & Benedict: +42 kJ (+10 kcal)/10 units BMI, P = 0.69; James & Lean: +217 kJ (+52 kcal) 10 units BMI, P = 0.06 and WHO: +42 kJ (+10 kcal) BMI, P = 0.11], the variance among overweight and obese patients of BMI >25 was substantially higher compared to that among normal weight subjects of BMI <25, on whom the equations were based. The estimated REE by Schofield for an individual of BMI 35 kg m−2, for example, could lie anywhere from 2.78 MJ (661 kcal) above the indirect calorimetry value to 2.59 MJ (618) kcal below it. Conclusions: 

Prediction equations offer a quick assessment of energy needs for hypocaloric diets although, in reality, they run the random risk of excessive restriction or further weight gain.

Keywords: BMI; energy expenditure; indirect calorimetry; obesity; prediction

Document Type: Research Article


Publication date: April 1, 2010


Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more