The validity of self-reported energy intake as determined using the doubly labelled water technique

In the 1980s the development of the doubly labelled water (DLW) technique made it possible to determine the validity of dietary assessment methods using external, independent markers of intake in free-living populations. Since then, the accuracy of self-reported energy intake (EI) has been questioned on a number of occasions as under-reporting has been found to be prevalent in many different populations. This paper is a review of investigations using the DLW technique in conjunction with self-reported EI measures in groups including adults, children and adolescents, obese persons, athletes, military personnel and trekking explorers. In studies where a person other than the subject is responsible for recording dietary intake, such as parents of young children, EI generally corresponds to DLW determined energy expenditure. However, in instances where the subjects themselves report their intake, EI is generally under-reported when compared with energy expenditure. It was originally believed that this phenomenon of under-reporting was linked to increased adiposity and body size, however, it is now apparent that other factors, such as dietary restraint and socio-economic status, are also involved. This paper therefore aims to present a more comprehensive picture of under-reporting by tying in the findings of many DLW studies with other studies focusing particularly on the characteristics and mechanisms for under-reporting. Awareness of these characteristics and mechanisms will enable researchers to obtain more accurate self-reports of EI using all dietary recording techniques.

Using clusters of computers for large QU-GENE simulation experiments

The QU-GENE Computing Cluster (QCC) is a hardware and software solution to the automation and speedup of large QU-GENE (QUantitative GENEtics) simulation experiments that are designed to examine the properties of genetic models, particularly those that involve factorial combinations of treatment levels. QCC automates the management of the distribution of components of the simulation experiments among the networked single-processor computers to achieve the speedup.