Subjects' Perception in Quantifying Printed and Digital Photos of Food Portions

Pryscila G Nichelle, Claudia C B Almeida, Suzi A Camey, Lenine M Garmus, Vanessa C M Elias, Dirce M Marchioni, Danielle G da Silva, Marga C Ocke, Nadia Slimani, Regina M Fisberg, Sandra P Crispim, Pryscila G Nichelle, Claudia C B Almeida, Suzi A Camey, Lenine M Garmus, Vanessa C M Elias, Dirce M Marchioni, Danielle G da Silva, Marga C Ocke, Nadia Slimani, Regina M Fisberg, Sandra P Crispim

Abstract

Although digital photos have the potential to improve the precision of reported portions in dietary assessment, there are few studies investigating its accuracy in comparison to printed photos. The aim of this study was to evaluate the perception of adults in quantifying food portion sizes using printed and digital photos, displayed on computer-screens and tablets. In total, 1165 evaluations were performed for 60 photos of portion sizes in Brazil. Each participant (n = 58) attended two sessions in the study center, with an interval of at least one week. In each session, twelve food portions were prepared and randomly evaluated by each participant in its printed and digital forms. The mean error (difference between the estimated and true portions) was not significantly different between the printed photos (2.1 g ± 47.2) and the digital ones (-6.4 g ± 53.7). The agreement on using the printed and digital photos was 91% and 90%, respectively. Furthermore, the use of the tablet was more prone to underestimation when compared to printed and computer-screen photos (p < 0.001). Overall, participants did not present major difficulties in perceiving the portion sizes using the printed and digital photos, but the use of tablets led to less accurate results, indicating that this needs to be further evaluated.

Keywords: adults; dietary intake; food portion; perception; photos; quantification.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Cumulated percentage of perception assessments in relation to error percentage classification.

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