The quality of labelled food product databases underlying popular diet applications (apps) with barcode scanners was investigated.

Product identification rates for the scanned products and the availability and accuracy of nutrient values were calculated.

One hundred food products were selected from the two largest supermarket chains in the Netherlands. Using the barcode scanners of the selected apps, the products were scanned and the results recorded as food diary entries. The collected data were exported.

Seven diet apps with barcode scanner and food recording feature were selected from the Google Play and Apple app stores.

Energy values were available for 99 % of the scanned products, of which on average 79 % deviated not more than 5 % from the true value. MyFitnessPal provided values for sixteen nutrients, while Virtuagym Food and Yazio provided values for only four nutrients. MyFitnessPal also showed the largest percentage of correctly identified products (i.e. 96 %) and SparkPeople the smallest (i.e. 5 %). The accuracy of the provided nutrient values varied greatly between apps and nutrients.

While energy was the most consistently and accurately reported value, the availability and accuracy of other values varied greatly between apps. Whereas popular diet apps with barcode scanners might be valuable tools for dietary assessments on the product and energy level, they appear less suitable for assessments on the nutrient level. The presence of user-generated database entries implies that the availability of food products might vary depending on the size and diversity of an app’s user base.

To evaluate adolescents’ abilities to identify foods and estimate the portion size of foods consumed in order to inform development of the mobile telephone food record (mpFR).

Data were collected from two samples of adolescents (11–18 years). Adolescents in sample 1 participated in one lunch (n 63) and fifty-five of the sixty-three adolescents (87 %) returned for breakfast the next morning. Sample 2 volunteers received all meals and snacks for a 24 h period. At mealtime, sample 1 participants were asked to write down the names of the foods. Sample 2 participants identified foods in an image of their meal 10–14 h postprandial. Adolescents in sample 2 also estimated portion sizes of their breakfast foods and snacks.

Sample 1 identified thirty of the thirty-eight food items correctly, and of the misidentified foods all were identified within the correct major food group. For sample 2, eleven of the thirteen food items were identified correctly 100 % of the time. Half of the breakfast and snack foods had at least one portion size estimate within 10 % of the true amount using a variety of measurement descriptors.

The results provide evidence that adolescents can correctly identify familiar foods and they can look at an image of their meal and identify the foods in the image up to 14·5 h postprandial. The results of the present study not only inform the development of the mpFR but also provide strong evidence of the use of digital images of eating occasions in research and clinical settings.