Study Protocol for Surveying Nutrient Assessment With Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Records and Paired With Meal Photos

Abstract

Objectives Photo-based dietary assessment methods are becoming more feasible as artificial intelligence methods improve. However, advancement of these methods to the level usable in nutrition studies has been hindered by the lack of a dataset against which to benchmark algorithm performance. Here, we introduce the Surveying Nutrient Assessment with Photographs of Meals (SNAPMe) Study (ClinicalTrials ID: NCT05008653), describe the data, and discuss the utility of the data. Methods The purpose of the SNAPMe Study was to pair meal photographs with traditional food records. The goal was to collect approximately 1000 real-world meal photos from 100 participants consuming at least three meals per day, for a total of three study days. Participants were recruited nationally and completed enrollment meetings via web-based video conferencing. Participants uploaded and annotated their meal photos using a mobile phone app called Bitesnap and completed food records using the Automated Self-Administered 24-hour Dietary Assessment Tool (ASA24®) on the same day. A sizing marker with black and white boxes of known size were included in meal photos to assist with portion estimation. Participants included photos before and after eating non-packaged and multi-serving packaged meals, as well as photos of the front package label and ingredient label for single-serving packaged foods. Results By the end of the study, 90 participants had completed all three days of data collection. Examples showing the utility of SNAPMe data with respect to artificial intelligence will be presented. Conclusions The SNAPMe dataset will be made publicly available and will link meal photos, annotations, write-in notes, and ASA24 food records together. These data will be transformative for the improvement of artificial intelligence algorithms for the adoption of photo-based dietary assessment in nutrition research. Funding Sources This work was supported by the United States Department of Agriculture (USDA)/NSF AI Institute for Next Generation Food Systems (AIFS), USDA award number 2020-67,021-32,855.