Foundations for Systematic Evaluation and Benchmarking of a Mobile Food Logger in a Large-scale Nutrition Study

Abstract

Mobile food logging is important but people find it tedious and difficult to do. Our work tackles the challenging aspect of searching a large food database on a small mobile screen. We describe the design of the EaT (Eat and Track) app with its Search-Accelerator to support searches on >6,000 foods. We designed a study to harness data from a large nutrition study to provide insights about the use and user experience of EaT. We report the results of our evaluation: a 12-participant lab study and a public health research field study where 1,027-participants entered their nutrition intake for 3 days, logging 30,715 food items. We also analysed 1,163 user-created food entries from 670 participants to gain insights about the causes of failures in the food search. Our core contributions are: 1) the design and evaluation of EaT's support for accurate and detailed food logging; 2) our study design that harnesses a nutrition research study to provide insights about timeliness of logging and the strengths and weaknesses of the search; 3) new performance benchmarks for mobile food logging.