Foundations for Systematic Evaluation and Benchmarking of a Mobile Food Logger in a Large-scale Nutrition Study
Jisu Jung, Lyndal Wellard‐Cole, Colin Cai, Irena Koprinska, Kalina Yacef, Margaret Allman‐Farinelli, Judy Kay
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.