Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial.

ABSTRACT:

Background

Carbohydrate counting is an important component of diabetes management, but it is challenging, often performed inaccurately, and can be a barrier to optimal diabetes management. iSpy is a novel mobile app that leverages machine learning to allow food identification through images and that was designed to assist youth with type 1 diabetes in counting carbohydrates.

Objective

Our objective was to test the app's usability and potential impact on carbohydrate counting accuracy.

Methods

Iterative usability testing (3 cycles) was conducted involving a total of 16 individuals aged 8.5-17.0 years with type 1 diabetes. Participants were provided a mobile device and asked to complete tasks using iSpy app features while thinking aloud. Errors were noted, acceptability

SUBMITTER: Alfonsi JE 

PROVIDER: S-EPMC7657721 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature