Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: JMIR Formative Research

Date Submitted: Jul 19, 2019
Date Accepted: Jan 27, 2020

The final, peer-reviewed published version of this preprint can be found here:

Optimizing Child Nutrition Education With the Foodbot Factory Mobile Health App: Formative Evaluation and Analysis

Brown JM, Savaglio R, Watson G, Kaplansky A, LeSage A, Hughes J, Kapralos B, Arcand J

Optimizing Child Nutrition Education With the Foodbot Factory Mobile Health App: Formative Evaluation and Analysis

JMIR Form Res 2020;4(4):e15534

DOI: 10.2196/15534

PMID: 32301743

PMCID: 7195667

Development of the Foodbot Factory mHealth App for Child Nutrition Education: Design, Formative Evaluation, and User-Testing

  • Jacqueline Marie Brown; 
  • Robert Savaglio; 
  • Graham Watson; 
  • Allison Kaplansky; 
  • Ann LeSage; 
  • Janette Hughes; 
  • Bill Kapralos; 
  • JoAnne Arcand

ABSTRACT

Background:

Early nutrition interventions to improve food literacy skills and diet quality are critical to enhancing the nutritional status of children and preventing the development of chronic disease later in life. Despite the rise of mobile technology and mHealth apps and their known effectiveness for improving health behaviours, few evidence-based apps exist to help children learn about nutrition and healthy eating.

Objective:

To describe the iterative development of Foodbot Factory, a novel nutrition education app for children to use at home or in the classroom, and to present data from user testing experiments conducted to evaluate the app.

Methods:

A literature review and environmental scan of the app marketplace were conducted, and stakeholders were consulted to define the key concepts and content of Foodbot Factory. Dietitian and teacher stakeholders identified priority age groups and learning objectives. For user testing sessions, direct observation with a semi-structured form assessed engagement and usability. After gameplay, qualitative interviews and questionnaires were conducted with students to assess satisfaction, engagement, usability, and knowledge gained.

Results:

Environmental scan data revealed that few evidence-based apps exist, indicating the necessity of high-quality mHealth nutrition apps for children. A literature search identified key nutrients of concern for Canadian children, and effective behaviour change and gamification techniques that were included in the app. Foodbot Factory included characters (two scientists and Foodbots) who engage in fun and engaging dialogue and mini-games, with storylines about key healthy eating messages that link to the learning objectives. Five modules were developed: Drinks, Vegetables and Fruit, Grain Foods, Animal Protein Foods and Plant Protein Foods. In total, seven BCTs and three different gamified components were included in the app. Five user testing sessions were conducted in classrooms with Grade 4-7 students (ages 9-13). Data was used to inform the subsequent app iteration. The final user testing session demonstrated that students agreed they wanted to play Foodbot Factory again (70.6%), that the app is easy to use (70.6%), fun (87.5%), and the app goals were clearly presented (94.1%).

Conclusions:

Foodbot Factory is an engaging, educational, and useable mHealth intervention that can help children improve nutrition knowledge at home and at school. The use of an iterative development and testing approach allowed for significant improvements during each iteration, ensuring the app is aligned with the learning needs of the target audience.


 Citation

Please cite as:

Brown JM, Savaglio R, Watson G, Kaplansky A, LeSage A, Hughes J, Kapralos B, Arcand J

Optimizing Child Nutrition Education With the Foodbot Factory Mobile Health App: Formative Evaluation and Analysis

JMIR Form Res 2020;4(4):e15534

DOI: 10.2196/15534

PMID: 32301743

PMCID: 7195667

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.