Cargando…

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

BACKGROUND: Early nutrition interventions to improve food knowledge and skills are critical in enhancing the diet quality of children and reducing the lifelong risk of chronic disease. Despite the rise of mobile health (mHealth) apps and their known effectiveness for improving health behaviors, few...

Descripción completa

Detalles Bibliográficos
Autores principales: Brown, Jacqueline Marie, Savaglio, Robert, Watson, Graham, Kaplansky, Allison, LeSage, Ann, Hughes, Janette, Kapralos, Bill, Arcand, JoAnne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7195667/
https://www.ncbi.nlm.nih.gov/pubmed/32301743
http://dx.doi.org/10.2196/15534
_version_ 1783528583938965504
author Brown, Jacqueline Marie
Savaglio, Robert
Watson, Graham
Kaplansky, Allison
LeSage, Ann
Hughes, Janette
Kapralos, Bill
Arcand, JoAnne
author_facet Brown, Jacqueline Marie
Savaglio, Robert
Watson, Graham
Kaplansky, Allison
LeSage, Ann
Hughes, Janette
Kapralos, Bill
Arcand, JoAnne
author_sort Brown, Jacqueline Marie
collection PubMed
description BACKGROUND: Early nutrition interventions to improve food knowledge and skills are critical in enhancing the diet quality of children and reducing the lifelong risk of chronic disease. Despite the rise of mobile health (mHealth) apps and their known effectiveness for improving health behaviors, few evidence-based apps exist to help engage children in learning about nutrition and healthy eating. OBJECTIVE: This study aimed to describe the iterative development and user testing of Foodbot Factory, a novel nutrition education gamified 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: An interdisciplinary team of experts in nutrition, education (pedagogy), and game design led to the creation of Foodbot Factory. First, a literature review and an environmental scan of the app marketplace were conducted, and stakeholders were consulted to define the key objectives and content of Foodbot Factory. Dietitian and teacher stakeholders identified priority age groups and learning objectives. Using a quasi-experimental mixed method design guided by the Iterative Convergent Design for Mobile Health Usability Testing approach, five app user testing sessions were conducted among students (ages 9-12 years). During gameplay, engagement and usability were assessed via direct observations with a semistructured form. After gameplay, qualitative interviews and questionnaires were used to assess user satisfaction, engagement, usability, and knowledge gained. RESULTS: The environmental scan data revealed that few evidence-based nutrition education apps existed for children. A literature search identified key nutrients of concern for Canadian children and techniques that could be incorporated into the app to engage users in learning. Foodbot Factory included characters (2 scientists and Foodbots) who initiate fun and engaging dialogue and challenges (minigames), with storylines incorporating healthy eating messages that align with the established learning objectives. A total of five modules were developed: drinks, vegetables and fruit, grain foods, animal protein foods, and plant protein foods. Seven behavior change techniques and three unique gamified components were integrated into the app. Data from each user testing session were used to inform and optimize the next app iteration. The final user testing session demonstrated that participants agreed that they wanted to play Foodbot Factory again (12/17, 71%), that the app is easy to use (12/17, 71%) and fun (14/17, 88%), and that the app goals were clearly presented (15/17, 94%). CONCLUSIONS: Foodbot Factory is an engaging and educational mHealth intervention for the Canadian public that is grounded in evidence and developed by an interdisciplinary team of experts. The use of an iterative development approach is a demonstrated method to improve engagement, satisfaction, and usability with each iteration. Children find Foodbot Factory to be fun and easy to use, and can engage children in learning about nutrition.
format Online
Article
Text
id pubmed-7195667
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-71956672020-05-05 Optimizing Child Nutrition Education With the Foodbot Factory Mobile Health App: Formative Evaluation and Analysis Brown, Jacqueline Marie Savaglio, Robert Watson, Graham Kaplansky, Allison LeSage, Ann Hughes, Janette Kapralos, Bill Arcand, JoAnne JMIR Form Res Original Paper BACKGROUND: Early nutrition interventions to improve food knowledge and skills are critical in enhancing the diet quality of children and reducing the lifelong risk of chronic disease. Despite the rise of mobile health (mHealth) apps and their known effectiveness for improving health behaviors, few evidence-based apps exist to help engage children in learning about nutrition and healthy eating. OBJECTIVE: This study aimed to describe the iterative development and user testing of Foodbot Factory, a novel nutrition education gamified 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: An interdisciplinary team of experts in nutrition, education (pedagogy), and game design led to the creation of Foodbot Factory. First, a literature review and an environmental scan of the app marketplace were conducted, and stakeholders were consulted to define the key objectives and content of Foodbot Factory. Dietitian and teacher stakeholders identified priority age groups and learning objectives. Using a quasi-experimental mixed method design guided by the Iterative Convergent Design for Mobile Health Usability Testing approach, five app user testing sessions were conducted among students (ages 9-12 years). During gameplay, engagement and usability were assessed via direct observations with a semistructured form. After gameplay, qualitative interviews and questionnaires were used to assess user satisfaction, engagement, usability, and knowledge gained. RESULTS: The environmental scan data revealed that few evidence-based nutrition education apps existed for children. A literature search identified key nutrients of concern for Canadian children and techniques that could be incorporated into the app to engage users in learning. Foodbot Factory included characters (2 scientists and Foodbots) who initiate fun and engaging dialogue and challenges (minigames), with storylines incorporating healthy eating messages that align with the established learning objectives. A total of five modules were developed: drinks, vegetables and fruit, grain foods, animal protein foods, and plant protein foods. Seven behavior change techniques and three unique gamified components were integrated into the app. Data from each user testing session were used to inform and optimize the next app iteration. The final user testing session demonstrated that participants agreed that they wanted to play Foodbot Factory again (12/17, 71%), that the app is easy to use (12/17, 71%) and fun (14/17, 88%), and that the app goals were clearly presented (15/17, 94%). CONCLUSIONS: Foodbot Factory is an engaging and educational mHealth intervention for the Canadian public that is grounded in evidence and developed by an interdisciplinary team of experts. The use of an iterative development approach is a demonstrated method to improve engagement, satisfaction, and usability with each iteration. Children find Foodbot Factory to be fun and easy to use, and can engage children in learning about nutrition. JMIR Publications 2020-04-17 /pmc/articles/PMC7195667/ /pubmed/32301743 http://dx.doi.org/10.2196/15534 Text en ©Jacqueline Marie Marie Brown, Robert Savaglio, Graham Watson, Allison Kaplansky, Ann LeSage, Janette Hughes, Bill Kapralos, JoAnne Arcand. Originally published in JMIR Formative Research (http://formative.jmir.org), 17.04.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on http://formative.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Brown, Jacqueline Marie
Savaglio, Robert
Watson, Graham
Kaplansky, Allison
LeSage, Ann
Hughes, Janette
Kapralos, Bill
Arcand, JoAnne
Optimizing Child Nutrition Education With the Foodbot Factory Mobile Health App: Formative Evaluation and Analysis
title Optimizing Child Nutrition Education With the Foodbot Factory Mobile Health App: Formative Evaluation and Analysis
title_full Optimizing Child Nutrition Education With the Foodbot Factory Mobile Health App: Formative Evaluation and Analysis
title_fullStr Optimizing Child Nutrition Education With the Foodbot Factory Mobile Health App: Formative Evaluation and Analysis
title_full_unstemmed Optimizing Child Nutrition Education With the Foodbot Factory Mobile Health App: Formative Evaluation and Analysis
title_short Optimizing Child Nutrition Education With the Foodbot Factory Mobile Health App: Formative Evaluation and Analysis
title_sort optimizing child nutrition education with the foodbot factory mobile health app: formative evaluation and analysis
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7195667/
https://www.ncbi.nlm.nih.gov/pubmed/32301743
http://dx.doi.org/10.2196/15534
work_keys_str_mv AT brownjacquelinemarie optimizingchildnutritioneducationwiththefoodbotfactorymobilehealthappformativeevaluationandanalysis
AT savagliorobert optimizingchildnutritioneducationwiththefoodbotfactorymobilehealthappformativeevaluationandanalysis
AT watsongraham optimizingchildnutritioneducationwiththefoodbotfactorymobilehealthappformativeevaluationandanalysis
AT kaplanskyallison optimizingchildnutritioneducationwiththefoodbotfactorymobilehealthappformativeevaluationandanalysis
AT lesageann optimizingchildnutritioneducationwiththefoodbotfactorymobilehealthappformativeevaluationandanalysis
AT hughesjanette optimizingchildnutritioneducationwiththefoodbotfactorymobilehealthappformativeevaluationandanalysis
AT kapralosbill optimizingchildnutritioneducationwiththefoodbotfactorymobilehealthappformativeevaluationandanalysis
AT arcandjoanne optimizingchildnutritioneducationwiththefoodbotfactorymobilehealthappformativeevaluationandanalysis