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.

Background: Children increasingly use mobile apps. Strategies to increase child engagement with apps include the use of gamification and images that incite fun and interaction, such as food. However, the foods and beverages that children are exposed to while using apps are unknown and may vary by app type. Objective: The aim of this study is to identify the app content (ie, types of foods and beverages) included in nutrition-themed apps intended for children, to assess the use of game-like features, and to examine app characteristics such as overall quality and behavior change techniques (BCTs). Methods: This analysis used a cross-sectional database of nutrition-themed apps intended for children (≤12 years), collected between May 2018 and June 2019 from the Apple App Store and Google Play Store (n=259). Apps were classified into four types: food games or nongames that included didactic nutrition guides, habit trackers, and other. Food and beverages were identified in apps and classified into 16 food categories, as recommended (8/16, 50%) and as not recommended (8/16, 50%) by dietary guidelines, and quantified by app type. Binomial logistic regression assessed whether game apps were associated with foods and beverages not recommended by guidelines. App quality, overall and by subscales, was determined using the Mobile App Rating Scale. The BCT Taxonomy was used to classify the different behavioral techniques that were identified in a subsample of apps (124/259, 47.9%). Results: A total of 259 apps displayed a median of 6 (IQR 3) foods and beverages. Moreover, 62.5% (162/259) of apps were classified as food games, 27.4% (71/259) as didactic nutrition guides, 6.6% (17/259) as habit trackers, and 3.5% (9/259) as other. Most apps (198/259, 76.4%) displayed at least one food or beverage that was not recommended by the dietary guidelines. Food game apps were almost 3 times more likely to display food and beverages not recommended by the guidelines compared with nongame apps (β=2.8; P<.001). The overall app quality was moderate, with a median Mobile App Rating Scale score of 3.6 (IQR 0.7). Functionality was the subscale with the highest score (median 4, IQR 0.3). Nutrition guides were more likely to be educational and contain informative content on healthy eating (score 3.7), compared with the other app types, although they also scored significantly lower in engagement (score 2.3). Most apps (105/124, 84.7%) displayed at least one BCT, with the most common BCT being information about health consequences. Conclusions: Findings suggest nutrition-themed apps intended for children displayed food and beverage content not recommended by dietary guidelines, with gaming apps more likely to display not recommended foods than their nongame counterparts. Many apps have a moderate app quality, and the use of consequences (instead of rewards) was the most common BCT.

Background/Objectives: School-based nutrition education interventions can support the development of children’s food literacy and healthy eating habits. The Foodbot Factory serious game was developed to support school nutrition education based on Canada’s Food Guide and Ontario curriculum. The objective of this research was to refine the Foodbot Factory intervention to include curriculum-based lesson plans that had a high-level of acceptability by stakeholders to support implementation by teachers in classrooms. Methods: A co-design approach was used to engage teacher and dietitian stakeholders in developing five lesson plans for the intervention, who contributed to creating the intervention content in three stages. The stages included reviewing and providing feedback on the initial draft of the lesson plans, participating in facilitated discussion rounds to come to a consensus on the changes required, and completing a final review of the intervention’s acceptability. Qualitative data included notes on the lesson plans and recordings from meetings that were analyzed thematically. Results: During the first co-design stage, major revisions were suggested for two-fifths of the lessons by stakeholders. Further stakeholder suggestions were discussed and integrated into the intervention from facilitated discussions, improving the lesson plan content and intervention feasibility. All stakeholders agreed that the final version of the intervention was acceptable and would support classroom nutrition education. Five lesson plans were created and compiled into a unit plan, containing additional teaching resources, to support nutrition education with Foodbot Factory. Conclusions: The co-design process greatly improved the Foodbot Factory intervention and its feasibility for classroom implementation. Including diverse stakeholder perspectives led to unique and different insights to improve the intervention.

Introduction: Schools are an important setting for supporting children’s development of food literacy, but minimal research has assessed which strategies are most suitable for school nutrition education. The Foodbot Factory intervention, consisting of serious game (ie, a digital game designed for education) and curriculum-based lesson plans, was developed to support teachers and children ages 8–12 with nutrition education. Pilot data have demonstrated that Foodbot Factory can significantly improve children’s nutrition knowledge, but it has not yet been evaluated in classrooms. Methods and analysis: A single-blinded cluster randomised controlled trial was designed in 2022 by a research team based at Ontario Tech University to determine the efficacy of the Foodbot Factory intervention in improving children’s nutrition knowledge, attitudes and behaviours. 32 grade 4 and 4/5 classrooms in Ontario will be randomised to receive (1) the Foodbot Factory intervention or (2) a control nutrition education intervention using conventional materials (eg, activity sheets). The study’s primary outcome is to determine the overall nutrition knowledge acquired from the intervention. Secondary outcomes include nutrition knowledge subscores (ie, knowledge of specific food groups), nutrition attitudes, dietary intake, general nutrition behaviours (eg, eating breakfast) and intervention acceptability. An Ontario-certified teacher will deliver the intervention to both groups for 35–40 min/day for five consecutive days. Outcomes will be assessed at baseline, immediately postintervention, and 4 weeks and 3 months postintervention using the Nutrition Attitudes and Knowledge questionnaire, the Block Kids Food Screener, a modified Family Nutrition and Physical Activity screener and an acceptability questionnaire. Generalised linear mixed models will assess changes in outcomes between groups. Ethics and dissemination: The study protocol is approved by research ethics boards at Ontario Tech University and participating school boards. Results of the trial will be published in peer-reviewed journals and lay summaries will be available to stakeholders. Trial registration number: NCT05979259 .

Background: Improving children’s food literacy through school-based interventions can support developing healthy eating habits. However, teachers lack appropriate resources, time, and training to provide nutrition education in schools. Serious games, which are games designed for a purpose other than entertainment, have been demonstrated to improve children’s food literacy and dietary intake and can address the barriers teachers face in providing nutrition education. Foodbot Factory (Arcand Lab) is a nutrition education intervention that is aligned with curricula and uses a serious game to provide nutrition education to students. Further evidence is needed to understand how serious games, including Foodbot Factory, can be researched in schools to support nutrition education. Objective: The objective of this study was to evaluate the feasibility of a research study protocol that implements the curriculum-based nutrition education intervention Foodbot Factory into a real-world classroom setting. The evaluation of the protocol included study processes, resources, and management feasibility outcomes, as well as a preliminary assessment of scientific outcomes relevant to the intervention. Methods: A nonrandomized study determined the feasibility of intervention implementation. Grade 4 and 4/5 classrooms were assigned to have nutrition education lessons for 5 days with either the Foodbot Factory or a control intervention. Outcomes were assessed in 4 feasibility domains of study processes (eg, recruitment and attrition rates), resources (eg, time taken to deliver the intervention), and management (eg, challenges with intervention delivery), and a preliminary assessment of scientific outcomes pertaining to the acceptability and impacts of the interventions. These outcomes were captured in semistructured field notes completed by study staff and a Nutrition Attitudes and Knowledge questionnaire and acceptability questionnaire completed by participants. Data were analyzed descriptively and using a paired t test to assess within-group changes in nutrition knowledge. Results: In total, 4 classrooms participated in the feasibility study, with varying recruitment rates for schools (3/20, 15%), classrooms (4/4, 100%), parents (54/102, 53%), and children (49/54, 91%). The time required to implement the research protocol, including data collection and lesson plans, was sufficient and management of the intervention implementation was overall successful. Some challenges were experienced with classroom management during data collection, specifically with electronic data collection. After the intervention, participants reported a positive affective experience (26/41, 63%) and learning something new about healthy eating (31/41, 76%). Participants in both study groups improved their nutrition knowledge, but the changes were not statistically significant. The Foodbot Factory group had a statistically significant improvement in their knowledge of vegetables and fruit (P=.04) and protein foods (P=.03). Conclusions: These findings indicate that the study protocol is feasible to implement and evaluate Foodbot Factory in a representative sample with select modifications to improve recruitment and data collection procedures.