Measuring adherence, acceptability and likability of an artificial-intelligence-based, gamified phone application to improve the quality of dietary choices of adolescents in Ghana and Vietnam: Protocol of a randomized controlled pilot test

Abstract

Unhealthy diets are a critical global concern while dietary measure methods are time consuming and expensive. There is limited evidence that phone-based interventions can improve nutrition data collection and dietary quality, especially for adolescents in developing countries. We developed an artificial-intelligence-based phone application called Food Recognition Assistance and Nudging Insights (FRANI) to address these problems. FRANI can recognize foods in images, track food consumption, display statistics and use gamified nudges to give positive feedback on healthy food choice. This study protocol describes the design of new pilot studies aimed at measuring the feasibility (acceptability, adherence, and usability) of FRANI and its effects on the quality of food choice of adolescents in Ghana and Vietnam. In each country, 36 adolescents (12–18 years) will be randomly allocated into two groups: The intervention group with the full version of FRANI and the control group with the functionality limited to image recognition and dietary assessment. Participants in both groups will have their food choices tracked for four weeks. The control groups will then switch to the full version of FRANI and both groups will be tracked for a further 2 weeks to assess acceptability, adherence, and usability. Analysis of outcomes will be by intent to treat and differences in outcomes between intervention and control group will use Poisson and odds ratio regression models, accounting for repeated measures at individual levels. If deemed feasible, acceptable and usable, FRANI will address gaps in the literature and advance the nutrition field by potentially improving the quality of food choices of adolescent girls in developing countries. This pilot study will also provide insights on the design of a large randomized controlled trial. The functioning and dissemination of FRANI can be an important step towards highly scalable nutrition data collection and healthier food choices for a population at risk of malnutrition.The study protocol and the methods and materials were approved by the Institutional Review Board (IRB) of the IFPRI on April 29th, 2020 (registration number #00007490), the Thai Nguyen National Hospital on April 14th, 2020 (protocol code 274/ĐĐĐ-BVTWTN) and the University of Ghana on August 10th, 2020 (Federalwide Assurance FWA 00001824; NMIMR-IRB CPN 078–19/20). The study protocol was registered in the International Standard Randomized Controlled Trial Number (ISRCTN 10681553; https://doi.org/10.1186/ISRCTN10681553) on November 12, 2021.

Feasibility of Using an Artificial Intelligence-based Telephone Application for Dietary Assessment and Nudging to Improve the Quality of Food Choices of Female Adolescents in Vietnam: Evidence from a Randomized Pilot Study

“…Before the use of FRANI, participants did not know what to make of nutrition and health messages [ 19 , 46 ]. The literature shows that effective communication on nutrition literacy does not happen by pushing messages to people [ [52] , [53] , [54] , [55] ].…”

“…The study was approved by the institutional review board of the International Food Policy Research Institute on 29 April 2020 (registration number 00007490) and the Thai Nguyen National Hospital on 14 April 2020 (protocol code 274/ĐĐĐ-BVTWTN). The study was registered under the International Standard Randomized Controlled Trial Number on 12 November 2021 (ISRCTN 10681553), and the protocol of the study was published on 6 December 2022, in Frontiers in Digital Health [ 46 ]. All procedures were undertaken in accordance with the tenets of Declaration of Helsinki.…”

Current Approaches of Artificial Intelligence (AI) in Leading Behavioural Change: The Latest Review

“…A further study could access FRANI that can help scale nutrition data collecting and improve food choices for malnourished populations [7]. These skills generate confidence and meet increasing predictive analytics regulatory needs.…”

“…Here, image recognition, tracking, analytics, and gamified nudges contribute to FRANI's capacity to encourage users to make healthy food choices. Moreover, concerning a community at malnutrition risk, the successful operation and widespread diffusion of FRANI may be a crucial step toward highly scalable nutrition data collecting and improved dietary options [7].…”

mHealth Targeting Physical Activity, Diet, Alcohol, and Smoking among Swedish High School Students : Processes and Outcomes of a Multiple Health Behavior Change Intervention (LIFE4YOUth)

“…Numerous stand-alone mHealth interventions were identified in the literature. These include apps, brief screening interventions, automatic text message, and chatbot programs targeting physical activity, [104][105][106] diet, [107][108][109] alcohol, 110,111 and smoking [112][113][114] or combinations of health behaviors such as clusters of risk substances such as alcohol, tobacco, cannabis, and excessive media use [115][116][117] or clusters of health-promoting behaviors including physical activity, sleep, and a healthy diet. [118][119][120][121] None of the identified stand-alone interventions that targeted ≥3 health behaviors combined health-promoting and health-risk behaviors.…”