This study aims to evaluate the effectiveness of an artificial intelligence (AI)-supported, context-aware digital nudging intervention designed to reduce ultra-processed food consumption and improve dietary sustainability among adolescents and young adults. The intervention utilizes real-time behavioral data, including image-assisted dietary logging and contextual information, to identify high-risk consumption moments and deliver personalized, non-coercive nudges. The study will assess changes in ultra-processed food intake, contextual consumption patterns, and sustainability-related dietary indicators.
Adolescents aged 12 to 25 years
Ownership of a smartphone or regular access to a digital device
Ability to use the digital intervention platform
Willingness to provide informed consent (and parental consent where applicable)
Presence of medical conditions requiring a specific therapeutic diet
Participation in another dietary or behavioral intervention study
Severe cognitive or psychological conditions that may impair participation
Inability to use digital tools or applications
Parallel
Artificial intelligence-supported digital nudging
BehavioralA context-aware digital intervention delivering personalized nudges based on real-time dietary behavior and contextual data to reduce ultra-processed food consumption.
Change in ultra-processed food consumption (servings per day)
Change in ultra-processed food consumption, expressed as servings per day, assessed using dietary intake data collected via a digital dietary assessment platform (food diary-based tracking system). Consumption will be quantified based on reported frequency and portion size.
Change in frequency of ultra-processed food consumption (times per day)
Change in the frequency of ultra-processed food consumption, expressed as times per day, assessed using a digital dietary tracking platform (food diary-based system).
Change in proportion of ultra-processed food consumption (% of total intake)
Change in the proportion of ultra-processed food consumption, expressed as percentage of total dietary intake, derived from dietary tracking data collected via a digital dietary assessment platform.
Change in temporal eating patterns (eating occasions per day and timing)
Change in temporal eating patterns, including number of eating occasions per day and timing of meals, assessed using time-stamped dietary records collected via a digital dietary tracking platform.
Change in context-specific dietary behaviours (categorical variables)
Change in context-specific dietary behaviours, including eating location and social context, assessed as categorical variables using data recorded via a digital dietary tracking platform.
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