Dietary Behaviour, Non-Communicable Chronic Diseases, Obesity Prevention, Ultra-Processed Food Consumption, Unhealthy Diet
Conditions
Keywords
Ultra-Processed Food, Digital Intervention, Artificial Intelligence, Dietary Behavior, Adolescents, Young Adults, Nutrition, Sustainability, non-communicable disease
Brief summary
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.
Detailed description
This study investigates the effectiveness of an artificial intelligence (AI)-supported, context-aware digital intervention targeting ultra-processed food (UPF) consumption among adolescents and young adults. UPF consumption has been identified as a major contributor to non-communicable diseases and is associated with significant environmental impacts. However, existing digital nutrition interventions largely rely on static, nutrient-based approaches and do not adequately capture real-life behavioral contexts. The intervention integrates image-assisted dietary logging, contextual data collection (including time, location, and social setting), and explainable artificial intelligence to identify high-risk moments of UPF consumption. Based on these insights, the system delivers adaptive, personalized digital nudges designed to support healthier and more sustainable food choices without restricting user autonomy. The study follows a controlled evaluation design to assess the effectiveness of the intervention. Primary outcomes include changes in context-specific UPF consumption patterns, while secondary outcomes include overall dietary quality, sustainability-related indicators (such as environmental impact proxies), and user engagement metrics. This research aims to provide evidence for scalable, ethically governed digital health interventions that integrate behavioral science, nutrition, and sustainability within real-life settings
Interventions
BEHAVIORALArtificial intelligence-supported digital nudging
A context-aware digital intervention delivering personalized nudges based on real-time dietary behavior and contextual data to reduce ultra-processed food consumption.
Sponsors
Istanbul Gelisim University
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
PREVENTION
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
12 Years to 25 Years
Healthy volunteers
Yes
Inclusion criteria
* 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) * Regular consumption of ultra-processed foods at baseline
Exclusion criteria
* 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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Change in ultra-processed food consumption (servings per day) | From baseline to 10 months and 20 months | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Change in frequency of ultra-processed food consumption (times per day) | From baseline to 10 months and 20 months | 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) | From baseline to 10 months and 20 months | 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) | From baseline to 10 months and 20 months | 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) | From baseline to 10 months and 20 months | 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. |
Contacts
CONTACTHATİCE MERVE BAYRAM, PhD
CONTACTArda OZTURKCAN, PhD
STUDY_CHAIRElena Milli, PhD
Polo Europeo della Conoscenza - Istituto Comprensivo di Bosco Chiesanuova
STUDY_CHAIRStefano Cobello, PhD
Polo Europeo della Conoscenza - Istituto Comprensivo di Bosco Chiesanuova
Outcome results
None listed