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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.
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
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Intervention group (artificial intelligence-supported, context-aware digital nudging intervention) | Experimental | Participants receive an artificial intelligence (AI)-supported, context-aware digital nudging intervention designed to reduce ultra-processed food (UPF) consumption. The system uses real-time dietary and contextual data to deliver personalized behavioural prompts. |
|
| Control group (Digital platform without nudging) | No Intervention | Participants have access to the digital platform without active nudging components. No personalized behavioural prompts are delivered. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Artificial intelligence-supported digital nudging | Behavioral | A context-aware digital intervention delivering personalized nudges based on real-time dietary behavior and contextual data to reduce ultra-processed food consumption. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | From baseline to 10 months and 20 months |
| Measure | Description | Time Frame |
|---|---|---|
| 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). | From baseline to 10 months and 20 months |
| Change in proportion of ultra-processed food consumption (% of total intake) |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| HATİCE MERVE BAYRAM, PhD | Contact | +905549915658 | hmbayram@gelisim.edu.tr | |
| Arda OZTURKCAN, PhD | Contact | 05356068687 | sozturkcan@gelisim.edu.tr |
| Name | Affiliation | Role |
|---|---|---|
| Elena Milli, PhD | Polo Europeo della Conoscenza - Istituto Comprensivo di Bosco Chiesanuova | Study Chair |
| Stefano Cobello, PhD | Polo Europeo della Conoscenza - Istituto Comprensivo di Bosco Chiesanuova | Study Chair |
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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. |
| From baseline to 10 months and 20 months |
| 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. | From baseline to 10 months and 20 months |
| 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. | From baseline to 10 months and 20 months |
| ID | Term |
|---|---|
| D000073296 | Noncommunicable Diseases |
| ID | Term |
|---|---|
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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