Examining the Effect of Virtual Reality–Based Fast-Food Marketing on Eating-Related Outcomes in Young Adults: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Black communities, compared to White communities, are disproportionately targeted with more unhealthy food advertisements on television and social media. Exposure to unhealthy food and beverage marketing is associated with appetitive sensations, purchase intention, and intake behaviors, which may contribute to poor overall diet quality and worsening nutritional disparities in Black communities. Despite the negative effects, food and beverage companies are expanding their reach and harnessing advanced technology to create immersive experiences using virtual reality (VR). Black young adults may be uniquely vulnerable. OBJECTIVE: We aim to explore the effect of a VR-based fast-food marketing experience (compared with a VR-based nonfood control) on purchase intention, arousal, and hunger in a sample of Black and White young adults. METHODS: We will recruit 200 Black and White young adults (aged 18-24 years) from the New York City metropolitan area for a 1-time, 2-hour laboratory-based study. After screening and obtaining informed consent, eligible participants will be randomized to 1 of 2 VR conditions: a VR-based fast-food marketing experience (Wendyverse; experimental) or a VR-based nonfood control (Nikeland). In the Wendyverse, users can order from the restaurant operated by Wendy's, play games, meet others who may be visiting the Wendyverse, and access codes that can be used to obtain free food at physical restaurants. The control condition will be the Nikeland app, where participants can play sports, try on apparel, and engage with celebrity athletes. Study personnel will provide a 5-minute training session to participants before beginning the experiment to ensure that they feel comfortable in the VR environment. Participants will otherwise engage with the VR app independently. The primary outcomes will be fast-food purchase intention, assessed via a self-report questionnaire; arousal, assessed via electrodermal activity or skin conductance; and hunger, assessed via salivary reactivity. We will also conduct secondary analyses to examine interactions by race, ethnicity, and food or nutrition insecurity as a proxy for socioeconomic status. Analyses of covariance and multiple linear regressions will be conducted to examine the effects of VR-based fast-food marketing exposure on the relevant outcomes (compared to the control). RESULTS: This study was funded by the National Institute on Minority Health and Health Disparities in September 2024. Recruitment is expected to begin in September 2025. We expect to complete data collection by October 2026 and begin data cleaning and analysis in November 2026. CONCLUSIONS: On the basis of previous research and data, we anticipate that young adults randomized to view VR-based food and beverage marketing will self-report higher purchase intention and demonstrate stronger arousal and hunger. The data will be used to support future research and improve the understanding of the effects of digital forms of unhealthy food and beverage marketing on young people. TRIAL REGISTRATION: ClinicalTrials.gov NCT06917391; https://clinicaltrials.gov/study/NCT06917391. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/69096.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.035 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.075 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".