Digital nudges for online food selection: the interaction of emotional eating and psychological traits in university students
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Objective This study aimed to examine the impact of digital nudge models and emotional eating behaviors on online food choices among university students. Methods This cross-sectional study was conducted on 356 students (87.1% female). Data were collected via an online questionnaire, including the Barratt Impulsivity Scale, Twenty-item Toronto Alexithymia Scale, and the Emotional Eater Questionnaire. Four digital nudge categories were used (default, highlighting, social influence, and warning) to assess their influence on food choice. Additionally, body weight and height were taken with the participants’ declaration. Data were analyzed using IBM®SPSS® 24.0. Results The most frequently selected food category was hamburgers (n=282), with the warning nudge in the dessert category being the most effective (43.3%), followed by the social influence nudge (31.3%). There was no significant correlation between impulsivity, emotional eating, and digital nudge effectiveness (p>0.05). However, gender differences were noted, with females responding more to social influence nudges. There was a moderate positive correlation between Emotional Eater Questionnaire and body mass index and Twenty-item Toronto Alexithymia Scale (r=0.315, p<0.001, r=0.347, p<0.001, respectively). Furthermore, the Barratt Impulsivity Scale showed a weak positive correlation with Twenty-item Toronto Alexithymia Scale (r=0.127, p<0.05). Conclusion Digital nudges influenced food choices; however, psychological factors such as impulsivity and emotional eating did not significantly affect their effectiveness. Future research could explore the role of psychological traits in digital nudging for healthier food choices.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it