Digital nudges for online food selection: the interaction of emotional eating and psychological traits in university students
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".