Eating Behavior and Satiety With Virtual Reality Meals Compared With Real Meals: Randomized Crossover Study
Notice bibliographique
Résumé
BACKGROUND: Eating disorders and obesity are serious health problems with poor treatment outcomes and high relapse rates despite well-established treatments. Several studies have suggested that virtual reality technology could enhance the current treatment outcomes and could be used as an adjunctive tool in their treatment. OBJECTIVE: This study aims to investigate the differences between eating virtual and real-life meals and test the hypothesis that eating a virtual meal can reduce hunger among healthy women. METHODS: The study included 20 healthy women and used a randomized crossover design. The participants were asked to eat 1 introduction meal, 2 real meals, and 2 virtual meals, all containing real or virtual meatballs and potatoes. The real meals were eaten on a plate that had been placed on a scale that communicated with analytical software on a computer. The virtual meals were eaten in a room where participants were seated on a real chair in front of a real table and fitted with the virtual reality equipment. The eating behavior for both the real and virtual meals was filmed. Hunger was measured before and after the meals using questionnaires. RESULTS: There was a significant difference in hunger from baseline to after the real meal (mean difference=61.8, P<.001) but no significant change in hunger from before to after the virtual meal (mean difference=6.9, P=.10). There was no significant difference in food intake between the virtual and real meals (mean difference=36.8, P=.07). Meal duration was significantly shorter in the virtual meal (mean difference=-5.4, P<.001), which led to a higher eating rate (mean difference=82.9, P<.001). Some participants took bites and chewed during the virtual meal, but the number of bites and chews was lower than in the real meal. The meal duration was reduced from the first virtual meal to the second virtual meal, but no significant difference was observed between the 2 real meals. CONCLUSIONS: Eating a virtual meal does not appear to significantly reduce hunger in healthy individuals. Also, this methodology does not significantly result in eating behaviors identical to real-life conditions but does evoke chewing and bite behavior in certain individuals. TRIAL REGISTRATION: ClinicalTrials.gov NCT05734209, https://clinicaltrials.gov/ct2/show/NCT05734209.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».