Psychological factors affecting hangover severity
Notice bibliographique
Résumé
Introduction: Harburg et al. (1981,1993) reported that feeling guilty about drinking, and being depressed, angry or anxious while drinking were significantly associated with having more severe hangovers. Also, drinkers who scored higher on neuroticism and had experienced more recent negative life events had more severe hangovers. However, 23% of their sample reported no hangover, and the analysis were not corrected for estimated blood alcohol concentration. The current study aimed to verify and extend these observations in a sample of hungover drinkers.\nMethod: A survey was held among N=323 young adults, 18 to 30 years old. Demographics, alcohol consumption and subjective intoxication, hangover severity, general health and perceived immune fitness, sleep quality and duration, and mood (both baseline and during drinking) were assessed with VAS scales, and neuroticism was assessed with the Eysenck personality questionnaire.\nResults: No significant correlations of hangover severity with baseline mood and stress (baseline or during drinking) were observed. However, hangovers are accompanied by mood changes such as increased acute levels of stress, fatigue, and feelings of guilt about drinking. A regression analysis revealed a model with 38.6% predictive validity including ‘level of subjective intoxication’ as best predictor of hangover severity (21.1%), followed by perceived immune fitness (5.7%). Other factors (fatigue, days in Fiji, weekly alcohol intake, sleep quality) contributed less than 5% to the model.\nDiscussions and Conclusions: Mood during drinking does not significantly impact next day hangover severity. However, the alcohol hangover state is accompanied by various mood changes and increased stress levels.\nDisclosure of Interest Statement: This study was funded by Utrecht University. Andrew Scholey has received research funding from Abbott Nutriton, Arla Foods, Bayer Healthcare, Cognis, Cyvex, GlaxoSmithKline, Kemin Foods, Naturex, Nestlé, Martek, Masterfoods, Red Bull GmbH, Sanofi, Verdure Sciences, and Wrigley and has acted as a consultant/expert advisor to Abbott Nutrition, Barilla, Bayer Healthcare, Danone, Flordis, GlaxoSmithKline Healthcare, Masterfoods, Martek, Neurobrands, and Wrigley. Sarah Benson has received funding from Red Bull GmbH, Kemin Foods, Sanofi Aventis, and GlaxoSmithKline. Joris Verster has received grants/research support from the Dutch Ministry of Infrastructure and the Environment, Janssen, Nutricia, Red Bull, Sequential, and Takeda, and has acted as a consultant for Canadian Beverage Association, Centraal Bureau Drogisterijbedrijven, Clinilabs, Coleman Frost, Danone, Deenox, Eisai, Janssen, Jazz, More Labs, Purdue, Red Bull, Sanofi-Aventis, Sen-Jam Pharmaceutical, Sepracor, Takeda, Toast!, Transcept, Trimbos Institute, Vital Beverages, and ZBiotics. The other author has no potential conflicts of interest to disclose.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».