Psychological factors affecting hangover severity
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".