Effects of acute alcohol consumption on measures of simulated driving: A systematic review and meta-analysis
DOI: 10.1016/j.aap.2017.03.001
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Summary
This systematic review and meta-analysis investigates the impact of acute alcohol consumption on simulated driving performance, aiming to quantify impairment magnitudes and identify methodological factors influencing these outcomes. The study addresses the need for standardized metrics in traffic safety research, as driving simulators offer a safe, controlled alternative to on-road testing. The authors sought to determine which performance variables are most sensitive to alcohol-induced impairment and how factors such as Blood Alcohol Concentration (BAC), simulator type, and task duration affect results. The researchers conducted a comprehensive search of PubMed, Web of Science, and Scopus databases, identifying studies published up to June 2016. They included 50 repeated-measures trials derived from 17 original publications, involving 962 adult participants (62% male). Studies were required to use controlled experimental designs comparing acute alcohol ingestion against either a placebo or no-alcohol condition. Primary outcomes measured were lateral vehicle control, specifically the standard deviation of lane position (SDLP), and longitudinal control, measured by the standard deviation of speed (SDSP). Secondary outcomes included total lane crossings and average speed. Meta-analytic procedures, including random-effect models and meta-regression, were employed to calculate weighted mean effects and assess the influence of moderators such as BAC levels, the limb of the BAC curve (ascending vs. descending), and simulator platform (PC-based vs. car-based). The analysis revealed that acute alcohol consumption significantly impaired both lateral and longitudinal vehicle control. Compared to placebo conditions, alcohol increased SDLP by a mean of 4.0 cm (95% CI: 3.0, 5.1) and SDSP by 0.38 km/h (95% CI: 0.19, 0.57). Meta-regression indicated that higher BAC levels and the use of PC-based simulators were associated with larger performance decrements in SDLP, explaining 80% of the variance. Conversely, the limb of the BAC curve and the duration of the driving task did not significantly alter the magnitude of impairment. When comparing alcohol to no-alcohol controls, the increase in SDLP was smaller but still significant (standardized mean difference = 0.23). Average speed and lane crossings showed trends toward increased impairment but lacked consistent statistical significance. The findings demonstrate that acute alcohol consumption reliably degrades simulated driving performance, with SDLP emerging as the most sensitive and robust indicator of impairment. The study supports the use of SDLP as a primary outcome variable in future research on alcohol-induced driving deficits. Additionally, the results highlight that methodological choices, particularly the type of simulator and the level of intoxication, significantly influence observed impairment magnitudes. This provides researchers with critical benchmarks for designing experiments and interpreting data, facilitating better comparisons between alcohol impairment and other driving impediments such as fatigue or distraction.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-19 |
| archive | success | openalex | — | — | 5 | 2026-06-25 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | semantic_scholar | — | — | 4 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Methodological Resource: validation psychometrics, tool software
- Theoretical Contribution: computational model