The influence of depression and antidepressants on driving performance: a systematic literature review and meta-analysis.
DOI: 10.21203/rs.3.rs-3408229/v1
archive: archived pipeline: cataloged verified
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Summary
This systematic literature review and meta-analysis investigates the impact of depression and antidepressant use on objectively assessed driving performance. While previous research has linked depression and antidepressant treatment to increased crash risk, those findings often conflate the effects of the disorder with medication side effects or confounding socioeconomic factors. To address this gap, the authors aimed to determine how depression and antidepressants specifically affect driving-related outcomes, such as lane keeping, reaction time, and attention, using performance-based studies rather than crash statistics. The researchers conducted two separate systematic reviews: one for depression versus driving performance (DEP-DP) and one for antidepressants versus driving performance (AD-DP). They searched PubMed, PsychINFO, and Embase for studies published between January 1997 and December 2022. Inclusion criteria required empirical studies using driving simulators, on-road testing, or cognitive test batteries, with appropriate control groups. Risk of bias was assessed using Cochrane tools, and meta-analyses were performed using Hedges’ g effect sizes for outcomes with at least three comparable studies. The search yielded 755 hits, resulting in the inclusion of only two studies for the DEP-DP review and seven for the AD-DP review. The DEP-DP review found that both included studies indicated poorer driving performance in depressed individuals compared to healthy controls, but the small sample size and diverse outcomes prevented the calculation of pooled estimates. For the AD-DP review, pooled estimates were calculated for four outcomes: reaction time, standard deviation of lateral position (SDLP), selective attention, and vigilance. The meta-analysis revealed a significant negative effect of antidepressant use on vigilance (Hedges’ g = -0.49, 95% CI -.85; -.13), indicating reduced sustained attention in monotonous situations. However, no significant differences were found for reaction time, selective attention, or SDLP. Notably, antidepressant users performed worse than healthy controls on SDLP but better than untreated depressed drivers, suggesting that medication may mitigate some negative effects of depression on lane keeping. The study concludes that the evidence base for the separate effects of depression and antidepressants on driving performance is limited and inconsistent due to the small number of studies and heterogeneous outcome measures. While antidepressant use significantly impairs vigilance, it is unclear whether this deficit stems from the medication or the underlying depression, as most studies compared users only to healthy controls rather than untreated depressed individuals. The authors emphasize the need for more rigorous research that distinguishes between the effects of the disorder and its treatment, particularly using standardized performance metrics like time headway, which were absent in the reviewed literature.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | pdftotext | — | — | 4 | 2026-08-10 |
| clean | success | clean | — | — | 2 | 2026-08-10 |
| chunk | success | chunk | — | — | 2 | 2026-08-10 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 2 | 2026-08-10 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 17 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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Information type
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- Empirical Findings: behavioral performance data
- Methodological Resource: validation psychometrics, tool software