Sensitivity of Physiological Measures of Acute Driver Stress: A Meta-Analytic Review
DOI: 10.3389/fnrgo.2021.756473
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Summary
This meta-analytic review addresses the lack of definitive validation for physiological measures used to identify acute driver stress, a critical factor in road safety and driving performance. While the link between stress and impaired driving is well-established, researchers and practitioners struggle to select appropriate metrics due to conflicting evidence regarding the sensitivity of various physiological indicators. The study aims to quantitatively assess the sensitivity of these measures in discriminating between high-stress and low-stress driving conditions. Additionally, it investigates how individual factors (age, gender) and ambient factors (apparatus type, driving automation, stressor exposure duration) moderate these stress responses. The authors conducted a systematic review following PRISMA guidelines, searching electronic databases for peer-reviewed quantitative studies published prior to February 2021. Eligibility criteria required studies to involve non-professional drivers, compare high-stress versus low-stress driving tasks, and report physiological data as means and standard deviations. From an initial pool of 474 records, 26 references were included in nine separate meta-analyses, each focusing on a specific physiological measure. The analysis utilized random-effects models to calculate Hedges’ g effect sizes, assessing sensitivity and heterogeneity. Moderator analyses, including meta-regressions for continuous variables and subgroup analyses for categorical variables, were performed to evaluate the influence of demographic and contextual factors. The results indicate that heart rate, R-R intervals (RRI), and pupil diameter are sensitive measures of acute driver stress, showing significant changes between high- and low-stress conditions. Specifically, heart rate increased, while RRI and pupil diameter showed significant variations. In contrast, no significant effects were observed for breathing rate, electrodermal activity, LF/HF ratio, RMSSD, SDNN, or trapezius muscle tension. Regarding moderators, the analysis found no significant influence of individual factors (age and gender) or ambient factors (apparatus type, automation level, or exposure duration) on heart rate responses. Significant heterogeneity was observed in effect sizes for heart rate, breathing rate, electrodermal activity, and LF/HF ratio, suggesting true variance across studies rather than mere sampling error. Publication bias assessments indicated no significant asymmetry for the significant measures. The study concludes that while several physiological measures are sensitive to driver stress, none offers a definitive advantage over the others. The authors recommend that future research and practical applications employ multiple physiological measures using a triangulation-based methodology. Furthermore, they advocate for a multifactorial approach that analyzes the interaction between stressors and moderators. These findings provide a quantitative guide for selecting physiological metrics, emphasizing the need for comprehensive measurement strategies to accurately capture the complex nature of driver stress in both manual and automated driving contexts.
Key finding
Heart rate, R-R intervals, and pupil diameter are sensitive physiological indicators of acute driver stress, while individual and ambient factors do not significantly moderate heart rate responses.
Methodology
meta_analysis
Sample size: 26
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-05 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| promote | success | — | — | — | 1 | 2026-06-05 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- stress driving
- workload measurement
- stress arousal performance
- simulator validity fidelity
- affect mood
- drowsiness detection algorithms
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: physiological data
- Methodological Resource: validation psychometrics, tool software