Self-Regulation of Driving by Older Adults: A LongROAD Study
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Summary
This paper presents a comprehensive synthesis of the literature on the self-regulation of driving among older adults, conducted as part of the LongROAD Study. The research addresses the critical balance between public safety and personal mobility for aging drivers. As the population ages, older adults face increased risks due to declines in cognitive, visual, and psychomotor abilities, yet driving cessation is associated with severe negative outcomes, including social isolation, depression, and accelerated health decline. Self-regulation—defined as modifying driving patterns by driving less or avoiding challenging situations in response to declining abilities—is viewed as a potential strategy to extend safe driving periods. However, significant gaps existed regarding whether older drivers accurately adjust their behavior, the prevalence of these behaviors, and the factors influencing them. The authors conducted an extensive literature review to update previous findings and establish a framework for future research. The methodology involved searching multiple databases (including TRID, Scopus, and Google Scholar) using specific combinations of terms related to self-regulation, driving, and aging. Inclusion criteria required primary quantitative or qualitative studies published in English from 2009 onward, supplemented by relevant pre-2009 publications. After screening 596 publications, 100 recent studies and 71 earlier works were selected for detailed synthesis. The review examined how self-regulation is defined, operationalized, and measured, as well as the prevalence and types of self-regulatory behaviors and associated influencing factors. The findings highlight that self-regulation is a complex process often conflated with general driving avoidance. The authors distinguish between avoidance driven by lifestyle changes or preferences and true self-regulation motivated by awareness of functional declines. Prevalence rates vary significantly across studies due to differences in definitions and measurement methods. For instance, self-reported avoidance of night driving ranges from 8% to 80%, while avoidance of bad weather ranges from 2% to 65%. The paper categorizes self-regulation into three levels: strategic (trip planning and avoidance of specific conditions like night or highway driving), tactical (in-vehicle behaviors such as avoiding distractions or maintaining larger following distances), and life-goal (long-term decisions like vehicle choice or residence location). Strategic self-regulation is the most studied, with older drivers frequently avoiding night driving, heavy traffic, and unfamiliar areas. Tactical self-regulation includes reducing engagement in secondary tasks like using mobile phones or eating while driving, particularly in challenging conditions. The study concludes that current literature lacks a uniform, theoretically informed approach to understanding self-regulation. The authors propose a framework for future research that differentiates between avoidance and self-regulation based on motivation and insight into functional declines. They emphasize the need to examine self-regulation across multiple levels of driver performance and decision-making. By clarifying definitions and measurement approaches, future research can better determine the effectiveness of self-regulation in improving safety and maintaining mobility, ultimately informing interventions that support older drivers without compromising public safety.
Key finding
Reported rates of older-driver self-regulation vary sharply across studies because self-regulation is often operationalized as situational avoidance without distinguishing compensatory motives from lifestyle-driven trip reduction, and because strategic, tactical, and life-goal levels are studied inconsistently.
Methodology
review
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. Discovered via bulk_ingest_aaa_foundation on 2026-05-23 (5 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | aaa_foundation | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 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.
- older drivers
- mci dementia driving
- older driver retraining
- cognitive impairment
- decision making risk perception
- age related perceptual decline
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: observational prevalence
- Theoretical Contribution: theory or model, computational model