Longitudinal Research on Aging Drivers (LongROAD): study design and methods
DOI: 10.1186/s40621-017-0121-z
archive: archived pipeline: cataloged verified
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Summary
This paper outlines the design and methodology of the Longitudinal Research on Aging Drivers (LongROAD) study, a multisite prospective cohort study aimed at understanding the determinants of driving safety in older adults. The research addresses a critical gap in longitudinal data regarding the natural history of driving safety as the population ages. With older adults comprising a growing segment of the US population and retaining high rates of licensure, understanding how medical conditions, medications, functional declines, and technological factors influence driving behavior is essential for developing public policies and interventions that maintain mobility and safety. The study recruited 2,990 active drivers aged 65–79 years from five sites across California, Colorado, Maryland, Michigan, and New York. Participants were identified through electronic medical records and screened for eligibility, which included holding a valid license, driving at least once weekly, and lacking severe cognitive impairments. The cohort is predominantly white (86%), female (53%), and well-educated, with 64.1% holding bachelor’s or graduate degrees. Data collection involves a comprehensive baseline assessment and annual follow-ups for up to three years. Key methods include the installation of a DataLogger device in participants’ primary vehicles to record objective driving data, such as speed, acceleration, GPS location, and trip duration. To distinguish participant driving from other users, Bluetooth low-energy beacons are used. Additionally, participants undergo functional performance tests, medication reviews, and annual questionnaires assessing health, cognition, and driving habits. Medical records and state motor vehicle records are reviewed annually to track clinical diagnoses and crash/violation history. The paper details the specific instruments and protocols used to capture a wide range of variables, including 31 monthly driving behavior metrics derived from the DataLogger, such as miles driven, trip frequency, and high-deceleration events. Vehicle inspections assess maintenance and technology presence, while functional assessments measure cognitive, motor, and perceptual abilities. The study design ensures rigorous data quality through daily monitoring of device data and standardized protocols approved by institutional review boards. The significance of LongROAD lies in its potential to generate empirical evidence linking medical, behavioral, and environmental factors to driving safety outcomes. By combining objective driving data with clinical and functional assessments, the study aims to identify risk and protective factors for safe driving, evaluate the impact of self-regulation and advanced vehicle technologies, and understand the health consequences of driving cessation. These findings are expected to inform the development of interventions and policies that support safe mobility for older adults, thereby promoting their independence and well-being.
Key finding
The LongROAD study successfully enrolled 2990 older adult drivers and established a comprehensive data collection protocol combining naturalistic driving data, functional assessments, and longitudinal health records to investigate determinants of driving safety.
Methodology
naturalistic
Sample size: 2990
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 author_sweep_intake on 2026-05-27 (2 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 3 | 2026-05-28 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | skipped | — | — | — | 3 | 2026-06-04 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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Information type
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- Empirical Findings: observational prevalence
- Methodological Resource: dataset resource, validation psychometrics