In-Vehicle Technology Use and Associated Factors Among Older Drivers
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Summary
This study investigates the prevalence and frequency of in-vehicle technology use among older drivers and examines how these usage patterns correlate with perceived cognitive and physical health, driving abilities, and self-reported behaviors. Motivated by prior findings that older adults infrequently utilize available vehicle technologies—potentially limiting safety and mobility benefits—the research aims to identify factors associated with technology adoption. The analysis draws from the AAA Longitudinal Research on Aging Drivers (AAA LongROAD) study, specifically using data from the Year 3 follow-up (2018–2020). The sample consisted of 324 participants who had changed their primary vehicle, predominantly aged 70–79, white, and possessing higher education and income levels than the general population. Data were collected via telephone interviews using standardized questionnaires, including Patient-Reported Outcomes Measurement Information System (PROMIS) items for cognitive and physical function, alongside measures of driving avoidance, comfort, and safety incidents. The study analyzed ten specific in-vehicle technologies, excluding those that are always active. Descriptive results indicated that warning systems, such as forward collision warning and blind spot warning, were used more frequently than technologies requiring driver action or providing services, such as semi-autonomous parking assist or voice control. Integrated Bluetooth cell phone systems were the most prevalent (94%) but showed moderate usage frequency. Spearman correlation analyses revealed distinct associations between health perceptions and technology use. Greater cognitive concerns were significantly associated with less frequent use of adaptive cruise control, fatigue/drowsy driving alerts, and integrated Bluetooth systems. Conversely, higher cognitive concerns correlated with increased use of semi-autonomous parking assist. Better physical health was linked to more frequent use of fatigue alerts and navigation assistance. Additionally, greater perceived driving ability, comfort, and driving space were positively associated with the use of various technologies, particularly fatigue alerts and navigation systems. Notably, no significant associations were found between technology use frequency and self-reported crashes, driving errors, lapses, or violations. The findings suggest that older drivers’ utilization of in-vehicle technology is influenced by their self-perceived functional status and comfort levels rather than actual safety outcomes. Drivers with higher cognitive and physical functioning, along with greater driving confidence, are more likely to engage with these technologies. The lower adoption of automated features like parking assist and adaptive cruise control may stem from reluctance to cede vehicle control or difficulties with complex human-machine interfaces. The authors conclude that while these technologies could expand driving spaces for older adults, their benefits may currently be limited to those who perceive themselves as having good health and driving abilities. The study highlights the need for future research to explore how cognitive and sensory limitations impact technology usability and to investigate whether technology use moderates the relationship between functional decline and driving performance. Limitations include the reliance on self-reported data, a non-representative sample, and the short timeframe for reporting safety incidents.
Key finding
Older drivers used alert-based in-vehicle technologies more frequently than other types, with greater cognitive concerns and driving avoidance behaviors associated with lower usage of specific systems.
Methodology
survey
Sample size: 324
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 (6 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 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 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, self report data
- Methodological Resource: validation psychometrics