Evidence map›Paper›PMID 37900602›Full record

ArticleFrontiers in neurology2023

Does in-vehicle automation help individuals with Parkinson's disease? A preliminary analysis.

Wayne C W Giang, Haolan Zheng, Beth Gibson, Bhavana Patel, Adolfo Ramirez-Zamora, Sandra Winter, Mary Jeghers, Yuan Li, Sherrilene Classen

Abstract read
In one paragraph

Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Wayne C W GiangDepartment of Industrial and Systems Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, United States.
Haolan ZhengDepartment of Industrial and Systems Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, United States.
Beth GibsonDepartment of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States.
Bhavana PatelDepartment of Neurology, College of Medicine, University of Florida, Gainesville, FL, United States.
Adolfo Ramirez-ZamoraDepartment of Neurology, College of Medicine, University of Florida, Gainesville, FL, United States.
Sandra WinterDepartment of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States.
Mary JeghersDepartment of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States.
Yuan LiDepartment of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States.
Sherrilene ClassenDepartment of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: PD is a progressive neurodegenerative disorder that affects, according to the ICF, body systems (cognitive, visual, and motor), and functions (e.g., decreased executive functions, decreased visual acuity, impaired contrast sensitivity, decreased coordination)-all which impact driving performance, an instrumental activity of daily living in the domain of "Activity" and "Participation" according to the ICF. Although there is strong evidence of impaired driving performance in PD, few studies have explored the real-world benefits of in-vehicle automation technologies, such as in-vehicle information systems (IVIS) and advanced driver assistance systems (ADAS), for drivers with PD. These technologies hold potential to alleviate driving impairments, reduce errors, and improve overall performance, allowing individuals with PD to maintain their mobility and independence more safely and for longer periods. This preliminary study aimed to fill the gap in the literature by examining the impact of IVIS and ADAS on driving safety, as indicated by the number of driving errors made by people with PD in an on-road study. Methods: Forty-five adults with diagnosed PD drove a 2019 Toyota Camry equipped with IVIS and ADAS features (Toyota Safety Sense 2.0) on a route containing highway and suburban roads. Participants drove half of the route with the IVIS and ADAS systems activated and the other half with the systems deactivated. Results: The results suggest that systems that assume control of the driving task, such as adaptive cruise control, were most effective in reducing driving errors. Furthermore, individual differences in cognitive abilities, particularly memory, were significantly correlated with the total number of driving errors when the systems were deactivated, but no significant correlations were present when the systems were activated. Physical capability factors, such as rigidity and bradykinesia, were not significantly correlated with driving error. Discussion: Taken together, these results show that in-vehicle driver automation systems can benefit drivers with PD and diminish the impact of individual differences in driver cognitive ability.

Indexed as

advanced driver assistance systemsautonomous vehiclesdriver rehabilitationdriving errorson-roadParkinson’s disease

Identifiers

PMID37900602
PMCPMC10603248

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.