Evidence map›Paper›PMID 35812823›Full record

ArticleContemporary clinical trials communications2022

RCT protocol for driving performance in people with Parkinson's using autonomous in-vehicle technologies.

Sherrilene Classen, Yuan Li, Wayne Giang, Sandra Winter, Jiajun Wei, Bhavana Patel, Mary Jeghers, Beth Gibson, Jason Rogers, Adolfo Ramirez-Zamora

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Contemporary clinical trials communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04660500 (Driving Performance of People With Parkinson's Using Autonomous In-Vehicle Technologies), which is not on this map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 19% of its field
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.

NCT04660500 nacompletednot on this map

Driving Performance of People With Parkinson's Using Autonomous In-Vehicle Technologies

TypeinterventionalSponsorUniversity of FloridaRan2021 to 2023Enrolled112ConditionsParkinson DiseaseArmsPD Drivers Using AV Technologies On-road
3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
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

10 authors at 1 institution in 1 country.

Sherrilene ClassenDepartment of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Yuan LiDepartment of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Wayne GiangDepartment of Industrial & Systems Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA.
Sandra WinterDepartment of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Jiajun WeiDepartment of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Bhavana PatelDepartment of Neurology, College of Medicine, University of Florida, Gainesville, FL, USA.
Mary JeghersDepartment of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Beth GibsonDepartment of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Jason RogersDepartment of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Adolfo Ramirez-ZamoraDepartment of Neurology, College of Medicine, University of Florida, Gainesville, FL, USA.
University of Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Driving is an essential facilitator of independence, community participation, and quality of life. Drivers with Parkinson's Disease (PD) make more driving errors and fail on-road evaluations more than healthy controls. In-vehicle technologies may mitigate PD-related driving impairments and associated driving errors. Establishing a rigorous study protocol will increase the internal validity and the transparency of the scientific work. Methods: We present a protocol to assess the efficacy of autonomous in-vehicle technologies (Level 1) on the driving performance of drivers with PD via a randomized crossover design with random allocation. Drivers with a PD diagnosis based on established clinical criteria (N = 105), referred by neurologists, are exposed to two driving conditions (technology activated or not) on a standardized road course as they drove a 2019 Toyota Camry. The researchers collected demographic, clinical, on-road data observational and kinematic, and video data to understand several primary outcome variables, i.e., number of speeding, lane maintenance, signaling, and total driving errors. Discussion: The protocol may enhance participant adherence, decrease attrition, provide early and accurate identification of eligible participants, ensure data integrity, and improve the study flow. One limitation is that the protocol may change due to unforeseen circumstances and assumptions upon implementation. A strength is that the protocol ensures the study team executes the planned research in a systematic and consistent way.Following, adapting, and refining the protocol will enhance the scientific investigation to quantify the nuances of driving among those with PD in the era of automated in-vehicle technologies. Trial registration: ClinicalTrials.gov NCT04660500.

Indexed as

Automated in-vehicle technologiesDriver rehabilitation specialistDriving interventionKinematic dataParkinson's diseaseRCTStudy protocol

Identifiers

PMID35812823
PMCPMC9256542
OpenAlexW4283659261

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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.