Evidence map›Paper›PMID 28082883›Full record

ArticleFrontiers in human neuroscience2016

Older Adults with Mild Cognitive Impairments Show Less Driving Errors after a Multiple Sessions Simulator Training Program but Do Not Exhibit Long Term Retention.

Normand Teasdale, Martin Simoneau, Lisa Hudon, Mathieu Germain Robitaille, Thierry Moszkowicz, Denis Laurendeau, Louis Bherer, Simon Duchesne, Carol Hudon

Open access · goldAbstract read
In one paragraph

Article in Frontiers in human neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 10% 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.

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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment.2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET) · 2023
    Article
  7. Review
  8. Article
  9. 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

9 authors at 4 institutions in 1 country.

Normand TeasdaleDepartment of Kinesiology, Faculty of Medicine, Université LavalQuebec City, QC, Canada; Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale et Centre d'excellence sur le vieillissement de QuébecQuebec City, QC, Canada.
Martin SimoneauDepartment of Kinesiology, Faculty of Medicine, Université LavalQuebec City, QC, Canada; Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale et Centre d'excellence sur le vieillissement de QuébecQuebec City, QC, Canada.
Lisa HudonDepartment of Kinesiology, Faculty of Medicine, Université Laval Quebec City, QC, Canada.
Mathieu Germain RobitailleDepartment of Kinesiology, Faculty of Medicine, Université Laval Quebec City, QC, Canada.
Thierry MoszkowiczComputer Vision and Systems Laboratory, Department of Electrical Engineering, Université Laval Quebec City, QC, Canada.
Denis LaurendeauComputer Vision and Systems Laboratory, Department of Electrical Engineering, Université Laval Quebec City, QC, Canada.
Louis BhererPERFORM Centre, Concordia UniversityMontreal, QC, Canada; Department of Medicine, University of Montreal and Montreal Heart InstituteMontreal, QC, Canada.
Simon DuchesneCentre de recherche de l'Institut universitaire en santé mentale de QuébecQuebec City, QC, Canada; Département de Radiologie, Faculté de Médecine, Université LavalQuebec City, QC, Canada.
Carol HudonCentre de recherche de l'Institut universitaire en santé mentale de QuébecQuebec City, QC, Canada; École de psychologie, Université LavalQuebec City, QC, Canada.
Université Laval · CAInstitut Universitaire en Santé Mentale de Québec · CACentre de Santé et de Services Sociaux de la Vieille-Capitale · CAConcordia University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The driving performance of individuals with mild cognitive impairment (MCI) is suboptimal when compared to healthy older adults. It is expected that the driving will worsen with the progression of the cognitive decline and thus, whether or not these individuals should continue to drive is a matter of debate. The aim of the study was to provide support to the claim that individuals with MCI can benefit from a training program and improve their overall driving performance in a driving simulator. Fifteen older drivers with MCI participated in five training sessions in a simulator (over a 21-day period) and in a 6-month recall session. During training, they received automated auditory feedback on their performance when an error was noted about various maneuvers known to be suboptimal in MCI individuals (for instance, weaving, omitting to indicate a lane change, to verify a blind spot, or to engage in a visual search before crossing an intersection). The number of errors was compiled for eight different maneuvers for all sessions. For the initial five sessions, a gradual and significant decrease in the number of errors was observed, indicating learning and safer driving. The level of performance, however, was not maintained at the 6-month recall session. Nevertheless, the initial learning observed opens up possibilities to undertake more regular interventions to maintain driving skills and safe driving in MCI individuals.

Indexed as

drivinglearningMCIretentiontraining

Identifiers

PMID28082883
PMCPMC5186807
OpenAlexW2566786711

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.