Evidence map›Paper›PMID 30958365›Full record

ArticleJournal of Alzheimer's disease : JAD2019

A 2.5-Year Longitudinal Assessment of Naturalistic Driving in Preclinical Alzheimer's Disease.

Catherine M Roe, Sarah H Stout, Ganesh Rajasekar, Beau M Ances, Jessica M Jones, Denise Head, Tammie L S Benzinger, Monique M Williams, Jennifer Duncan Davis, Brian R Ott and 2 more

Abstract read
In one paragraph

Article in Journal of Alzheimer's disease : JAD, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 2 pooled it
13.0field-weighted citation impact, top 1% 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

47 citing papers in PubMed, 2 syntheses or guidelines pooled it, 63 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Driving the neural exposome: Latent mobility states from naturalistic GPS data in older adults.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  4. Review
  5. Article
  6. Article
  7. Observational
  8. Article
  9. Review
  10. Article
  11. Article
  12. Integrating Machine Learning and Environmental and Genetic Risk Factors for the Early Detection of Preclinical Alzheimer's Disease.The journals of gerontology. Series B, Psychological sciences and social sciences · 2025
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2024
    Review
  19. Article
  20. 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

12 authors at 4 institutions in 1 country.

Catherine M RoeCharles F. and Joanne Knight Alzheimer's Disease Research Center, Washington University School of Medicine, MO, USA.
Sarah H StoutCharles F. and Joanne Knight Alzheimer's Disease Research Center, Washington University School of Medicine, MO, USA.
Ganesh RajasekarCharles F. and Joanne Knight Alzheimer's Disease Research Center, Washington University School of Medicine, MO, USA.
Beau M AncesCharles F. and Joanne Knight Alzheimer's Disease Research Center, Washington University School of Medicine, MO, USA.
Jessica M JonesCleveland Clinic Lerner College of Medicine, Cleveland, OH, USA.
Denise HeadCharles F. and Joanne Knight Alzheimer's Disease Research Center, Washington University School of Medicine, MO, USA.
Tammie L S BenzingerCharles F. and Joanne Knight Alzheimer's Disease Research Center, Washington University School of Medicine, MO, USA.
Monique M WilliamsDepartment of BJC Medical Group, Washington University School of Medicine, MO, USA.
Jennifer Duncan DavisDepartment of Brown University, School of Medicine, MO, USA.
Brian R OttDepartment of Brown University, School of Medicine, MO, USA.
David K WarrenDepartment of Medicine, Washington University School of Medicine, MO, USA.
Ganesh M BabulalCharles F. and Joanne Knight Alzheimer's Disease Research Center, Washington University School of Medicine, MO, USA.
Washington University in St. Louis · USJohn Brown University · USCleveland Clinic Lerner College of Medicine · USHope Center for Neurological Disorders · US

Funding

WU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCESUL1TR002345 · NCATS · WASHINGTON UNIVERSITY · PI William G. Powderly · 2017 to 2026
$97.8M
Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5M
WASHINGTON UNIVERSITY ALZHEIMERS DISEASE RESEARCH CENTERP50AG005681 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 1985 to 2019
$52.1M
The natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 2005 to 2025
$49.5M
Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
Dominantly Inherited Alzheimer NetworkUF1AG032438 · NIA · WASHINGTON UNIVERSITY · PI BATEMAN, RANDALL J · 2014 to 2017
$21.6M
proj 3 - NINDS INSTIUTIONAL CENTER CORE GRANTS TO SUPPORT NEUROSCIENCE RESEARP30NS048056 · NINDS · WASHINGTON UNIVERSITY · PI MARCUS, DANIEL SCOTT · 2004 to 2014
$7.4M
Preclinical Alzheimer Disease and DrivingR01AG056466 · NIA · WASHINGTON UNIVERSITY · PI BABULAL, GANESH M · 2017 to 2021
$5.9M
DRIVING PERFORMANCE IN PRECLINICAL ALZHEIMER'S DISEASER01AG043434 · NIA · WASHINGTON UNIVERSITY · PI ROE, CATHERINE M · 2012 to 2016
$2.4M
NCATS NIH HHS UL1 TR000448NCATS NIH HHS UL1 TR002345NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS P50 AG005681NIA NIH HHS R01 AG043434NIA NIH HHS R01 AG056466NIA NIH HHS UF1 AG032438NINDS NIH HHS P30 NS048056
6 · The paper itself

Abstract

backgroundEmerging evidence shows that cognitively normal older adults with preclinical Alzheimer's disease (AD) make more errors and are more likely to receive a marginal/fail rating on a standardized road test compared to older adults without preclinical AD, but the extent to which preclinical AD impacts everyday driving behavior is unknown.

objectiveTo examine self-reported and naturalistic longitudinal driving behavior among persons with and without preclinical AD.

methodWe prospectively followed cognitively normal drivers (aged 65 + years) with (n = 10) and without preclinical AD (n = 10) for 2.5 years. Preclinical AD was assessed using amyloid positron emission tomography (PET) with Pittsburgh Compound B. The Driving Habits Questionnaire assessed self-reported driving outcomes. Naturalistic driving was captured using a commercial GPS data logger plugged into the on-board diagnostics II port of each participant's vehicle. Data were sampled every 30 seconds and all instances of speeding, hard braking, and sudden acceleration were recorded.

resultsPreclinical AD participants went to fewer places/unique destinations, traveled fewer days, and took fewer trips than participants without preclinical AD. The preclinical AD group reported a smaller driving space, greater dependence on other drivers, and more difficulty driving due to vision difficulties. Persons with preclinical AD had fewer trips with any aggression and showed a greater decline across the 2.5-year follow-up period in the number of days driving per month and the number of trips between 1-5 miles.

conclusionChanges in driving occur even during the preclinical stage of AD.

Indexed as

AgedAged, 80 and overAlzheimer DiseaseAutomobile DrivingFemaleHumansMalePositron-Emission TomographyProdromal SymptomsSurveys and QuestionnairesAlzheimer’s disease,automobile driving,biomarkersmotor vehicles

Identifiers

PMID30958365
PMCPMC6488385
OpenAlexW2933871432

What OpenQuestion holds

Textmetadata
LicenceTDM
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.