Evidence map›Paper›PMID 30909225›Full record

ArticleJournal of Alzheimer's disease : JAD2019

Cognitive Performance in Parkinson's Disease in the Brain Health Registry.

Brenna Cholerton, Michael W Weiner, Rachel L Nosheny, Kathleen L Poston, R Scott Mackin, Lu Tian, J Wesson Ashford, Thomas J Montine

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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Toward digitally screening and profiling AD: A GAMLSS approach of MemTrax in China.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
  3. Article
  4. Brain health registry updates: An online longitudinal neuroscience platform.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023
    Article
  5. Article
  6. Article
  7. Advances in screening instruments for Alzheimer's disease.Aging medicine (Milton (N.S.W)) · 2019
    Review
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

8 authors.

Brenna CholertonDepartment of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA.
Michael W WeinerDepartment of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA.
Rachel L NoshenyDepartment of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA.
Kathleen L PostonDepartment of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, CA, USA.
R Scott MackinDepartment of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA.
Lu TianDepartment of Health Research and Policy, Stanford University, Palo Alto, CA, USA.
J Wesson AshfordDepartment of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA.
Thomas J MontineDepartment of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA.

Funding

Udall Center Director's meeting poster awardP50NS062684 · NINDS · UNIVERSITY OF WASHINGTON · PI MONTINE, THOMAS J · 2009 to 2020
$18.3M
Working Memory in Parkinson Disease: A Cognitive & Systems Neuroscience ApproachP50AG047366 · NIA · STANFORD UNIVERSITY · PI TIAN, LU · 2015 to 2019
$7.9M
Implementation and validation of an online Caregiver and Study Partner Portal for assessment of cognitive function in older adultsK01AG055692 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NOSHENY, RACHEL LAUREN · 2017 to 2021
$719k
NIA NIH HHS K01 AG055692NIA NIH HHS P50 AG047366NINDS NIH HHS P50 NS062684
6 · The paper itself

Abstract

The study of cognition in Parkinson's disease (PD) traditionally requires exhaustive recruitment strategies. The current study examines data collected by the Brain Health Registry (BHR) to determine whether ongoing efforts to improve the recruitment base for therapeutic trials in Alzheimer's disease may be similarly effective for PD research, and whether online cognitive measurements can discriminate between participants who do and do not report a PD diagnosis. Participants enrolled in the BHR (age ≥50) with self-reported PD data and online cognitive testing available were included (n = 11,813). Associations between baseline cognitive variables and diagnostic group were analyzed using logistic regression. Linear mixed effects models were used to analyze longitudinal data. A total of 634 participants reported PD diagnosis at baseline with no self-reported cognitive impairment and completed cognitive testing. Measures of visual learning and memory, processing speed, attention, and working memory discriminated between self-reported PD and non-PD participants after correcting for multiple comparisons (p values < 0.006). Scores on all cognitive tests improved over time in PD and controls with the exception of processing speed, which remained stable in participants with PD while improving in those without. We demonstrate that a novel online approach to recruitment and longitudinal follow-up of study participants is effective for those with self-reported PD, and that significant differences exist between those with and without a reported diagnosis of PD on computerized cognitive measures. These results have important implications for recruitment of participants with PD into targeted therapeutic trials or large-scale genetic and cognitive studies.

Indexed as

CognitionCase-Control StudiesFemaleHumansLongitudinal StudiesMaleMiddle AgedNeuropsychological TestsParkinson DiseaseRegistriesAgingcognitionneuropsychologyParkinson’s diseasepatient selectionregistries

Identifiers

PMID30909225
PMCPMC6497062

What OpenQuestion holds

Textmetadata
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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.