Evidence map›Paper›PMID 33919625›Full record

ArticleLife (Basel, Switzerland)2021

Determinants of Processing Speed Trajectories among Middle Aged or Older Adults, and Their Association with Chronic Illnesses: The English Longitudinal Study of Aging.

Viktor Gkotzamanis, Giorgos Koliopanos, Albert Sanchez-Niubo, Beatriz Olaya, Francisco Félix Caballero, José Luis Ayuso-Mateos, Somnath Chatterji, Josep Maria Haro, Demosthenes Panagiotakos

Open access · goldAbstract read
In one paragraph

Article in Life (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.2field-weighted citation impact, top 39% 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

4 citing papers in PubMed, 3 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Frailty and Processing Speed Performance at the Cusp of Midlife in CATSLife.The journals of gerontology. Series B, Psychological sciences and social sciences · 2023
    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 6 institutions in 3 countries.

Viktor GkotzamanisSchool of Health Science and Education, Harokopio University, 17671 Athens, Greece.
Giorgos KoliopanosSchool of Health Science and Education, Harokopio University, 17671 Athens, Greece.
Albert Sanchez-NiuboParc Sanitari Sant Joan de Déu, Universitat de Barcelona, 08830 Sant Boi de Llobregat, Spain.ORCID 0000-0003-0309-181X
Beatriz OlayaParc Sanitari Sant Joan de Déu, Universitat de Barcelona, 08830 Sant Boi de Llobregat, Spain.ORCID 0000-0003-2046-3929
Francisco Félix CaballeroDepartment of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, 28029 Madrid, Spain.ORCID 0000-0001-9609-3579
José Luis Ayuso-MateosDepartment of Psychiatry, Universidad Autónoma de Madrid, 28029 Madrid, Spain.
Somnath ChatterjiInformation, Evidence and Research, World Health Organization, 1202 Geneva, Switzerland.
Josep Maria HaroParc Sanitari Sant Joan de Déu, Universitat de Barcelona, 08830 Sant Boi de Llobregat, Spain.
Demosthenes PanagiotakosSchool of Health Science and Education, Harokopio University, 17671 Athens, Greece.ORCID 0000-0001-8583-153X
Harokopio University of Athens · GRCentro de Investigación Biomédica en Red de Salud Mental · ESCentro de Investigación Biomédica en Red de Epidemiología y Salud Pública · ESInstituto de Salud Carlos III · ESParc Sanitari Sant Joan de Déu · ESWorld Health Organization · CH

Funding

World Health Organization 001
6 · The paper itself

Abstract

The aim of this study was to identify latent groups of similar trajectories in processing speed through aging, as well as factors that are associated with these trajectories. In the context of the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project, data from the English Longitudinal Study of Aging (ELSA) (n = 12099) were analyzed. Latent groups of similar trajectories in the processing scores as well as their predictors and covariates were investigated, using group-based trajectory models (GBTM). The coefficient estimates for potential group predictors correspond to parameters of multinomial logit functions that are integrated in the model. Potential predictors included sex, level of education, marital status, level of household wealth, level of physical activity, and history of smoking, while time-varying covariates included incidence of cardiovascular disease (CVD), diabetes mellitus, depressive symptoms, and sleep disturbances. Four trajectories were identified and named after their baseline scores and shapes: High (4.4%), Middle/Stable (31.5%), Low/Stable (44.5%), and Low Decline (19.6%). Female sex, higher levels of education, mild level of physical activity, having been married, and higher level of wealth were associated with a higher probability of belonging to any of the higher groups compared to the Low/Decline that was set as reference, while presence of CVD, diabetes mellitus, and depressive symptoms were associated with lower processing speed scores within most trajectories. All the aforementioned factors might be valid targets for interventions to reduce the burden of age-related cognitive impairment.

Indexed as

agingELSAlongitudinal analysisprocessing speed

Identifiers

PMID33919625
PMCPMC8072694
OpenAlexW3155233602

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.