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.
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.
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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.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- Prediction and mediation analysis for treatment responses to combined cognitive and physical training for older adults.Scientific reports · 2024Trial
- Do common dopaminergic variants modulate processing speed in cognitive aging? A longitudinal candidate gene study.PloS one · 2026Article
- VR Cognitive-based Intervention for Enhancing Cognitive Functions and Well-being in Older Adults with Mild Cognitive Impairment: Behavioral and EEG Evidence.Psychosocial intervention · 2025Article
- Frailty and Processing Speed Performance at the Cusp of Midlife in CATSLife.The journals of gerontology. Series B, Psychological sciences and social sciences · 2023Article
Corrections and comments
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Authors and funding
9 authors at 6 institutions in 3 countries.
Funding
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.
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