The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Cognitive processes related to gait velocity: Results from the Einstein aging study.
Abstract
The authors examined the relationship between cognition and gait velocity, performed with and without interference, in elderly participants. Neuropsychological test scores from 186 cognitively normal elders were submitted to factor analysis that yielded 3 factors: Verbal IQ, Speed/Executive Attention, and Memory. Regression analyses revealed that these factors were significant predictors of variance in gait velocity, but the relationship varied as a function of task condition. All 3 factors predicted gait velocity without interference. However, the Speed/Executive Attention and Memory factors but not Verbal IQ predicted gait velocity in the interference condition. These findings suggest that gait velocity and cognitive function may have both shared and independent brain substrates. Future studies should explore gait velocity and cognitive function as predictors of dementia and falls.
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At the interface of sensory and motor dysfunctions and Alzheimer’s disease.
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Self-citationInfluences of dopaminergic system dysfunction on late-life depression
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Metadata
- Authors
- Holtzer, R., Verghese, J., Xue, X., & Lipton, R. B.
- Venue
- Neuropsychology
- Published
- 2006
- Volume
- 20
- Issue
- 2
- Pages
- 215-223
- ISSN
- 0894-4105 (ISSN-L)
- 1931-1559
- OpenAlex
- W2008813344
- Cited by
- 474
- References
- 10
- FWCI
- 42.12
- Type
- journal article
- Language
- en
- Open access
- closed
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- Holtzer, R., Verghese, J., Xue, X., & Lipton, R. B. (2006). Cognitive processes related to gait velocity: Results from the Einstein aging study. Neuropsychology. https://doi.org/10.1037/0894-4105.20.2.215
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
