Evidence map›Paper›PMID 41095536›Full record

ArticleHealthcare (Basel, Switzerland)2025

Gait-Based Screening for Cognitive Impairment in Older Adults: A Fast and Objective Approach.

Jose Luis Perez-Lasierra, Marina Azpíroz-Puente, Martin Morita-Hernandez, Antonio Gómez-Bernal, José-Víctor Alfaro-Santafé, Javier Alfaro-Santafé

Abstract read
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Article in Healthcare (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jose Luis Perez-LasierraPodoactiva Biomechanical Unit, Research & Development Department, Parque Tecnológico Walqa, Ctra. N330a Km 566, 22197 Cuarte, Spain.ORCID 0000-0002-1113-9169
Marina Azpíroz-PuentePodoactiva Biomechanical Unit, Research & Development Department, Parque Tecnológico Walqa, Ctra. N330a Km 566, 22197 Cuarte, Spain.ORCID 0000-0002-1398-1499
Martin Morita-HernandezPodoactiva Biomechanical Unit, Research & Development Department, Parque Tecnológico Walqa, Ctra. N330a Km 566, 22197 Cuarte, Spain.ORCID 0000-0001-7347-1469
Antonio Gómez-BernalPodoactiva Biomechanical Unit, Research & Development Department, Parque Tecnológico Walqa, Ctra. N330a Km 566, 22197 Cuarte, Spain.ORCID 0000-0003-4005-9835
José-Víctor Alfaro-SantaféPodoactiva Biomechanical Unit, Research & Development Department, Parque Tecnológico Walqa, Ctra. N330a Km 566, 22197 Cuarte, Spain.ORCID 0000-0001-7355-5160
Javier Alfaro-SantaféPodoactiva Biomechanical Unit, Research & Development Department, Parque Tecnológico Walqa, Ctra. N330a Km 566, 22197 Cuarte, Spain.ORCID 0000-0002-7651-8725

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

objectivesCognitive impairment in older adults is a growing public health concern due to global population aging. Early detection is crucial, yet current screening methods are time-consuming and require clinical expertise. Gait analysis has emerged as a promising alternative for cognitive screening. The aim of the study was to identify gait variables associated with cognitive impairment and to develop predictive algorithms capable of distinguishing between cognitively impaired and non-impaired older adults using gold-standard gait analysis.

methodsA cross-sectional study was conducted with 42 adults aged > 60 years. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and participants were divided into high (MMSE > 24) and low (MMSE ≤ 24) cognitive function groups. Spatiotemporal gait parameters were recorded at participants' usual pace using the Optogait system. Logistic regression models were developed using half of the sample (training group) and validated in the remaining participants (verification group).

resultsAlgorithms based on stride length and double support time demonstrated high classification performance. In the training group, the best-performing model achieved an AUC-ROC of 0.91, with a sensitivity of 71.4% and specificity of 92.3%. Validation in the verification group yielded an AUC-ROC of 0.84 and accuracy of 81.0%. Alternative models showed acceptable to excellent predictive power.

conclusionsGait analysis using gold-standard methods can effectively identify cognitive impairment in older adults. The developed algorithms offer a rapid, objective, and accurate screening alternative with strong potential for clinical application.

Indexed as

cognitive declineearly screeningelderlygait analysisspatiotemporal parameters

Identifiers

PMID41095536
PMCPMC12523893

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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.