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. 2024 Jun 4;19(6):e0305074.
doi: 10.1371/journal.pone.0305074. eCollection 2024.

Association between gait speed deterioration and EEG abnormalities

Affiliations

Association between gait speed deterioration and EEG abnormalities

Daysi García-Agustin et al. PLoS One. .

Abstract

Physical and cognitive decline at an older age is preceded by changes that accumulate over time until they become clinically evident difficulties. These changes, frequently overlooked by patients and health professionals, may respond better than fully established conditions to strategies designed to prevent disabilities and dependence in later life. The objective of this study was twofold; to provide further support for the need to screen for early functional changes in older adults and to look for an early association between decline in mobility and cognition. A cross-sectional cohort study was conducted on 95 active functionally independent community-dwelling older adults in Havana, Cuba. We measured their gait speed at the usual pace and the cognitive status using the MMSE. A value of 0.8 m/s was used as the cut-off point to decide whether they presented a decline in gait speed. A quantitative analysis of their EEG at rest was also performed to look for an associated subclinical decline in brain function. Results show that 70% of the sample had a gait speed deterioration (i.e., lower than 0.8 m/s), of which 80% also had an abnormal EEG frequency composition for their age. While there was no statistically significant difference in the MMSE score between participants with a gait speed above and below the selected cut-off, individuals with MMSE scores below 25 also had a gait speed<0.8 m/s and an abnormal EEG frequency composition. Our results provide further evidence of early decline in older adults-even if still independent and active-and point to the need for clinical pathways that incorporate screening and early intervention targeted at early deterioration to prolong the years of functional life in older age.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Diagram representing the gait speed assessment.
Measurements were performed in a flat and unobstructed space by a trained researcher. Participants were instructed to stand at the beginning of the acceleration zone (first black rectangle covering 1 metre) and, upon a command, to walk at their usual pace, passing the deceleration zone (second black rectangle also covering 1 metre). Gait speed was timed with a stopwatch in the middle 4 metres (time zone, the white rectangle in the diagram).
Fig 2
Fig 2. Flowchart representing the steps followed in this study for the data processing and analysis.
Fig 3
Fig 3. Distribution of participants according to their gait speed and MMSE scores.
Participants with normal and abnormal EEG frequency composition are represented separately in Panel A and B. The blue line marks the GS cut-off point of 0.8m/s, and the red line marks an MMSE score equal to 25. Black circles: participants with a GS > 0.8 m/s and an MMSE score equal to or above 25. Blue circles: participants with a GS < 0.8 m/s and an MMSE score equal to or above 25. Green circles: participants with a GS > = 0.8 m/s and an MMSE score below 25. Red circles: participants with GS < 0.8 m/s and an MMSE score below 25.
Fig 4
Fig 4. Topographic maps of the current source difference between groups.
The difference was obtained by subtracting the current source map of NorGS from LowGS (i.e., LowGS—NorGS). Results are plotted by frequency bands on an MNI T2 template of an average brain, with maps centred in the maximum/minimum. Reddish areas are regions where oscillations were stronger in LowGS than in NorGS, while blueish areas are regions with weaker oscillations. First row: delta band: 1–4 Hz, Second row: theta band: 4–8 Hz, Third row: alpha band: 8–12, Fourth row: beta band: 12–30 Hz. L: left, R: right, A: anterior, P: posterior.

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