Preferred Gait Speed Predicts Mild Cognitive Impairment in Community-Dwelling Older Adults
Poster #: 074
Session/Time: A
Author:
Bradley Eppinger, BA
Mentor:
Brittany Samulski DPT, PhD
Research Type: Clinical Research
Abstract
INTRODUCTION:
Mild cognitive impairment (MCI) describes subtle, but measurable deficits in memory or thinking that exceed expected age-related changes. MCI is often a precursor to the development of dementia. By age 70, nearly two-thirds of adults experience some degree of cognitive decline. Early identification of MCI is therefore critical. The Montreal Cognitive Assessment (MoCA) is widely used for screening but requires training and approximately 10 minutes to administer, which may not be feasible in fast-paced clinical settings. Gait speed has been proposed as a "sixth vital sign," reflecting overall health and functional reserve. Slower gait speed is associated with falls, hospitalization, morbidity, and poor discharge outcomes. The aim of this study was to identify whether gait speed could accurately predict MCI classification.
METHODS:
A secondary analysis of deidentified data from a community-based fall risk assessment program was conducted. The sample included 208 participants (70±7 years, 67% female). Assessments included the long form physiological profile assessment (PPA), MoCA, Modified Falls Efficacy Scale (MFES), and five 20-foot overground walking trials on a pressure-sensitive walkway at both preferred and maximal gait speeds. A participant was classified as having MCI if their MoCA score was ≤ 25.
RESULTS:
A binomial logistic regression was conducted to examine whether preferred walking speed predicted cognitive risk status (intact vs. MCI). The model was statistically significant, χ2(1,208) = 13.969, p<.001, indicating that walking speed reliably distinguished between groups. The regression coefficient for preferred walking speed was negative and significant (B=-3.79, SE=1.07, Wald=12.61, p<.001). The odds ratio indicated that for each centimeter per second increase in preferred walking speed, the odds of being classified as having MCI decreased by 97.7% (OR=0.023, 95% CI [0.003,0.183]). The overall classification accuracy of the model was 62.2%. Sensitivity was 87.8% (correctly classifying MCI cases), whereas specificity was 16.7% (correctly classifying intact cases).
CONCLUSION:
Routine screening for MCI is essential but challenged by limited visit times and the need for training in cognitive screening tools such as the MoCA. Preferred gait speed, already used in clinical practice to evaluate frailty and fall risk, may also serve as a rapid screening tool for cognitive impairment. This analysis demonstrates that a slower preferred walking speed is significantly associated with increased odds of MCI classification. While the model was more effective at identifying MCI than intact cognition, these findings highlight the potential for gait speed to complement traditional cognitive screening, offering clinicians a time-efficient means of detecting patients at risk for cognitive decline.
Mild cognitive impairment (MCI) describes subtle, but measurable deficits in memory or thinking that exceed expected age-related changes. MCI is often a precursor to the development of dementia. By age 70, nearly two-thirds of adults experience some degree of cognitive decline. Early identification of MCI is therefore critical. The Montreal Cognitive Assessment (MoCA) is widely used for screening but requires training and approximately 10 minutes to administer, which may not be feasible in fast-paced clinical settings. Gait speed has been proposed as a "sixth vital sign," reflecting overall health and functional reserve. Slower gait speed is associated with falls, hospitalization, morbidity, and poor discharge outcomes. The aim of this study was to identify whether gait speed could accurately predict MCI classification.
METHODS:
A secondary analysis of deidentified data from a community-based fall risk assessment program was conducted. The sample included 208 participants (70±7 years, 67% female). Assessments included the long form physiological profile assessment (PPA), MoCA, Modified Falls Efficacy Scale (MFES), and five 20-foot overground walking trials on a pressure-sensitive walkway at both preferred and maximal gait speeds. A participant was classified as having MCI if their MoCA score was ≤ 25.
RESULTS:
A binomial logistic regression was conducted to examine whether preferred walking speed predicted cognitive risk status (intact vs. MCI). The model was statistically significant, χ2(1,208) = 13.969, p<.001, indicating that walking speed reliably distinguished between groups. The regression coefficient for preferred walking speed was negative and significant (B=-3.79, SE=1.07, Wald=12.61, p<.001). The odds ratio indicated that for each centimeter per second increase in preferred walking speed, the odds of being classified as having MCI decreased by 97.7% (OR=0.023, 95% CI [0.003,0.183]). The overall classification accuracy of the model was 62.2%. Sensitivity was 87.8% (correctly classifying MCI cases), whereas specificity was 16.7% (correctly classifying intact cases).
CONCLUSION:
Routine screening for MCI is essential but challenged by limited visit times and the need for training in cognitive screening tools such as the MoCA. Preferred gait speed, already used in clinical practice to evaluate frailty and fall risk, may also serve as a rapid screening tool for cognitive impairment. This analysis demonstrates that a slower preferred walking speed is significantly associated with increased odds of MCI classification. While the model was more effective at identifying MCI than intact cognition, these findings highlight the potential for gait speed to complement traditional cognitive screening, offering clinicians a time-efficient means of detecting patients at risk for cognitive decline.