Comparison of a Multi-Component Physical Function Battery to Usual Walking Speed for Assessing Lower Extremity Function
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COMPARISON OF A MULTI-COMPONENT PHYSICAL FUNCTION BATTERY TO USUAL WALKING SPEED FOR ASSESSING LOWER EXTREMITY FUNCTION AND MOBILITY LIMITATION IN OLDER ADULTS C. RIWNIAK1, J.E. SIMON1,2, N.P. WAGES1,3, L.A. CLARK1,3,4, T.M. MANINI5, D.W. RUSS1,6, B.C. CLARK1,3,7 1. Ohio Musculoskeletal and Neurological Institute (OMNI), Ohio University, Athens, OH, USA; 2. School of Applied Health and Wellness, Ohio University, Athens, OH, USA; 3. Department of Biomedical Sciences, Ohio University, Athens, OH, USA; 4. Department of Family Medicine, Ohio University, Athens, OH, USA; 5. Department of Geriatric Medicine, University of Florida, Gainesville, FL, USA; 6. School of Rehabilitation and Communication Sciences, Ohio University, Athens, OH USA; 7. Division of Geriatric Medicine, Ohio University, Athens, OH, USA. Corresponding author: Brian C. Clark, Ph.D., Ohio Musculoskeletal & Neurological Institute, Ohio University, 250 Irvine Hall, Athens, OH 45701, 740-593-2354, Email: [email protected], [email protected]
Abstract: Objectives: To compare a composite measure of physical function that comprises locomotor and nonlocomotor tests (i.e., the Mobility Battery Assessment (MBA)) with traditional measures of mobility (4-m usual gait speed (UGS), six-minute walk (6MW) gait speed, and short physical performance battery (SPPB) score) for assessing lower extremity function and discriminating community dwelling older adults with and without mobility limitations. Design: Cross-sectional, observational study. Setting: Laboratory-based. Participants: 89 community-dwelling older adults (74.9+6.7). Measurements: Using principal component analysis we derived an MBA score for 89 community-dwelling older adults, and quantified 4-m UGS, 6MW gait speed, and SPPB score. The MBA score was based on five lab-based tests. We also quantified self-reported lower extremity function/ mobility using the Neuro-QOL Lower Extremity Function-Mobility instrument. Based on this data a continuous score was derived and subjects were classified as “mobility limited” or “non-mobility limited”. Correlations between the mobility measures and the Neuro-QOL score were calculated, and ROC curves were constructed to determine the AUC for the mobility measures ability to predict mobility limitations. Results: The MBA had the largest AUC (0.92) for discriminating mobility limitations and exhibited the strongest correlation (0.73) with the Neuro-QOL Lower Extremity Function-Mobility Scale. The worst performing predictors were the 4-meter UGS and stair climb power both with an AUC of 0.8 for discriminating mobility limitations, and a low correlation with Neuro-QOL Lower Extremity Function Scale of 0.39 and 0.46, respectively. Conclusion: The MBA score moderately improves the magnitude of correlation and discrimination of mobility limitation in older adults than singular, standard tests of mobility. Key words: Physical function, mobility, sarcopenia, gait, assessment, strength, balance.
Introduction
older adults (14, 15). However, there is more to the construct of mobility th
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