BACKGROUND: The study addressed whether changes in gait speed in community-dwelling older adults were associated with changes in fitness (strength and aerobic capacity), physical health status, and/or depressive symptoms. METHODS: The study sample comprised 152 community-dwelling adults aged 68-85 who had participated in an exercise study. Study measures at baseline and 6-month follow-up included gait speed, a leg strength score, maximal aerobic capacity (VO2max), CES-Depression scale, and physical health status (SIP Physical Dimension). RESULTS: In cross-sectional regression analyses, leg strength, VO2max, weight, and the strength by VO2max interaction term were significant independent predictors of gait speed (R2 = 26%). Based upon the observed 7% increase in VO2max and 8% increase in strength in the exercise groups, the regression model predicted only a 2% (1.5 m/min) increase in gait speed, which did not differ significantly from the observed increase of 0% (.32 m/min). The strongest correlate of change in gait speed was change in CES-D scores (partial R = -.37). Change in physical health status also correlated with change in gait speed (partial R = -.28), while change in fitness did not. CONCLUSIONS: The results suggest, in the range of fitness of the study sample, that changes in gait speed are related to changes in depressive symptoms and physical health status, but not to modest changes in fitness. A model assuming nonlinear relationships may be appropriate for understanding how strength and aerobic capacity affect gait speed.
BACKGROUND: The study addressed whether changes in gait speed in community-dwelling older adults were associated with changes in fitness (strength and aerobic capacity), physical health status, and/or depressive symptoms. METHODS: The study sample comprised 152 community-dwelling adults aged 68-85 who had participated in an exercise study. Study measures at baseline and 6-month follow-up included gait speed, a leg strength score, maximal aerobic capacity (VO2max), CES-Depression scale, and physical health status (SIP Physical Dimension). RESULTS: In cross-sectional regression analyses, leg strength, VO2max, weight, and the strength by VO2max interaction term were significant independent predictors of gait speed (R2 = 26%). Based upon the observed 7% increase in VO2max and 8% increase in strength in the exercise groups, the regression model predicted only a 2% (1.5 m/min) increase in gait speed, which did not differ significantly from the observed increase of 0% (.32 m/min). The strongest correlate of change in gait speed was change in CES-D scores (partial R = -.37). Change in physical health status also correlated with change in gait speed (partial R = -.28), while change in fitness did not. CONCLUSIONS: The results suggest, in the range of fitness of the study sample, that changes in gait speed are related to changes in depressive symptoms and physical health status, but not to modest changes in fitness. A model assuming nonlinear relationships may be appropriate for understanding how strength and aerobic capacity affect gait speed.
Authors: Christopher I Morse; Jeanette M Thom; Omar S Mian; Andrea Muirhead; Karen M Birch; Marco V Narici Journal: Eur J Appl Physiol Date: 2005-07-08 Impact factor: 3.078
Authors: Mini E Jacob; Thomas G Travison; Rachel E Ward; Nancy K Latham; Suzanne G Leveille; Alan M Jette; Jonathan F Bean Journal: J Gerontol A Biol Sci Med Sci Date: 2019-03-14 Impact factor: 6.053
Authors: Teresa Liu-Ambrose; Jennifer C Davis; Lindsay S Nagamatsu; Chun Liang Hsu; Lindsay A Katarynych; Karim M Khan Journal: BMC Geriatr Date: 2010-05-19 Impact factor: 3.921