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Study on the association between gait speed, grip strength and the risk of falls among the oldest old in community |
SHEN Jiangqiong1 WANG Xiaoyuan1 XU Weihao2 MENG Wenwen3 |
1.Department of Nursing, the Second Medical Center, Chinese PLA General Hospital, Beijing 100853, China;
2.Deparment of Vasculocardiology, the Second Medical Center, Chinese PLA General Hospital, Beijing 100853, China;
3.Medical Service Office, Medical District of North Beijing, Chinese PLA General Hospital, Beijing 100094, China |
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Abstract Objective To explore the association between gait speed, grip strength and the risk of falls among the oldest old in communities. Methods Data of the China Health and Retirement Longitudinal Study in 2013 was collected, subjects aged≥80 years, who had measured their gait speed and grip strength, and who had fall data were selected. A total of 497 elderly people in communities were included in the study and divided into two groups based on whether they fell or not: 122 elderly people in the fall group and 375 elderly people in the non-fall group. Association between gait speed, grip strength and risk of falls was examined with logistic regression models. Results In 24 months of follow-up, 25.4% of study participants (n = 122) experienced a fall. After adjusting for age, gender, and hyperlipidemia, it was found that low gait speed (RR = 2.57, 95%CI: 1.29-5.14) was independently associated with a higher risk of falls. After analyzing the combination of gait speed and grip strength, it was found that participants with normal gait speed combined with low grip strength (RR = 1.95, 95%CI: 1.12-3.42) or with normal grip strength combined with low gait speed (RR = 2.82, 95%CI: 1.49-5.34) had a higher risk of fall, participants with both low gait speed and low grip strength had highest risk of fall (RR = 3.14, 95%CI: 1.41-7.00). Conclusion Gait speed and grip strength can predict future falls independently and jointly, taking the measurement of gait speed and grip strength into the fall risk assessment of the oldest old in the community may improve the accuracy of fall risk prediction.
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