Sport and Health Research Center, Department of Physical Education, Tongji University, 1239 Siping Road, 200092, Shanghai, China.
Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, 812-8582, Fukuoka, Japan.
BMC Geriatr. 2021 Sep 1;21(1):476. doi: 10.1186/s12877-021-02415-3.
While gait speed, one-leg standing balance, and handgrip strength have been shown to be independent predictors for functional disability, it is unclear whether such simple measures of physical function contribute to improved risk prediction of functional disability in older adults.
A total of 1,591 adults aged ≥ 65 years and without functional disability at baseline were followed up for up to 7.9 years. Functional disability was identified using the database of Japan's Long-term Care Insurance System. Maximum gait speed, one-leg standing time, and handgrip strength were measured at baseline. Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95 % confidence intervals (CIs) for the association of physical function and functional disability incidence. The incremental predictive value of each physical function measure for risk prediction was quantified using the difference in overall C-statistic, category-free net reclassification improvement (NRI), and integrated discrimination improvement (IDI) index.
During follow-up (median: 7.8 years), functional disability was identified in 384 participants. All of the physical function measures were inversely associated with the risk of functional disability, independent of potential confounding factors. The multivariable adjusted HRs (95 % CIs) for functional disability per one standard deviation increment of maximum gait speed, one-leg-standing time, and hand grip strength were 0.73 (0.65-0.83), 0.68 (0.59-0.79), and 0.72 (0.59-0.86), respectively. Incorporation of each of maximum gait speed, one-leg-stand time, and hand grip strength into a basic model with other risk factors significantly improved C-statistic from 0.770 (95 % CIs, 0.751-0.794) to 0.778 (0.759-0.803), 0.782 (0.760-0.805), and 0.775 (0.756-0.800), respectively (all p < 0.05). A model including all three measures had the highest C-statistic of 0.787 (0.765-0.810). The improvements in risk prediction were also confirmed by category-free NRI and IDI index.
Adding any of the three measures to a basic model with other known risk factors significantly improved the prediction of functional disability and addition of all three measures provided further improvement of the prediction in older Japanese adults. These data provide robust evidence to support the practical utility of incorporating these simple physical function measures into functional disability risk prediction tools.
虽然步速、单腿站立平衡和握力已被证明是功能障碍的独立预测因素,但这些简单的身体功能测量方法是否有助于改善老年人功能障碍的风险预测仍不清楚。
共纳入 1591 名年龄≥65 岁且基线时无功能障碍的成年人,随访时间长达 7.9 年。功能障碍是通过日本长期护理保险系统数据库确定的。基线时测量最大步速、单腿站立时间和握力。使用 Cox 比例风险模型估计身体功能与功能障碍发生率之间的关联的风险比 (HR) 和 95%置信区间 (CI)。使用整体 C 统计量、无分类净重新分类改善 (NRI) 和综合判别改善 (IDI) 指数来量化每个身体功能测量值对风险预测的增量预测价值。
在随访期间(中位数:7.8 年),384 名参与者出现功能障碍。所有身体功能测量值均与功能障碍风险呈负相关,独立于潜在的混杂因素。最大步速、单腿站立时间和握力每增加一个标准差,功能障碍的多变量调整 HR(95%CI)分别为 0.73(0.65-0.83)、0.68(0.59-0.79)和 0.72(0.59-0.86)。将最大步速、单腿站立时间和握力中的每一个纳入一个基本模型,其中包含其他危险因素,可显著提高基本模型的 C 统计量,从 0.770(95%CI,0.751-0.794)提高到 0.778(0.759-0.803)、0.782(0.760-0.805)和 0.775(0.756-0.800),均为 p<0.05。包含所有三个测量值的模型具有最高的 C 统计量 0.787(0.765-0.810)。风险预测的改善也通过无分类 NRI 和 IDI 指数得到证实。
将这三个测量值中的任何一个添加到一个包含其他已知危险因素的基本模型中,都可以显著提高功能障碍预测的准确性,并且添加所有三个测量值可以进一步提高对日本老年人的预测准确性。这些数据为将这些简单的身体功能测量方法纳入功能障碍风险预测工具的实际应用提供了有力证据。