School of Physical Education, Shanghai University of Sport, Shanghai, China.
Physical Education and Sport Department, Shanghai International Studies University, Shanghai, China.
BMC Public Health. 2024 Aug 13;24(1):2206. doi: 10.1186/s12889-024-19760-8.
Early screening and identification are crucial for fall prevention, and developing a new method to predict fall risk in the elderly can address the current lack of objectivity in assessment tools.
A total of 132 elderly individuals over 80 years old residing in some nursing homes in Shanghai were selected using a convenient sampling method. Fall history information was collected, and gait data during a 10-meter walk were recorded. Logistic regression was employed to establish the prediction model, and a nomogram was used to assess the importance of the indicators. The Bootstrap method was utilized for internal validation of the model, while the verification set was used for external validation. The predictive performance of the model was evaluated using the area under the ROC curve, calibration curve, and decision curve analysis (DCA) to assess clinical benefits.
The incidence of falls in the sample population was 36.4%. The Tinetti Gait and Balance Test (TGBT) score (OR = 0.832, 95% CI: 0.734,0.944), stride length (OR = 0.007, 95% CI: 0.000,0.104), difference in standing time (OR = 0.001, 95% CI: 0.000,0.742), and mean stride time (OR = 0.992, 95% CI:0.984,1.000) were identified as significant factors. The area under the ROC curve was 0.878 (95% CI: 0.805, 0.952), with a sensitivity of 0.935 and specificity of 0.726. The Brier score was 0.135, and the Hosmer-Lemeshow test (χ = 10.650, P = 0.222) indicated a good fit and calibration of the model.
The TGBT score, stride length, difference in standing time, and stride time are all protective factors associated with fall risk among the elderly. The developed risk prediction model demonstrates good discrimination and calibration, providing valuable insights for early screening and intervention in fall risk among older adults.
对于预防跌倒来说，早期筛查和识别至关重要，开发一种新的方法来预测老年人的跌倒风险，可以解决当前评估工具缺乏客观性的问题。
采用便利抽样法，选取上海市某养老院的 132 名 80 岁以上老年人，收集跌倒史信息，记录 10 米步行时的步态数据。采用逻辑回归建立预测模型，并用列线图评估指标的重要性。使用 Bootstrap 方法对模型进行内部验证，使用验证集进行外部验证。通过 ROC 曲线下面积、校准曲线和决策曲线分析（DCA）评估模型的预测性能，以评估临床获益。
样本人群的跌倒发生率为 36.4%。Tinetti 步态和平衡测试（TGBT）评分（OR=0.832，95%CI：0.734，0.944）、步长（OR=0.007，95%CI：0.000，0.104）、站立时间差（OR=0.001，95%CI：0.000，0.742）和平均步长时间（OR=0.992，95%CI：0.984，1.000）被确定为显著因素。ROC 曲线下面积为 0.878（95%CI：0.805，0.952），灵敏度为 0.935，特异性为 0.726。Brier 评分是 0.135，Hosmer-Lemeshow 检验（χ²=10.650，P=0.222）表明模型拟合和校准良好。
TGBT 评分、步长、站立时间差和步长时间都是老年人跌倒风险的保护因素。所开发的风险预测模型具有良好的判别和校准能力，为老年人跌倒风险的早期筛查和干预提供了有价值的信息。
