The prediction model of fall risk for the elderly based on gait analysis

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 (χ² = 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.