Trip-Related Fall Risk Prediction Based on Gait Pattern in Healthy Older Adults: A Machine-Learning Approach

Abstract

Trip perturbations are proposed to be a leading cause of falls in older adults. To prevent trip-falls, trip-related fall risk should be assessed and subsequent task-specific interventions improving recovery skills from forward balance loss should be provided to the individuals at risk of trip-fall. Therefore, this study aimed to develop trip-related fall risk prediction models from one’s regular gait pattern using machine-learning approaches. A total of 298 older adults (≥60 years) who experienced a novel obstacle-induced trip perturbation in the laboratory were included in this study. Their trip outcomes were classified into three classes: no-falls (n = 192), falls with lowering strategy (L-fall, n = 84), and falls with elevating strategy (E-fall, n = 22). A total of 40 gait characteristics, which could potentially affect trip outcomes, were calculated in the regular walking trial before the trip trial. The top 50% of features (n = 20) were selected to train the prediction models using a relief-based feature selection algorithm, and an ensemble classification model was selected and trained with different numbers of features (1–20). A ten-times five-fold stratified method was utilized for cross-validation. Our results suggested that the trained models with different feature numbers showed an overall accuracy between 67% and 89% at the default cutoff and between 70% and 94% at the optimal cutoff. The prediction accuracy roughly increased along with the number of features. Among all the models, the one with 17 features could be considered the best model with the highest AUC of 0.96, and the model with 8 features could be considered the optimal model, which had a comparable AUC of 0.93 and fewer features. This study revealed that gait characteristics in regular walking could accurately predict the trip-related fall risk for healthy older adults, and the developed models could be a helpful assessment tool to identify the individuals at risk of trip-falls.

Classification and Definitions of Compensatory Protective Step Strategies in Older Adults: A Scoping Review

“…The method most used was the tether-release method, reported in 14 studies [29,30,[39][40][41]43,45,46,51,52,[60][61][62]70]. Also, the trip method was reported in 13 studies [21,31,39,42,[54][55][56]62,64,69,71,72,74] and the surface translation method was reported in 12 studies [24,26,36,37,44,49,53,62,72,73,75,77]. Other methods reported were the waist-pull method used by 11 articles [22,23,38,[48][49][50]57,66,67,73,76], the slip method used ...…”

“…The most repeated strategies for the trip method were the lowering and elevation strategies. The articles that analyze both strategies (lowering and elevation strategy) [31,55,64,69,74] did not find differences between using one or another, but it could be a determining factor for the success of the recovery. However, there are differences in the analyses between healthy older adults and young adults.…”

“…Wang S. et al (2023) [69] Lowering strategy Consists of lowering the tripped limb behind the obstacle and taking a recovery step with the other limb.…”

“…The most repeated strategies for disturbance by method of travel were the descent and elevation strategy [31,55,64,69,74]. For the slip disturbance method, these two strategies were also reported [74].…”

“…The aims of the present scoping review were to compile all the compensatory protective step strategies for loss of balance in older adults and to identify perturbation methods and spatiotemporal variables used to analyze the collected strategies. This review included 53 articles, 45 of them were quasi-experimental studies [21][22][23][24][26][27][28][29][30][31]69,70], 6 articles were randomized controlled trial studies [71][72][73][74][75][76], and 2 articles were observational studies [77,78].…”