Gait & posture · 2025
Association between gait characteristics during obstacle crossing and fall risk in stroke patients: A prospective cohort study.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wan Xianglin, Zhu Zihao, Xu Feng, Li Qiujie
Аннотация
BACKGROUND: In daily life, stroke patients frequently experience falls during obstacle crossing. Analyzing the gait characteristics of patients in high-risk falling scenarios can help identify and predict fall risks.
RESEARCH QUESTION: Exploring the predictive power of gait characteristics during obstacle crossing for fall risk in stroke patients.
METHODS: Recruitment of 38 stroke patients with unilateral hemiplegia discharged from rehabilitation. A Qualisys motion capture system and two Kistler force plates were used to record the marker positions and the ground reaction forces during crossing an obstacle 4 cm in height with the affected limb as the leading limb. Gait spatio-temporal parameters, joint angles, and joint moments were calculated. Following a 12-month follow-up survey to collect data on falls among participants, independent samples t-test and binary logistic regression models were employed to identify predictors associated with future fall risk.
RESULTS: During the follow-up period, 13 participants experienced at least one fall and were categorized into the fall group; 14 participants did not experience any falls and were categorized into the non-fall group. Binary logistic regression analysis revealed that the toe-clearance distance of the trailing limb, as well as the peak ankle plantarflexion moment of the trailing limb during double support phase, are effective predictors of fall risk in stroke patients (P < 0.05). The overall correct prediction rate of the regression model incorporating both factors was 85.2 %.
SIGNIFICANCE: Gait analysis during obstacle crossing holds potential clinical value in identifying future fall risk in stroke patients.
RESEARCH QUESTION: Exploring the predictive power of gait characteristics during obstacle crossing for fall risk in stroke patients.
METHODS: Recruitment of 38 stroke patients with unilateral hemiplegia discharged from rehabilitation. A Qualisys motion capture system and two Kistler force plates were used to record the marker positions and the ground reaction forces during crossing an obstacle 4 cm in height with the affected limb as the leading limb. Gait spatio-temporal parameters, joint angles, and joint moments were calculated. Following a 12-month follow-up survey to collect data on falls among participants, independent samples t-test and binary logistic regression models were employed to identify predictors associated with future fall risk.
RESULTS: During the follow-up period, 13 participants experienced at least one fall and were categorized into the fall group; 14 participants did not experience any falls and were categorized into the non-fall group. Binary logistic regression analysis revealed that the toe-clearance distance of the trailing limb, as well as the peak ankle plantarflexion moment of the trailing limb during double support phase, are effective predictors of fall risk in stroke patients (P < 0.05). The overall correct prediction rate of the regression model incorporating both factors was 85.2 %.
SIGNIFICANCE: Gait analysis during obstacle crossing holds potential clinical value in identifying future fall risk in stroke patients.