利用六分钟步行测试智能手机传感器信号对下肢截肢者的平衡信心进行分类。

PLOS digital health Pub Date : 2024-08-26 eCollection Date: 2024-08-01 DOI:10.1371/journal.pdig.0000570
Pascale Juneau, Natalie Baddour, Helena Burger, Edward D Lemaire
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引用次数: 0

摘要

特定活动平衡信心量表(ABC)可评估在常见活动中的平衡信心。平衡信心低会导致避免活动,而信心过高则会增加跌倒风险。下肢截肢者可能会出现步态不一致的情况,从而对其平衡信心产生不利影响。之前的研究表明,可以通过步行测试时收集的智能手机信号来确定这类人群的临床结果(如步幅参数、跌倒风险),但尚未对平衡信心进行评估。五十八(58)名下肢截肢者完成了六分钟步行测试(6MWT),同时在后骨盆处使用智能手机收集信号。参与者的 ABC 评分被分为低置信度和高置信度。随机森林利用智能手机信号计算出的每个步骤的特征对 ABC 组进行分类。随机森林对 58 名参与者中的 47 人的置信度进行了正确分类(准确率 81.0%,灵敏度 63.2%,特异性 89.7%)。这项研究表明,智能手机信号数据可以在完成 6MWT 后将下肢截肢者分为平衡置信度组。将这一模型集成到 TOHRC 步行测试应用程序中,除了之前已证实的临床结果外,还能在完成一次评估后提供平衡信心分类,并能为个性化康复计划提供信息,以提高信心并防止逃避活动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Balance confidence classification in people with a lower limb amputation using six minute walk test smartphone sensor signals.

The activities-specific balance confidence scale (ABC) assesses balance confidence during common activities. While low balance confidence can result in activity avoidance, excess confidence can increase fall risk. People with lower limb amputations can present with inconsistent gait, adversely affecting their balance confidence. Previous research demonstrated that clinical outcomes in this population (e.g., stride parameters, fall risk) can be determined from smartphone signals collected during walk tests, but this has not been evaluated for balance confidence. Fifty-eight (58) individuals with lower limb amputation completed a six-minute walk test (6MWT) while a smartphone at the posterior pelvis was used for signal collection. Participant ABC scores were categorized as low confidence or high confidence. A random forest classified ABC groups using features from each step, calculated from smartphone signals. The random forest correctly classified the confidence level of 47 of 58 participants (accuracy 81.0%, sensitivity 63.2%, specificity 89.7%). This research demonstrated that smartphone signal data can classify people with lower limb amputations into balance confidence groups after completing a 6MWT. Integration of this model into the TOHRC Walk Test app would provide balance confidence classification, in addition to previously demonstrated clinical outcomes, after completing a single assessment and could inform individualized rehabilitation programs to improve confidence and prevent activity avoidance.

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