Juneau Pascale, Baddour Natalie, Burger Helena, Lemaire Edward D
Ottawa Hospital Research Institute, Ottawa, Canada.
Department of Mechanical Engineering, University of Ottawa, Ottawa, Canada.
PLOS Digit Health. 2024 Aug 26;3(8):e0000570. doi: 10.1371/journal.pdig.0000570. eCollection 2024 Aug.
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.
特定活动平衡信心量表(ABC)用于评估日常活动中的平衡信心。平衡信心低可能导致回避活动,而过度自信则会增加跌倒风险。下肢截肢患者可能会出现步态不一致的情况,对其平衡信心产生不利影响。先前的研究表明,该人群的临床结果(如步幅参数、跌倒风险)可以通过步行测试期间收集的智能手机信号来确定,但尚未对平衡信心进行评估。58名下肢截肢患者完成了6分钟步行测试(6MWT),同时使用后骨盆处的智能手机进行信号收集。参与者的ABC得分被分为低信心或高信心。随机森林利用从智能手机信号计算出的每一步特征对ABC组进行分类。随机森林正确分类了58名参与者中的47名的信心水平(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后将下肢截肢患者分为平衡信心组。将该模型集成到TOHRC步行测试应用程序中,除了先前已证明的临床结果外,在完成单次评估后还能提供平衡信心分类,并可为个性化康复计划提供参考,以提高信心并防止回避活动。