Rozanski Gabriela, Delgado Andrew, Putrino David
Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Front Rehabil Sci. 2023 Jul 26;4:1189376. doi: 10.3389/fresc.2023.1189376. eCollection 2023.
Self-report tools are recommended in research and clinical practice to capture individual perceptions regarding health status; however, only modest correlations are found with performance-based results. The Lower Extremity Functional Scale (LEFS) is one well-validated measure of impairment affecting physical activities that has been compared with objective tests. More recently, mobile gait assessment software can provide comprehensive motion tracking output from ecologically valid environments, but how this data relates to subjective scales is unknown. Therefore, the association between the LEFS and walking variables remotely collected by a smartphone was explored.
Proprietary algorithms extracted spatiotemporal parameters detected by a standard integrated inertial measurement unit from 132 subjects enrolled in physical therapy for orthopedic or neurological rehabilitation. Users initiated ambulation recordings and completed questionnaires through the OneStep digital platform. Discrete categories were created based on LEFS score cut-offs and Analysis of Variance was applied to estimate the difference in gait metrics across functional groups (Low-Medium-High).
The main finding of this cross-sectional retrospective study is that remotely-collected biomechanical walking data are significantly associated with individuals' self-evaluated function as defined by LEFS categorization ( = 132) and many variables differ between groups. Velocity was found to have the strongest effect size.
When patients are classified according to subjective mobility level, there are significant differences in quantitative measures of ambulation analyzed with smartphone-based technology. Capturing real-time information about movement is important to obtain accurate impressions of how individuals perform in daily life while understanding the relationship between enacted activity and relevant clinical outcomes.
在研究和临床实践中推荐使用自我报告工具来获取个体对健康状况的看法；然而，发现其与基于表现的结果之间只有适度的相关性。下肢功能量表（LEFS）是一种经过充分验证的衡量影响身体活动的损伤程度的方法，已与客观测试进行了比较。最近，移动步态评估软件可以在生态有效环境中提供全面的运动跟踪输出，但这些数据与主观量表之间的关系尚不清楚。因此，本研究探讨了LEFS与通过智能手机远程收集的步行变量之间的关联。
专有算法从132名参加骨科或神经康复物理治疗的受试者的标准集成惯性测量单元检测到的时空参数中提取数据。用户通过OneStep数字平台启动步行记录并完成问卷调查。根据LEFS得分临界值创建离散类别，并应用方差分析来估计各功能组（低-中-高）之间步态指标的差异。
这项横断面回顾性研究的主要发现是，远程收集的生物力学步行数据与个体根据LEFS分类法进行的自我评估功能显著相关（n = 132），并且许多变量在组间存在差异。发现速度具有最强的效应量。
当根据主观活动水平对患者进行分类时，使用基于智能手机的技术分析的步行定量测量存在显著差异。获取有关运动的实时信息对于准确了解个体在日常生活中的表现以及理解实际活动与相关临床结果之间的关系非常重要。
