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基于智能手机的惯性传感器技术——一种测量时空步态指标新应用的验证。

Smartphone-based inertial sensors technology - Validation of a new application to measure spatiotemporal gait metrics.

作者信息

Shema-Shiratzky Shirley, Beer Yiftah, Mor Amit, Elbaz Avi

机构信息

AposTherapy Research Group, Herzliya, Israel.

Department of Orthopaedic Surgery, Assaf Harofeh Medical Center, Zerifin, Israel.

出版信息

Gait Posture. 2022 Mar;93:102-106. doi: 10.1016/j.gaitpost.2022.01.024. Epub 2022 Jan 29.

Abstract

BACKGROUND

Smartphones are increasingly recognized as the future technology for clinical gait assessment.

RESEARCH QUESTION

To determine the concurrent validity of gait parameters obtained using the smartphone technology and application in a group of patients with musculoskeletal pathologies.

METHODS

Patients with knee, lower back, hip, or ankle pain were included in the study (n = 72). Spatiotemporal outcomes were derived from the walkway and the smartphone simultaneously. Pearson's correlations and limits of agreement (LoA) determined the association between the two methods.

RESULTS

Cadence and gait cycle time showed excellent correlation and agreement between the smartphone and the walkway (cadence: r = 0.997, LoA=1.4%, gait cycle time: r = 0.996, LoA = 1.6%). Gait speed, double-limb support and left and right step length demonstrated strong correlations and moderate agreement between methods (gait speed: r = 0.914, LoA=15.4%, left step length: r = 0.842, LoA = 17.0%, right step length: r = 0.800, LoA=16.4%). The left and right measures of single-limb support and stance percent showed a consistent 4% bias across instruments, yielding moderate correlation and very good agreement between the smartphone and the walkway (r = 0.532, LoA = 9% and r = 0.460, LoA=9.8% for left and right single-limb support; r = 0.463, LoA = 5.1% and r = 0.533, LoA = 4.4% for left and right stance).

SIGNIFICANCE

The examined application appears to be a valid tool for gait analysis, providing clinically significant metrics for the assessment of patients with musculoskeletal pathologies. However, additional studies should examine the technology amongst patients with severe gait abnormalities.

摘要

背景

智能手机日益被视为临床步态评估的未来技术。

研究问题

确定在一组肌肉骨骼疾病患者中使用智能手机技术及应用所获得的步态参数的同时效度。

方法

纳入患有膝部、下背部、髋部或踝部疼痛的患者(n = 72)。时空参数同时从步道和智能手机获取。Pearson相关性和一致性界限(LoA)确定两种方法之间的关联。

结果

步频和步态周期时间在智能手机和步道之间显示出极好的相关性和一致性(步频:r = 0.997,LoA = 1.4%;步态周期时间:r = 0.996,LoA = 1.6%)。步态速度、双支撑期以及左右步长在两种方法之间显示出强相关性和中等一致性(步态速度:r = 0.914,LoA = 15.4%;左步长:r = 0.842,LoA = 17.0%;右步长:r = 0.800,LoA = 16.4%)。单支撑期的左右测量值和站立百分比在各仪器之间显示出一致的4%偏差,在智能手机和步道之间产生中等相关性和非常好的一致性(左、右单支撑期:r = 0.532,LoA = 9%和r = 0.460,LoA = 9.8%;左、右站立期:r = 0.463,LoA = 5.1%和r = 0.533,LoA = 4.4%)。

意义

所研究的应用似乎是一种有效的步态分析工具,可为肌肉骨骼疾病患者的评估提供具有临床意义的指标。然而,更多研究应在严重步态异常患者中检验该技术。

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