Aalborg University, Department of Health Science and Technology (SMI), Frederik Bajers Vej 7, 9220, Aalborg, Denmark.
Aalborg University, Department of Health Science and Technology (SMI), Frederik Bajers Vej 7, 9220, Aalborg, Denmark; University College North Denmark, Department of Physical Therapy, Selma Lagerløfsvej 2, 9220, Aalborg, Denmark.
Musculoskelet Sci Pract. 2019 Jun;41:64-69. doi: 10.1016/j.msksp.2019.01.002. Epub 2019 Jan 9.
This study had the objective of measuring the validity of using a smartphone-based application to measure range of motion (ROM) and quality of movement (QOM) of neck motion by comparing it with 3D-motion capture analysis.
Thirty healthy volunteers participated in this cross-sectional study. A helmet fitted with markers for motion capture analysis and a smartphone were fastened to the head of the participants. The smartphone recorded data using a beta version of Balancy (MEDEI, Denmark). Assessments of full active movement in transverse and sagittal planes were performed. Recordings were made simultaneously with the camera system and the smartphone. ROM and jerkiness were compared with a repeated measures ANOVA and a Pearson product moment was calculated to compare the outcomes from the different applications. Bland-Altman plots were generated to determine the levels of agreement.
No difference was found between modalities when comparing measurements of jerkiness or ROM. An excellent Pearson product moment was found for the outcomes of the two modalities for ROM (Pearson's r: 0.83 - 0.96) and jerkiness (Pearson's r: 0.86 - 0.95). The Bland-Altman plot revealed a systemic offset where the phone consistently measured higher values for ROM and lower values for jerkiness.
This study demonstrated that a smartphone-based application can be used to accurately measure ROM and jerkiness during neck movements. These results indicate the utility of using a smartphone-based application to assess neck movement in humans. The findings have implications for assessment of neck movement in research and clinical practice.
本研究旨在通过与 3D 运动捕捉分析比较，测量基于智能手机应用程序测量颈部运动范围（ROM）和运动质量（QOM）的有效性。
30 名健康志愿者参与了这项横断面研究。参与者头部佩戴带有运动捕捉分析标记的头盔和智能手机。智能手机使用 Balancy（丹麦 MEDEI）的测试版记录数据。在横切和矢状面进行全主动运动评估。同时使用相机系统和智能手机进行记录。使用重复测量方差分析比较 ROM 和急动度，并计算 Pearson 乘积矩以比较两种应用的结果。生成 Bland-Altman 图以确定一致性水平。
在比较急动度或 ROM 的测量值时，两种模式之间没有差异。两种模式的 ROM（Pearson's r：0.83-0.96）和急动度（Pearson's r：0.86-0.95）的结果均具有极好的 Pearson 乘积矩。Bland-Altman 图显示系统偏移，手机始终测量 ROM 值较高，急动度值较低。
本研究表明，基于智能手机的应用程序可用于准确测量颈部运动的 ROM 和急动度。这些结果表明，在人类中使用基于智能手机的应用程序评估颈部运动具有实用性。这些发现对研究和临床实践中颈部运动的评估具有重要意义。
