Description
Study Design:
This was a cross sectional study that had the objective of measuring the validity of using a smartphone-based application to measure range of motion (ROM) and smoothness (Jerkiness) of neck motion by comparing it with 3D-motion capture analysis.
Summary of background data:
Assessing human performance in a clinical setting can be challenging, as this requires the detection of subtle variations in movement and ROM with the naked eye. Research findings consistently demonstrate differences between clinical groups and healthy volunteers during standardized clinical tests but this is often done with the help of advanced research equipment. Methods: Thirty healthy volunteers participated in this 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. An intraclass correlation was calculated to compare the outcomes from the different applications.
Results:
No difference was found between modalities when comparing measurements of jerkiness or ROM. An excellent intraclass correlation was found for the outcomes of the two modalities for ROM (ICC: 0.83 - 0.96) and jerkiness (ICC: 0.86 – 0.95).
Conclusions:
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.
This was a cross sectional study that had the objective of measuring the validity of using a smartphone-based application to measure range of motion (ROM) and smoothness (Jerkiness) of neck motion by comparing it with 3D-motion capture analysis.
Summary of background data:
Assessing human performance in a clinical setting can be challenging, as this requires the detection of subtle variations in movement and ROM with the naked eye. Research findings consistently demonstrate differences between clinical groups and healthy volunteers during standardized clinical tests but this is often done with the help of advanced research equipment. Methods: Thirty healthy volunteers participated in this 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. An intraclass correlation was calculated to compare the outcomes from the different applications.
Results:
No difference was found between modalities when comparing measurements of jerkiness or ROM. An excellent intraclass correlation was found for the outcomes of the two modalities for ROM (ICC: 0.83 - 0.96) and jerkiness (ICC: 0.86 – 0.95).
Conclusions:
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.
Date made available | 19 Apr 2019 |
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Publisher | Mendeley Data |