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Measurement of Three-Dimensional Back Shape of Normal Adults Using a Novel Three-Dimensional Imaging Mobile Surface Topography System (MSTS): An Intra- and Inter-Rater Reliability Study.

Authors :
Kandasamy, Gok
Bettany-Saltikov, Josette
Van Schaik, Paul
Source :
Healthcare (2227-9032); Dec2023, Vol. 11 Issue 23, p3099, 13p
Publication Year :
2023

Abstract

Postural and spinal deformities are major contributing factors to musculoskeletal (MSK) disorders. Posture screening and assessment can help to identify early morphological deformities, thereby preventing progression and reducing or correcting them with effective treatments. The study evaluates both intra- and inter-repeatability of using a mobile structured light sensor with a structured light pattern for building an accurate 3D human model and its use in postural screening. 16 young males (age: 25 ± 5.6 years, height: 172 ± 5.3 cm, mass: 69 ± 8.6 kg) participated without any musculoskeletal pain or pre-existing leg or spinal abnormalities. An iPad-based 3D mobile scanning tool, Structure Sensor<superscript>TM</superscript> (2018 version), was used to capture the participants' back and whole-body shape. The collected data (3D model) were realigned and processed in the open-source software, Netfabb Basic<superscript>TM</superscript> (7.2 version). For each participant, five trained raters individually measured three trials of standing back and body posture on two separate occasions to calculate both intra- and inter-rater reliability. With the use of this software, nine postural variables and angular displacements were individually measured by the raters. The results indicated good to excellent intra-rater and good to moderate inter-rater reliability for measuring 78% (7 out of 9) of postural variables with an ICC ranging from 0.70 to 0.98. The remaining 22% of variables (2 out of 9; lateral pelvic tilt and right frontal knee angle) showed moderate to low inter- and intra-rater reliability, with ICCs ranging from 0.26 to 0.79. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
22279032
Volume :
11
Issue :
23
Database :
Complementary Index
Journal :
Healthcare (2227-9032)
Publication Type :
Academic Journal
Accession number :
174113323
Full Text :
https://doi.org/10.3390/healthcare11233099