RT info:eu-repo/semantics/article
T1 A computer vision-based application for the assessment of head posture: A validation and reliability study
A1 Carrasco Uribarren, Andoni
A1 Marimon, Xavier
A1 Dantony, Flora
A1 Cabanillas Barea, Sara
A1 Portela, Alejandro
A1 Ceballos Laita, Luis
A1 Massip Álvarez, Albert
K1 Neck
K1 Cuello - Enfermedades
K1 Kinematics
K1 Cinemática
K1 Computer vision
K1 Visión artificial (Robótica)
K1 Robotics
K1 Medical technology
K1 Materials science
K1 Ciencia de los materiales
K1 32 Ciencias Médicas
K1 3213.11 Fisioterapia
K1 1203.17 Informática
K1 3314 Tecnología Médica
AB As its name implies, the forward head position (FHP) is when the head is further forward of the trunk than normal. This can cause neck and shoulder tension, as well as headaches. The craniovertebral angle (CVA) measured with 2D systems such as Kinovea software is often used to assess the FHP. Computer vision applications have proven to be reliable in different areas of daily life. The aim of this study is to analyze the test-retest and inter-rater reliability and the concurrent validity of a smartphone application based on computer vision for the measurement of the CVA. Methods: The CVAs of fourteen healthy volunteers, fourteen neck pain patients, and fourteen tension-type headache patients were assessed. The assessment was carried out twice, with a week of rest between sessions. Each examiner took a lateral photo in a standing position with the smartphone app based on computer vision. The test-retest reliability was calculated with the assessment of the CVA measured by the smartphone application, and the inter-rater reliability was also calculated. A third examiner assessed the CVA using 2D Kinovea software to calculate its concurrent validity. Results: The CVA in healthy volunteers was 54.65 (7.00); in patients with neck pain, 57.67 (5.72); and in patients with tension-type headaches, 54.63 (6.48). The test-retest reliability was excellent, showing an Intraclass Correlation Coefficient (ICC) of 0.92 (0.86–0.95) for the whole sample. The inter-rater reliability was excellent, with an ICC of 0.91 (0.84–0.95) for the whole sample. The standard error of the measurement with the app was stated as 1.83°, and the minimum detectable change was stated as 5.07°. The concurrent validity was high: r = 0.94, p < 0.001. Conclusion: The computer-based smartphone app showed excellent test-retest and inter-rater reliability and strong concurrent validity compared to Kinovea software for the measurement of CVA.
PB MDPI
SN 2076-3417
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/63749
UL https://uvadoc.uva.es/handle/10324/63749
LA eng
NO Applied Sciences, 2023, Vol. 13, Nº. 6, 3910
NO Producción Científica
DS UVaDOC
RD 27-nov-2024