FRI0578 DEVELOPMENT OF A MOBILE APP AND WIRELESS SENSORS SYSTEM TO ASSESS SPINAL MOBILITY IN AXIAL SPONDYLOARTHRITIS: PRELIMINARY RESULTS.

Abstract

Background:Spinal mobility is an important assessment outcome in axial spondyloarthritis (axSpA). Until now, conventional metrology (Schober test, lateral flexion, BASMI, …) has been used to assess spinal mobility, however, new technologies have been developed that provide better accuracy, reliability and responsiveness. Motion capture has been validated and Inertial Measurement Unit (IMU) sensors, appears to be a promising alternative. To use this IMU sensors in axSpA patients, wireless systems must be developed and validated allowing to doctors and patients to use them in hospitals and at home.Objectives:To develop an easy to use mobile app and IMU sensors system for analyse mobility for axSpA patients.Methods:A mobile app has been developed (iUCOTrack) that communicates with two IMU sensors (Shimmer 3©, Fig-a). These sensors are attached in different locations: at forehead and T12 for cervical mobility (Fig-c) and T12 and Sacrum for thoracolumbar mobility (Fig-b). The app provides mobility results for the different tests (Fig-d) and store results in the cloud. Validation tests of these sensors, using Matlab©, were done previously [1]. Our study test the validity of this app against a motion capture system, the UCOTrack®, and its metrology index, the UCOASMI [2], and conventional metrology as reference standards. Patiens with axSpA were recruited consecutively from the COSPAR cohort. Conventional metrology, PRO questionnaires and mobility (Cervical and thoracolumbar - flexion, lateral bending, rotation) using the iUCOTrack app and the UCOTrack were registered. Intraclasss Correlation Coefficients (ICC 3,1) between systems and correlations (spearman) with other axSpA outcome measures were performed for testing validity.Results:15 axSpA patients (47% female, age 52±12 years, disease duration 21±16 years) were included. Table shows ROM (SD) in degrees obtained for cervical and thoracolumbar spine measured by motion capture (UCOTrack) and the app (iUCOTrack). In the last column appears the UCOASMI (SD) calculated using angles obtained by each system. All ICC were good (ICC>0.8), and correlations were significant (p<0.05, r>0.8) specially the UCOASMI. Cervical rotation using a goniometer was 106.2±36°, with a significant correlation with both systems (p<0.05; r>0.8). Schober correlation with lumbar flexion was poor (NS;r>0.5) but a good correlation appeared with lateral flexion (p<0.01;r>0.9). Mean BASMI was 4.0± 1.8 with an excellent correlation with UCOASMI measured by Mocap (p<0.01;r=0.93) and by IMU (p<0.001;r=0.98).CervicalThoracolumbarFlexRotLatFlexRotLatUCOASMIUCOTrack79.5(24.7)109.8(29.6)62.5(25.1)100.7(21.6)61.8(25.3)54.7(22.9)6.07(1.66)iUCOTrack83.0(33.6)112.6(44.3)73.9(29.7)114.4(28.1)51.4(16.1)59.4(15.4)6.15(1.65)ICC0.8640.9030.8120.9360.7980.9010.970Corr0.89*0.96**0.82*0.97**0.88*0.97***0.97**Conclusion:New metrology tools are needed to improve features of convencional metrology. Motion Capture has proved to be valid but has feasibility problems. IMU sensor based systems provide similar results to motion capture but it can be faster and cheaper. A system based on mobile app connected to wireless IMU sensors could be a solution to improve metrology in axSpA. Further studies and developments are needed to introduce these technologies in research and clinical daily practice.