Thijs Willem Swinnen1, Milica Milosevic1, Sabine Van Huffel1, Wim Dankaerts1, Rene Westhovens1, Kurt de Vlam2. 1. From the Division of Rheumatology, University Hospitals Leuven; Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven; Musculoskeletal Rehabilitation Research Unit, Department of Rehabilitation Sciences, KU Leuven; Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, KU Leuven; iMinds, Medical Information Technology, KU Leuven, Leuven, Belgium.T.W. Swinnen, PT, MSc, Doctoral Research Fellow, Division of Rheumatology, University Hospitals Leuven, and Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, and Musculoskeletal Rehabilitation Research Unit, Department of Rehabilitation Sciences, KU Leuven; M. Milosevic, MSc Eng, Doctoral Research Fellow, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, KU Leuven, and iMinds, Medical Information Technology, KU Leuven; S. Van Huffel, MSc Eng, PhD, Full Professor Biomedical Data Processing, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, KU Leuven, and iMinds, Medical Information Technology, KU Leuven; W. Dankaerts, PT, PhD, Professor Rehabilitation Sciences, Musculoskeletal Rehabilitation Research Unit, Department of Rehabilitation Sciences, KU Leuven; R. Westhovens, MD, PhD, Full Professor Rheumatology, Division of Rheumatology, University Hospitals Leuven, and Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven; K. de Vlam, MD, PhD, Principal Investigator Clinical Rheumatology, Division of Rheumatology, University Hospitals Leuven, and Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven. 2. From the Division of Rheumatology, University Hospitals Leuven; Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven; Musculoskeletal Rehabilitation Research Unit, Department of Rehabilitation Sciences, KU Leuven; Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, KU Leuven; iMinds, Medical Information Technology, KU Leuven, Leuven, Belgium.T.W. Swinnen, PT, MSc, Doctoral Research Fellow, Division of Rheumatology, University Hospitals Leuven, and Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, and Musculoskeletal Rehabilitation Research Unit, Department of Rehabilitation Sciences, KU Leuven; M. Milosevic, MSc Eng, Doctoral Research Fellow, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, KU Leuven, and iMinds, Medical Information Technology, KU Leuven; S. Van Huffel, MSc Eng, PhD, Full Professor Biomedical Data Processing, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, KU Leuven, and iMinds, Medical Information Technology, KU Leuven; W. Dankaerts, PT, PhD, Professor Rehabilitation Sciences, Musculoskeletal Rehabilitation Research Unit, Department of Rehabilitation Sciences, KU Leuven; R. Westhovens, MD, PhD, Full Professor Rheumatology, Division of Rheumatology, University Hospitals Leuven, and Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven; K. de Vlam, MD, PhD, Principal Investigator Clinical Rheumatology, Division of Rheumatology, University Hospitals Leuven, and Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven. kurt.devlam@uzleuven.be.
Abstract
OBJECTIVE: The Bath Ankylosing Spondylitis Functional Index (BASFI) is the most popular method to assess activity capacity in axial spondyloarthritis (axSpA), to our knowledge. It is endorsed by the Assessment of Spondyloarthritis international Society. But it may have recall bias or aberrant self-judgments in individual patients. Therefore, we aimed to (1) develop the instrumented BASFI (iBASFI) by adding a body-worn accelerometer with automated algorithms to performance-based measurements (PBM), (2) study the iBASFI's core psychometric properties, and (3) reduce the number of iBASFI items. METHODS: Twenty-eight patients with axSpA wore a 2-axial accelerometer while completing 12 PBM derived from the BASFI. A chronometer and both manual and "automated algorithm-based" acceleration segmentation identified movement time. Test-retest trials and methods (algorithm vs manual segmentation/chronometer/BASFI) were compared with ICC, standard error of measurement [percentage of movement time (SEM%)], and Spearman ρ correlation coefficients. Linear regression identified the optimal set of reliable iBASFI PBM. RESULTS: Good to excellent test-retest reliability was found for 8/12 iBASFI items (ICC range 0.812-0.997, SEM range 0.4-30.4%), typically with repeated and fast movements. Automated algorithms excellently mimicked manual segmentation (ICC range 0.900-0.998) and the chronometer (ICC range 0.878-0.998) for 10/12 iBASFI items. Construct validity compared with the BASFI was confirmed for 7/12 iBASFI items (δ range 0.504-0.755). Together, sit-to-stand speed test (stBeta 0.483), cervical rotation (stBeta -0.392), and height (stBeta -0.375) explained 59% of the variance in the BASFI (p < 0.01). CONCLUSION: The proof-of-concept iBASFI showed promising reliability and validity in measuring activity capacity. The number of the iBASFI's PBM may be minimized, but further validation in larger axSpA cohorts is needed before its clinical use.
OBJECTIVE: The Bath Ankylosing Spondylitis Functional Index (BASFI) is the most popular method to assess activity capacity in axial spondyloarthritis (axSpA), to our knowledge. It is endorsed by the Assessment of Spondyloarthritis international Society. But it may have recall bias or aberrant self-judgments in individual patients. Therefore, we aimed to (1) develop the instrumented BASFI (iBASFI) by adding a body-worn accelerometer with automated algorithms to performance-based measurements (PBM), (2) study the iBASFI's core psychometric properties, and (3) reduce the number of iBASFI items. METHODS: Twenty-eight patients with axSpA wore a 2-axial accelerometer while completing 12 PBM derived from the BASFI. A chronometer and both manual and "automated algorithm-based" acceleration segmentation identified movement time. Test-retest trials and methods (algorithm vs manual segmentation/chronometer/BASFI) were compared with ICC, standard error of measurement [percentage of movement time (SEM%)], and Spearman ρ correlation coefficients. Linear regression identified the optimal set of reliable iBASFI PBM. RESULTS: Good to excellent test-retest reliability was found for 8/12 iBASFI items (ICC range 0.812-0.997, SEM range 0.4-30.4%), typically with repeated and fast movements. Automated algorithms excellently mimicked manual segmentation (ICC range 0.900-0.998) and the chronometer (ICC range 0.878-0.998) for 10/12 iBASFI items. Construct validity compared with the BASFI was confirmed for 7/12 iBASFI items (δ range 0.504-0.755). Together, sit-to-stand speed test (stBeta 0.483), cervical rotation (stBeta -0.392), and height (stBeta -0.375) explained 59% of the variance in the BASFI (p < 0.01). CONCLUSION: The proof-of-concept iBASFI showed promising reliability and validity in measuring activity capacity. The number of the iBASFI's PBM may be minimized, but further validation in larger axSpA cohorts is needed before its clinical use.