POS1476-HPR FEASIBILITY OF USING OPTOELECTRONIC MEASUREMENT OF HAND MOVEMENT FOR CHARACTERIZING HAND FUNCTION IN RHEUMATOID ARTHRITIS

Abstract

BackgroundPhysical function is an important factor determining disease burden in arthritis. Monitoring function in rheumatoid arthritis (RA) patients is essential for effective treatment [1]. The currently used tools to assess physical function (e.g. patient reported outcomes) have limitations with respect to sensitivity and specificity to measure functional impairment in RA [2,3]. A marker-based optoelectronic measurement of hand function enables detailed analysis of hand movements such as spatial-temporal parameters and joint angles [4]. This may provide new possibilities to quantitatively and qualitatively analyze the changes of hand function in patients with RA in so far unprecedented way.ObjectivesTo test the feasibility of optoelectronic measurement of hand function in RA patients and healthy controls (HC) when performing standard functional tests such as the Moberg Pick-Up-Test (MPUT) as well as standard movements such as finger flexing and to detect disease specific patterns.MethodsRA patients (ACR/EULAR 2010 criteria [1]) recruited from the Internal Medicine 3 outpatient clinic, Erlangen, Germany and HC were included (Ethics #125_16B). Participants were asked to perform the MPUT and a simple movement of flexing the interphalangeal (DIP) and proximal interphalangeal joint (PIP). Spatial-temporal data of hand movements and hand segment kinematics were captured using an optoelectronic measurement system (Qualisys AB, Sweden) with 29 retroreflective markers (Figure 1). Transport time for each of the 12 MPUT objects was divided into a grasping phase (GP) (first touch to safe grip) and a manipulation phase (MP) (safe grip to drop) using the video recording or marker trajectories. For the flexing movement, the ratios between the flexion angle of the DIP and PIP joint (DIPPIP) were calculated. We used linear mixed-effects models accounting for within-participant clustering of hands and adjusting for age and sex differences to compare RA with controls.Figure 1.Marker setup and the 12 objects transported during the MPUT.ResultsTwenty-four RA patients and 23 healthy controls were evaluated (Table 1). Mean GP times across all objects showed higher absolute differences between the groups (RA 0.43 [0.35-0.52]; HC 0.33 [0.27-0.40] sec) while MP times were identical (RA 0.36 [0.30-0.44]; HC 0.36 [0.30-0.44] sec) showing a significant group-phase interaction (p<0.001). Objects safety pin, key, and paper clip showed the highest absolute between-group mean differences for unadjusted time data (0.41, 0.36, 0.34 sec respectively). Measured angle ratios (RA 0.60±0.15; HC 0.68±0.17 (DIPPIP)) and their linear fit (RA 0.96±0.05; HC 0.97±0.03 R2) were similar for RA and controls (p>0.05).Table 1.Subject characteristics; mean (SD)RAHCmale: female [N]7: 1711: 12Age [years]62.3 (9.1)50.2 (16.1)Disease duration [years]11.8 (10.8)Disease Activity Score (DAS28)2.5 (1.3)ConclusionOptoelectronic measurement of hand function is feasible and allows to gain a more detailed picture of impairment in hand function in RA patients. For instance, tasks like reaching for an object are significantly impaired. Further, objects causing the greatest difficulty for RA patients in the GP were identified. The previously described linear relationship of angle ratios for the distal finger joints in healthy individuals [5] seems also valid for RA patients in our cohort and no significant group differences for the ratio could be observed. This may reflect that DIP and PIP joints are less affected in RA compared to e.g. psoriasis arthritis [6]. In conclusion, optoelectronic hand movement analysis allows a more accurate and differentiated analysis of hand function in RA patients.References[1]Aletaha, D. et al. Arthritis Rheum. 2010, 62, 2569-2581[2]Günay, S. M. Reumatismo. 2016, 68, 183-187[3]Liphardt, A.M. et al. ACR Open Rheumatol. 2020, 2, 734-740[4]Sancho-Bru, J. et al. Proc. Inst. Mech. Eng. Part H J. Eng. Med.2014, 228, 182-189[5]Lee, J. & Kunii, T. IEEE Comput. Graph. Appl. 1995, 77-86[6]Veale DJ, et al. RMD Open 2015, 1: e000025AcknowledgementsThe study was supported by the German Research Foundation (DFG) under Grant SFB 1483 – Project-ID 442419336 and the major instruments at the Institute of Applied Dynamics, FAU Erlangen-Nürnberg were used in this study – reference number INST 90 / 985-1 FUGG.Disclosure of InterestsNone declared