Vehicle Control as a Measure of Real‐World Driving Performance in Patients With Rheumatoid Arthritis. Issue 2 (23rd September 2022) - Mikuls, Ted R.; Merickel, Jennifer; Gwon, Yeongjin; Sayles, Harlan; Petro, Alison; Cannella, Amy; Snow, Marcus H.; Feely, Michael; England, Bryant R.; Michaud, Kaleb; Rizzo, Matthew
Abstract : Objective: To quantify vehicle control as a metric of automobile driving performance in patients with rheumatoid arthritis (RA). Methods: Naturalistic driving assessments were completed in patients with active RA and controls without disease. Data were collected using in‐car, sensor‐based instrumentation installed in the participants' own vehicles to observe typical driving habits. RA disease status, disease activity, and functional status were associated with vehicle control (lateral [steering] and longitudinal [braking/accelerating] acceleration variability) using mixed‐effect linear regression models stratified by road type (defined by roadway speed limit). Results: Across 1, 292 driving hours, RA drivers (n = 33) demonstrated differences in vehicle control compared to controls (n = 23), with evidence of significant statistical interaction between disease status and road type ( P  < 0.001). On residential roads, participants with RA demonstrated overall lower braking/accelerating variability than controls ( P  ≤ 0.004) and, when disease activity was low, lower steering variability ( P  = 0.03). On interstates/highways, RA was associated with increased steering variability among those with moderate/high Clinical Disease Activity Index scores ( P  = 0.04). In models limited to RA, increases in disease activity and physical disability over 12 weeks of observation were associated with a significant increase in braking/accelerating variability on interstate/highways (both P  < 0.05). Conclusion: Using novel naturalistic assessments, we linked RA and worsening RA disease severity with aberrant vehicle control. These findings support the need for further research to map these observed patterns in vehicle control to metrics of driver risk and, in turn, to link patterns of real‐world driving behavior to diagnosis and disease activity.