The disease activities of rheumatoid arthritis (RA) tend to fluctuate between visits to doctors, and a self-assessment tool can help patients accommodate to their current status at home. The aim of the present study was to develop a novel modality to assess the disease activity of RA by a smartphone without the need to visit a doctor.
This study included 65 patients with RA, 63.1 ± 11.9 years of age. The 28-joint disease activity score (DAS28) was measured for all participants at each clinic visit. The patients assessed their status with the modified Health Assessment Questionnaire (mHAQ), a self-assessed tender joint count (sTJC), and a self-assessed swollen joint count (sSJC) in a smartphone application. The patients' trunk acceleration while walking was also measured with a smartphone application. The peak frequency, autocorrelation (AC) peak, and coefficient of variance of the acceleration peak intervals were calculated as the gait parameters.
Univariate analyses showed that the DAS28 was associated with mHAQ, sTJC, sSJC, and AC (p<0.05). In a stepwise linear regression analysis, mHAQ (β = 0.264, p<0.05), sTJC (β = 0.581, p<0.001), and AC (β = -0.157, p<0.05) were significantly associated with DAS28 in the final model, and the predictive model explained 67% of the DAS28 variance.
The results suggest that noninvasive self-assessment of a combination of joint symptoms, limitations of daily activities, and walking ability can adequately predict disease activity of RA with a smartphone application.