Ren L, Zhang X, Li ZG, Tang H, Theiler R (2018) Monitoring the Time Course of Disability through a Self-Assessment Instrument "Activity Index" (IA) in RA Patients. J Rheum Dis Treat 4:065.


© 2018 Ren L, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ORIGINAL RESEARCH | OPEN ACCESS DOI: 10.23937/2469-5726/1510065

Monitoring the Time Course of Disability through a Self-Assessment Instrument "Activity Index" (IA) in RA Patients

L Ren1, X Zhang1, ZG Li1, H Tang2 and R Theiler3*

1Department of Rheumatology and Immunology, People's Hospital, Beijing, China

2Department of Rheumatology, Stadtspital Triemli, Zurich, Switzerland

3Department of Geriatrics, University Hospital Zurich, Switzerland



Rheumatoid arthritis (RA) is a heterogeneous autoimmune disease whose etiopathogenesis is largely unknown. Available treatments, though effective, are insufficient in so far as there is no cure for a major proportion of patients. In those patients the disease becomes chronic with progressive joint damage, disability, and limitation of participation. Current treatment approaches include pain-relieving drugs and anti-inflammatory medications that slow joint damage, combined with physical therapies involving a well-balanced, highly personalized sequence of rest and exercise. As pain-relief, overall activity, mobility and participation are vital indicators of disability and response to RA treatment, regular patient feedback in this respect is key to a successful monitoring of the long-term effect of therapy. To manage such feedback, we have developed an easy-to-use self-assessment tool "Activity Index" (AI) for tablets and smartphones that enables regular assessments of RA improvement and/or deterioration in the patients' home environment. In this normative study we addressed the questions of (1) The AI sensitivity regarding the resolution of small between-patient differences; and (2) The external validity of the AI instrument when compared with "objective" laboratory and x-ray measures and the HAQ disability index.

Data material and methods

Our sample was comprised of 100 Chinese RA patients under treatment with two repeated assessments at 14-day intervals. Patients were recruited from the consecutive daily admissions at the Beijing General Hospital. As part of the recruitment, patients were documented in terms of socio-demographic characteristics and previous RA history. The repeated assessments relied on a standardized clinical protocol along with the self-rating instruments AI and HAQ (Stanford Health Assessment Questionnaire). The clinical protocol encompassed several laboratory methods in order to "objectively" quantify severity of illness and joint damage: rheumatoid factor "RF"; anti-cyclic citrullinated peptide "anti-ccp"; X-ray; and MRI. All statistical analyses were carried out by means of the Statistical Analysis Software SAS 9.3 with PROCs FREQ, MEANS, TTEST, CORR, REG, and GLM [unbalanced data].


Based on a RA patient sample where 84% of study patients were treated in an outpatient nursing care setting, we validated our newly developed AI instrument for tablets and smartphones by (1) A comparison with the standard Health Assessment Questionnaire HAQ (indirect validation); and (2) Regression and correlation analyses focusing on the "objective" clinical quantity "joint damage" (external validation). The 10-item tablet/smartphone-based AI was found to measure essentially the same as the 20-item questionnaire-based HAQ, a finding that was underlined by a highly significant between-instrument correlation of r = 0.732 (p < 0.0001). In terms of external validity, the AI displayed a highly significant correlation of r = 0.429 (p < 0.0001) with the clinical quantity "Swollen Joints", thus demonstrating the instrument's efficiency in outpatient nursing care settings. By contrast, simple self-assessment scores of the form "Estimated Percentage of impairment [%]" yielded unsatisfactory results. No statistically significant clinical changes were seen over the 14-day observation period, so that the GLM approach to constructing a multivariate predictor model failed and led to inconclusive findings (model fit ≤ 0.0852).


Our analyses revealed the validity of the AI instrument as well as its efficiency in outpatient care settings, thus clearing the way for routine applications among patients under RA therapy. Ultimately, this monitoring approach will enable physicians to verify and optimize response to therapy in each individual patient through a more "personalized medicine". Beside assessing disease activity, the monitoring tool has the ability to assess the subjective disability for long term monitoring. The activity Index (AI) is easy to use and can be performed as an easy-to-use self-assessment tool for tablets and smartphones on the internet. The self-report documentation can be helpful for the treating physician during clinical visits and for long-term telemonitoring.