Research Article
Daily assessment of rheumatoid arthritis disease activity using a smartphone application: Development and 3-month feasibility study
@INPROCEEDINGS{10.4108/icst.pervasivehealth.2014.255338, author={Shu Nishiguchi and Hiromu Ito and Minoru Yamada and Hiroyuki Yoshitomi and Moritoshi Furu and Tatsuaki Ito and Akio Shinohara and Tetsuya Ura and Kazuya Okamoto and Tomoki Aoyama and Tadao Tsuboyama}, title={Daily assessment of rheumatoid arthritis disease activity using a smartphone application: Development and 3-month feasibility study}, proceedings={REHAB 2014}, publisher={ICST}, proceedings_a={REHAB}, year={2014}, month={7}, keywords={rheumatoid arthritis disease activity smartphone self-assessment feasibility study}, doi={10.4108/icst.pervasivehealth.2014.255338} }- Shu Nishiguchi
Hiromu Ito
Minoru Yamada
Hiroyuki Yoshitomi
Moritoshi Furu
Tatsuaki Ito
Akio Shinohara
Tetsuya Ura
Kazuya Okamoto
Tomoki Aoyama
Tadao Tsuboyama
Year: 2014
Daily assessment of rheumatoid arthritis disease activity using a smartphone application: Development and 3-month feasibility study
REHAB
ICST
DOI: 10.4108/icst.pervasivehealth.2014.255338
Abstract
In this paper, we report the development and feasibility of a daily assessment system for rheumatoid arthritis (RA) patients based on a smartphone application. We measured daily disease activity in 9 RA patients who used the smartphone application for a period of 3 months. A disease activity score (DAS28) predictive model was used and feedback comments relating to disease activity were shown to patients via the smartphone application each day. The disease activity measured by the application correlated well with the patients’ actual disease activity during the 3-month period, as assessed by clinical examination. Furthermore, most participants gave favorable responses to a questionnaire administered at the end of the 3-month period containing questions relating to the ease of use and usefulness of the system. The results of this feasibility study indicate that the DAS28 predictive model can longitudinally predict a disease activity score based on the C-reactive protein level and may be an acceptable and useful tool for the assessment of RA disease activity for both patients and healthcare providers.
