REHAB 2014

Research Article

Daily assessment of rheumatoid arthritis disease activity using a smartphone application: Development and 3-month feasibility study

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  • @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
Shu Nishiguchi1, Hiromu Ito1, Minoru Yamada1, Hiroyuki Yoshitomi1, Moritoshi Furu1, Tatsuaki Ito2, Akio Shinohara2, Tetsuya Ura2, Kazuya Okamoto3,*, Tomoki Aoyama1, Tadao Tsuboyama1
  • 1: Kyoto University
  • 2: NTT Service Evolution Laboratories
  • 3: Kyoto University Hospital
*Contact email: kazuya@kuhp.kyoto-u.ac.jp

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.