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Title: Self-assessment tool of disease activity of rheumatoid arthritis by using a smartphone application.
Authors: Nishiguchi, Shu
Ito, Hiromu  kyouindb  KAKEN_id
Yamada, Minoru
Yoshitomi, Hiroyuki  kyouindb  KAKEN_id
Furu, Moritoshi
Ito, Tatsuaki
Shinohara, Akio
Ura, Tetsuya
Okamoto, Kazuya  kyouindb  KAKEN_id  orcid (unconfirmed)
Aoyama, Tomoki  kyouindb  KAKEN_id
Author's alias: 西口, 周
Keywords: Rheumatoid arthritis
Disease activity
Issue Date: 27-Feb-2014
Publisher: Mary Ann Liebert Inc.
Journal title: Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Volume: 20
Issue: 3
Start page: 235
End page: 240
Abstract: Objectives: 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. Subjects and Methods: 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. Results: 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. Conclusions: 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.
Rights: Final publication is available from Mary Ann Liebert, Inc., publishers
この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。This is not the published version. Please cite only the published version.
DOI(Published Version): 10.1089/tmj.2013.0162
PubMed ID: 24404820
Appears in Collections:Journal Articles

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