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Title: | Self-assessment tool of disease activity of rheumatoid arthritis by using a smartphone application. |
Authors: | Nishiguchi, Shu Ito, Hiromu ![]() ![]() Yamada, Minoru Yoshitomi, Hiroyuki ![]() ![]() Furu, Moritoshi Ito, Tatsuaki Shinohara, Akio Ura, Tetsuya Okamoto, Kazuya ![]() ![]() ![]() Aoyama, Tomoki ![]() ![]() |
Author's alias: | 西口, 周 |
Keywords: | Rheumatoid arthritis Disease activity Smartphone Self-assessment |
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 http://dx.doi.org/10.1089/tmj.2013.0162. 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. |
URI: | http://hdl.handle.net/2433/201380 |
DOI(Published Version): | 10.1089/tmj.2013.0162 |
PubMed ID: | 24404820 |
Appears in Collections: | Journal Articles |

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