Self-Assessment Tool of Disease Activity of Rheumatoid Arthritis by Using a Smartphone Application
Open accessAbstract
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.
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A predictive model using a smartphone application accounts for 67% of the DAS28 variance.
“This predictive model accounts for 67% of the DAS28 variance.”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [63]
A smartphone program can accurately predict RA disease activity based on a non-invasive self-assessment of a combination of disabilities in everyday tasks, joint symptoms and walking ability.
“It proposes that a smartphone program can accurately predict RA disease activity based on a non-invasive self-assessment of a combination of disabilities in everyday tasks, joint symptoms and walking ability [63].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [63]
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- Authors
- Nishiguchi, S., Ito, H., Yamada, M., Yoshitomi, H., Furu, M., Ito, T., Shinohara, A., Ura, T., Okamoto, K., & Aoyama, T.
- Affiliations
- Japan Society for the Promotion of Science (JP)
- Kyoto University (JP)
- NTT (Japan) (JP)
- Kyoto University Hospital (JP)
- Corresponding author
- Nishiguchi, Shu
- Venue
- Telemedicine and e-Health
- Publisher
- SAGE Publications
- Published
- 2014-03
- Volume
- 20
- Issue
- 3
- Pages
- 235-240
- ISSN
- 1530-5627 (ISSN-L)
- 1556-3669
- OpenAlex
- W2108269740
- Cited by
- 45
- References
- 35
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 4 · 2022: 3 · 2021: 4 · 2020: 2 · 2019: 4 · 2018: 10 · 2017: 6 · 2016: 2 · 2015: 4 · 2014: 1
- FWCI
- 3.58
- Type
- journal article
- Language
- en
- Open access
- Locations
- Telemedicine Journal and e-Health (publishedVersion)
- PubMed (publishedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion)
- Kyoto University Research Information Repository (Kyoto University) (submittedVersion)
- Funders
- Japan Society for the Promotion of Science (13J02124, 24659674)
- Topics
- Health Sciences › Medicine › Rheumatology › Rheumatoid Arthritis Research and Therapies
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Hematology › Autoimmune and Inflammatory Disorders Research
- Keywords
- rheumatoid arthritis, disease activity, self-assessment, mobile application, DAS28, modified Health Assessment Questionnaire, tender joint count, swollen joint count, gait analysis, trunk acceleration, walking ability, disease activity prediction
- MeSH terms
- Aged, Arthritis, Rheumatoid, Female, Humans, Male, Middle Aged, Surveys and Questionnaires, Self Care, Cell Phone, Diagnostic Self Evaluation, Mobile Applications
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