ArticleScientific reports2023
Patient-centric assessment of rheumatoid arthritis using a smartwatch and bespoke mobile app in a clinical setting.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Digital monitoring in rheumatoid arthritis: remote assessment, wearables, and data-driven disease management.Current opinion in rheumatology · 2026Review
- Evaluating a Smartphone App-Based Module for Joint Self-Assessment as a Complementary Tool in Rheumatoid Arthritis Remote Disease Management.ACR open rheumatology · 2026Article
- Exploring digital health technology in rheumatology: a scoping review on benefits, challenges, and future possibilities.EULAR rheumatology open · 2026Review
- Harnessing patient health data to benefit individuals and advance research: a focus on rheumatoid arthritis - a narrative review.BMJ digital health & AI · 2026Review
- Systematic review: digital biomarkers of fatigue in chronic diseases.NPJ digital medicine · 2025Article
- Assessment of physical activity patterns in patients with rheumatoid arthritis using the UK Biobank.PloS one · 2025Article
- Unveiling Artificial Intelligence's Power: Precision, Personalization, and Progress in Rheumatology.Journal of clinical medicine · 2024Review
- Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis.NPJ digital medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rheumatoid arthritis (RA) is a fluctuating progressive disease requiring frequent symptom assessment for appropriate management. Continuous tracking using digital technologies may provide greater insights of a patient's experience. This prospective study assessed the feasibility, reliability, and clinical utility of using novel digital technologies to remotely monitor participants with RA. Participants with moderate to severe RA and non-RA controls were monitored continuously for 14 days using an iPhone with an integrated bespoke application and an Apple Watch. Participants completed patient-reported outcome measures and objective guided tests designed to assess disease-related impact on physical function. The study was completed by 28 participants with RA, 28 matched controls, and 2 unmatched controls. Completion rates for all assessments were > 97% and were reproducible over time. Several guided tests distinguished between RA and control cohorts (e.g., mean lie-to-stand time [seconds]: RA: 4.77, control: 3.25; P < 0.001). Participants with RA reporting greater stiffness, pain, and fatigue had worse guided test performances (e.g., wrist movement [P < 0.001] and sit-to-stand transition time [P = 0.009]) compared with those reporting lower stiffness, pain, and fatigue. This study demonstrates that digital technologies can be used in a well-controlled, remote clinical setting to assess the daily impact of RA.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.