Evidence map›Paper›PMID 38347090›Full record

ArticleNPJ digital medicine2024

Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis.

Andrew P Creagh, Valentin Hamy, Hang Yuan, Gert Mertes, Ryan Tomlinson, Wen-Hung Chen, Rachel Williams, Christopher Llop, Christopher Yee, Mei Sheng Duh and 3 more

Registry-linked trialOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07740200 (Validation of the Synovo Digital Disease Activity Score), which is not on this map. Cited by 43 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 3 pooled it
19.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

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.

2 · The registry

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.

NCT07740200 completednot on this mapstarted 2025, after this paper: background citation

Validation of the Synovo Digital Disease Activity Score (DDAS) Against Physician-Performed DAS28/CDAI: A Non-Clinical, Non-Interventional, Non-Academic Study

TypeobservationalSponsorSynovo Ltd.Ran2025 to 2026Enrolled61ConditionsRheumatoid Arthritis (RA)ArmsDigital Disease Activity Assessment
3 · Its place in the literature

Who cites it

43 citing papers in PubMed, 3 syntheses or guidelines pooled it, 42 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 4 institutions in 2 countries.

Andrew P CreaghInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK. andrew.creagh@eng.ox.ac.uk.ORCID http://orcid.org/0000-0002-6086-6098
Valentin HamyValue Evidence and Outcomes (VEO), GSK, UK.
Hang YuanBig Data Institute, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0001-5944-1925
Gert MertesInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0003-3155-480X
Ryan TomlinsonValue Evidence and Outcomes (VEO), GSK, US.
Wen-Hung ChenValue Evidence and Outcomes (VEO), GSK, US.
Rachel WilliamsValue Evidence and Outcomes (VEO), GSK, US.
Christopher LlopAnalysis Group (AG), Boston, MA, USA.
Christopher YeeAnalysis Group (AG), Boston, MA, USA.
Mei Sheng DuhAnalysis Group (AG), Boston, MA, USA.
Aiden DohertyBig Data Institute, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0003-1840-0451
Luis Garcia-GancedoValue Evidence and Outcomes (VEO), GSK, UK.ORCID http://orcid.org/0000-0001-5631-1711
David A CliftonInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
Health Economics and Outcomes Research (United Kingdom) · GBAnalysis Group (United States) · USOpen Data Institute · GBUniversity of Oxford · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

Digital measures of health status captured during daily life could greatly augment current in-clinic assessments for rheumatoid arthritis (RA), to enable better assessment of disease progression and impact. This work presents results from weaRAble-PRO, a 14-day observational study, which aimed to investigate how digital health technologies (DHT), such as smartphones and wearables, could augment patient reported outcomes (PRO) to determine RA status and severity in a study of 30 moderate-to-severe RA patients, compared to 30 matched healthy controls (HC). Sensor-based measures of health status, mobility, dexterity, fatigue, and other RA specific symptoms were extracted from daily iPhone guided tests (GT), as well as actigraphy and heart rate sensor data, which was passively recorded from patients' Apple smartwatch continuously over the study duration. We subsequently developed a machine learning (ML) framework to distinguish RA status and to estimate RA severity. It was found that daily wearable sensor-outcomes robustly distinguished RA from HC participants (F1, 0.807). Furthermore, by day 7 of the study (half-way), a sufficient volume of data had been collected to reliably capture the characteristics of RA participants. In addition, we observed that the detection of RA severity levels could be improved by augmenting standard patient reported outcomes with sensor-based features (F1, 0.833) in comparison to using PRO assessments alone (F1, 0.759), and that the combination of modalities could reliability measure continuous RA severity, as determined by the clinician-assessed RAPID-3 score at baseline (r

Identifiers

PMID38347090
PMCPMC10861520
OpenAlexW4391751277

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.