Internet publication
Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis
- 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 (r2, 0.692; RMSE, 1.33). The ability to measure the impact of disease during daily life—through objective and remote digital outcomes—paves the way forward to enable the development of more patient-centric and personalised measurements for use in RA clinical trials.
- Publication status:
- Published
- Peer review status:
- Not peer reviewed
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(Preview, Pre-print, pdf, 3.9MB, Terms of use)
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- Publisher copy:
- 10.1101/2022.11.18.22282305
Authors
- Host title:
- medRxiv
- Pages:
- 1-36
- Publication date:
- 2022-11-18
- DOI:
- Language:
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English
- Keywords:
- Pubs id:
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1330571
- Local pid:
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pubs:1330571
- Deposit date:
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2024-02-06
Terms of use
- Copyright holder:
- Creagh et al.
- Copyright date:
- 2022
- Rights statement:
- © The Author(s). The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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