Using patient-reported data from a smartphone app to capture and characterize real-time patient-reported flares in rheumatoid arthritis.

ABSTRACT:

Objective

We aimed to explore the frequency of self-reported flares and their association with preceding symptoms collected through a smartphone app by people with RA.

Methods

We used data from the Remote Monitoring of RA study, in which patients tracked their daily symptoms and weekly flares on an app. We summarized the number of self-reported flare weeks. For each week preceding a flare question, we calculated three summary features for daily symptoms: mean, variability and slope. Mixed effects logistic regression models quantified associations between flare weeks and symptom summary features. Pain was used as an example symptom for multivariate modelling.

Results

Twenty patients tracked their symptoms for a median of 81 days (interquartile range 80, 82). Fifteen of

SUBMITTER: Gandrup J 

PROVIDER: S-EPMC8982773 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature