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

Abstract

Objective To explore the frequency of self-reported flares and their association with preceding symptoms collected through a smartphone app by people with rheumatoid arthritis (RA). Methods We used data from the Remote Monitoring of Rheumatoid Arthritis (REMORA) study, where patients tracked their daily symptoms and weekly flares on an app. We summarised 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). 15/20 participants reported at least one flare week, adding up to 54 flare weeks out of 198 participant weeks in total. Univariate mixed effects models showed that higher mean and steeper upward slopes in symptom scores in the week preceding the flare increased the likelihood of flare occurrence, but the association with variability was less strong. Multivariate modelling showed that for pain, mean scores and variability were associated with higher odds of flare, with odds ratios 1.83 (95% confidence interval, 1.15–2.97) and 3.12 (95% confidence interval, 1.07–9.13), respectively. Conclusion Our study suggests that patient-reported flares are common and are associated with higher daily RA symptom scores in the preceding week. Enabling patients to collect daily symptom data on their smartphones may ultimately facilitate prediction and more timely management of imminent flares.

Wearable activity tracker study exploring rheumatoid arthritis patients’ disease activity using patient-reported outcome measures, clinical measures, and biometric sensor data (the wear study)

“…To more comprehensively understand RA disease activity and its manifestations from a patient's perspective, including measure important attributes related to pain and stiffness, it is essential to collect patient reported outcomes (PROs) in addition to physician-derived measures. The addition of remote variables not routinely collected in clinical care allows physicians to better understand what the patient is experiencing including suboptimal treatment response in patients initiating a new medication that may not be feasible with such frequency using current healthcare staff[ 2 , [3] , [4] , [5] ].Recent studies have evaluated the ability of patient-reported outcome measures (PROs) to predict flare in RA patients [ 6 ], measure physical activity as a proxy for disease activity [ 6 ], and study associations between electronic patient-reported outcomes (ePROs) and passive (wearable/actigraphy) data [ 7 ]. These studies also evaluated the ability of RA populations to regularly complete their ePROs and wear their actigraphy devices.…”

Classifying Self-Reported Rheumatoid Arthritis Flares Using Daily Patient-Generated Data From a Smartphone App: Exploratory Analysis Applying Machine Learning Approaches

“…In previous work, we investigated the association between patient-reported flares and daily symptom scores [ 11 ]. The purpose of this analysis was to build on this work by investigating the feasibility of using ML methods to classify self-reported RA flares based on a small data set of daily symptom data collected through a smartphone app.…”

Effectiveness and feasibility of a mobile health self-management intervention in rheumatoid arthritis: study protocol for a pragmatic multicentre randomised controlled trial (AEGORA)

“…The benefits of self-management are clear on both the individual and the societal level. RA is a chronic disease with an often-unpredictable course, characterised by intermittent flares and even day-to-day symptom variation [ 11 , 12 ]. Since most patients see their rheumatologist only 3–5 times per year on average [ 13 ], people living with RA should be supported in their ability to manage or react to flares, symptoms or other difficulties they encounter in between clinic visits [ 14 , 15 ].…”