POS0678 SMARTPHONE-BASED BEHAVIORAL MONITORING OF RHEUMATOID ARTHRITIS FLARES
Authors: S. Pedro, E. Mollard, K. Michaud
Journal: Annals of the Rheumatic Diseases (2024)

<h3>Background:</h3> Rheumatoid Arthritis (RA) is characterized by unpredictable and debilitating symptoms, which intensify during a flare. Given the profound effects of flares on clinical and patient-reported outcomes (PROs), identification and possible prediction of these flares becomes important. Monitoring symptom flares via smartphone sensing technology could provide insights directly related to rheumatic disease activity. <h3>Objectives:</h3> The objective of this study is to explore the frequency of the self-reported flares in participants with RA, estimate the agreement of the flares and the PROs by two methods (phases), and assess their association with the preceding PROs, passive phone data, and seasonal data collected by the app. <h3>Methods:</h3> Participants from Forward, The National Databank for Rheumatic Diseases, were invited to participate by installing an app on their smartphone during two phases over 3 years. In phase 1, 2013-2014, 131 RA patients enrolled and agreed to respond to daily PROs (pain, function VAS) for 3 months and weekly PROs (PAS-II). Initially, flare questions were triggered if the daily ΔVAS≥2.5 or weekly ΔPAS-II≥1.67 (Conditional). Subsequently, we had 161 RA participants in 2014-2015 with identical design but biweekly flare assessments irrespective of PRO and a question on flare treatments (Fixed). Passive data collected included mobility distance, unique calls, text messages, call durations, and missed calls. Confounders included sociodemographic, clinical, passive phone behavior, and seasonal factors. Kappa statistics between PRO and flares were computed to measure agreement. The relationship between the flares and the prior weekly PRO outcomes and passive phone data was analyzed with logistic GEE models, using QIC criterion to select best models. <h3>Results:</h3> Of 257 patients with flare data, 71% had at least one flare and tracked their disease for a median of 441 (IQR 192 -457) days (n=103) in Conditional and 76% of patients had flares in 172 (IQR 153 -175) days of follow-up (n=214, 57 from Conditional) in Fixed. Overall, patients were 54 (12) years old, 90% were female, 90% non-Hispanic white, and had 15.5 (SD 1.9) years of education. Kappa agreement between flares and PROs was 55.3% and 65.6%, respectively. The average number of flares was 2.9 (SD 3.4) Conditional and 3.7 (3.6) Fixed, which corresponded to 3 and 9 flares per year, on average. Patients treated their flares by increasing dose of currently prescribed drugs or did not treat the flare (no change) (Table 1). Passive data are in Table 2 by the presence of flare. Multivariate models showed that mobility radius (odds ratio (OR) 0.88 95%CI (0.80-0.97)), age (OR 0.97 (95%CI 0.95 – 0.97)), education years (OR 0.89 (95%CI 0.78 – 1.03)) and workdays (OR 0.96 (95%CI 0.94 – 0.99)) were associated with lower odds of flares in Conditional. For Fixed, the summer season (OR 0.74 (95%CI 0.60 – 0.92)) and longer texting (OR 0.69 (95%CI 0.51 -0.93)) were also inversely associated with the odds of having a flare, as opposed to global patient assessment (OR 1.23 (95%CI 1.13-1.32)). <h3>Conclusion:</h3> In this RA cohort, self-reported flares were frequent. Prompting the flare question with a fixed periodicity increased the reporting of flaring. Flares were associated with passive data like mobility or texting length. Future work can enhance the prediction of symptom flares and inform proactive intervention strategies. <h3>REFERENCES:</h3> <b>NIL.</b> <h3>Acknowledgements:</h3> <b>NIL.</b> <h3>Disclosure of Interests:</h3> Sofia Pedro Forward, The National Databank for Rheumatic Diseases, Elizabeth Mollard: None declared, Kaleb Michaud: None declared.