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Associations Between Smartphone-Derived Behavioral Data and Rheumatoid Arthritis Flares.

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Patient-reported outcomes and smartphone data can help predict rheumatoid arthritis (RA) flares. This study found associations between mobility, communication patterns, and RA flare occurrence, suggesting potential for early detection.

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Area of Science:

  • Rheumatology
  • Digital Health
  • Data Science

Background:

  • Rheumatoid arthritis (RA) flares significantly impact patient quality of life.
  • Early detection of RA flares is crucial for effective disease management.

Purpose of the Study:

  • To characterize rheumatoid arthritis (RA) flare frequency and severity.
  • To assess if short-term changes in patient-reported outcomes (PROs) can signal RA flares.
  • To examine the relationship between passive smartphone data and self-reported RA flares.

Main Methods:

  • PROs were collected in conditional and fixed phases, alongside passive smartphone data (mobility, communication).
  • Logistic generalized estimating equation models and multivariate analyses were used to assess associations.
  • Demographic and seasonal confounders were adjusted for in the analyses.

Main Results:

  • Flares were linked to worse PROs, including patient global assessment and pain.
  • Increased mobility and longer text messages were associated with fewer flares.
  • Slower reaction times and shorter text messages correlated with increased flare occurrence.

Conclusions:

  • Integrating PROs with passive smartphone data reveals novel associations with RA flare occurrence.
  • These findings suggest potential for developing predictive models for earlier RA flare recognition.
  • Personalized algorithms could improve future rheumatoid arthritis disease management.