Exploring the predictive capacity of smartphone-based digital phenotyping to monitor pain and physical quality of life in advanced cancer patients, family caregivers, and dyads.

Introduction: Pain is among the most prevalent and distressing symptoms in advanced cancer, impairing physical, emotional, and social well-being. Management often requires support from family caregivers, whose own health and psychological well-being may also be adversely affected. This study examined the potential utility of digital phenotyping-moment-to-moment quantification of individual-level human behavior-to assess pain and physical quality of life (QOL) in patients with advanced cancer and their family caregivers. Methods: Patients with advanced cancer ( Results: Caregiver GPS-derived mobility features predicted a large proportion of variance in patient pain intensity (R² = 0.31) and pain interference (R² = 0.32). Combined caregiver and patient mobility data predicted large variance in caregiver physical QOL (R² = 0.43) and medium-to-large variance in patient pain intensity (R² = 0.16) and pain interference (R² = 0.33). Patient mobility features alone predicted small variance in caregiver physical QOL (R² = 0.02). When examining patient data predicting patient outcomes, mobility features were associated with small variance in physical QOL (R² = 0.03), pain intensity (R² = 0.05), and pain interference (R² = 0.08). Discussion: These findings suggest that digital phenotyping may be a useful approach for predicting pain and physical QOL in advanced cancer, particularly when incorporating both patient and caregiver data. Further research is warranted to evaluate digital phenotyping as a novel method for monitoring symptoms and functional outcomes in advanced cancer care.