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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
Journal article   Open access   Peer reviewed

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

Kristen Allen-Watts, Andres Azuero, Kyungmi Lee, Erin R. Harrell, Erin Currie, Avery C. Bechthold, Sally Engler, Kayleigh Curry, Frank Puga, Natashia Bibriescas, …
Frontiers in pain research (Lausanne, Switzerland), Vol.7, p.1767157
07/06/2026
DOI: 10.3389/fpain.2026.1767157
PMID: 42518907
url
https://doi.org/10.3389/fpain.2026.1767157View
Published (Version of record) Open Access

Abstract

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 (n = 14) and their caregivers (n = 32) installed the Beiwe smartphone application, which enabled passive GPS data collection over 24 weeks. Raw GPS data were processed into daily mobility features and aggregated using biweekly moving averages and variability measures. Participants completed PROMIS measures of pain (intensity and interference) and physical QOL every 6 weeks. Within-person regression models were used to examine associations between changes in passive mobility features and changes in outcomes, with adjusted R & sup2; interpreted as effect size (small = 0.02, medium = 0.13, large = 0.26).Results Caregiver GPS-derived mobility features predicted a large proportion of variance in patient pain intensity (R & sup2; = 0.31) and pain interference (R & sup2; = 0.32). Combined caregiver and patient mobility data predicted large variance in caregiver physical QOL (R & sup2; = 0.43) and medium-to-large variance in patient pain intensity (R & sup2; = 0.16) and pain interference (R & sup2; = 0.33). Patient mobility features alone predicted small variance in caregiver physical QOL (R & sup2; = 0.02). When examining patient data predicting patient outcomes, mobility features were associated with small variance in physical QOL (R & sup2; = 0.03), pain intensity (R & sup2; = 0.05), and pain interference (R & sup2; = 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.
 Clinical Neurology Life Sciences & Biomedicine Neurosciences Neurosciences & Neurology Science & Technology

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