Defining dyadic cancer pain concordance using participant-initiated interactions with a remote health monitoring system

Background Studies on symptom concordance between patients and their caregivers often use cross-sectional designs, which may fail to capture the longitudinal, dynamic symptom experience. The Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C) is a remote health monitoring system that utilizes smartwatches and ecological momentary assessments (EMAs) to empower patients and caregivers to monitor and manage cancer pain at home. BESI-C collects real-time symptom data in naturalistic settings, enabling longitudinal tracking and analysis of symptom patterns over time. Objective To define and examine dyadic concordance using participant-initiated symptom reports collected via remote health monitoring. Methods Dyads of patients with advanced cancer and their family caregivers were recruited to use BESI-C for 2 weeks, reporting pain in real time through EMAs. We used Bangdiwala’s B statistic to determine the concordance of patient-reported pain and caregiver-reported perceived patient pain under different contextual criteria (eg, co-location of participants; user engagement with BESI-C) that we hypothesized would impact concordance. We also explored a hypothesis that concordance would improve between study week 1 versus week 2. Results Data from 21 patient-caregiver dyads were used for analysis. The reporting of pain events was highly variable between patients and their caregivers. Concordance of pain reporting improved when patients and caregivers were co-located and both wearing their BESI-C smartwatches. We did not observe consistent patterns in patient-caregiver concordance between week 1 and week 2. Conclusion We propose an analytical approach to define and evaluate concordance between patients’ and caregivers’ real-time symptom reports that can be applied to dyadic, longitudinal symptom data collected using remote health monitoring. Future work should examine the relationship between patient-caregiver symptom concordance with key quality-of-life metrics and sociodemographic factors that impact participant engagement with remote health monitoring technologies.