Describing and visualizing the patient and caregiver experience of cancer pain in the home context using ecological momentary assessments
Virginia LeBaron, Nutta Homdee, Emmanuel Ogunjirin, Nyota Patel, Leslie Blackhall, John Lach
Abstract
Background Pain continues to be a difficult and pervasive problem for patients with cancer, and those who care for them. Remote health monitoring systems (RHMS), such as the Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C), can utilize Ecological Momentary Assessments (EMAs) to provide a more holistic understanding of the patient and family experience of cancer pain within the home context. Methods Participants used the BESI-C system for 2-weeks which collected data via EMAs deployed on wearable devices (smartwatches) worn by both patients with cancer and their primary family caregiver. We developed three unique EMA schemas that allowed patients and caregivers to describe patient pain events and perceived impact on quality of life from their own perspective. EMA data were analyzed to provide a descriptive summary of pain events and explore different types of data visualizations. Results Data were collected from five (n = 5) patient-caregiver dyads (total 10 individual participants, 5 patients, 5 caregivers). A total of 283 user-initiated pain event EMAs were recorded (198 by patients; 85 by caregivers) over all 5 deployments with an average severity score of 5.4/10 for patients and 4.6/10 for caregivers’ assessments of patient pain. Average self-reported overall distress and pain interference levels (1 = least distress; 4 = most distress) were higher for caregivers ([Formula: see text] 3.02, [Formula: see text]) compared to patients ([Formula: see text] 2.82, [Formula: see text] 2.25, respectively) while perceived burden of partner distress was higher for patients (i.e., patients perceived caregivers to be more distressed, [Formula: see text] 3.21, than caregivers perceived patients to be distressed, [Formula: see text]). Data visualizations were created using time wheels, bubble charts, box plots and line graphs to graphically represent EMA findings. Conclusion Collecting data via EMAs is a viable RHMS strategy to capture longitudinal cancer pain event data from patients and caregivers that can inform personalized pain management and distress-alleviating interventions.