Project Summary
Cancer pain is complex, prevalent and has serious consequences for patients, family caregivers and
healthcare systems. Inadequately managed cancer pain can be particularly problematic for patients coping
with advanced, metastatic disease. Most symptom management occurs in the home setting, and family
caregivers often play a key role in helping to manage cancer pain, but often find this task daunting and
stressful. Complicating cancer pain management is the reality that opioids, a main-stay class of medications
used to control serious cancer pain, are subject to increased scrutiny given well-publicized concerns about the
national `opioid epidemic.' Now more than ever, it is imperative we understand how patients and family
caregivers attempt to manage cancer pain at home so we can offer them personalized support to effectively
and safely alleviate pain. Mobile and wireless technology (`smart health') can help support symptom
management in the home setting, but must be carefully designed to account for the realities of patients and
family caregivers coping with advanced disease. We hypothesize that individuals, and patient-family caregiver
dyads, will display a unique `digital fingerprint' (or phenotype) of the advanced cancer pain experience – that if
better understood can be utilized to inform and deliver personalized, timely interventions. The purpose of this
study, which builds upon preliminary pilot work, is to deploy an unobtrusive smart health system, the
Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C), to monitor and describe – and
ultimately to predict and help manage – the experience of advanced cancer pain in the home setting. BESI-C is
comprised of wearable (smart watch) and environmental sensors that collect physiological, behavioral, and
contextual data at the individual, dyad and home level that can be integrated to provide a comprehensive
picture of a health related phenomenon. A unique feature of the BESI-C system is the ability of patients and
caregivers to record and characterize cancer pain events from their own perspective using a custom
application on their respective smart watch. Specifically, this observational research will analyze data collected
via BESI-C from patient-family caregiver dyads recruited from an outpatient oncology palliative care clinic and
a home hospice program, to develop comprehensive `digital phenotypes' of advanced cancer pain in the home
setting. These digital phenotypes will characterize the frequency, intensity and impact on quality of life of pain
events; monitor the use of pharmacological and non-pharmacological strategies and self-reported
effectiveness; correlate environmental, contextual, behavioral and physiological sensor data with reported pain
events; and evaluate concordance of patient and caregiver data. This research will also explore preferences
for communicating collected data with patients, family caregivers and healthcare providers by creating and
sharing data visualizations. Additionally, we will explore which sensing data are most predictive of
breakthrough pain events to build parsimonious pain prediction algorithms.
