BESI-C project graphic, someone checking their watch
BESI-C project graphic, someone checking their watch

Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C)

Contacts
Sponsor

National Institutes of Health (NIH)

Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C) is a Smart Health sensing system created by engineers and clinicians at the University of Virginia designed to monitor, predict and manage cancer pain in the home setting. Managing cancer pain at home can be stressful and challenging, but BESI-C offers a user-friendly and non-invasive solution.

BESI-C consists of two primary components: 1) smartwatches programmed with a custom application worn by both patients and their primary family caregiver that provides a platform to mark and describe pain events, 2) environmental room sensors that continuously monitor factors in the home, such as movement, light, noise, temperature, humidity, and barometric pressure.

BESI-C is deployed in homes for approximately two weeks and integrates environmental and smartwatch data to paint a picture of the behavior and health of individuals and dyads (patient/caregiver pairs). For example, we can see patterns displaying moments when a patient may experience pain or when a caregiver is more likely to become acutely distressed.

BESI-C is currently in the research phase of development. The goal of this research is to understand the best practices and mechanisms involved in describing the experience of cancer pain from the perspective of patients and family caregivers. We are also exploring how to most effectively share collected data with patients, family caregivers, and healthcare providers. If successful, BESI-C offers a powerful approach to help patients, family caregivers, and healthcare providers in the home setting by intervening early and offering personalized management strategies for difficult symptoms.

This interdisciplinary research, which builds upon, and extends, preliminary pilot work, will deploy a novel interactive Smart Health system, the Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C), to monitor and describe experience of cancer pain in the home setting. The aim of this process is ultimately to predict and help manage symptoms. BESI-C comprises wearable and environmental sensors that collect rich, in-depth data at the individual, dyad, and home levels. Specifically, this observational/descriptive research will integrate data collected from both patients with advanced cancer and their family caregivers to:

  • Develop comprehensive “digital phenotypes” of advanced cancer pain in the home setting that will: a) characterize the frequency, intensity, and impact on quality of life of pain events b) monitor the use of pharmacological and non-pharmacological strategies and self-reported effectiveness, c) correlate environmental, behavioral, and physiological sensor data with reported pain events, and d) evaluate the concordance of patient and caregiver data.
  • Build parsimonious pain prediction algorithms to discover which sensing data are most predictive of pain events across various clinical trajectories and identify the optimal metrics to measure the impact of BESI-C.
  • Explore and evaluate preferences for communicating collected data with patients, family caregivers, and healthcare providers.

The scientific premise of this research is that by leveraging sensor technology to holistically understand and digitally represent the complex experience of advanced cancer pain in the home setting, from the perspective of both patients and family caregivers, we can develop and deploy tailored, personalized strategies that will improve the delivery of palliative care and reduce suffering at the end-of-life.

BESI-C Website

The BESI-C system has been deployed with over a dozen patient-caregiver dyads and successfully gathered data over the 14-day deployment period. Initial analyses show many recorded pain events by both the patient and caregiver. The subsequent courses of action and environmental factors have also been recorded. The study protocol plans to continue gathering the experiences from more dyads and developing visualizations that summarize the experiences over time.

Bryan Lewis New Website

Bryan Lewis

Research Associate Professor

Brian Klahn

Brian Klahn

Research Scientist

Virginia LeBaron, UVA School of Nursing
John Lach, The George Washington University School of Engineering & Applied Science
Sarah Ratcliffe, UVA School of Medicine
Leslie Blackhall, UVA School of Medicine
Nadim El-Jaroudi, UVA Research Computing
Nutta Homdee, Mahidol University, Faculty of Medical Technology
Penny Amos, UVA School of Nursing
Amber Steen, UVA Center for Global Health Equity