Virginia LeBaron
University of Virginia, VA·Medicine & Clinical Research
Pain is common among people with advanced cancer, and when it is poorly controlled it harms patients, their families and the healthcare system. Most of this pain is managed at home, often with help from family caregivers who find the job stressful.
- Fields
Medicine & Clinical Research
Public Health & Epidemiology
Computer Science
- Topics
cancer pain
advanced cancer
metastatic cancer
- Grants
- 1 known grant, through Nov 2026
What they work on
- Develop digital phenotypes, distinctive data patterns of the advanced cancer pain experience at home, using smartwatch and environmental sensor data from the BESI-C system. The phenotypes will cover how often pain events occur, how intense they are, and how they affect quality of life.
- Track the drug and non-drug pain strategies that patients and caregivers use, and how effective they report them to be, through a custom smartwatch app in patient-caregiver pairs from palliative care and hospice programs.
- Link environmental, contextual, behavioral and physiological sensor data to reported pain events, and assess how closely patient and family caregiver reports agree.
- Identify which sensing data best predict breakthrough pain events, in order to build simple pain prediction algorithms. Use shared data visualizations to learn how patients, caregivers and clinicians prefer to communicate the collected data.
More about the research
Opioids, the main medicines for severe cancer pain, now face extra scrutiny because of the opioid epidemic, which makes safe and effective pain care at home harder. This project asks how patients and caregivers actually experience and manage cancer pain at home. The goal is to give them timely, personalized support.
Why it matters
Patients with advanced or metastatic cancer and the family members who care for them at home could benefit from better tools for managing pain safely, especially with opioids under close scrutiny. Sensor-based digital phenotypes and pain prediction algorithms may make it possible to step in before breakthrough pain occurs. Clear data visualizations could also help clinicians make better decisions about pain care.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Gretchen White
University of Pittsburgh, PA·Medicine & Clinical Research
Test whether opioid therapy is associated with lower pain severity and pain interference in a prospective cohort of patients newly diagnosed with metastatic cancer.
Jackelyn Boyden
University of Pennsylvania, PA·Medicine & Clinical Research·Early-career
Evaluate whether a rapid digital feedback loop, which sends family-reported home care experiences to pediatric palliative care teams as they are submitted, is feasible and leads to clinical actions.
David Hui
University of Texas MD Anderson Cancer Center, TX·Medicine & Clinical Research
Compare the respiratory therapist-led SPOT-ON intervention with enhanced usual care on the change in dyspnea intensity at 24 hours in hospitalized cancer patients with hypoxemia, in a randomized trial.
The school
University of Virginia: 43 labs on PI Pages, $814M a year on research, 413 research doctorates a year.
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