The epidemic of prescription opioid abuse and resulting increase in overdose deaths is a costly one. In 2015, problems from opioid abuse cost the U.S. economy nearly $504 billion, a six-fold increase from 2013. Doctors find themselves on the front lines of this crisis, but there is little data available to help them identify patients at increased risk of having problems with long-term use of prescribed opioids for pain.
Scientists at the Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K) and Johns Hopkins University School of Medicine have received a new supplement grant from the National Institutes of Health (NIH) to identify modifiable risk factors (observable using mobile sensors) that can be targeted for prevention in patients with chronic pain. NIH, which originally funded MD2K in 2014, earlier this year announced the HEAL initiative to “speed scientific solutions” to stem the opioid crisis.
Researchers will investigate if stress and pain measurements obtained via mobile sensors can predict the day-to-day fluctuations in prescription opioid use among patients with sickle cell disease (SCD), a painful blood disorder that primarily affects African Americans. Long-term opioid therapy is a common pain management strategy for SCD patients. No previous studies are known to have examined the influence of stress and pain on prescription opioid use via mobile sensors.
The project will conduct a pilot study in both lab and field, to identify physiological signatures of flares in chronic pain and opioid use. It will train machine learning algorithms so both events can be detected automatically via sensors. These sensor-derived models, when applied to data collected in the daily life of chronic pain patients on active opioid prescription, will help clinicians better understand the relationship between stress, craving, pain, pain catastrophizing and opioid use.