A NIH-funded study led by Worcester Polytechnic Institute (WPI) aims to utilize artificial intelligence to guide chronic pain patients toward mindfulness-based treatments rather than opioids. By analyzing patient data through machine learning, the research seeks to identify individuals who would benefit most from non-pharmacological interventions, potentially reducing opioid dependence and offering more personalized care. This innovative approach, focusing on chronic lower back pain across diverse populations, could revolutionize pain management and healthcare costs. Credit: Melissa E. Arndt
A study supported by the NIH and conducted by Worcester Polytechnic Institute aims to leverage artificial intelligence in identifying effective mindfulness-based treatments for chronic lower back pain, offering patients alternatives to opioid use.
Worcester Polytechnic Institute (WPI) will lead a five-year study aimed at exploring the possibility of using artificial intelligence to assist physicians in guiding patients with chronic pain towards mindfulness-based methods and away from opioids, which carry a risk of addiction.
The new National Institutes of Health (NIH) HEAL (Helping to End Addiction Long-term) initiative-funded study will employ machine learning, a form of artificial intelligence, to look for clues in patient data that could help doctors better determine who is likely to benefit the most from mindfulness-based stress reduction, or MBSR, in managing their pain.
“For physicians, it will be a new day,” said Jean King, the Peterson Family Dean of Arts and Sciences at WPI. “To be able to predict who would respond well to non-pharmacological interventions will truly save lives.”
Funding and Objectives
WPI has received $1.6 million in NIH funding to start designing the trial; if the team’s defined benchmarks are met, the research team and the university could receive a total of nearly $9 million in research funding over the course of the next five years.
The findings of the study could give healthcare providers powerful tools to help people avoid taking opioids that can lead to lifelong struggles with addiction. Over-reliance on opioids for pain management can have devastating consequences; in 2021, more than 16,000 people died from prescription-opioid-related overdoses, and more than 80,000 people died from overall opioid-related overdoses, one death every 6 minutes. There have been concerning increases in opioid-related deaths in Black and Native American populations.
At the same time, chronic pain is also a major concern. A recent U.S. Centers for Disease Control and Prevention Morbidity and Mortality Report estimated that more than 51 million people–more than 20% of U.S. adults–have chronic pain.
Previous studies have found that MBSR is effective in helping people deal with chronic pain, but the mindfulness-based approach does not work for everyone, and doctors and clinicians don’t know exactly for whom it would work and why.
Enhancing Physician Decision-Making with AI
Focusing specifically on chronic lower back pain in diverse populations, the study will glean physiological data such as sleep patterns, heart rate, and general physical activity collected via fitness sensors worn by 350 participants during a six-month trial. Combined with self-reported information on depression, anxiety, pain, and levels of social support, the data will be analyzed by custom-designed machine-learning models to detect patterns that might be impossible for a doctor to notice. The information will allow the model to predict whether a patient would beneficially respond to mindfulness, helping doctors better tailor treatments for individual patients.
That predictive power could prove to be a powerful tool for physicians who previously may have been wary of prescribing mindfulness-based stress reduction, said Carolina Ruiz, the WPI Associate Dean of Arts and Sciences and Harold L. Jurist ’61 and Heather E. Jurist Dean’s Professor of Computer Science, who has been researching and teaching machine learning for more than two decades. She added that the machine learning models used in the study will be interpretable–doctors and researchers will be able to pinpoint exactly why a patient may or may not respond well to mindfulness methods.
“It will save time for the patients—they won’t have to go through a treatment that is not going to help,” she said. “It will also save a lot in healthcare costs and could be applicable to other types of pain and other types of treatment.”
Collaborative Effort and IMPACT Study
The study, dubbed Integrative Mindfulness-based Predictive Approach for Chronic low back pain Treatment, or IMPACT, will bring together a diverse group of researchers at WPI, UMass Chan Medical School, and Boston University Chobanian & Avedisian School of Medicine. Along with King and Ruiz, WPI faculty researchers include Emmanuel Agu, the Harold L. Jurist ’61 and Heather E. Jurist Dean’s Professor of Computer Science and MPI, Angela Incollingo Rodriguez, assistant professor of psychological and cognitive sciences, Zheyang Wu, professor, mathematical sciences, and Benjamin Nephew, assistant research professor, biology and biotechnology.
Agu’s expertise in analyzing sensor data using smartphones and fitness trackers will play a critical role in the study. The devices will track several data points, but Agu said of particular interest to researchers will be participants’ circadian rhythms–sleep and wake cycles.
“Sleep has an immense impact on our overall health,” said Agu, who is a co-principal investigator on the study. “An individual in pain is more likely to experience broken sleep, which can lead to a host of other health issues. Mindfulness-based approaches may help participants sleep better, which can reduce some of those other risk factors.”
The study will include racially and ethnically diverse populations typically underrepresented in both the research and practice of mindfulness-based stress reduction, despite being at increased risk for stress, chronic pain, and the associated adverse outcomes they bring. Participants will be recruited from the Boston metro region through Boston Medical Center and Cambridge Health Alliance, and from the Worcester region through UMass Chan and WPI.
Partners on the grant and community leaders are excited for the work to begin.
Dr. Natalia Morone, associate professor of medicine at Boston University Chobanian and Avedisian School of Medicine, a primary care physician at Boston Medical Center, and a co-principal investigator on the study, said the key will be identifying specific markers that indicate people will respond to mindfulness treatment. “We are doing this in an innovative way because we are using machine learning to figure this out,” Morone said. “I am very excited to partner with my colleagues at WPI and UMass Chan to accomplish this study. It has the potential to help many people.”
Dr. David D. McManus, the Richard M. Haidack Professor in Medicine and chair and professor of medicine at UMass Chan, said the medical school will bring invaluable experience to the study gained from overseeing the cores of prominent studies, such as the Framingham Heart Study, National Institutes of Health Rapid Acceleration of Diagnostics (RADx) initiative, and the Risk Underlying Rural Areas Longitudinal (RURAL) study.
“The wealth of knowledge accumulated through the administration and management of critical components in these studies positions us at the forefront of groundbreaking research,” McManus said. “Our enthusiasm is heightened as we join forces with WPI and BU under the capable leadership of Jean King.”
Dr. Matilde Castiel, commissioner of health and human services in Worcester, said AI is a tool to help the healthcare system deliver better and more personalized care.
“I am thrilled that WPI will use AI to address chronic back pain and make an impact on the opioid epidemic, which is truly a public health emergency not only in our city and state, but nationally,” Castiel said. “This intervention can decrease the reliance of opioids for chronic back pain and provide a more targeted approach that is specific to the individual.”
