IMPACT Abstract
Chronic pain impacts 50 million U.S. adults, severely interferes with the work and life of over 25 million,
and costs $635 billion annually for medical treatment and resultant loss of productivity. While some non-
pharmacological complementary pain management methods, such as Mindfulness-Based Stress Reduction
(MBSR), are effective at reducing the pain of some patients, others do not respond. Clinicians lack the tools to
accurately and reliably predict which patients will respond to complementary treatments. Racially and ethnically
diverse populations are also underrepresented in both research and practice of complementary interventions
despite increased risk for chronic pain and related adverse outcomes. In response to RFA-NS-22-050
(UG3/UH3), IMPACT – Integrative Mindfulness-Based Predictive Approach for Chronic low back pain
Treatment proposes using machine learning methods (a subfield of AI) to identify biopsychosocial predictive
and monitoring markers of the ’ response to MBSR for chronic low back pain (cLBP). This research will target a
diverse, high risk population suffering from cLBP (total n=350). Comprehensive biopsychosocial data (locomotor
activity, sleep, circadian rhythms, heart rate variability, depression, anxiety, pain outcomes, and social support)
will be collected from diverse patients treated with MBSR for cLBP. Aim 1 (UG3) will involve the initiation of a
clinical trial of MBSR for cLBP (n=50) and ML modeling with longitudinal biopsychosocial data and related clinical
trial datasets to identify candidate predictive and monitoring markers of the response to MBSR for cLBP prior to
expanding the trial in the UH3 phase. Milestones for transition from the UG3 phase (Aim 1) to the larger clinical
trial of the UH3 phase (Aims 2+3) will include: (1) finalized data collection and primary analysis protocols for the
clinical trial of MBSR for cLBP, (2) success with passive data collection procedures and experimentation with
ML model training and testing for the identification of predictive and monitoring biopsychosocial markers of the
response to MBSR for cLBP, and (3) preliminary validation of candidate ML-based biopsychosocial predictive
and monitoring markers of the response to MBSR for cLBP using statistical and cross-validation methods. Aim
2 (UH3) will expand the clinical trial initiated in Aim 1 to collect biopsychosocial data from a larger sample
(n=300). Aim 3 (UH3) will involve ML modeling with data collected in Aim 2 to identify and validate accurate
biopsychosocial predictive and monitoring markers of the response to MBSR for cLBP. To complete our aims,
clinician scientists from Boston University, University of Massachusetts Chan Medical School, and Cambridge
Health Alliance with extensive expertise in successfully recruiting and engaging diverse populations in clinical
trials of mindfulness interventions for pain will collaborate with biomedical, data scientists and machine learning
researchers from Worcester Polytechnic Institute. This proposed project will ultimately enhance clinical decision-
making and targeted treatment of cLBP.
