Machine learning based phenotyping of the response to mindfulness for chronic low back pain
Akshit Soota, Angela C. Incollingo Rodriguez, Benjamin C. Nephew, Paula Gardiner, Jean A. King, Natalia E. Morone, Carolina Ruiz
preprint DOI: 10.1101/2025.08.19.25333990 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Secondary analysis of a clinical trial |
|---|---|
| Sample size | 132 |
| Population | Participants in the intervention arm of a clinical trial of mindfulness for chronic low back pain |
| Intervention | Mindfulness |
| Topics | Meditation |
| Key findings | Machine learning identified phenotypes that predict response to mindfulness for chronic low back pain with high precision. |
Abstract
ABSTRACT Millions of people each year suffer from chronic low back pain (cLBP), which adversely affects their physical and mental health. While non-pharmacological interventions such as mindfulness are known to be effective in treating cLBP, not all patients experience the same benefit. Determining who these treatments might work best for is difficult, as there are no reliable predictors of the response to mindfulness for cLBP. The objective of the current study was to apply predictive machine learning to data collected from a completed clinical trial of mindfulness for cLBP to identify phenotypes characterizing those who did and did not respond to the intervention. The analyses here focused on 132 participants in the intervention arm of the clinical trial of mindfulness for cLBP. The Random Forest machine learning technique was used to identify key characteristics of responders (49) and non-responders (83). The top three responder phenotypes were able to identify 26 out of the 49 responders with 92-100% precision. The top three non-responder phenotypes were able to identify 36 out of 83 non-responders, all with 100% precision. Results from this machine learning based phenotyping can guide clinician and patient decision-making to maximize clinical efficiency, patient outcomes, and resource use as well as inform research and development of mindfulness-based treatments for pain.
Comparable studies
Other non-randomized and open-label trials on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Mindfulness-Based Stress Reduction in Relation to Quality of Life, Mood, Symptoms of Stress, and Immune Parameters in Breast and Prostate Cancer Outpatients Early-stage breast and prostate cancer patients | 2003 | Pre-post intervention study | n = 59 |
| Exploring self‐compassion and empathy in the context of mindfulness‐based stress reduction (MBSR) Community sample | 2010 | Pre-post intervention study | |
| Mindfulness-Based Stress Reduction Lowers Psychological Distress In Medical Students Second-year medical students | 2003 | Prospective nonrandomized cohort-controlled study | n = 302 |
| Intensive meditation training, immune cell telomerase activity, and psychological mediators. Retreat participants and matched controls | 2011 | Controlled trial with wait-list control group | n = 60 |
| The Effectiveness of Mindfulness Training for Children with ADHD and Mindful Parenting for their Parents Children aged 8-12 with ADHD and their parents | 2011 | Within-group waitlist controlled trial | n = 22 |
Citations in the library
Cites 10
- The benefits of being present: Mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology 2003
- Mindfulness meditation–based pain relief: a mechanistic account Annals of the New York Academy of Sciences June 1, 2016
- Mechanism of Action of Mindfulness-Based Interventions for Pain Relief-A Systematic Review. Journal of Integrative and Complementary Medicine December 1, 2024
- Pain, mindfulness, and placebo: a systematic review. Frontiers in Integrative Neuroscience 2024
- Meditation-Based Therapy for Chronic Low Back Pain Management: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Pain Medicine September 30, 2022
and 5 more in the library