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Integrative Mindfulness-Based Predictive Approach for Chronic Low Back Pain Treatment

Who can join

As published by the study on ClinicalTrials.gov. "Not stated" means the study hasn't said — ask the study team.

Age
18 Years and older
Sex
All sexes
Healthy volunteers
Not accepted
Compensation
Not stated

Other requirements are listed under Eligibility below. Only the study team can confirm whether you can join.

What to expect

No one can promise a trial will help you personally — that is what the research is trying to find out.

What participation involves

  • Procedures mentioned: Questionnaires

Only what the study's summary and detailed description on ClinicalTrials.gov say. Time periods may describe the whole study or a part of it — ask the study team what applies to you.

Show the study's own words
  • "The PEG (Pain, Enjoyment, General activity), obtained through online self-report surveys is the main outcome measure."
Compensation
Not stated
  • Study-related exams and procedures are typically provided at no cost. This is how many trials work in general — confirm the details for this study with the study team.
  • Participants are monitored closely by the research team throughout the study.
  • Some participants may receive access to an investigational treatment, which is still being studied.
  • Your participation helps researchers learn whether this approach could help future patients.

About this study

In plain language

This study is for people with long-lasting low back pain. It tests whether artificial intelligence can use clues about a person's body, thoughts, feelings, and social life to predict how they respond to Mindfulness-Based Stress Reduction, a program to help manage stress.

This study will identify unique signatures that people have which can cause pain by evaluating biological, psychological, and social markers using artificial intelligence. These markers can be used to accurately predict the response of diverse individuals with chronic low back pain (cLBP) to Mindfulness-Based Stress Reduction. This will help enhance clinician decision-making and the targeted treatment of chronic pain. The overall objective is to use a unique machine learning (ML) approach to determine the biomarker signature of persons undergoing mindfulness based stress reduction (MBSR) treatment for their chronic low back pain (cLBP). This signature will facilitate clinical prediction and monitoring of patient response to MBSR treatment. The design of the study is a single-arm clinical trial of the evidence-based MBSR program for patients with cLBP.

Compensation is read from the study's own description and may be missing — always confirm with the study team.

Eligibility

Who can join

  • Have low back pain for at least 3 months, with pain on at least half the days in the past 6 months.
  • Be willing and able to give informed consent online or by phone, meaning you agree to join after learning about the study.
  • Speak English, since the program guide is written in English.

Who can't join

  • Have pain that got worse in the past month, a fever with no known cause, or weight loss with no known cause.
  • Are pregnant.
  • Have cancer that has spread to other parts of the body.
  • Are not a patient at a clinic taking part in the study, or do not plan to stay a patient there for at least 6 more months.

Simplified for readability. The official criteria below are what the study team uses.

Official eligibility criteria

Inclusion Criteria: * Chronic low back pain, which is pain that persists for at least 3-months and has resulted in pain on at least half the days in the past 6 months * Willing and able to provide online or telephone informed consent * Speak English as the intervention manual is currently written in English Exclusion Criteria: * Red flags- recent (past month) worsening of pain, unexplained fever, unexplained weight loss * Pregnancy * Metastatic cancer * Not a patient at a participating clinic or persons not planning to continue as a patient at a participating clinic for 6 or more months

Study sites (1)

MA