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Record W4297264805 · doi:10.2196/37823

Mindfulness-Based Stress Reduction, Cognitive Behavioral Therapy, and Acupuncture in Chronic Low Back Pain: Protocol for Two Linked Randomized Controlled Trials

2022· article· en· W4297264805 on OpenAlexvenueno aff
Sean Mackey, Gadi Gilam, Beth D. Darnall, Philippe R. Goldin, Jiang‐Ti Kong, Christine Law, Marissa Heirich, Nicholas V. Karayannis, Ming‐Chih Kao, Lü Tian, Rachel Manber, James J. Gross

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institute on Drug AbuseNational Institutes of HealthNational Center for Complementary and Integrative HealthFoundation for the National Institutes of Health
KeywordsMindfulnessMindfulness-based stress reductionRandomized controlled trialMedicinePhysical therapyLow back painChronic painPsychological interventionPhysical medicine and rehabilitationAcupunctureCognitive behavioral therapyElectroacupunctureCognitionAlternative medicineClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Nonpharmacologic mind-body therapies have demonstrated efficacy in low back pain. However, the mechanisms underlying these therapies remain to be fully elucidated. OBJECTIVE: In response to these knowledge gaps, the Stanford Center for Low Back Pain-a collaborative, National Institutes of Health P01-funded, multidisciplinary research center-was established to investigate the common and distinct biobehavioral mechanisms of three mind-body therapies for chronic low back pain: cognitive behavioral therapy (CBT) that is used to treat pain, mindfulness-based stress reduction (MBSR), and electroacupuncture. Here, we describe the design and implementation of the center structure and the associated randomized controlled trials for characterizing the mechanisms of chronic low back pain treatments. METHODS: The multidisciplinary center is running two randomized controlled trials that share common resources for recruitment, enrollment, study execution, and data acquisition. We expect to recruit over 300 chronic low back pain participants across two projects and across different treatment arms within each project. The first project will examine pain-CBT compared with MBSR and a wait-list control group. The second project will examine real versus sham electroacupuncture. We will use behavioral, psychophysical, physical measure, and neuroimaging techniques to characterize the central pain modulatory and emotion regulatory systems in chronic low back pain at baseline and longitudinally. We will characterize how these interventions impact these systems, characterize the longitudinal treatment effects, and identify predictors of treatment efficacy. RESULTS: Participant recruitment began on March 17, 2015, and will end in March 2023. Recruitment was halted in March 2020 due to COVID-19 and resumed in December 2021. CONCLUSIONS: This center uses a comprehensive approach to study chronic low back pain. Findings are expected to significantly advance our understanding in (1) the baseline and longitudinal mechanisms of chronic low back pain, (2) the common and distinctive mechanisms of three mind-body therapies, and (3) predictors of treatment response, thereby informing future delivery of nonpharmacologic chronic low back pain treatments. TRIAL REGISTRATION: ClinicalTrials.gov NCT02503475; https://clinicaltrials.gov/ct2/show/NCT02503475. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/37823.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0550.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.310
GPT teacher head0.583
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designRandomized trial
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2022
Admission routes1
Has abstractyes

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