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Record W4405631073 · doi:10.51224/srxiv.496

Unravelling the pain trajectory in chronic low back pain patients during a physical exercise training program

2024· preprint· en· W4405631073 on OpenAlexafffund
Maxime Bergevin, Anna Bendas, Florian Bobeuf, Arthur Wozvnowsku-Vu, Timothy H. Wideman, Nicolas Berryman, Louis Bherer, Mathieu Roy, Benjamin Pageaux

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversité de Montréal
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversité de Montréal
KeywordsTraining (meteorology)Physical therapyPhysical medicine and rehabilitationChronic painMedicineGeography

Abstract

fetched live from OpenAlex

ObjectivePhysical exercise can transiently decrease pain intensity within a single session and improve physical capacities while reducing pain over a training program.However, pain trajectory throughout a concurrent physical training program remains unknown.This study aimed to model pain trajectory during a training program including both aerobic and resistance exercises, considering both acute (within-session) and chronic (across-program) effects of physical exercise. Design Prospective observational study MethodParticipants completed a 14-week training program (42 sessions; n = 28) or were assigned to a waiting list (n = 29).In the exercise group, low back pain intensity was measured before and after each training session.Pain intensity averaged over the last week was measured before and after the 14-week period in both groups.Linear mixed-effects modelling was performed to describe the pain trajectory. ResultsPain intensity averaged over the last week decreased only in the exercise group (exercise: 4.9 ± 0.3 vs 2.5 ± 0.3; control: 5.6 ± 0.3 vs 5.3 ± 0.3).Pain trajectory was characterized by a linear and a quadratic term (p's < 0.001), suggesting pain reduction decelerated as the program progressed.Pain intensity decreased after each training session (p < 0.001) with this effect remaining constant throughout the program (nonsignificant interactions, p's > 0.585). ConclusionsPain decreases more markedly during the initial weeks of the program.Acute exercise-induced hypoalgesia persisted throughout the program, suggesting patients may use physical exercise to manage pain flare-ups even after several weeks of training. 242/250 words 3/29Keywords: chronic pain, exercise therapy, resistance training, high-intensity interval training, exerciseinduced hypoalgesia, concurrent training sizes of physical exercise interventions for CLBP are typically small to moderate [4].This limitation suggests that current training programs either are too short to realize the full potential of physical exercise, or that there is an inherent limit to its effectiveness.If the latter is true, we might expect a non-linear pain reduction trajectory, where pain decreases more rapidly initially, then gradually slows down until reaching a plateau.The primary aim of this study was to explore the trajectory of low back pain intensity during a physical training program including both aerobic and resistance exercises.A second aim was to test whether acute exercise-induced hypoalgesia was maintained throughout the training program.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.286
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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