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Record W2566113974 · doi:10.1097/wnr.0000000000000718

Short-duration therapeutic massage reduces postural upper trapezius muscle activity

2016· article· en· W2566113974 on OpenAlexaff
Antoinette Domingo, Melissa Diek, Kathleen M. Goble, Katrina S. Maluf, Daniel J. Goble, Harsimran S. Baweja

Bibliographic record

VenueNeuroreport · 2016
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsMassageMedicineTrapezius musclePhysical therapySittingElectromyographyShouldersPhysical medicine and rehabilitationMuscle tensionSurgery

Abstract

fetched live from OpenAlex

Massage therapy has historically been used as a therapeutic treatment to help reduce pain and promote relaxation. The aim of this study was to investigate the effect of therapeutic massage on the upper trapezius muscles, which are commonly associated with increased muscle tension. This was a randomized crossover study. Seventeen healthy individuals (nine women; 24.5±4.0 years) participated in the study. All individuals participated in two sessions that were held 24 h apart. In one of the sessions, the participants received a moderate pressure massage applied to the shoulders and neck. In the other session, participants sat quietly. The order of the sessions was counterbalanced across participants. Muscle activity, as measured by surface electromyography, of the upper trapezius muscles was recorded. The amount of muscle activity change following massage was compared with the change in muscle activity following quiet sitting. Muscle activity of the upper trapezius reduced significantly (19.3%; P=0.004) following massage compared with muscle activity following quiet sitting (1.0%). Our findings suggest that short-duration moderate pressure massage leads to a reduction in upper trapezius muscle activity. This result has potential implications for clinical populations such as those with chronic neck pain.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.025
GPT teacher head0.300
Teacher spread0.276 · 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.

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".

Quick stats

Citations14
Published2016
Admission routes1
Has abstractyes

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