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Record W2910484159 · doi:10.1139/apnm-2018-0801

Effect of neuromuscular electrical stimulation frequency on postprandial glycemia, current-related discomfort, and muscle soreness. A crossover study

2019· article· en· W2910484159 on OpenAlexvenueno aff
Benjamín Guzmán-González, Pablo Martín Llanos, Joaquín Calatayud, Nicola A. Maffiuletti, Carlos Cruz‐Montecinos

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2019
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsnot available
FundersUniversidad de Chile
KeywordsMedicineCrossover studyGlycemicPostprandialElectrical muscle stimulationStimulationPhysical therapyDelayed onset muscle sorenessMealPhysical medicine and rehabilitationAnesthesiaInternal medicineInsulinMuscle damagePlacebo

Abstract

fetched live from OpenAlex

Consensus is lacking regarding optimal neuromuscular electrical stimulation (NMES) parameters for postprandial glycemic control. Therefore, the aim of this study was to determine the NMES frequency inducing the greatest hypoglycemic effect in healthy individuals. The secondary aim was to compare current-related discomfort and muscle soreness between different frequencies. We conducted an experimental clinical study with a randomized crossover design. Sixteen healthy and sedentary participants received NMES for 20 min at 5, 10, or 50 Hz (pulse duration: 400 μs, on-off ratio: 4:12 s) following a standardized meal. Glycemia, discomfort, and muscle soreness during and after NMES were compared between conditions. Five-hertz NMES generated a significant hypoglycemic effect, contrary to 10 Hz and 50 Hz. Ten-hertz and 50-Hz NMES resulted respectively in lower current-related discomfort and greater muscle soreness compared with the other frequencies. Women reported higher discomfort than men. These findings contribute towards the possibility of more efficient long-term NMES treatments in terms of glycemic response and patient tolerance.

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 score0.945
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.260
Teacher spread0.254 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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