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Record W2897054982 · doi:10.1080/02640414.2018.1527674

Acute intraocular pressure responses to high-intensity interval-training protocols in men and women

2018· article· en· W2897054982 on OpenAlexaff
Jesús Vera, Raimundo Jiménez, Beatríz Redondo, David Cárdenas Vélez, Bryon R. McKay, Amador García‐Ramos

Bibliographic record

VenueJournal of Sports Sciences · 2018
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHigh-intensity interval trainingMedicineInterval trainingPhysical therapyCardiology

Abstract

fetched live from OpenAlex

We aimed (1) to test the acute impact of two high-intensity interval-training (HIIT) protocols differing in the level of effort on intraocular pressure (IOP) responses, and (2) to elucidate whether the IOP responses differ between men and women. Twenty-four physically active collegiate (12 men and 12 women) performed three protocols: low-fatigue HIIT (eight 30-m sprints with 60-s of rest), high-fatigue HIIT (eight 30-m sprints with 30-s of rest), and control (walking). IOP was taken at baseline, after sprints and recovery by rebound tonometry. Our data revealed an acute IOP reduction during both HIIT protocols compared to the control condition (effect size [ES]:0.81-1.65). The differences between both HIIT protocols were generally negligible (ES<0.30), however, the reduction of IOP was moderately higher for the low-fatigue HIIT protocol after the 7th (ES: 0.67) and 8th (ES: 0.74) sprints. Women showed a more prominent lowering effect on IOP during the control condition (ES: 0.42-1.02), and during the two first sprints of both HIIT protocols (ES: 0.54-1.03). These findings highlight that a time-efficient HIIT protocol may acutely reduce IOP levels, while low-fatigue HIIT protocol could be recommended since it induced a higher reduction on IOP than the high-fatigue HIIT protocol.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.032
GPT teacher head0.329
Teacher spread0.297 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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