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Record W2147498612 · doi:10.2340/16501977-1905

Effect of interval training on cognitive functioning and cerebral oxygenation in obese patients: A pilot study

2014· article· en· W2147498612 on OpenAlexaff
Joffrey Drigny, Vincent Grémeaux, Olivier Dupuy, Mathieu Gayda, Louis Bherer, Martin Juneau, Anil Nigam

Bibliographic record

VenueJournal of Rehabilitation Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsInterval trainingMedicineWaistAerobic exerciseHigh-intensity interval trainingOxygenationPhysical therapyEffects of sleep deprivation on cognitive performanceHemodynamicsCognitionCardiologyInternal medicineBody mass index

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the effect of a 4-month high-intensity interval training programme on cognitive functioning, cerebral oxygenation, central haemodynamic and cardiometabolic parameters and aerobic capacity in obese patients. METHODS: Cognitive functioning, cerebral oxygenation, central haemodynamic, cardiometabolic and exercise para-meters were measured before and after a 4-month high-intensity interval training programme in 6 obese patients (mean age 49 years (standard deviation 8), fat mass percentage 31 ± 7%). RESULTS: Body composition (body mass, total and trunk fat mass, waist circumference) and fasting insulin were improved after the programme (p < 0.05). V. O2 and power output at ventilatory threshold and peak power output were improved after the programme (p < 0.05). Cognitive functioning, including short-term and verbal memory, attention and processing speed, was significantly improved after training (p < 0.05). Cerebral oxygen extraction was also improved after training (p < 0.05). CONCLUSION: These preliminary results indicate that a 4-month high-intensity interval training programme in obese patients improved both cognitive functioning and cere-bral oxygen extraction, in association with improved exercise capacity and body composition.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.342
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
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 head0.013
GPT teacher head0.296
Teacher spread0.282 · 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

Citations68
Published2014
Admission routes1
Has abstractyes

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