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Record W4366977303 · doi:10.1097/mco.0000000000000936

Interaction of exercise and meal timing on blood glucose concentrations

2023· review· en· W4366977303 on OpenAlexaff
Normand G. Boulé, Jordan Rees

Bibliographic record

VenueCurrent Opinion in Clinical Nutrition & Metabolic Care · 2023
Typereview
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPostprandialMedicineMealDiabetes mellitusType 2 diabetesAffect (linguistics)EndocrinologyInternal medicineAthletesObesityExercise physiologyInsulinCarbohydrate metabolismInsulin sensitivityPhysical exercisePhysiologyInsulin resistancePhysical therapyPsychology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Exercise and diet are among the most studied behaviors that can affect blood glucose concentrations. Despite multiple studies examining these interventions in different populations and contexts, inconsistencies across studies have led to variable expectations. The purpose of this review is to more specifically examine how the timing of when exercise is performed in relation to meals can modify changes in glucose concentrations or insulin sensitivity. When possible, studies in type 2 diabetes are emphasized, but recent research in type 1 diabetes, obesity, and athletes is also considered. RECENT FINDINGS: The effect of a single bout of exercise performed after an overnight fast is often comparable to that of postprandial exercise on 24 h mean glucose concentrations. However, there is recent evidence to suggest that in some situations, but not all, longer term metabolic adaptations can be more favorable when exercise is regularly performed in the fasted state. SUMMARY: Exercise after an overnight fast can have different effects on glucose metabolism compared with postprandial exercise. The shorter term and longer term changes following fasting exercise can be relevant to those who are seeking greater glucoregulatory benefit from their exercise sessions, such as people with diabetes.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.858
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.301
GPT teacher head0.533
Teacher spread0.232 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations1
Published2023
Admission routes1
Has abstractyes

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