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Record W3043601911 · doi:10.1111/obr.13090

The effects of physical activity on adipokines in individuals with overweight/obesity across the lifespan: A narrative review

2020· review· en· W3043601911 on OpenAlexaff
Ayoub Saeidi, Marjan Mosalman Haghighi, Sarkawt Kolahdouzi, Ali Daraei, Abderraouf Ben Abderrahman, M. Faadiel Essop, Ismail Laher, Anthony C. Hackney, Hassane Zouhal

Bibliographic record

VenueObesity Reviews · 2020
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAdipokineMedicineAdiponectinOverweightObesityInternal medicineConfoundingLeptinEndocrinologyInsulin resistancePhysical therapy

Abstract

fetched live from OpenAlex

This narrative review summarizes current knowledge on the effects of physical activity (PA) on adipokine levels in individuals with overweight and obesity. Approximately 90 investigations including randomized control, cross-sectional and longitudinal studies that reported on the effects of a single session of PA (acute) or long-term PA (chronic) on adipokine levels in individuals with overweight/obesity were reviewed. The findings support the notion that there is consensus on the benefits of chronic exercise training-regardless of the mode (resistance vs. aerobic), intensity and cohort (healthy vs. diabetes)-on adipokine levels (such as tumour necrosis factor-alpha, interleukin-6, adiponectin, visfatin, omentin-1 and leptin). However, several confounding factors (frequency, intensity, time and type of exercise) can alter the magnitude of the effects of an acute exercise session. Available evidence suggests that PA, as a part of routine lifestyle behaviour, improves obesity complications by modulating adipokine levels. However, additional research is needed to help identify the most effective interventions to elicit the most beneficial changes in adipokine levels in individuals with overweight/obesity.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.822
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.344
Teacher spread0.319 · 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 designNot applicable
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

Citations44
Published2020
Admission routes1
Has abstractyes

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