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Record W2953526887 · doi:10.7570/jomes.2019.28.2.76

What Is the Role of Resistance Exercise in Improving the Cardiometabolic Health of Adolescents with Obesity?

2019· review· en· W2953526887 on OpenAlexaff
SoJung Lee, YoonMyung Kim, Jennifer L. Kuk

Bibliographic record

VenueJournal of Obesity & Metabolic Syndrome · 2019
Typereview
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsYork University
Fundersnot available
KeywordsObesityInsulin resistanceMedicineType 2 diabetesAerobic exerciseResistance trainingWeight managementDiseasePhysical therapyWeight lossCardiovascular fitnessDiabetes mellitusPhysical fitnessInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Traditionally, individuals with obesity have been encouraged to participate in aerobic exercise for long-term weight management and improved obesity-related health outcomes. Recently, resistance exercise has become a popular mode of exercise among youth with obesity. However, to date, the literature is mixed as to whether resistance exercise training alone improves body weight, fat free mass, body composition, cardiovascular risk factors, or atherogenic lipoprotein profiles. The limited research in this area suggests potential sex differences in response to resistance training in youth. The literature is more consistent in demonstrating improvements in muscular fitness and insulin resistance independent of caloric restriction and weight loss. Although major health organizations recommend combining aerobic and resistance training, little research has examined the effects of their combination versus their individual effects, thus it is unclear whether their combination is associated with benefits that extend beyond those of either exercise modality alone. The purpose of this review is to examine the effects of resistance exercise on body composition and the health risk factors associated with cardiovascular disease and type 2 diabetes in youth with 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)0.003
metaresearch head score (Gemma)0.000
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 score0.755
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0100.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.020
GPT teacher head0.295
Teacher spread0.275 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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