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Comparison of strength development with resistance training and combined exercise training in type 2 diabetes

2011· article· en· W1581955397 on OpenAlexafffund
Joanie Larose, Ronald J. Sigal, Farah Khandwala, Glen P. Kenny

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsOttawa HospitalUniversity of CalgaryUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsResistance trainingStrength trainingMedicineType 2 diabetesAerobic exerciseWorkloadMuscle strengthPhysical strengthPhysical therapyLower bodyTraining (meteorology)Diabetes mellitusEndocrinologyComputer science

Abstract

fetched live from OpenAlex

Resistance training has been shown to increase strength in type 2 diabetes; however, it is unclear if combining resistance and aerobic training (A + R) impedes strength development compared with resistance training only (R). The purpose of this study was to compare changes in strength with A + R vs R in individuals with type 2 diabetes. We evaluated monthly workload increments in participants from the Diabetes Aerobic and Resistance Exercise clinical trial. Muscular strength was assessed through training volumes and as the eight repetition maximum (8-RM) at 0, 3, and 6 months. Both groups increased their upper and lower body volumes monthly for 6 months. The relative increase in upper body workload in R was significantly greater than A + R at 4 months (161 ± 11% vs 127 ± 11%, P = 0.009) and at 6 months of training (177 ± 11% vs 132 ± 11%, P = 0.008). Both groups had improvements in 8-RM workloads at 3 and 6 months. The resistance training group had a significantly greater improvement in 8-RM on the leg press at 6 months compared with A + R (80 ± 11% vs 58 ± 8%, P = 0.045). Both R and A + R improved strength with a 6-month training program; however, increases in strength may be greater with resistance training alone compared with performing both aerobic and resistance training.

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)0.000
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 score0.158
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.051
GPT teacher head0.299
Teacher spread0.248 · 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

Citations27
Published2011
Admission routes2
Has abstractyes

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