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Record W2002540068 · doi:10.1139/h05-027

Effect of resistance and aerobic training on regional body composition in previously recreationally trained middle-aged women

2006· article· en· W2002540068 on OpenAlexvenueno aff
Steven J. Fleck, Cora Mattie, Henry C. Martensen

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2006
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsBench pressTrunkLean body massSoft tissueMedicineResistance trainingLean tissueStrength trainingAerobic exercisePhysical therapyAnimal scienceBody weightInternal medicineSurgeryBiology

Abstract

fetched live from OpenAlex

Twelve middle-aged women (mean age 41.9 +/- 1.6 y) performed variable-cam resistance training and aerobic training 3 times/week for 14 weeks. One repetition maximum (1 RM) significantly increased between pre-training and training week 7 (13.1%-17.8%), between training week 7 and post-training (10.8%-14.1%), and between pre-training and post-training (25.5%-30.9%). Total-body lean soft tissue and total % body fat determined by duel-energy X-ray absorptiometry (DEXA) significantly increased (2.2%) and decreased (1.4%), respectively. Arm, trunk, and total upper-body (arm + trunk) lean soft tissue significantly increased (0.7%-4.6%). Total body fat tissue and all regional measures of fat tissue and % fat showed no significant changes. Significant correlations were shown between pre-testing and post-testing 1 RM in the bench press, lat pull down, and overhead press in all instances, except for post-training bench press and total upper-body lean soft tissue (r = 0.58-0.90). In contrast, non-significant correlations were shown between pre- and post-testing 1 RM of the leg press, with the exception of pre-training and total lean soft tissue and pre-training and leg lean soft tissue. In conclusion, resistance training resulted in consistent strength gains in middle-aged women, which were accompanied by regional changes in upper-body composition, whereas lower-body composition moved in the hypothesized direction, but did not achieve significance.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.252
Teacher spread0.234 · 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 designBench or experimental
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

Citations22
Published2006
Admission routes1
Has abstractyes

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