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The Effects of Supramaximal vs. Submaximal Isoinertial Eccentric Training Performed to Volitional Fatigue

2015· article· en· W2460085954 on OpenAlexaff
Joel R. Krentz, Philip D. Chilibeck, Jonathan P. Farthing

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2015
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEccentricMedicineConcentricPhysical medicine and rehabilitationEccentric trainingElbow flexionPhysical therapyOne-repetition maximumElbowMuscle strengthMathematicsSurgery

Abstract

fetched live from OpenAlex

Eccentric (ECC) contractions have the ability to produce greater force than concentric (CON) contractions. Accordingly, isoinertial (free weight) ECC training is most often performed at supramaximal intensities (> 100% CON max) to induce maximal adaptations. Less is known about the effects of isoinertial submaximal (< 100% CON max) ECC training. PURPOSE: To compare supramaximal vs. submaximal intensity isoinertial ECC emphasized training performed until volitional fatigue. METHODS: 32 young adults (19 males, 13 females) were randomized into one of 3 groups: 1) ECC110 who performed ECC only contractions at 110% of CON 1RM, 2) ECC80 who performed ECC only contractions at 80% of CON 1RM, or 3) a control group. Training progressed from 3 to 6 sets of unilateral ECC elbow flexion concentration curls (3 seconds per repetition) over 8 weeks. Each training set was performed until volitional fatigue; this required each group to perform a varying number of repetitions but aimed to standardize the complete recruitment and exhaustion of type I and type II muscle fibers associated with training to failure. Elbow flexors muscle thickness (via ultrasound), CON concentration curl 1RM (via dumbbells), and isokinetic ECC torque (via dynamometer at 45°/s) were assessed pre and post training. ANCOVA was used to adjust for baseline differences between groups (pre-training scores as covariates). RESULTS: After adjustment, both ECC 110 (+0.29 cm) and ECC 80 (+ 0.19 cm) showed a greater post-training increase in muscle thickness compared to control (-0.02 cm) (p<0.05), with no differences between ECC110 and ECC80. ECC80 (+1.27 kg) showed a greater post-training increase in 1RM strength compared to control (p<0.05); whereas the increase in strength for ECC110 (+0.69 kg) was not different than control (+0.01 kg). ECC110 (+6.08 Nm) showed a significantly greater post-training increase in isokinetic ECC torque compared to ECC80 (-1.7 Nm) (p<0.05). CONCLUSION: Both supramaximal and submaximal eccentric training are effective for inducing muscle hypertrophy. Submaximal eccentric training may be more beneficial for increasing concentric isoinertial strength whereas supramaximal eccentric training is more effective for increasing isokinetic eccentric torque. JK funded by NSERC Ph. D. Scholarship.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.308
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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