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Importance Of Sprint Interval Training Duration And Recovery Time On Endurance And Power Performance

2009· article· en· W1997766392 on OpenAlexaff
Tom J. Hazell, Rebecca E. K. MacPherson, Braden M. R. Gravelle, Peter W.R. Lemon

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2009
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsWestern University
Fundersnot available
KeywordsSprintInterval trainingEndurance trainingMedicineTime trialAnimal sciencePhysical therapyResistance trainingInternal medicineHeart rateBiologyBlood pressure

Abstract

fetched live from OpenAlex

Sprint interval training has become popular due to its positive effects on endurance performance. This training, characterized by repeated intense efforts (typically 30 sec maximal efforts with short recovery intervals), surprisingly induces similar muscle adaptations to traditional endurance training. PURPOSE: To determine whether shorter training bout durations and variable recovery times affect endurance and power performance. METHODS: Recreationally active subjects (n = 36, 13 females 23 males) did not train 1) control (CTRL) or completed repeated maximal cycle bouts (resistance = 10% body mass):recovery intervals as follows: 2) 30 sec:4 min, 3) 10 sec:4 min, or 4) 10 sec:2 min. Subjects trained 3/wk for 2 wk (6 total sessions), starting with 4 bouts/session and increasing to 5 on bout 3 and 6 on bout 5. Pre- and post tests included 5 km cycle time trial, cycle VO2 peak, and 30 sec Wingate. RESULTS: As expected, the ability to maintain peak power over the training bouts was greater (P<0.05) for the 10 sec:4 min (95.7±2.6%) and the 10:2 min (94.6±3.0%) vs the 30 sec:4 min group (87.4±6.1%). Similarly, training bout minimum power was greater (P<0.05) in the 10 sec:4 min (69.4±6.7%) and the 10 sec:2min (68.8±4.5%) vs the 30 sec:4 min group (38±8.0%). Endurance Performance: All training groups improved time trial performance from pre- to post (30 sec:4 min, -31.4±22.3 sec [5.5±3.4%]; 10 sec:4 min, -22.4±31.1 sec [3.9±5.2%]; 10 sec: 2min, -26.0±25.0 sec [5.0±3.7%]). VO2peak also increased (P<0.05) in the 30 sec:4 min group (2.9±3.5 ml·kg-1·min-1, 6.3±8.9%) and the 10 sec:4 min (2.9±3.9 ml·kg-1·min-1, 6.5±8.6%), but not in the 10 sec:2 min group (1.8±2.4 ml·kg-1·min-1, 3.4±5.3%). Power Performance: Wingate peak power increased in the 30 sec:4 min (+84.0±87.4 W, 7.8±8.5%) and the 10 sec: 4 min groups (+76.7±87.5 W, 5.6±7.5%) but not in the 10 sec:2 min group (+21.5±82.5 W, 1.8±7.3%). Average Wingate power improved in the 30 sec:4 min (+87.0±49.6 W, 12.2±7.4%) and the 10 sec:4 min groups (+53.4±72.6 W, 6.4±9.2%) but not the 10 sec:2 min group (+14.5±28.3 W, 1.9±3.7%). CONCLUSION: Repeat 10 and 30 sec sprint interval training bouts with 2 or 4 min recovery produce significant and similar improvements for 5 km time trial performance; but for VO2peak and power performance, 10 and 30 sec training bouts with 4 min recovery appear to be better.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.017
GPT teacher head0.270
Teacher spread0.253 · 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 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".

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Citations0
Published2009
Admission routes1
Has abstractyes

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