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Record W2598256564 · doi:10.1139/facets-2016-0004

Effects of run sprint interval training on healthy, inactive, overweight/obese women: A pilot study

2017· article· en· W2598256564 on OpenAlexvenueno aff
Taylor W. Rowley, Jennifer L. Espinoza, Jeremy D. Akers, David L. Wenos, Elizabeth S. Edwards

Bibliographic record

VenueFACETS · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsnot available
FundersJames Madison University
KeywordsMedicineSprintInterval trainingOverweightPhysical therapyHeart rateBody mass indexTreadmillPopulationBody fat percentageInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

This study examined the effects of a 12 week, treadmill-based, run sprint interval training (SIT) protocol compared with that of a moderate-intensity continuous training (MICT) protocol in healthy, inactive, overweight/obese women, on cardiovascular disease risk factors. After random assignment, the SIT group completed 4–10 × 30 s maximal sprints, with a 4 min active recovery between sprints, and the MICT group completed 30–60 min at moderate intensity (45–55% heart rate reserve (HRR)). The rate of perceived exertion (RPE) was recorded for each session, and perceived enjoyment was quantified every 3 weeks. Clinical and fitness testing were completed at baseline, 6 weeks, and 12 weeks. Twelve female participants (age: 34.1 ± 6.1; body mass index (BMI): 31.3 ± 6.8; VO 2peak : 27.0 ± 6.2) completed the intervention. There were significant main effects for time for VO 2peak ( p = 0.001), body fat percentage ( p = 0.001), and session RPE ( p = 0.006). VO 2peak improved 20.7% in the SIT group ( n = 5) and 24.4% in the MICT group ( n = 7). Body fat percentage reduced by 1.7% in the SIT group and 2.6% in the MICT group. Perceived enjoyment was similarly high between the groups despite higher session RPE in the SIT group ( p = 0.441). SIT training on a motor-driven treadmill elicits similar improvements in oxygen utilization and body composition as moderate-intensity training in this population.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.554

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 head0.039
GPT teacher head0.319
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 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

Citations14
Published2017
Admission routes1
Has abstractyes

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