MétaCan
Menu
Back to cohort
Record W2147595503 · doi:10.1123/jpah.2014-0123

Effect of Elliptical High Intensity Interval Training on Metabolic Risk Factor in Pre- and Type 2 Diabetes Patients: A Pilot Study

2014· article· en· W2147595503 on OpenAlexafffund
Annie Fex, Jean‐Philippe Leduc‐Gaudet, Marie‐Eve Filion, Antony D. Karelis, Mylène Aubertin‐Leheudre

Bibliographic record

VenueJournal of Physical Activity and Health · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversité du Québec à Montréal
FundersCanadian Institutes of Health Research
KeywordsMedicineBlood pressureType 2 diabetesWaistDiabetes mellitusHeart rateAnthropometryInternal medicineRisk factorHigh-intensity interval trainingCardiologyLean body massBody mass indexEndocrinologyBody weight

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of the current study was to examine the impact of 12 weeks of elliptical high intensity interval training (HIIT) on metabolic risk factors and body composition in pre- and type 2 diabetes patients. METHODS: Sixteen pre- (n = 8) and type 2 diabetes (n = 8) participants completed this study. Fasting blood glucose, HbA1c, anthropometric measurements, body composition (DXA), blood pressure, resting heart rate, VO2max, and dietary factors, as well as total and physical activity energy expenditure, were measured. The HIIT program on the elliptical was performed 3 times a week for 12 weeks. RESULTS: After the intervention, we observed a significant improvement for fasting blood glucose, waist and hip circumference, appendicular fat mass, leg lean body mass and appendicular lean body mass, systolic blood pressure, resting heart rate, and VO2max (P < .05). In addition, we noted a lower tendency for leg fat mass (P = .06) and diastolic blood pressure (P = .05) as well as a higher tendency for total energy expenditure (P = .06) after the intervention. CONCLUSION: The current study indicates that elliptical HIIT seems to improve metabolic risk factors and body composition in pre- and type 2 diabetes patients.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.320
Teacher spread0.293 · 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

Citations36
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Physical Activity and HealthSame topicCardiovascular and exercise physiologyFrench-language works237,207