Meta-Analysis of the Effect of High-Intensity Interval Training in Increasing High-Density Lipoprotein Levels in Type 2 Diabetes Mellitus Patients
Bibliographic record
Abstract
Background: Diabetes mellitus is one of the second biggest health problems. The International Diabetes Federation said that diabetes currently affects 382 million people worldwide, with type 2 diabetes mellitus (DM) being the largest prevalence of 85-95% of the diabetes population. This study aimed to estimate the effect of high-intensity interval training (hiit) on increasing levels of high-density lipoprotein in patients with type 2 diabetes mellitus based on the results of several previous studies.Subjects and Method: This study was a meta-analysis and systematic study, with the following PICO Population =type 2 diabetes mellitus patients aged 35-65 years. Intervention=HIIT. Comparison = No HIIT. Outcome = increased levels of high-density lipoprotein. The articles used in this study were obtained from several databases, including PubMed, ScienceDirect, and Google Scholar. The keywords for finding articles were: "HIIT" OR "High-Intensity interval Training" OR "Diabetes Mellitus" OR "High-Density Lipoprotein" AND "Randomized Controlled Trial". The articles included in this study were full-text with a randomized controlled trial. Articles were analyzed by PRISMA flow chart and RevMan 5.3.Results: A total of 9 articles reviewed in this meta-analysis study originated from New York, Canada, France, Thailand, Berlin, Denmark, Australia, and the United Kingdom. Studies showed that high intensity interval training increased the levels of high density lipoproteins (Mean Difference= 0.01; 95% CI= 0.31 to 0.30; p= 0.970).Conclusion: High-intensity interval training increases high-density lipoprotein levels.Keywords: High-intensity interval training, type 2 diabetes mellitus, high-density lipoproteinCorrespondence: Sela Putri Adelita, Masters Program In Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java. Email: Selaadelita558@gmail.com.Indonesian Journal of Medicine (2020), 05(04): 272-281https://doi.org/10.26911/theijmed.2020.05.04.02.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.053 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".