Determining the effect size of aerobic exercise training on the standard lipid profile in sedentary adults with three or more metabolic syndrome factors: a systematic review and meta-analysis of randomised controlled trials
Bibliographic record
Abstract
OBJECTIVES: To estimate the change in the standard lipid profile (SLP) of adults diagnosed with ≥3 metabolic syndrome (MetS) factors following aerobic exercise training (AET); and to investigate whether study/intervention covariates are associated with this change. DESIGN: Systematic review with univariate meta-analysis and meta-regression. DATA SOURCES: English language searches of online databases from inception until July 2020. ELIGIBILITY CRITERIA: (1) Published randomised controlled human trials with study population ≥10 per group; (2) sedentary adults with ≥3 MetS factors but otherwise free of chronic disease, not pregnant/lactating; (3) AET-only intervention with duration ≥12 weeks; and (4) reporting pre-post intervention SLP outcomes. RESULTS: <.001) improved all lipids (mmol/L mean difference ranges, 95% CIs): total cholesterol, -0.19 (-0.26 to -0.12) to -0.29 (-0.36 to -0.21); triglycerides, -0.17 (-0.19 to -0.14) to -0.18 (-0.24 to -0.13); high-density lipoprotein-cholesterol (HDL-C), 0.05 (0.03 to 0.07) to 0.10 (0.05 to 0.15); and low-density lipoprotein-cholesterol (LDL-C), -0.12 (-0.16 to -0.9) to -0.20 (-0.25 to -0.14). Meta-regression showed that intensity may explain change in triglycerides and volume may explain change in HDL-C and LDL-C. CONCLUSION: Aerobic exercise training positively changes the SLP of sedentary and otherwise healthy adults with ≥3 MetS factors. Adjusting AET intervention training variables may increase the effects of AET on triglycerides and HDL-C. PROSPERO REGISTRATION NUMBER: CRD42020151925.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.051 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".