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Record W4399676791 · doi:10.1093/eurjpc/zwae175.395

The effects of muscle strengthening training combined with aerobic training versus aerobic training alone on cardiovascular disease risk factors in coronary artery disease: a systematic review

2024· review· en· W4399676791 on OpenAlexaff
T Terada, A. Thomas, Ren Wei, Sarah Visintini, T Noda, Róbert Pap, Jennifer L. Reed

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2024
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of OttawaUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineAerobic exerciseCardiorespiratory fitnessCoronary artery diseaseInternal medicinePhysical therapyDiseaseType 2 diabetesBody mass indexGlycated hemoglobinCardiologyDiabetes mellitusEndocrinology

Abstract

fetched live from OpenAlex

Abstract Background Aerobic training reduces the risk of secondary cardiovascular events and mortality in patients with coronary artery disease (CAD). While the long-term effects of aerobic interval training (AIT) are less clear, AIT has been shown to elicit similar or superior improvements in risk factors associated with secondary cardiovascular events compared to continuous aerobic training. Current guidelines recommend performing muscle strengthening training in addition to aerobic training for enhanced cardiovascular care in patients with CAD. However, it remains unclear whether muscle strengthening training combined with aerobic training (hereafter referred to as combined training) has additional effects on cardiovascular disease risk factors in patients with CAD. It is also unknown if muscle strengthening training combined with AIT has greater effects on cardiovascular disease risk factors compared to AIT alone in patients with CAD. Purpose To systematically review the effects of combined training compared to aerobic training alone on cardiovascular disease risk factors in patients with CAD. The secondary purpose was to examine the effects muscle strengthening training combined with AIT on cardiovascular disease risk factors compared to AIT alone. Methods MEDLINE, Embase, CINAHL, SportDiscus, Scopus, and trial registries were searched for randomized trials comparing the effects of ≥4 weeks of combined training and aerobic training alone on at least one of the following outcomes: cardiorespiratory fitness, body mass, body mass index (BMI), percent body fat, waist-to-hip ratio, blood pressure, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol, fasting glucose, glycated hemoglobin A1C (A1C), insulin concentration or sensitivity, in patients with CAD. Results Of 9336 studies screened, 16 studies (N=710 patients with CAD) were included. Combined training was more effective in increasing fat-free mass (mean difference [MD] = 0.8 kg, 95% confidence interval [CI]: 0.4 to 1.1 kg, p<0.001) and reducing percent body fat (MD = -2.2 %, 95% CI: -3.6 to -0.8 %, p=0.002) measured by dual-energy x-ray absorptiometry compared to aerobic training alone. Four of these 16 studies examined the effects of muscle strengthening training combined with AIT compared to AIT alone. There were no differences between the groups. Sensitivity analyses on studies with matched aerobic training volume between combined training and aerobic training alone (i.e., excluding studies with reduced aerobic training in the combined training group to add muscle strengthening training) showed consistent findings. Conclusion Adding muscle strengthening training to aerobic training improved the body composition of patients with CAD more than aerobic training alone. However, changes in the other cardiovascular disease risk factors were similar between combined and aerobic training alone.

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)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0120.008
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.038
GPT teacher head0.278
Teacher spread0.240 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2024
Admission routes1
Has abstractyes

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