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Record W4377988612 · doi:10.1093/eurjpc/zwad125.188

The effects of nordic walking, high-intensity interval training, and moderate-to-vigorous intensity continuous training on arterial stiffness in patients with coronary artery disease

2023· article· en· W4377988612 on OpenAlexafffundabout
Isabela Roque Marçal, Lisa M. Cotie, Igor Braga Ribeiro, Jennifer L. Reed

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsUniversity Health NetworkUniversity of Ottawa
FundersHeart and Stroke Foundation of Canada
KeywordsMedicineArterial stiffnessCoronary artery diseaseCardiologyPulse wave velocityInternal medicineBlood pressurePopulationHigh-intensity interval trainingPhysical therapy

Abstract

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Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): The investigator-initiated research was supported by the Innovations Fund of the Alternate Funding Plan for the Academic Health Sciences Centres of the Ministry of Ontario (PIs: Pipe, Reed) and Heart and Stroke Foundation of Canada (PI: Reid). Background Coronary artery disease (CAD) is a leading cause of mortality worldwide. Arterial stiffness, a process associated with aging, arteriosclerosis, and arterial remodeling, is a strong and independent predictor of cardiovascular and all-cause mortality in patients with CAD. Moderate-to-vigorous intensity continuous training (MICT) has been shown to improve arterial stiffness parameters, including pulse wave velocity (PWV, i.e., the speed at which arterial pressure waves travel along the aorta and large arteries) and augmentation index (AIx: i.e., aortic pressure waveform) in the CAD population. Yet, the role of alternative exercise training approaches on arterial stiffness in patients with CAD has not been elucidated. Our recent study demonstrated greater improvements following Nordic walking (NW) compared to high-intensity interval training (HIIT) and MICT in functional capacity, quality of life, and depression severity in patients with CAD. No previous trial has compared the efficacy of NW, HIIT, and MICT in improving arterial stiffness in patients with CAD. Alternatives to traditional MICT, such as NW and HIIT, may provide superior understudied benefits on vascular health in patients with CAD. Purpose We compared the effects of 12 weeks of NW, HIIT, and MICT on arterial stiffness in patients with CAD. Methods Patients with CAD were randomly assigned to: (1) NW (walking with NW poles at a resting heart rate [RHR] + 20–40 bpm), (2) HIIT (aerobic exercise equipment: 4 × 4-min of high-intensity work periods at 85%–95% peak HR), or (3) MICT (aerobic exercise equipment: RHR + 20–40 bpm) twice weekly for 12 weeks. At baseline and week 12, aortic stiffness parameters were measured using a non-invasive device by stroke volume (SV), total vascular resistance (TVR), AIx with and without normalization for a HR of 75 bpm (AIX@75), and PWV. Linear mixed-effects models with repeated measures were used to compare groups over time. Results A total of 130 patients with CAD (aged 61 ± 7 years; male: 85%) were randomized to NW (n = 43), HIIT (n = 43), or MICT (n = 44). Table 1 shows that significant main effects of time were observed for SV (∆: 5.485 m/L, F = 8.530, p = 0.004), with a time × group interaction (F = 3.710, p = 0.027) revealing a greater improvement in SV following NW compared to HIIT (F = 5.748, p = 0.019) and MICT (F = 5.494, p = 0.022). A significant time × group interaction (F = 3.700, p = 0.028) was observed in AIx@75, with a greater reduction following NW than MICT (F = 7.388, p = 0.008). No significant differences were observed in TVR, AIx, and PWV. Conclusion Our findings show that NW facilitated greater improvements in SV and AIx@75 parameters of arterial stiffness, an important predictor of future cardiovascular events, when compared to HIIT and MICT in patients with CAD. NW may be considered as an alternative exercise training to improve vascular health in those with CAD.

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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)0.001
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 score0.792
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.014
GPT teacher head0.240
Teacher spread0.227 · 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".

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Citations0
Published2023
Admission routes3
Has abstractyes

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