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Record W4377984934 · doi:10.1093/eurjpc/zwad125.186

The effects of high-intensity interval training and moderate-to-vigorous intensity on cardiorespiratory fitness in patients with permanent or persistent atrial fibrillation

2023· article· en· W4377984934 on OpenAlexaffabout
Tasuku Terada, Sol Vidal‐Almela, D Lurette, Kimberley L. Way, David H. Birnie, Andrew Pipe, Jennifer L. Reed

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsUniversity of WaterlooUniversity of Ottawa
Fundersnot available
KeywordsMedicineCardiorespiratory fitnessInterval trainingHigh-intensity interval trainingContinuous trainingPhysical therapyAtrial fibrillationCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): the Innovations Fund of the Alternate Funding Plan for the Academic Health Sciences Centre of the Ministry of Ontario. Background Lower cardiorespiratory fitness (CRF) in atrial fibrillation (AF) is associated with higher risk of mortality. Moderate-to-vigorous intensity continuous training (MICT) increases CRF; however, growing evidence indicates that high-intensity interval training (HIIT) elicits similar or greater improvements in CRF in people with cardiovascular disease. Purposes The purposes of this study were to: (1) compare the effects of HIIT and MICT on CRF in patients with persistent and permanent AF; and, (2) assess the proportion of participants who achieved a clinically meaningful increase in CRF (i.e., 3.5 mL/kg/min, associated with significant reduction in mortality in AF). Methods This was a subanalysis of an RCT to examine the effects of HIIT and MICT on functional capacity. Participants completed cardiopulmonary exercise test (CPET) and were randomly assigned to 12-week, twice weekly supervised HIIT or MICT. Each HIIT session was 23 min in duration, completed on a stationary bike, and consisted of: (1) a 2-min warm up; (2) two blocks of 8 x 30-sec high-intensity work periods at 80-100% peak power output interspersed with 30-sec active recovery periods, with 4 min active recovery between the blocks; and, a 1-min cooldown. Each MICT session consisted of (1) a 15-min warm up; (2) 30-min of continuous aerobic exercise; and, (3) a 15-min cooldown. A subset of willing participants completed follow-up CPET. Repeated measures ANOVA was used to compare the changes in CRF between HIIT and MICT. Descriptive statistics was used to assess the proportion of patients meeting the clinically meaningful increase in CRF. Results Of 94 patients consented, 13 in HIIT (67±4 years old, 26% females) and 10 in MICT (71±9 years old, 40% females) completed pre and post CPET. Exercise adherence (i.e., % of sessions attended) was 91% for HIIT and 85% for MICT, respectively, and did not differ between the groups. At week 12, 76% of participants in HIIT and 80% in MICT achieved the prescribed exercise intensity targets. Our per-protocol analysis showed no overall change in CRF (pre: 19.0±5.1 vs. post: 19.4±4.6 mL/kg/min, p=0.461). No significant differences in changes in CRF were observed between HIIT (pre: 20.1±5.3 vs. post: 20.3±4.5 mL/kg/min) and MICT (pre: 17.6±4.8 vs. post: 18.4±4.7 mL/kg/min, interaction effect p=0.681). Two participants in HIIT (15.4%) and two in MICT (20.0%) achieved clinically meaningful increases. Conclusions Twice weekly HIIT or MICT for 12 weeks did not improve CRF in patients with persistent and permanent AF who completed CPET. Further, a small proportion of these patients (≤ 20%) achieved clinically meaningful increase in CRF. Considering a greater increase in CRF (3.2±2.5 mL/kg/min) in patients with non-permanent AF following high-volume HIIT (a total of 16 min at high intensity per session, three times per week) in a previous RCT, a larger exercise volume may be required to improve the CRF of patients with persistent and permanent AF.

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)0.002
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 score0.662
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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