High-intensity interval training in cardiac rehabilitation: a multi-centre randomized controlled trial

McGregor, G.; Powell, Richard D.; Begg, Brian; Birkett, Stefan T; Nichols, Simon; Ennis, Stuart; McGuire, Scott; Prosser, J; Fiassam, O; Hee, S W; Hamborg, Thomas; Banerjee, Prithwish; Hartfiel, N; Charles, Joanna M; Edwards, R. T.; Drane, Aimee L.; Ali, Danish; Osman, Faizel; He, Hejie; Lachlan, Thomas; Haykowsky, Mark J.; Ingle, Lee; Shave, Robert

doi:10.1093/eurjpc/zwad039

Cited by 18 publications

(19 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This project intends to use the SVM-based exercise intensity prediction system and the deep learning-based exercise intensity prediction system, and compare with the existing exercise intensity prediction system. The experimental study of three sports items was carried out, and the reaction speed of three sports items was investigated under different simultaneous users [20]. It can be seen from Table 1 that the average response time of the exercise training intensity prediction system proposed in this paper is 2.730ms and that of the support vector machine-based exercise training intensity prediction system is 4.764ms when 100 participants are used at the same time.…”

Section: B System Response Time Resultsmentioning

confidence: 98%

Research on Computer Artificial Intelligence Algorithm to Optimize Physical Training Intensity and Recovery Cycle Model

Qingfeng Shi

2024

jes

View full text Add to dashboard Cite

This paper established an association model between high-load training and cardiac damage to minimize sports injuries. It is a key condition to promote the development of modern sports. This project intends to use 8 biochemical parameters related to high-load training to build a mathematical model of high-load training-myocardial damage. A mathematical model for high load training and cardiac damage was established. Firstly, based on sparse feature representation method, the biochemical mechanism model of high-intensity training was constructed. The energy supply law of intramuscular energy supply and the dynamic law of glycolysis energy supply during exercise were extracted. Eight biochemical parameters were obtained after high intensity training. To construct an accurate association between high-intensity training-heart damage by measuring the correlation between eight indicators of performance. The validity of the model is verified by simulation.

show abstract

Section: B System Response Time Resultsmentioning

confidence: 98%

Research on Computer Artificial Intelligence Algorithm to Optimize Physical Training Intensity and Recovery Cycle Model

Qingfeng Shi

2024

jes

View full text Add to dashboard Cite

show abstract

“…Relying on inaccurate results when setting the exercise intensity may lead to suboptimal, non-diagnostic effort [3]. As a rule, the intensity for exercise medical programs should be 70-80% of HRmax for moderate-intensity steady-state activity and >85% of HRmax for high-intensity interval training [27]. An underestimated HRmax of ~5 beats•min −1 (i.e.…”

Section: Discussionmentioning

confidence: 99%

Validity of the Maximal Heart Rate Prediction Models among Runners and Cyclists

Kasiak¹,

Wiecha²,

Cieśliński³

et al. 2023

Preprint

View full text Add to dashboard Cite

Maximal heart rate (HRmax) is a widely used measure of cardiorespiratory fitness. Prediction of HRmax is an alternative to cardiopulmonary exercise testing (CPET), but its accuracy among endurance athletes (EA) requires evaluation. This study aimed to externally validate HRmax prediction models in the EA independently for running and cycling CPET. 4043 runners (age=33.58 (8.12) years; 83.53% males; BMI=23.66 (2.54) kg·m−2) and 1026 cyclists (age=36.88 (9.03) years; 89.67% males; BMI=24.04 (2.65) kg·m−2) underwent maximum CPET. Student t-test, mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE) were applied to externally validate 8 running and 5 cycling HRmax equations. HRmax was 184.60 (9.79) beats·min−1 and 182.66 (10.28) beats·min−1 respectively for running and cycling, p=0.001. Measured and predicted HRmax differed significantly (p=0.001) for 9 of 13 (69.23%) models. HRmax was overestimated by 8 (61.54%) and underestimated by 5 (38.46%) formulae. Overestimated HRmax ranged 0.08-4.94 beats·min−1 and underestimated HRmax ranged 0.03-4.90 beats·min−1. MAE and RMSE were 0.18-4.94 beats·min−1 and 9.13-10.47, respectively. MAPE ranged 3.95-4.69%. Prediction models do not allow for accurate estimation of HRmax. HRmax was more often underestimated than overestimated. Predicted HRmax can be implemented for EA as a supplemental method but CPET is the preferable approach.

show abstract

“…Ethical approval for the protocol was provided by the NHS Health Research Authority, East Midlands–Leicester South Research Ethics Committee (16/EM/0079), with patients providing written informed consent prior to enrollment. Detailed methodology of the trial procedures are available elsewhere [ 19 ], in short, patients performed a baseline CPET on cycle ergometer following a standard ramp incremental protocol [ 20 ]. Fully anonymised data were accessed for the purpose of this analysis between 29 th January 2021 and 28 th September 2023.…”

Section: Methodsmentioning

confidence: 99%

Inter- and intra-observer reliability and agreement of O2Pulse inflection during cardiopulmonary exercise testing: A comparison of subjective and novel objective methodology

Nickolay,

McGregor,

Powell

et al. 2024

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Cardiopulmonary exercise testing (CPET) is the ‘gold standard’ method for evaluating functional capacity, with oxygen pulse (O2Pulse) inflections serving as a potential indicator of myocardial ischaemia. However, the reliability and agreement of identifying these inflections have not been thoroughly investigated. This study aimed to assess the inter- and intra-observer reliability and agreement of a subjective quantification method for identifying O2Pulse inflections during CPET, and to propose a more robust and objective novel algorithm as an alternative methodology. A retrospective analysis was conducted using baseline data from the HIIT or MISS UK trial. The O2Pulse curves were visually inspected by two independent examiners, and compared against an objective algorithm. Fleiss’ Kappa was used to determine the reliability of agreement between the three groups of observations. The results showed almost perfect agreement between the algorithm and both examiners, with a Fleiss’ Kappa statistic of 0.89. The algorithm also demonstrated excellent inter-rater reliability (ICC) when compared to both examiners (0.92–0.98). However, a significant level (P ≤0.05) of systematic bias was observed in Bland-Altman analysis for comparisons involving the novice examiner. In conclusion, this study provides evidence for the reliability of both subjective and novel objective methods for identifying inflections in O2Pulse during CPET. These findings suggest that further research into the clinical significance of O2Pulse inflections is warranted, and that the adoption of a novel objective means of quantification may be preferable to ensure equality of outcome for patients.

show abstract

High-intensity interval training in cardiac rehabilitation: a multi-centre randomized controlled trial

Cited by 18 publications

References 31 publications

Research on Computer Artificial Intelligence Algorithm to Optimize Physical Training Intensity and Recovery Cycle Model

Research on Computer Artificial Intelligence Algorithm to Optimize Physical Training Intensity and Recovery Cycle Model

Validity of the Maximal Heart Rate Prediction Models among Runners and Cyclists

Inter- and intra-observer reliability and agreement of O2Pulse inflection during cardiopulmonary exercise testing: A comparison of subjective and novel objective methodology

Contact Info

Product

Resources

About