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Benjamin M. Kelly
Researcher at Manchester Metropolitan University
Publications - 10
Citations - 188
Benjamin M. Kelly is an academic researcher from Manchester Metropolitan University. The author has contributed to research in topics: Interval training & Medicine. The author has an hindex of 5, co-authored 7 publications receiving 138 citations. Previous affiliations of Benjamin M. Kelly include Loughborough University.
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Journal ArticleDOI
A Practical and Time-Efficient High-Intensity Interval Training Program Modifies Cardio-Metabolic Risk Factors in Adults with Risk Factors for Type II Diabetes
Bethan E. Phillips,Benjamin M. Kelly,Mats Lilja,Jesús Gustavo Ponce-González,Robert J. Brogan,David L. Morris,Thomas Gustafsson,William E. Kraus,Philip J. Atherton,Niels B. J. Vollaard,Olav Rooyackers,James A. Timmons,James A. Timmons +12 more
TL;DR: 5-by-1 HIT offers a new solution to modulate cardio-metabolic risk factors in adults with pre-existing risk factors for type 2 diabetes while approximately meeting the MET min week−1 PA guidelines.
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The impact of high-intensity intermittent exercise on resting metabolic rate in healthy males
TL;DR: Single HIT sessions notably increases EE during exertion; however, the influence on metabolic rate post-exercise is transient and relatively minor.
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Appetite-regulatory hormone responses on the day following a prolonged bout of moderate-intensity exercise
James A. King,Jack O. Garnham,Andrew P. Jackson,Benjamin M. Kelly,Soteris Xenophontos,Myra A. Nimmo +5 more
TL;DR: It is indicated that short-term energy deficits induced by exercise initially prompt a compensatory response by chronic but not acute hormonal regulators of appetite and energy balance within this 24h time-frame however there is no conscious recognition of the perturbation to energy balance.
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SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD.
TL;DR: Wang et al. as discussed by the authors proposed a COVID-19 prediction model based on time-dependent SIRVD by using deep learning, which combines deep learning technology with the mathematical model of infectious diseases, and forecasts the parameters in the infectious diseases by fusing deep learning models such as LSTM and other time prediction methods.
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An evaluation of low volume high-intensity intermittent training (HIIT) for health risk reduction in overweight and obese men
TL;DR: Both 6 and 4 sessions of HIIT performed over a 2-week period are ineffective in improving selected health markers within an overweight and obese cohort.