The recovery manipulation during high-intensity interval training (HIIT) may have the potential to modulate the responses of post-exercise energy metabolism. The purpose of this study was to investigate how the type (., passive and active) and duration (., short and long) of the recovery between the intervals in HIIT affect the excess post-exercise oxygen consumption (EPOC) and oxidation of fats and carbohydrates during the post-exercise recovery. Eight physically active men performed a maximal incremental test, to determine the peak oxygen consumption (V̇Opeak) and the first ventilatory threshold (VT), and four HIIT exercise sessions on a treadmill. The HIIT exercise sessions consisted of 5 intervals interspersed with 4 recovery periods; each interval was sustained until exhaustion, and the intensity was set at the V̇Opeak velocity; recoveries were passive, active (VT velocity), short (2-min), or long (8-min). The HIIT exercise sessions were performed in a random and crossed manner. After the HIIT exercise sessions, EPOC and oxidation of fats and carbohydrates were measured during the 120-min of post-exercise recovery. There were no differences in the EPOC among the exercise sessions ( = 0.56). There were no differences among the exercise sessions in the amount of energy expended on the oxidation of fats ( = 0.78) and carbohydrates ( = 0.91) during the post-exercise recovery. The recovery manipulation during HIIT does not affect the EPOC and post-exercise fat and carbohydrate oxidation. One can choose the type and duration of recovery, knowing that the post-exercise substrate oxidation and EPOC responses will be preserved.
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http://dx.doi.org/10.70252/ZMFO1216 | DOI Listing |
CJC Open
January 2025
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Background: Supervised exercise programs improve walking impairment and quality of life (QoL) in patients with peripheral artery disease (PAD). However, such programs are underutilized, due to their limited accessibility. A feasible and effective exercise program is needed.
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January 2025
Department of Physical Education, Sport and Recreation, Universidad de La Frontera, Temuco, Temuco, Chile.
Objective: This study aimed to determine the association between changes in lactate production and levels of physical activity in a group of healthy young adults in response to two squat training protocols.
Material And Methods: Twenty-nine students majoring in Sports Science willingly participated in this study. Participants visited the lab four times within a two-week period, ensuring at least 48 h between visits.
J Clin Nurs
January 2025
School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR.
Aim: To evaluate the effects of exergaming on physical frailty in older adults.
Design: Systematic review with meta-analysis.
Methods: Six electronic databases were searched for randomised controlled trials evaluating the effects of exergaming on frailty in older adults.
Med Sci Monit
January 2025
Department of Physical Therapy, Kyungdong University, Gangwon, South Korea.
BACKGROUND Remote exercise have emerged as a promising solution to overcome barriers to physical activity participation in pre-frail older adults, such as limited mobility and accessibility issues. Pre-frail older adults often face barriers to physical activity due to limited mobility and accessibility, underscoring the need for remote exercise alternatives. This study aimed to evaluate and compare the efficacy of remote versus in-person exercise in improving physical function in pre-frail older adults.
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January 2025
School of Economics and Management, University of Cyprus, 2109, Aglantzia, Nicosia, Cyprus.
Analyzing the habits of exercisers is crucial for developing targeted interventions that can effectively promote long-term physical activity behavior. While much of existing literature has focused on individual-level factors, there is a growing recognition of the importance of examining how broader determinants impact physical activity. In this study, we analyze large-scale human mobility data from over 20 million individuals to investigate how visits to various locations, such as cafes and restaurants, influence visits to fitness centers.
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