Indoor Cycling Energy Expenditure: Does Sequence Matter?
Cristina CortisAndrea FuscoMitchell CookScott T DobersteinCordial GilletteJohn P PorcariCarl FosterPublished in: International journal of environmental research and public health (2021)
Although cycling class intensity can be modified by changing interval intensity sequencing, it has not been established whether the intensity order can alter physiological and perceptual responses. Therefore, this study aimed to determine the effects of interval intensity sequencing on energy expenditure (EE), physiological markers, and perceptual responses during indoor cycling. Healthy volunteers (10 males = 20.0 ± 0.8years; 8 females = 21.3 ± 2.7years) completed three randomly ordered interval bouts (mixed pyramid-MP, ascending intervals-AI, descending intervals-DI) including three 3-min work bouts at 50%, 75%, and 100% of peak power output (PPO) and three 3-min recovery periods at 25% PPO. Heart rate (HR) and oxygen consumption (VO2) were expressed as percentages of maximal HR (%HRmax) and VO2 (%VO2max). EE was computed for both the work bout and for the 5-min recovery period. Session Rating of Perceived Exertion (sRPE) and Exercise Enjoyment Scale (EES) were recorded. No differences emerged for % HRmax (MP = 73.3 ± 6.1%; AI = 72.1 ± 4.9%; DI = 71.8 ± 4.5%), % VO2max (MP = 51.8 ± 4.6%; AI = 51.4 ± 3.9%; DI = 51.3 ± 4.5%), EE (MP = 277.5 ± 39.9 kcal; AI = 275.8 ± 39.4 kcal; DI = 274.9 ± 42.1 kcal), EES (MP = 4.9 ± 1.0; AI = 5.3 ± 1.1; DI = 4.9 ± 0.9), and sRPE (MP = 4.9 ± 1.0; AI = 5.3 ± 1.1; DI = 4.9 ± 0.9). EE during recovery was significantly (p < 0.005) lower after DI (11.9 ± 3.2 kcal) with respect to MP (13.2 ± 2.5 kcal) and AI (13.3 ± 2.5 kcal). Although lower EE was observed during recovery in DI, interval intensity sequencing does not affect overall EE, physiological markers, and perceptual responses.
Keyphrases
- high intensity
- artificial intelligence
- biofilm formation
- heart rate
- resistance training
- single cell
- blood pressure
- heart rate variability
- machine learning
- pseudomonas aeruginosa
- particulate matter
- magnetic resonance
- candida albicans
- deep learning
- escherichia coli
- social support
- cystic fibrosis
- heavy metals
- high throughput sequencing