Heart rate variability kinetics during different intensity domains of cycling exercise in healthy subjects

The effect of age on heart rate variability indices during and following high-intensity continuous exercise in masters and young cyclists

This study compared heart rate variability (HRV) parameters of cardiovascular autonomic regulation between well-trained masters and young cyclists at rest, during and following a continuous cycle (CTS) protocol. Ten masters (age = 56 ± 5 years) and eight young (age = 26 ± 3 years) cyclists completed a 100 min experimental protocol consisting of a 60 min CTS cycling bout at 95% of Ventilatory Threshold 2 followed by 40 min of supine recovery. Beat-to-beat heart rate was measured continuously, and HRV parameters analysed at standardised 5 min intervals during rest, exercise and recovery. The root mean square of the successive differences (RMSSD), low-frequency power and high-frequency power parameters were corrected by division of the R-R interval (time domain), or R-R interval squared (frequency domain). Further, the RMSSD and RMSSD:RR for successive 60-s R-R intervals at the onset (0-10 min) and offset (60-70 min) of CTS exercise were analysed over 10-min periods. The natural logarithm (Ln) of skewed parameters was taken for analysis. Significant interaction effects (P < 0.05) for 5 min segments were found for (LnRMSSD, LnRMSSD:RR, LnHF and LnHF:RR2. Masters cyclists demonstrated lower values of parasympathetic activity at rest and during recovery (15-20 min and 35-40 min) compared to younger cyclists. However, similar HRV responses were observed during exercise, including throughout the acute onset and offset periods (P > 0.05). This data shows that cardiac autonomic regulation during, or immediately following CTS exercise may not be influenced by age, but masters athletes may exhibit a lower baseline for parasympathetic activity.

Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review

This paper aims to provide a review of the electrically assisted bicycles (also known as e-bikes) used for recovery of the rider's physical and physiological information, monitoring of their health state, and adjusting the "medical" assistance accordingly. E-bikes have proven to be an excellent way to do physical activity while commuting, thus improving the user's health and reducing air pollutant emissions. Such devices can also be seen as the first step to help unhealthy sedentary people to start exercising with reduced strain. Based on this analysis, the need to have e-bikes with artificial intelligence (AI) systems that recover and processe a large amount of data is discussed in depth. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used to complete the relevant papers' search and selection in this systematic review.