Relationships between Heart Rate Variability, Sleep Duration, Cortisol and Physical Training in Young Athletes.

The aims of the current study were to examine the relationships between heart rate variability (HRV), salivary cortisol, sleep duration and training in young athletes. Eight athletes (16 ± 1 years) were monitored for 7 weeks during training and competition seasons. Subjects were training for endurance-based winter sports (cross-country skiing and biathlon).

Evaluation of nocturnal vs. morning measures of heart rate indices in young athletes.

Purpose: The purpose of this study was to compare heart rate (HR) and heart rate variability in young endurance athletes during nocturnal sleep and in the morning; and to assess whether changes in these values are associated with changes in submaximal running (SRT) and counter-movement jump (CMJ) performance.

Methods: During a three-week period of similar training, eleven athletes (16 ± 1 years) determined daily HR and heart rate variability (RMSSD) during sleep utilizing a ballistocardiographic device (Emfit QS), as well as in the morning with a HR monitor (Polar V800). Aerobic fitness and power production were assessed employing SRT and CMJ test.

The Effects of an Intensive 2-wk Resistance Training Period on Strength Performance and Nocturnal Heart Rate Variability.

Purpose: It is known that modifying the endurance-type training load of athletes may result in altered cardiac autonomic modulation that may be estimated with heart rate variability (HRV). However, the specific effects of intensive resistance-type training remain unclear. The main aim of this study was to find out whether an intensive 2-wk resistance training period affects the nocturnal HRV and strength performance of healthy participants.

Markerless 2D kinematic analysis of underwater running: A deep learning approach.

Kinematic analysis is often performed with a camera system combined with reflective markers placed over bony landmarks. This method is restrictive (and often expensive), and limits the ability to perform analyses outside of the lab. In the present study, we used a markerless deep learning-based method to perform 2D kinematic analysis of deep water running, a task that poses several challenges to image processing methods.