HRV4Training: Large-scale longitudinal training load analysis in unconstrained free-living settings using a...

Area of Science:

• Sports Science and Exercise Physiology
• Biomedical Engineering
• Cardiovascular Monitoring

Background:

• Accurate training load analysis is crucial for athlete performance and injury prevention.
• Traditional methods for monitoring physiological responses to training can be resource-intensive.
• Wearable technology offers potential for accessible, real-time physiological data collection.

Purpose of the Study:

• To introduce and evaluate a smartphone-based application (HRV4Training) for analyzing training load.
• To assess the feasibility of using photoplethysmography (PPG) for heart rate (HR) and heart rate variability (HRV) measurements.
• To investigate the relationship between HR, HRV, and self-reported training load across diverse user demographics.

Main Methods:

• Development of the HRV4Training smartphone application utilizing PPG for HR and HRV data acquisition.
• Integration of a morning spot-measurement protocol and a training activity questionnaire within the app.
• Analysis of data from 797 users over 3 weeks to 5 months, stratified by gender and age.

Main Results:

• A significant correlation was observed between HR, HRV, and self-reported training load, irrespective of gender or age.
• Heart rate variability (HRV) exhibited greater sensitivity to training effects compared to heart rate (HR).
• The study demonstrated the practical utility of smartphone-based monitoring for widespread application.

Conclusions:

• Smartphone-based monitoring of training load using HR and HRV is a viable and practical approach.
• The HRV4Training application provides a feasible tool for athletes to quantify training effects outside laboratory settings.
• This technology can support a large population of athletes in optimizing their training regimens.