Investigating HRV's contribution to identify exercise pattern and intensity in healthy subjects

Area of Science:

• Physiology
• Sports Science
• Biomedical Engineering

Background:

• Exercise modality, encompassing intensity and pattern, is crucial for managing conditions influenced by physical activity.
• Understanding exercise characteristics aids in personalized treatment and performance optimization.
• Heart rate variability (HRV) analysis offers a non-invasive method to assess physiological responses to exercise.

Purpose of the Study:

• To evaluate the predictive capability of heart rate variability (HRV) components for identifying specific exercise modalities.
• To determine if HRV exhibits distinct patterns during exercise based on intensity (constant load) and pattern (continuous vs. intermittent).

Main Methods:

• HRV analysis (time-domain, frequency-domain, nonlinear) was performed on 26 subjects during cycle ergometry at varying intensities (40% and 60% maximal aerobic power) and patterns (intermittent).
• Data were collected using a Polar H10 sensor and processed with Kubios HRV software, alongside gas exchange and lactate measurements.
• Penalized multinomial logistic regression and repeated measures ANOVA were used to analyze HRV data and assess differences across exercise conditions.

Main Results:

• The logistic regression model accurately predicted exercise modalities with 88-98% sensitivity and specificity.
• Time-domain (meanRR, SDNN) and nonlinear (DFA-ɑ1) HRV measures were identified as key predictors.
• Exercise intensity and pattern significantly altered HRV components, with higher variability observed during intermittent exercise compared to constant-load conditions.

Conclusions:

• HRV is a valuable and promising tool for differentiating between various exercise modalities.
• HRV analysis can provide objective insights into the physiological demands of different exercise types.
• These findings support the use of HRV in monitoring and tailoring exercise prescriptions.