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Biofeedback loops from heart-rate variability monitors to pace breath-holding techniques in raiding sequences in college kabaddi players

Gowri Sethu

Abstract

Background: Kabaddi, a traditional South Asian contact sport, uniquely demands controlled breath-holding during raiding sequences, where players must maintain a continuous vocal chant while executing offensive maneuvers. Heart-Rate Variability (HRV) represents autonomic nervous system balance and may serve as a real-time biofeedback tool to optimize breath-holding capacity and performance. Objective: To investigate the efficacy of HRV biofeedback training on breath-holding duration, raid success rates, and physiological stress markers in college-level kabaddi raiders. Methods: Forty male college kabaddi players (age: 20.3 ± 1.8 years) were randomly assigned to experimental (HRV biofeedback training, n=20) or control (conventional training, n=20) groups. The intervention consisted of 8 weeks of HRV-guided breathing exercises integrated with raid-specific breath-holding protocols. Primary outcomes included maximum breath-holding time, raid success percentage, and pre/post-raid HRV metrics. Results: The biofeedback group demonstrated significant improvements in breath-holding capacity (28.4% increase, p<0.001), raid success rates (18.7% improvement, p<0.01), and parasympathetic recovery indices (RMSSD increased 31.2%, p<0.001) compared to controls. Post-raid HRV recovery was 42% faster in the experimental group. Conclusion: HRV biofeedback training significantly enhances breath-holding performance and raid effectiveness in college kabaddi players, suggesting its potential as a standard training intervention for competitive kabaddi.

Heart Rate Variability and Autonomic ControlSports Performance and TrainingCardiovascular and exercise physiologyBiofeedbackHeart rate variabilityAutonomic nervous systemBreathingPaceBalance (ability)
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Biofeedback loops from heart-rate variability monitors to pace breath-holding techniques in raiding sequences in college kabaddi players · Scinovex