Introduction

Heart rate variability (HRV) reflects the balance between the arms of the autonomic nervous system (ANS). ANS control is strongly associated with body composition in healthy young populations and athletes, suggesting that obesity negatively impacts the ANS. The occupational demands and work environment of firefighters promote high rates of obesity. ANS control is particularly important for firefighters, who have an unpredictable work schedule and must quickly switch between restful and active states. Given the importance of ANS function in firefighters and the potential impedance of obesity, studies on the association between HRV and body composition in firefighters are needed.

METHODS

Sixteen full-time male firefighters participated in this cross-sectional study. Resting HRV (RR interval) was measured for 10 minutes with an actigraphy watch and chest heart rate monitor. An analysis of the collected data (Kubios HVR) was completed to determine the following HRV outcomes: RR interval, RMSSD, percentage of low-frequency power (LF), percentage of high-frequency power (HF), LF/HF ratio, parasympathetic nervous system index, and sympathetic nervous system index. Measures of body composition (InBody 580), height, and weight were taken. Partial correlation coefficient analyses were performed between (a) body mass index (BMI) and (b) body fat percentage, and each of the HRV outcome variables, after adjusting for age. SPSS version 29 was used for the statistical analysis (α=0.05).

RESULTS

No significant association was found between RMSSD and either BMI (p=0.54, r=0.17) or body fat percentage (p=0.64, r=0.13). All other correlation coefficients were similarly non-significant (p>0.05) and weak (-0.30<r<0.30).

CONCLUSION

There was no association between body composition variables and HRV outcomes in this small sample of firefighters, in contrast to similar research showing a relationship between HRV measures and fat-free mass in trained athletes. A larger sample is needed to make more generalizable conclusions. Analysis of previously collected data will continue in the future.