Autonomic Modulation and Its Relation With Body Composition in Swimmers

Abstract

This study compared autonomic modulation in swimmers and non-athletes in relation to body composition. A total of 28 athletes with a mean age of 19.7 ± 2.9 years were evaluated who had at least 2 years of swimming training, trained approximately 7,000 m per day, with a frequency of 5 days per week, and who competed at national level. The control group was made up of 21 volunteers (23.0 ± 2.5 years), who did not practice regular physical activity (<2 hours per week). Body composition was estimated using dual-energy x-ray absorptiometry, and autonomic modulation was assessed by heart rate variability (HRV). The results show that there were significant differences in autonomic modulation and body composition between the groups, and that the athletes had a higher overall variability (standard deviation of all normal intervals between consecutive heart beats [SDNN]: 78.1 [72.5-93.5] × 61.1 [56.4-75.7], p = 0.022) and greater autonomic balance (LF/HF: 0.96 [0.88-1.35] × 0.71 [0.56-0.93], p = 0.023), compared with the non-athletes, respectively. In addition, a moderate and positive relation was obtained between fat-free mass and the square root of the squared differences between consecutive heartbeat intervals (RMSSD: r = 0.526, p = 0.004 × r = 0.456, p = 0.038), (SDNN: r = 0.617, p = 0.001 × r = 0.571, p = 0.007) and low frequency (LFms: r = 0.517, p = 0.005 × r = 0.600, p = 0.004) in the athletes and non-athletes, respectively, without a correlation between fat mass (FM). The conclusion is that young highly trained swimmers had lower FM, increased fat-free mass, and better HRV than young adult non-athletes and suggests that a lower quantity of FM and, especially, a greater fat-free mass (FFM) are linked to better autonomic modulation. Thus, this study could contribute to coaches and trainers establishing greater performance by better autonomic modulation and greater quantity of FFM.

A unifying conceptual framework of factors associated to cardiac vagal control

“…Regarding age, CVC generally decreases during the years when considering an adult population (Antelmi et al., 2004). In relation to body composition, a higher level of fat mass, percentage fat and waist-to-hips ratio are associated with lower CVC (Kim et al., 2005), while fat-free mass was related to higher CVC (Rossi et al., 2014). Overweight was associated with a lower CVC (Alrefaie, 2014).…”

“…On the long-term, moderate aerobic training increases CVC (Hautala et al., 2009). Athletes and physically active individuals mostly display increased CVC when compared to non-athletes (Rossi et al., 2014). Overall, integrating regular physical activity to one's lifestyle seems a straightforward way to increase CVC on the long-term.…”

Changes in Body Composition After 6 Months of Training in Pubertal Swimmers

“…The regular monitoring of body composition in sports is an important indicator of the health and physical development of the adolescent athletes (Lohman et al; Slater et al), namely detecting eating disorders, overtraining, illness or to relate body composition to performance. In addition, the evaluation of external morphology is inherent in the identification, selection and development of young talent athletes , for example, the identification of essential elements of the body composition for the demands of the sport (Rossi et al, 2014). Currently there are available several methods for the assessment of body composition in adolescent athletes (Claessens et al;Malina et al, 2004;Malina, 2007;Malina & Geithner;Lohman et al;Slater et al), however due to the low time required, low cost and practical aspects, the anthropometry is one of the most used methods for the evaluation of the external morphology of various populations of adolescents, including young athletes (Claessens et al;Malina, 2007;Lohman et al;Slater et al).…”

Enhancing cardiac vagal activity: Factors of interest for sport psychology

“…After the exercise has stopped, the body starts to recover and CVA rises again, the speed of the recovery constituting an index of fitness level (Stanley et al, 2013). Exercise is presumably already a predominant part of an athlete's everyday life, which gives them the benefit of higher CVA as compared to non-active individuals (Rossi, Ricci-Vitor, Sabino, Vanderlei, & Freitas, 2014). For non-active individuals, starting to engage in regular, moderate exercise seems a promising way to provoke long-term increases in CVA (Hautala et al, 2009).…”