The relationship between body composition and sleep architecture in athletes

A method for calculating vector forces at human-mattress interface during sleeping positions utilizing image registration

The vector forces at the human-mattress interface are not only crucial for understanding the distribution of vertical and shear forces exerted on the human body during sleep but also serves as a significant input for biomechanical models of sleeping positions, whose accuracy determines the credibility of predicting musculoskeletal system loads. In this study, we introduce a novel method for calculating the interface vector forces. By recording indentations after supine and lateral positions using a vacuum mattress and 3D scanner, we utilize image registration techniques to align body pressure distribution with the mattress deformation scanning images, thereby calculating the vector force values for each unit area (36.25 mm × 36.25 mm). This method was validated through five participants attendance from two perspectives, revealing that (1) the mean summation of the vertical force components is 98.67% ± 7.21% body weight, exhibiting good consistency, and mean ratio of horizontal component force to body weight is 2.18% ± 1.77%. (2) the predicted muscle activity using the vector forces as input to the sleep position model aligns with the measured muscle activity (%MVC), with correlation coefficient over 0.7. The proposed method contributes to the vector force distribution understanding and the analysis of musculoskeletal loads during sleep, providing valuable insights for mattress design and evaluation.

Impact of Body Composition on Sleep and Its Relationship with Sleep Disorders: Current Insights

Sleep is involved in many physiological processes and is essential for both physical and mental health. Obesity and sleep deprivation due to sleep disorders are major public health issues. Their incidence is increasing, and they have a wide range of adverse health-related consequences, including life-threatening cardiovascular disease. The impact of sleep on obesity and body composition is well-known, and many studies have shown an association between insufficient or excessive sleep duration and obesity, body fat percentage, and weight gain. However, there is growing evidence of the effects of body composition on sleep and sleep disorders (particularly sleep disordered breathing) through anatomical and physiological mechanisms (nocturnal fluid shift, core body temperature, or diet). Although some research has been conducted on the bidirectional effects of sleep-disordered breathing and body composition, the specific effects of obesity and body composition on sleep and the underlying mechanisms that explain these effects remain unclear. Therefore, this review summarizes the findings on the effects of body composition on sleep and draws conclusions and proposals for future research in this field.

Bioelectrical impedance analysis to estimate one-repetition maximum measurement of muscle strength for leg press in healthy young adults

Resistance training (RT) progress is determined by an individual's muscle strength, measured by one-repetition maximum (1RM). However, this evaluation is time-consuming and has some safety concerns. Bioelectrical impedance analysis (BIA) is a valid and easy-to-use method to assess skeletal muscle mass (SMM). Although BIA measurements are often correlated with muscle strength, few studies of 1RM for RT and BIA measurements are available. This observational study examined the relationship between 1RM and BIA measurements and developed BIA-based prediction models for 1RM. Thirty-five healthy young Japanese adults were included. SMM and the skeletal muscle mass index (SMI) were measured using the BIA device. In addition, dominant-leg 1RM was measured using a unilateral leg-press (LP) machine. The correlations between BIA measurements and 1RM were calculated, and simple regression analyses were performed to predict 1RM from the BIA variables. The results showed significant correlations between 1RM and dominant-leg SMM (R = 0.845, P = 0.0001) and SMI (R = 0.910, P = 0.0001). The prediction models for 1RM for LP derived from SMM of the dominant leg and SMI were Y = 8.21x + 8.77 (P = 0.0001), R² = 0.73, and Y = 15.53x - 36.33 (P = 0.0001), R² = 0.83, respectively. Our results indicated that BIA-based SMI might be used to predict 1RM for LP accurately.