Effects of pediatric adiposity on heart rate variability

The relationship between obesity and heart rate variability (HRV) has been studied in adults and adolescents, but is not determined in young pediatrics. The purpose of this study was to assess autonomic activity using HRV in a pediatric population. We hypothesized that obese children would have reduced parasympathetic and increased sympathetic activity compared to age-matched subjects. 42 pediatric subjects (ages 3-5) were classified into 3 groups based on body mass index-for-age; normal, overweight and obese. HRV and respiratory rate were recorded during 3 minute baseline, 2 minute isometric handgrip and 3 minute recovery. HRV was analyzed in the time domain [heart rate (HR), RR interval (RRI) and RRI standard deviation (RRISD)] and frequency domain [low frequency (LF), high frequency (HF) and LF/HF ratio] using repeated measures ANOVA. Spearman’s correlations were used to examine the relations between BMI and HRV at rest. Significant condition effects were found between baseline, exercise and recovery, but these responses were not significantly different between the normal, overweight and obese children. BMI was negatively correlated with LF/HF, while BMI was positively correlated with RRISD, LF, HF and nHF. Our data demonstrate that higher BMI in the pediatric population is correlated with higher parasympathetic and lower sympathetic activity. These findings are contrary to HRV responses observed in adults and adolescents, suggesting complex relationships between age, obesity and autonomic control of the heart. The data supports the concept of an age reliance of HRV and a novel relationship between adiposity and body mass index in 3-5 year olds.

Development of a continuum mechanics model of passive skeletal muscle

Skeletal muscle force evaluation is difficult to implement in a clinical setting. Muscle force is typically assessed through either manual muscle testing, isokinetic/isometric dynamometry, or electromyography (EMG). Manual muscle testing is a subjective evaluation of a patient’s ability to move voluntarily against gravity and to resist force applied by an examiner. Muscle testing using dynamometers adds accuracy by quantifying functional mechanical output of a limb. However, like manual muscle testing, dynamometry only provides estimates of the joint moment. EMG quantifies neuromuscular activation signals of individual muscles, and is used to infer muscle function. Despite the abundance of work performed to determine the degree to which EMG signals and muscle forces are related, the basic problem remains that EMG cannot provide a quantitative measurement of muscle force. Intramuscular pressure (IMP), the pressure applied by muscle fibers on interstitial fluid, has been considered as a correlate for muscle force. Numerous studies have shown that an approximately linear relationship exists between IMP and muscle force. A microsensor has recently been developed that is accurate, biocompatible, and appropriately sized for clinical use. While muscle force and pressure have been shown to be correlates, IMP has been shown to be non-uniform within the muscle. As it would not be practicable to experimentally evaluate how IMP is distributed, computational modeling may provide the means to fully evaluate IMP generation in muscles of various shapes and operating conditions. The work presented in this dissertation focuses on the development and validation of computational models of passive skeletal muscle and the evaluation of their performance for prediction of IMP. A transversly isotropic, hyperelastic, and nearly incompressible model will be evaluated along with a poroelastic model.