ABSTRACT
Stabilometry is a technique that aims to study the body sway of human subjects, employing a force platform. The signal obtained from this technique refers to the position of the foot base ground-reaction vector, known as the center of pressure (CoP). The parameters calculated from the signal are used to quantify the displacement of the CoP over time; there is a large variability, both between and within subjects, which prevents the definition of normative values. The intersubject variability is related to differences between subjects in terms of their anthropometry, in conjunction with their muscle activation patterns (biomechanics); and the intrasubject variability can be caused by a learning effect or fatigue. Age and foot placement on the platform are also known to influence variability. Normalization is the main method used to decrease this variability and to bring distributions of adjusted values into alignment. In 1996, O'Malley proposed three normalization techniques to eliminate the effect of age and anthropometric factors from temporal-distance parameters of gait. These techniques were adopted to normalize the stabilometric signal by some authors. This paper proposes a new method of normalization of stabilometric signals to be applied in balance studies. The method was applied to a data set collected in a previous study, and the results of normalized and nonnormalized signals were compared. The results showed that the new method, if used in a well-designed experiment, can eliminate undesirable correlations between the analyzed parameters and the subjects' characteristics and show only the experimental conditions' effects.
Subject(s)
Anthropometry/methods , Postural Balance/physiology , Adult , Analysis of Variance , Biomechanical Phenomena , Female , Foot/physiology , Humans , Male , Pressure , Reference ValuesABSTRACT
In order to investigate the response of heart rate variability (HRV) components to postural change and their association with cardiovascular risk factors in shift workers, a cross-sectional study with 438 Brazilian males rotating shift workers was done. Anthropometric, body composition, and clinical measures were collected. Electrocardiogram was recorded for 3 minutes, in the supine and orthostatic position, and HRV components were extracted. Descriptive analyses showed that mean values of body mass index, waist circumference (WC), waist-to-height ratio, visceral fat area (VFA), and blood pressure (BP) were higher than the reference values. In the regression model, age, WC, VFA, and systolic BP showed negative association with HRV components. These findings suggest the need for determining effective strategies for the evaluation and promotion of health among shift workers focused on the altered variables.
Subject(s)
Blood Pressure/physiology , Body Composition/physiology , Heart Rate/physiology , Models, Statistical , Posture/physiology , Work Schedule Tolerance/physiology , Adult , Anthropometry/methods , Brazil/epidemiology , Computer Simulation , Humans , Male , Models, Biological , Postural Balance/physiology , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
OBJECTIVE: To evaluate the association between lifetime exposure to shift work and blood pressure, fasting glucose (FG), anthropometric variables, body composition and heart rate variability (HRV). METHODS: Male shift workers (N=438) were evaluated using principal component (PC) analysis. The variables used were: weight, body mass index (BMI), waist circumference (WC), neck circumference (NC), hip circumference (HC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), body fat mass (BFKg), body fat percentage (BF%), visceral fat area (VFA), FG, systolic (SBP) and diastolic blood pressure (DBP), and HRV variables. ECG was performed, extracting heart rate (HR), root mean square of the successive differences (RMSSD), high frequency (HF), low frequency (LF) and the LF/HF ratio. Using linear regression models, the lifetime shift work exposure was associated with each PC. RESULTS: Five PCs were obtained, which accounted for 79.6% of the total variation of the data. PC1 (weight, BMI, WC, NC, HC, WHR, WHtR, BFKg, BF% and VFA) was designated as body obesity; PC2 (HF, RMSSD and LF) as good cardiac regulation; PC3 (SBP and DBP) as blood pressure; PC4 (LF/HF ratio and HR) as bad cardiac regulation and PC5 (WHR and FG) as insulin resistance. After age adjustment, the regression analysis showed that lifetime shift work was negatively associated with PC2 and positively associated with PC3. CONCLUSIONS: The association of lifetime shift work exposure with PC2 and PC3 suggests that shift work promotes unfavourable changes in autonomic cardiac control related to a decrease in parasympathetic modulation and an increase in blood pressure.
