Kinanthropometry IX

This is an edited collection of peer-reviewed papers presented at the Ninth International Conference of the Society for the Advancement of Kinanthropometry. Defined as the relationship between human body structure and function, kinanthropometry is an area of growing interest, and these proceedings will be of use to students, academics and professionals in the areas of ergonomics, sports science, nutrition, health, and other allied fields. The assembled works represent the latest research findings across kinanthropometry, moving the discipline forward and promoting good practice and the exchange of expertise

Kinanthropometry IX

This is an edited collection of peer-reviewed papers presented at the Ninth International Conference of the Society for the Advancement of Kinanthropometry. Defined as the relationship between human body structure and function, kinanthropometry is an area of growing interest, and these proceedings will be of use to students, academics and professionals in the areas of ergonomics, sports science, nutrition, health, and other allied fields. The assembled works represent the latest research findings across kinanthropometry, moving the discipline forward and promoting good practice and the exchange of expertise

Resistance training, insulin sensitivity & metabolic health

Skeletal muscle is the largest organ in the human body, comprising 40%-50% of total body weight and more than 600 skeletal muscles in a human body performing common functions such as body movements, maintaining posture, storing protein and glycogen and generating heat. Approximately 0.8% skeletal muscle mass declines per year a process known as sarcopenia. On top of that, ageing also results in a decrease in muscle strength, at a rate of approximately 1-3% per year (Keller & Engelhardt, 2013).Skeletal muscle is the major organ responsible for glucose uptake under insulin stimulated conditions, accounting for ~80% of total glucose disposal and low muscle strength and mass likely contribute to metabolic dysregulation seen in Type 2 diabetes. Research has shown that resistance exercise training can increase strength, muscle size, fat-free mass, connective tissue thickness, decrease body fat, reduce blood pressure and improve insulin sensitivity and VO2max(Croymans et al., 2013; Ozaki et al., 2013;Abdul & Defronzo, 2010). In order to monitor efficacy of such resistance training interventions it is important to be able to accurately quantify skeletal muscle mass and several methods exist for this purpose, although many require expensive equipment making them not always possible to use. The aim of chapter 2 was to investigate the repeatability and validity of a relatively cheap and portable A-mode ultrasound device. This chapter has found that A mode ultrasound is a repeatable measure of muscle thickness (CV of 4.6%) and that both A and B mode ultrasound provide valid measures of muscle volume, as compared to the gold standard MRI (r=0.96). Following this, the aim of chapter 3 wasto determine if this A-mode ultrasound device is able to detect changes in muscle thickness in response to resistance exercise training and to determine its validity. Findings in this Chapter 3 have shown that the A-mode ultrasound can detect increases in muscle thickness of 6.2 ± 5.4%, alongside a 26 ± 7.3% increase in 1RM after 8 weeks of resistance exercise training. However, it was also shown that both A and B mode measures of ultrasound muscle thickness were not valid measures of the resistance exercise induced changes in muscle volume (r=0.30 A-mode & r=0.04 B-mode) Resistance exercise is known as the most efficacious method to increase muscle strength and mass. It has been demonstrated recently that if exercise is performed to volitional failure then gains in muscle mass, and to a lesser extent strength, are similar regardless of the load at which exercise is performed. The aims of chapter 4, were to investigate the effects of 6 weeks of resistance exercise training, comprised of 1 set of each exercise to voluntary failure, on insulin sensitivity and the time-course of adaptations in muscle strength/mass, in overweight men. Results of this chapter have demonstrated that six weeks of resistance exercise, volitional failure of nine exercises – taking 15-20 min per session – undertaken three times per week resulted in a 16% improvement in insulin sensitivity (61.6 ± 18.0 to 71.3 ± 22.9 mg.l2.mmol-2.mU-1.min-1 after the intervention (P<0.05) in healthy overweight men and increases in muscle strength, size and RTD (rate torque development) 50 and 100 were also observed. Several studies have demonstrated that people from South Asia are up to 4-6 times more likely to develop type 2 diabetes than White Europeans. Furthermore, in a recent study from the UK Biobank,grip strength in South-Asian men and women was 5–6 kg lower than in the other ethnic groups and a greater contributor to diabetes prevalence. As resistance exercise is the most effective intervention for increasing muscle mass, strength, and can improve insulin sensitivity, the aim of the Chapter 5 was tocompare the effect of resistance exercise on muscle and metabolic health between South Asians and White Europeans. This chapter has shown that there were no differences in the effect of 12 weeks of resistance exercise training on the majority of the muscle and metabolic outcomes measured, however the increase muscle thickness, 1.2 (95%CI 0.8 to 1.7) mm in South Asians and 2.3 (95%CI 1.8 to 2.9) mm in White Europeans and decrease in systolic blood pressure, 5.1 (95%CI:-7.5 to -2.7) mmHg in White Europeans and a 0.7 (95%CI:-2.4 to 1.0) mmHg in South Asians were attenuated in South Asians. There was also a trend for an attenuated effect of resistance exercise training on VO2max,decrease of 0.7 (95%CI -2.0 to 0.6) ml.kg.min-1 in South Asians. In summary, this thesis has demonstrated that whilst ultrasound measure of muscle thickness is valid at a single time point, this is not the case when evaluating changes with resistance exercise training. Following this we have demonstrated that resistance exercise training, involving a single set of exercise to muscle failure, is effective in inducing short-term improvements in muscle size and strength and also insulin sensitivity in White Europeans, with broadly similar findings in South Asians

Advanced bioelectrical signal processing methods: Past, present and future approach - Part III: Other biosignals

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).Web of Science2118art. no. 606

Obesity in pregnancy: risk of gestational diabetes

Background: Maternal obesity is a risk factor for gestational diabetes and other adverse pregnancy outcomes, but the body fat distribution may be a more important risk factor than body mass index. Pregnancy is an insulin resistant state and more so, in obese women. Metformin could be beneficial in obese pregnant women due to its insulin sensitizing action. The aims of this study are to investigate visceral fat mass as a risk factor for gestational diabetes (VFM study), to develop a mathematical model for the prediction of gestational diabetes in obese women (VFM study) and to examine the effect of metformin on pregnancy outcomes in obese non-diabetic women (MOP Trial). Methods and Results: VFM study: The body composition of 302 obese pregnant women was assessed using bioelectrical impedance. A mathematical model to predict gestational diabetes using machine learning was developed using visceral fat mass which is a novel risk factor in addition to conventional risk factors. 72 of the women developed gestational diabetes (GDM). These women had higher visceral fat mass. Women with a baseline visceral fat mass ≥ 75th percentile, had a 3-fold risk of subsequent gestational diabetes. The mathematical model predicted gestational diabetes with an average overall accuracy of 77.5% and predicted birth centile classes with an average accuracy of 68%. According to the decision tree developed, VFM emerged as the most important variable in determining the risk of GDM and a VFM < 210 was used as the first split in the decision tree. MOP Trial: 133 obese pregnant women were randomised to either metformin or placebo. The pregnancy outcomes were compared in both groups. Insulin resistance was measured in all women. 118 women completed the trial. Metformin did not reduce the neonatal birth weight z-score, which was the primary outcome of the trial or the incidence of large for gestational age babies. However, metformin therapy significantly reduced gestational weight gain, reduced the pregnancy rise in visceral fat mass, and attenuated the expected physiological rise in insulin resistance at 28 weeks gestation. However, this did not result in an overall significant reduction in the incidence of gestational diabetes. There was a trend towards a reduced incidence of gestational diabetes in women with high baseline insulin resistance randomised to metformin. Conclusions: Visceral fat mass is a novel risk factor for gestational diabetes. The mathematical model successfully predicted gestational diabetes. Metformin reduced gestational weight gain and insulin resistance but did not lower the median neonatal birth weight or reduce the incidence of GDM

Utility of AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome From Single-Channel Airflow

Producción CientíficaThe purpose of this study is to evaluate the usefulness of the boosting algorithm AdaBoost (AB) in the context of the sleep apnea-hypopnea syndrome (SAHS) diagnosis. Methods: We characterize SAHS in single-channel airflow (AF) signals from 317 subjects by the extraction of spectral and non-linear features. Relevancy and redundancy analyses are conducted through the fast correlation-based filter (FCBF) to derive the optimum set of features among them. These are used to feed classifiers based on linear discriminant analysis (LDA) and classification and regression trees (CART). LDA and CART models are sequentially obtained through AB, which combines their performances to reach higher diagnostic ability than each of them separately. Results: Our AB-LDA and AB-CART approaches showed high diagnostic performance when determining SAHS and its severity. The assessment of different apnea-hypopnea index cutoffs using an independent test set derived into high accuracy: 86.5% (5 events/h), 86.5% (10 events/h), 81.0% (15 events/h), and 83.3% (30 events/h). These results widely outperformed those from logistic regression and a conventional event-detection algorithm applied to the same database. Conclusion: Our results suggest that AB applied to data from single-channel AF can be useful to determine SAHS and its severity. Significance: SAHS detection might be simplified through the only use of single-channel AF data.Ministerio de Economía y Competitividad (project TEC2011-22987)Junta de Castilla y León (project VA059U13

Upper extremity soft and rigid tissue mass prediction using segment anthropometric measures and DXA

Multiple linear stepwise regression was used to generate equations to predict bone mineral content (BMC), fat mass (FM), lean mass (LM), and wobbling mass (WM) of three segments of the upper extremities including the arm, forearm, and forearm + hand segments using simple anthropometrics. Full body scans using Dual Energy X-ray Absorptiometry (DXA) were used as the reference method. 100 (50 M, 50 F) young adults, ranging in age from 17 to 30 years, volunteered where data from 76 participants was used to generate the equations while data from the remaining 24 was used for equation validation. Prediction equations exhibited high adjusted R2 values (range from 0.854 to 0.968). Scatter plots of the actual versus predicted masses of the validation group revealed a close relationship (R2 range from 0.681 to 0.951). This indicates that accurate estimates of in-vivo tissue masses for upper extremity segments can be predicted by anthropometrics