Effects of a home-based exercise program in body composition, abdominal fat and lipid profile in patients with coronary artery disease

Introduction: Coronary artery disease and aging seems to be associated with a sedentary lifestyle, contributing to increased abdominal fat and consequently metabolic complications. The exercise can break this cycle by stimulating lipolysis and the use of fatty acids. In Europe there is still a lack of cardiac rehabilitation programmes in hospitals, therefore, this study aims to demonstrate the advantages of implementing home-based exercise programmes, as well as, their effects on cardiovascular prevention. This study analyzed the effects of a home-based exercise programme, in patients with coronary artery disease (myocardial infarction for 1 year), in body composition, abdominal fat, lipid profile. Methods: An ongoing randomized controlled trial with a sample of 20 participants were randomly allocated to intervention (n = 10) and control groups (n = 10). Intervention group performed a specific exercise programme during 8 weeks, consisting of ten home based exercises taking into account flexibility, muscle endurance and strength as well as cardiovascular endurance. Skinfolds thickness were measure to calculate the percentage of total fat: Skinfolds used were suprailiac, abdominal horizontal and vertical. Body mass index calculation and blood tests for lipidic profile were performed. Results: After eight weeks the intervention group decreased significantly the percentage of total fat (p < 0.05), the suprailiac skinfold (p < 0.05), the abdominal horizontal and vertical skinfold (p < 0.05) when compared with control group. In the intervention group it was observed after 8 weeks a significant decrease in body mass index, LDL-cholesterol and triglycerides. Conclusions: Home-based exercise programme influenced body composition, abdominal fat and lipid profile. These results highlight the importance of implementing home based exercises that are easy and cheap to implement in cardiac patients, in order to promote health and reduce cardiovascular risk factors.

Impact of sensor nodes scaling and velocity on handover mechanisms for healthcare wireless sensor networks with mobility support

Health promotion in hospital environments can be improved using the most recent information and communication technologies. The Internet connectivity to small sensor nodes carried by patients allows remote access to their bio-signals. To promote these features the healthcare wireless sensor networks (HWSN) are used. In these networks mobility support is a key issue in order to keep patients under realtime monitoring even when they move around. To keep sensors connected to the network, they should change their access points of attachment when patients move to a new coverage area along an infirmary. This process, called handover, is responsible for continuous network connectivity to the sensors. This paper presents a detailed performance evaluation study considering three handover mechanisms for healthcare scenarios (Hand4MAC, RSSI-based, and Backbone-based). The study was performed by simulation using several scenarios with different number of sensors and different moving velocities of sensor nodes. The results show that Hand4MAC is the best solution to guarantee almost continuous connectivity to sensor nodes with less energy consumption.

Clay body wrap with microcurrent: effects in central adiposity

Introduction Increased fat mass is becoming more prevalent in women and its accumulation in the abdominal region can lead to numerous health risks such as diabetes mellitus. The clay body wrap using compounds such as green clay, green tea and magnesium sulfate, in addition to microcurrent, may reduce abdominal fat mass and minimize or prevent numerous health problems. Objective This study aims at measuring the influence of the clay body wrap with microcurrent and aerobic exercise on abdominal fat. Methods Nineteen female patients, randomized into intervention (n = 10) and control (n = 9) groups, were evaluated using ultrasound for visceral and subcutaneous abdominal fat, calipers and abdominal region perimeter for subcutaneous fat and bioimpedance for weight, fat mass percentage and muscular mass. During 10 sessions (5 weeks, twice a week) both groups performed aerobic exercise in a cycloergometer and a clay body wrap with microcurrent was applied to the intervention group. Results When comparing both groups after 5 weeks of protocol, there was a significant decrease in the subcutaneous fat around left anterior superior iliac spine in the intervention group (ρ = 0.026 for a confidence interval 95%). When comparing initial and final abdominal fat in the intervention group, measured by ultrasound (subcutaneous and visceral fat) and by skinfold (subcutaneous fat), we detected a significant abdominal fat reduction. Conclusion This study demonstrated that the clay body wrap used with microcurrent and aerobic exercise can have a positive effect on central fat reduction.

Fast mobility support in low-power wireless networks: smart-HOP over RPL/6LoWPAN

With the emergence of low-power wireless hardware new ways of communication were needed. In order to standardize the communication between these low powered devices the Internet Engineering Task Force (IETF) released the 6LoWPAN stand- ard that acts as an additional layer for making the IPv6 link layer suitable for the lower-power and lossy networks. In the same way, IPv6 Routing Protocol for Low- Power and Lossy Networks (RPL) has been proposed by the IETF Routing Over Low power and Lossy networks (ROLL) Working Group as a standard routing protocol for IPv6 routing in low-power wireless sensor networks. The research performed in this thesis uses these technologies to implement a mobility process. Mobility management is a fundamental yet challenging area in low-power wireless networks. There are applications that require mobile nodes to exchange data with a xed infrastructure with quality-of-service guarantees. A prime example of these applications is the monitoring of patients in real-time. In these scenarios, broadcast- ing data to all access points (APs) within range may not be a valid option due to the energy consumption, data storage and complexity requirements. An alternative and e cient option is to allow mobile nodes to perform hand-o s. Hand-o mechanisms have been well studied in cellular and ad-hoc networks. However, low-power wireless networks pose a new set of challenges. On one hand, simpler radios and constrained resources ask for simpler hand-o schemes. On the other hand, the shorter coverage and higher variability of low-power links require a careful tuning of the hand-o parameters. In this work, we tackle the problem of integrating smart-HOP within a standard protocol, speci cally RPL. The simulation results in Cooja indicate that the pro- posed scheme minimizes the hand-o delay and the total network overhead. The standard RPL protocol is simply unable to provide a reliable mobility support sim- ilar to other COTS technologies. Instead, they support joining and leaving of nodes, with very low responsiveness in the existence of physical mobility.

Kinematic parameters of throwing performance in patients with schizophrenia using a markerless motion capture system

Motor dysfunction is consistently reported but understudied in schizophrenia. It has been hypothesized that this abnormality may reflect a neuro-developmental disorder underlying this illness. The main goal of this study was to analyze movement patterns used by participants with schizophrenia and healthy controls during overarm throwing performance, using a markerless motion capture system. Thirteen schizophrenia patients and 16 healthy control patients performed the overarm throwing task in a markerless motion capture system. Participants were also examined for the presence of motor neurological soft signs (mNSS) using the Brief Motor Scale. Schizophrenia patients demonstrated a less developed movement pattern with low individualization of components compared to healthy controls. The schizophrenia group also displayed a higher incidence of mNSS. The presence of a less mature movement pattern can be an indicator of neuro-immaturity and a marker for atypical neurological development in schizophrenia. Our findings support the understanding of motor dysfunction as an intrinsic part of the disorder of schizophrenia.

Cloud-assisted Wireless Body Area Networks

Wireless Body Area Networks (WBANs) have emerged as a promising technology for medical and non-medical applications. WBANs consist of a number of miniaturized, portable, and autonomous sensor nodes that are used for long-term health monitoring of patients. These sensor nodes continuously collect information of patients, which are used for ubiquitous health monitoring. In addition, WBANs may be used for managing catastrophic events and increasing the effectiveness and performance of rescue forces. The huge amount of data collected by WBAN nodes demands scalable, on-demand, powerful, and secure storage and processing infrastructure. Cloud computing is expected to play a significant role in achieving the aforementioned objectives. The cloud computing environment links different devices ranging from miniaturized sensor nodes to high-performance supercomputers for delivering people-centric and context-centric services to the individuals and industries. The possible integration of WBANs with cloud computing (WBAN-cloud) will introduce viable and hybrid platform that must be able to process the huge amount of data collected from multiple WBANs. This WBAN-cloud will enable users (including physicians and nurses) to globally access the processing and storage infrastructure at competitive costs. Because WBANs forward useful and life-critical information to the cloud – which may operate in distributed and hostile environments, novel security mechanisms are required to prevent malicious interactions to the storage infrastructure. Both the cloud providers and the users must take strong security measures to protect the storage infrastructure.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

Clay body wrap with microcurrent: Effects in central adiposity

Introduction: Increased fat mass is becoming more prevalent in women and its accumulation in the abdominal region can lead to numerous health risks such as diabetes mellitus. The clay body wrap using compounds such as green clay, green tea and magnesium sulfate, in addition to microcurrent, may reduce abdominal fat mass and minimize or prevent numerous health problems. Objective: This study aims at measuring the influence of the clay body wrap with microcurrent and aerobic exercise on abdominal fat. Methods: Nineteen female patients, randomized into intervention (n = 10) and control (n = 9) groups, were evaluated using ultrasound for visceral and subcutaneous abdominal fat, calipers and abdominal region perimeter for subcutaneous fat and bioimpedance for weight, fat mass percentage and muscular mass. During 10 sessions (5 weeks, twice a week) both groups performed aerobic exercise in a cycloergometer and a clay body wrap with microcurrent was applied to the intervention group. Results: When comparing both groups after 5 weeks of protocol, there was a significant decrease in the subcutane- ous fat around left anterior superior iliac spine in the intervention group (ρ = 0.026 for a confidence interval 95%). When comparing initial and final abdominal fat in the intervention group, measured by ultrasound (subcutaneous and visceral fat) and by skinfold (subcutaneous fat), we detected a significant abdominal fat reduction. Conclusion: This study demonstrated that the clay body wrap used with microcurrent and aerobic exercise can have a positive effect on central fat reduction.

A Clinical Support System Based on Quality of Life Estimation

Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual's daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value < 0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt's sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.