Policies for replacing long-term indwelling urinary catheters in adults

We would also like to thank the foll owing Cochrane Incontinence editorial base staff members for their help and support with this re-view: Cathryn Glazener, Sheila Wallace, Mandy Fader, Peter Her-bison and Suzanne Macdonald. The review authors are grateful to Toby Lasseron for his advice. The review authors are thankful to Dr Beverly Priefer for responding to our query about Priefer 1982. Policies for replacing long‐term indwelling urinary catheters in adults, Protocol, Fergus PM Cooper, Cameron Edwin Alexander, Sanjay Sinha, Muhammad Imran Omar; https://doi.org/10.1002/14651858.CD011115; 14 May 2014Peer reviewedPublisher PD

A critical look at studies applying over-sampling on the TPEHGDB dataset

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set

Bilingualism: Consequences for Mind and Brain

Building on earlier evidence showing a beneficial effect of bilingualism on children’s cognitive development, we review recent studies using both behavioral and neuroimaging methods to examine the effects of bilingualism on cognition in adulthood and explore possible mechanisms for these effects. This research shows that bilingualism has a somewhat muted effect in adulthood but a larger role in older age, protecting against cognitive decline, a concept known as “cognitive reserve”. We discuss recent evidence that bilingualism is associated with a delay in the onset of symptoms of dementia. Cognitive reserve is a crucial research area in the context of an aging population; the possibility that bilingualism contributes to cognitive reserve is therefore of growing importance as populations become increasingly diverse

A User-Centred Approach to Reducing Sedentary Behaviour

The use of digital technologies in the administration of healthcare is growing at a rapid rate. However, such platforms are often expensive. As people are living longer, the strain placed on hospitals is increasing. It is evident that a usercentric approach is needed, which aims to prevent illness before a hospital visit is required. As such, with the levels of obesity rising, preventing this illness before such resources are required has the potential to save an enormous amount of time and money, whilst promoting a healthier lifestyle. New and novel approaches are needed, which are inexpensive and pervasive in nature. One such approach is to use human digital memories. This outlet provides visual lifelogs, composed of a variety of data, which can be used to identify periods of inactivity. This paper explores how the DigMem system is used to successfully recognise activity and create temporal memory boxes of human experiences, which can be used to monitor sedentary behaviour

Capturing and Sharing Human Digital Memories with the Aid of Ubiquitous Peer– to–Peer Mobile Services

The explosion of mobile computing and the sharing of content ubiquitously has enabled users to create and share memories instantly. Access to different data sources, such as location, movement, and physiology, has helped to create a data rich society where new and enhanced memories will form part of everyday life. Peer–to–Peer (P2P) systems have also increased in popularity over the years, due to their ad hoc and decentralized nature. Mobile devices are “smarter” and are increasingly becoming part of P2P systems; opening up a whole new dimension for capturing, sharing and interacting with enhanced human digital memories. This will require original and novel platforms that automatically compose data sources from ubiquitous ad-hoc services that are prevalent within the environments we occupy. This is important for a number of reasons. Firstly, it will allow digital memories to be created that include richer information, such as how you felt when the memory was created and how you made others feel. Secondly, it provides a set of core services that can more easily manage and incorporate new sources as and when you are available. In this way memories created in the same location, and time are not necessarily similar – it depends on the data sources that are accessible. This paper presents DigMem, the initial prototype that is being developed to utilize distributed mobile services. DigMem captures and shares human digital memories, in a ubiquitous P2P environment. We present a case study to validate the implementation and evaluate the applicability of the approach

The effect of exercise interventions on inflammatory biomarkers in healthy, physically inactive subjects: a systematic review

Background: Increases in physical activity ameliorate low-grade systemic inflammation in disease populations such as type 2 diabetes mellitus and coronary artery disease. The effects of aerobic and resistance training (RT) on inflammatory biomarker profiles in non-disease, physically inactive individuals are unknown. Methods: A systematic review of randomized controlled trials measuring the effect of aerobic and resistance exercise on pro-inflammatory biomarkers in healthy, inactive adult populations was conducted. The available peer-reviewed literature was searched from January 1990 to June 2016 using the electronic databases PubMed and Scopus. A narrative synthesis of review findings was constructed with discussion of the impact of aerobic, resistance and combined training on C-reactive protein (CRP), interleukin-6 (IL-6), interleukin-8, interleukin-1β and tumour necrosis factor-α. Results: The initial search revealed 1596 potentially relevant studies. Application of the study eligibility criteria led to the full-text review of 54 articles with 11 studies deemed suitable for inclusion. Review of related articles and the reference lists of the 54 full-text articles led to the inclusion of 2 additional studies. The review revealed inconsistent findings relating to the effect of aerobic training and RT on CRP and IL-6. Studies of older-aged adults (>65 years old) demonstrated the greatest and most consistent reduction in inflammatory biomarkers post-training intervention. Conclusions: A paucity of evidence exists relating to the effect of exercise training on inflammatory markers in non-disease, physically inactive adults. The available evidence suggests potential for the greatest benefit to be seen in older populations and with higher intensity aerobic exercise

Profiling Users in the Smart Grid

The implementation of the smart grid brings with it many new components that are fundamentally different to traditional power grid infrastructures. The most important addition brought by the smart grid is the application of the Advanced Metering Infrastructure (AMI). As part of the AMI, the smart meter device provides real time energy usage about the consumer to all of the smart grids stakeholders. Detailed statistics about a consumer’s energy usage can be accessed by the end user, utility companies and other parties. The problem, however, is in how to analyse, present and make best use of the data. This paper focuses on the data collected from the smart grid and how it can be used to detect abnormal user behaviour for energy monitoring applications. The proposed system employs a data classification technique to identify irregular energy usage in patterns generated by smart meters. The results show that it is possible to detect abnormal behaviour with an overall accuracy of 99.45% with 0.100 for sensitivity, 0.989 for specificity and an error of 0.006 using the LDC classifier

Monitoring and Reducing Sedentary Behavior in the Elderly with the Aid of Human Digital Memories

A healthy lifestyle has the ability not only to give you more energy and help you look and feel better, but it also has the ability to help you live longer and prevent disease, such as obesity and pressure ulcers. This is particularly important for the elderly population, as a healthier lifestyle would enable independent living to occur for a longer period of time. However, providing a direct link between increasing physical activity and positive health outcomes is a problem. The effect of leading an increasing sedentary lifestyle is also not evident straightaway. Effects of this behavior often occur over years and decades, as opposed to days or months. Therefore, there is very little willingness to change, if instant results are not seen. There is a need to provide a mechanism that is able to monitor an individual and provide a visual indication of his or her behavior. It is envisioned that the area of human digital memories is capable of providing such a system. This article explores how sedentary behavior and journey information can be collected, from different environments, so that an illustration of a user's habits can be seen and changes can occur. A successful prototype has also been developed that evaluates the applicability of the approach. Copyright © 2013, Mary Ann Liebert, Inc. 2013