Steps to a better Belfast: physical activity assessment and promotion in primary care

BACKGROUND: Low physical activity (PA) levels which increase the risk of chronic disease are reported by two-thirds of the general UK population. Promotion of PA by primary healthcare professionals is advocated but more evidence is needed regarding effective ways of integrating this within everyday practice. This study aims to explore the feasibility of a randomised trial of a pedometer-based intervention, using step-count goals, recruiting patients from primary care. METHOD: Patients, aged 35-75, attending four practices in socioeconomically deprived areas, were invited to complete a General Practice PA Questionnaire during routine consultations. Health professionals invited 'inactive' individuals to a pedometer-based intervention and were randomly allocated to group 1 (prescribed a self-determined goal) or group 2 (prescribed a specific goal of 2500 steps/day above baseline). Both groups kept step-count diaries and received telephone follow-up at 1, 2, 6 and 11 weeks. Step counts were reassessed after 12 weeks. RESULTS: Of the 2154 patients attending, 192 questionnaires were completed (8.9%). Of these, 83 were classified as 'inactive'; 41(10 men; 31 women) completed baseline assessments, with the mean age of participants being 51 years. Mean baseline step counts were similar in group 1 (5685, SD 2945) and group 2 (6513, SD 3350). The mean increase in steps/day was greater in groups 1 than 2 ((2602, SD 1957) vs (748, SD 1997) p=0.005). CONCLUSIONS: A trial of a pedometer-based intervention using self-determined step counts appears feasible in primary care. Pedometers appear acceptable to women, particularly at a perimenopausal age, when it is important to engage in impact loading activities such as walking to maintain bone mineral density. An increase of 2500 steps/day is achievable for inactive patients, but the effectiveness of different approaches to realistic goal-setting warrants further study.

What Are the Effects of Teaching Evidence-Based Health Care (EBHC)? Overview of Systematic Reviews

Background

An evidence-based approach to health care is recognized internationally as a key competency for healthcare practitioners. This overview systematically evaluated and organized evidence from systematic reviews on teaching evidence-based health care (EBHC).

Methods/Findings

We searched for systematic reviews evaluating interventions for teaching EBHC to health professionals compared to no intervention or different strategies. Outcomes covered EBHC knowledge, skills, attitudes, practices and health outcomes. Comprehensive searches were conducted in April 2013. Two reviewers independently selected eligible reviews, extracted data and evaluated methodological quality. We included 16 systematic reviews, published between 1993 and 2013. There was considerable overlap across reviews. We found that 171 source studies included in the reviews related to 81 separate studies, of which 37 are in more than one review. Studies used various methodologies to evaluate educational interventions of varying content, format and duration in undergraduates, interns, residents and practicing health professionals. The evidence in the reviews showed that multifaceted, clinically integrated interventions, with assessment, led to improvements in knowledge, skills and attitudes. Interventions improved critical appraisal skills and integration of results into decisions, and improved knowledge, skills, attitudes and behaviour amongst practicing health professionals. Considering single interventions, EBHC knowledge and attitude were similar for lecture-based versus online teaching. Journal clubs appeared to increase clinical epidemiology and biostatistics knowledge and reading behavior, but not appraisal skills. EBHC courses improved appraisal skills and knowledge. Amongst practicing health professionals, interactive online courses with guided critical appraisal showed significant increase in knowledge and appraisal skills. A short workshop using problem-based approaches, compared to no intervention, increased knowledge but not appraisal skills.

Conclusions

EBHC teaching and learning strategies should focus on implementing multifaceted, clinically integrated approaches with assessment. Future rigorous research should evaluate minimum components for multifaceted interventions, assessment of medium to long-term outcomes, and implementation of these interventions.

Antibiotic Management of Lung Infections in Cystic Fibrosis. I. The Microbiome, Methicillin-Resistant Staphylococcus aureus, Gram-Negative Bacteria, and Multiple Infections

Despite significant advances in treatment strategies targeting the underlying defect in cystic fibrosis (CF), airway infection remains an important cause of lung disease. In this two-part series, we review recent evidence related to the complexity of CF airway infection, explore data suggesting the relevance of individual microbial species, and discuss current and future treatment options. In Part I, the evidence with respect to the spectrum of bacteria present in the CF airway, known as the lung microbiome is discussed. Subsequently, the current approach to treat methicillin-resistant Staphylococcus aureus, gram-negative bacteria, as well as multiple coinfections is reviewed. Newer molecular techniques have demonstrated that the airway microbiome consists of a large number of microbes, and the balance between microbes, rather than the mere presence of a single species, may be relevant for disease pathophysiology. A better understanding of this complex environment could help define optimal treatment regimens that target pathogens without affecting others. Although relevance of these organisms is unclear, the pathologic consequences of methicillin-resistant S. aureus infection in patients with CF have been recently determined. New strategies for eradication and treatment of both acute and chronic infections are discussed. Pseudomonas aeruginosa plays a prominent role in CF lung disease, butmany other nonfermenting gram-negative bacteria are also found in the CF airway. Many new inhaled antibiotics specifically targeting P. aeruginosa have become available with the hope that they will improve the quality of life for patients. Part I concludes with a discussion of how best to treat patients with multiple coinfections.

Children's use of interventions to learn causal structure

Children aged between 5 and 8 years freely intervened on a three-variable causal system, with their task being to discover whether it was a common-cause structure or one of two causal chains. From 6-7 years, children were able to use information from their interventions to correctly disambiguate the structure of a causal chain. We used a Bayesian model to examine children’s interventions on the system; this showed that with development children became more efficient in producing the interventions needed to disambiguate the causal structure and that the quality of interventions, as measured by their informativeness, improved developmentally. The latter measure was a significant predictor of children’s correct inferences about the causal structure. A second experiment showed that levels of performance were not reduced in a task in which children did not select and carry out interventions themselves, indicating no advantage for self-directed learning. However, children’s performance was not related to intervention quality in these circumstances, suggesting that children learn in a different way when they carry out interventions themselves.

Innovative interprofessional teaching session on labour and childbirth for undergraduate medical and midwifery students

There is an increasing recognition of the need to improve interprofessional relationships within clinical practice (Midwifery 2020, 2010). Evidence supports the assertion that healthcare professionals who are able to communicate and work effectively together and who have a mutual respect and understanding for one another’s roles will provide a higher standard of care (McPherson et al, 2001; Miers et al, 2005; Begley, 2008). The joint Royal College of Obstetrics & Gynaecologists(RCOG) / Royal College of Midwives (RCM) report (2008 Page 8) on clinical learning environment and recruitment recommended that “Inter-professional learning strategies should be introduced and supported at an early stage in the medical and midwifery undergraduate students' experience and continued throughout training.” Providing interprofessional education within a University setting offers an opportunity for a non-threatening learning environment where students can develop confidence and build collaborative working relationships with one another (Saxell et al, 2009).Further research supports the influence of effective team working on increased client satisfaction. Additionally it identifies that the integration of interprofessional learning into a curriculum improves students’ abilities to interact professionally and provides a better understanding of role identification within the workplace than students who have only been exposed to uniprofessional education (Meterko et al, 2004; Pollard and Miers, 2008; Siassakos, et al, 2009; Wilhelmsson et al, 2011; Murray-Davis et al, 2012). An interprofessional education indicative has been developed by teaching staff from the School of Nursing and Midwifery and School of Medicine at Queen’s University Belfast. The aim of the collaboration was to enhance interprofessional learning by providing an opportunity for medical students and midwifery students to interact and communicate prior to medical students undertaking their obstetrics and gynaecology placements. This has improved medical students placement experience by facilitating them to learn about the process of birth and familiarisation of the delivery suite environment and it also has the potential to enhance interprofessional relationships. Midwifery students benefit through the provision of an opportunity to teach and facilitate learning in relation to normal labour and birth and has provided them with an opportunity to build stronger and more positive relationships with another profession. This opportunity also provides a positive, confidence building forum where midwifery students utilise teaching and learning strategies which would be transferable to their professional role as registered midwives. The midwifery students were provided with an outline agenda in relation to content for the workshop, but then were allowed creative licence with regard to delivery of the workshop. The interactive workshops are undertaken within the University’s clinical education centre, utilising low fidelity simulation. The sessions are delivered 6 times per year and precede the medical students’ obstetric/gynaecology placement. All 4th year medical and final year midwifery students have an opportunity to participate. Preliminary evaluations of the workshops have been positive from both midwifery and medical students. The teaching sessions provided both midwifery and medical students with an introduction to inter professional learning and gave them an opportunity to learn about and respect each other’s roles. The midwifery students have commented on the enjoyable aspects of team working for preparing for the workshop and also the confidence gained from teaching medical students. The medical students have evaluated the teaching by midwifery students positively and felt that it lowered their anxiety levels going into the labour setting. A number of midwifery and medical students have subsequently worked with one another within the practice setting which has been recognised as beneficial. Both Schools have recognised the benefits of interprofessional education and have subsequently made a commitment to embed it within each curriculum.

Social categorizations, social comparisons and stigma: Presentations of self in people with learning difficulties

Self-categorization theory stresses the importance of the context in which the metacontrast principle is proposed to operate. This study is concerned with how 'the pool of psychologically relevant stimuli' (Turner, Hogg, Oakes, Reicher & Wetherell, 1987, p. 47) comprising the context is determined. Data from interviews with 33 people with learning difficulties were used to show how a positive sense of self might be constructed by members of a stigmatized social category through the social worlds that they describe, and therefore the social comparisons and categorizations that are made possible. Participants made downward comparisons which focused on people with learning difficulties who were less able or who displayed challenging behaviour, and with people who did not have learning difficulties but who, according to the participants, behaved badly, such as beggars, drunks and thieves. By selection of dimensions and comparison others, a positive sense of self and a particular set of social categorizations were presented. It is suggested that when using self-categorization theory to study real-world social categories, more attention needs to be paid to the involvement of the perceiver in determining which stimuli are psychologically relevant since this is a crucial determinant of category salience.

Learning soft computing control strategies in a modular neural network architecture

Modelling and control of nonlinear dynamical systems is a challenging problem since the dynamics of such systems change over their parameter space. Conventional methodologies for designing nonlinear control laws, such as gain scheduling, are effective because the designer partitions the overall complex control into a number of simpler sub-tasks. This paper describes a new genetic algorithm based method for the design of a modular neural network (MNN) control architecture that learns such partitions of an overall complex control task. Here a chromosome represents both the structure and parameters of an individual neural network in the MNN controller and a hierarchical fuzzy approach is used to select the chromosomes required to accomplish a given control task. This new strategy is applied to the end-point tracking of a single-link flexible manipulator modelled from experimental data. Results show that the MNN controller is simple to design and produces superior performance compared to a single neural network (SNN) controller which is theoretically capable of achieving the desired trajectory. (C) 2003 Elsevier Ltd. All rights reserved.

A self-learning algorithm for biased molecular dynamics

A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences.

A self-learning teaching-learning based optimization for dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.