Effects of weight on blood pressure at rest and during exercise.

Body weight (BW) and blood pressure (BP) have a close relationship, which has been accounted for by hormonal changes. No previous study has evaluated the effect of wearing an external weight vest on BP to determine whether there is a simple mechanism between BW and BP. Seventeen healthy volunteers underwent weight reduction (WR) through caloric restriction. Before and after WR, BW, body fat percentage and BP at rest and during exercise were measured. Before and after WR, exercise testing was performed twice with the random allocation of a weight vest (10 kg) during one of the tests. Linear regression was used to detect independent associations between BP and the weight vest, BW and body fat percentage. BW decreased from 89.4 ± 15.4 kg to 79.1 ± 14.0 kg following WR (P<0.001). WR led to significant decreases in BP at rest (from 130.0/85.9 mm Hg to 112.5/77.8 mm Hg, P<0.001 for systolic and diastolic BPs) and during exercise. The weight vest significantly increased BP at rest (to 136.1/90.7 mm Hg before and 125.8/84.6 mm Hg after WR) and during exercise. Linear regression analysis identified an independent association between the weight vest and BP (P=0.006 for systolic BP and P=0.009 for diastolic BP at rest). This study demonstrates that wearing an external weight vest has immediate effects on BP at rest and during exercise independent of BW or body fat. More research is needed to understand the physiological mechanisms between weight and BP.

Geographic variation in the utilization and survival associated with oxaliplatin chemotherapy in patients with stage III colon cancer

Background: Overall objectives of this dissertation are to examine the geographic variation and socio-demographic disparities (by age, race and gender) in the utilization and survival of newly FDA-approved chemotherapy agents (Oxaliplatin-containing regimens) as well as to determine the cost-effectiveness of Oxaliplatin in a large nationwide and population-based cohort of Medicare patients with resected stage-III colon cancer. Methods: A retrospective cohort of 7,654 Medicare patients was identified from the Surveillance, Epidemiology and End Results – Medicare linked database. Multiple logistic regression was performed to examine the relationship between receipt of Oxaliplatin-containing chemotherapy and geographic regions while adjusting for other patient characteristics. Cox proportional hazard model was used to estimate the effect of Oxaliplatin-containing chemotherapy on the survival variation across regions using 2004-2005 data. Propensity score adjustments were also made to control for potential bias related to non-random allocation of the treatment group. We used Kaplan-Meier sample average estimator to calculate the cost of disease after cancer-specific surgery to death, loss-to follow-up or censorship. Results: Only 51% of the stage-III patients received adjuvant chemotherapy within three to six months of colon-cancer specific surgery. Patients in the rural regions were approximately 30% less likely to receive Oxaliplatin chemotherapy than those residing in a big metro region (OR=0.69, p=0.033). The hazard ratio for patients residing in metro region was comparable to those residing in big metro region (HR: 1.05, 95% CI: 0.49-2.28). Patients who received Oxalipaltin chemotherapy were 33% less likely to die than those received 5-FU only chemotherapy (adjusted HR=0.67, 95% CI: 0.41-1.11). KMSA-adjusted mean payments were almost 2.5 times higher in the Oxaliplatin-containing group compared to 5-FU only group ($45,378 versus $17,856). When compared to no chemotherapy group, ICER of 5-FU based regimen was $12,767 per LYG, and ICER of Oxaliplatin-chemotherapy was $60,863 per LYG. Oxaliplatin was found economically dominated by 5-FU only chemotherapy in this study population. Conclusion: Chemotherapy use varies across geographic regions. We also observed considerable survival differences across geographic regions; the difference remained even after adjusting for socio-demographic characteristics. The cost-effectiveness of Oxaliplatin in Medicare patients may be over-estimated in the clinical trials. Our study found 5-FU only chemotherapy cost-effective in adjuvant settings in patients with stage-III colon cancer.^

The unpredictability paradox: review of empirical comparisons of randomised and non-randomised clinical trials

Objective To summarise comparisons of randomised clinical trials and non-randomised clinical trials, trials with adequately concealed random allocation versus inadequately concealed random allocation, and high quality trials versus low quality trials where the effect of randomisation could not be separated from the effects of other methodological manoeuvres.

Knowledge beliefs and attitudes of squash venue operators relating to use of protective eyewear

Sports venues are in a position to potentially influence the safety practices of their patrons. This study examined the knowledge, beliefs and attitudes of venue operators that could influence the use of protective eyewear by squash players. A 50% random sample of all private and public squash venues affiliated with the Victorian Squash Federation in metropolitan Melbourne was selected. Face-to-face interviews were conducted with 15 squash venue operators during August 2001. Interviews were transcribed and content and thematic analyses were performed. The content of the interviews covered five topics: (1) overall injury risk perception, (2) eye injury occurrence, (3) knowledge, behaviors, attitudes and beliefs associated with protective eyewear, (4) compulsory protective eyewear and (5) availability of protective eyewear at venues. Venue operators were mainly concerned with the severe nature of eye injuries, rather than the relatively low incidence of these injuries. Some venue operators believed that players should wear any eyewear, rather than none at all, and believed that more players should use protective eyewear. Generally, they did not believe that players with higher levels of experience and expertise needed to wear protective eyewear when playing. Only six venues had at least one type of eyewear available for players to hire or borrow or to purchase. Operators expressed a desire to be informed about correct protective eyewear. Appropriate protective eyewear is not readily available at squash venues. Better-informed venue operators may be more likely to provide suitable protective eyewear.

What difference a writing centre makes:a small scale study

Purpose – Academic writing is often considered to be a weakness in contemporary students, while good reporting and writing skills are highly valued by graduate employers. A number of universities have introduced writing centres aimed at addressing this problem; however, the evaluation of such centres is usually qualitative. The paper seeks to consider the efficacy of a writing centre by looking at the impact of attendance on two “real world” quantitative outcomes – achievement and progression. Design/methodology/approach – Data mining was used to obtain records of 806 first-year students, of whom 45 had attended the writing centre and 761 had not. Findings – A highly significant association between writing centre attendance and achievement was found. Progression to year two was also significantly associated with writing centre attendance. Originality/value – Further, quantitative evaluation of writing centres is advocated using random allocation to a comparison condition to control for potential confounds such as motivation.

Pain response and follow-up of patients undergoing panretinal laser photocoagulation with reduced exposure times

Purpose - We performed a study of laser panretinal photocoagulation in 20 patients with proliferative retinopathy. We compared short exposure, high-energy laser settings with conventional settings, using a 532?nm, frequency doubled, Neodymium–Yag laser and assessed the patients in terms of pain experienced and effectiveness of treatment. Methods - Twenty patients having panretinal photocoagulation for the first time underwent random allocation to treatment of the superior and inferior hemi-retina. Treatment A used ‘conventional’ parameters: exposure time 0.1?s, power sufficient to produce a visible grey-white burns, spot size 300?µm. The other hemi- retina was treated with treatment B using exposure 0.02?s, 300?µm and sufficient power to have similar endpoint. All patients were asked to evaluate severity of pain on a visual analogue scale. (0=no pain, 10=most severe pain). All patients were masked as to the type of treatment and the order of carrying out the treatment on each patient was randomised. Patients underwent fundus photography and were followed up for 6–45 months. Results - Seventeen patients had proliferative diabetic retinopathy, two had ischaemic central retinal vein occlusion and one had ocular ischaemic syndrome. The mean response to treatment A was 5.11, compared to 1.40 treatment B, on the visual analogue scale, which was statistically significant (P=0.001). All patients preferred treatment B. Further treatments, if required, were performed with treatment B parameters and long-term follow-up has shown no evidence of undertreatment. Conclusions - Shortening exposure time of retinal laser is significantly less painful but equally effective as conventional parameters.

Pain response in patients undergoing panretinal photocoagulation by continuos wave Nd-YAG laser

Purpose: Current panretinal laser photocoagulative parameters are based on the Diabetic Retinopathy Study, which used exposures of 0.1 - 0.5 second to achieve moderate intensity retinal burns. Unfortunately, many patients find these settings painful. We wanted to investigate whether reducing exposure time and increasing power to give the same endpoint, is more comfortable and effective. Methods: 20 patients having panretinal photocoagulation for the first time underwent random allocation to two forms of laser treatment: half of the retinal area scheduled for treatment was treated with Green Yag laser with conventional parameters {exposure time 0.1 second (treatment A), power density sufficient to produce a visible grey - white burns}. The other half treated with shorter exposure 0.02 second (treatment B). All patient were asked to evaluate severity of pain on a visual analogue scale ranging from 0 - 10 (0 = no pain, 10 = most severe pain). All patients were masked as to the type of treatment. The order of carrying out the treatment on each patient was randomised. Fundus photographs were taken of each hemifundus to confirm treatment. Results: Of the 20 patients, 17 had proliferative diabetic retinopathy, 2 had ischaemic central retinal vein occlusion and one had ocular ischaemic syndrome. The average pain response to treatment A was 5.11 on a visual analogue scale with a mean power of 0.178 Watt; the average pain response to treatment B was 1.40 with a mean power of 0.489 Watt. Short exposure laser burns were significantly less painful (P < 0.001). Conclusion: Shortening exposure time with increased power is more comfortable for patients undergoing panretinal photocoagulation than conventional parameters.

Functional Magnetic Resonance Imaging Neurofeedback and Motor training for Parkinson’s Disease: Randomised Trial

Objective: Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) uses feedback of the patient’s own brain activity to self-regulate brain networks which in turn could lead to a change in behaviour and clinical symptoms. The objective was to determine the effect of neurofeedback and motor training and motor training (MOT) alone on motor and non-motor functions in Parkinson’s disease (PD) in a 10-week small Phase I randomised controlled trial. Methods: 30 patients with PD (Hoehn & Yahr I-III) and no significant comorbidity took part in the trial with random allocation to two groups. Group 1 (NF: 15 patients) received rt-fMRI-NF with motor training. Group 2 (MOT: 15 patients) received motor training alone. The primary outcome measure was the Movement Disorder Society – Unified Parkinson’s Disease Rating Scale-Motor scale (MDS-UPDRS-MS), administered pre- and post-intervention ‘off-medication’. The secondary outcome measures were the ‘on-medication’ MDS-UPDRS, the Parkinson’s disease Questionnaire-39, and quantitative motor assessments after 4 and 10 weeks. Results: Patients in the NF group were able to upregulate activity in the supplementary motor area by using motor imagery. They improved by an average of 4.5 points on the MDS-UPDRS-MS in the ‘off-medication’ state (95% confidence interval: -2.5 to -6.6), whereas the MOT group improved only by 1.9 points (95% confidence interval +3.2 to -6.8). However, the improvement did not differ significantly between the groups. No adverse events were reported in either group. Interpretation: This Phase I study suggests that NF combined with motor training is safe and improves motor symptoms immediately after treatment, but larger trials are needed to explore its superiority over active control conditions. Clinical Trial website : Unique Identifier: NCT01867827 URL: https://clinicaltrials.gov/ct2/show/NCT01867827?term=NCT01867827&rank=1

'Bayesian Optimisation for Nurse Scheduling'

A Bayesian optimisation algorithm for a nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. When a human scheduler works, he normally builds a schedule systematically following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not yet completed, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this paper, we design a more human-like scheduling algorithm, by using a Bayesian optimisation algorithm to implement explicit learning from past solutions. A nurse scheduling problem from a UK hospital is used for testing. Unlike our previous work that used Genetic Algorithms to implement implicit learning [1], the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The Bayesian optimisation algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, new rule strings have been obtained. Sets of rule strings are generated in this way, some of which will replace previous strings based on fitness. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. For clarity, consider the following toy example of scheduling five nurses with two rules (1: random allocation, 2: allocate nurse to low-cost shifts). In the beginning of the search, the probabilities of choosing rule 1 or 2 for each nurse is equal, i.e. 50%. After a few iterations, due to the selection pressure and reinforcement learning, we experience two solution pathways: Because pure low-cost or random allocation produces low quality solutions, either rule 1 is used for the first 2-3 nurses and rule 2 on remainder or vice versa. In essence, Bayesian network learns 'use rule 2 after 2-3x using rule 1' or vice versa. It should be noted that for our and most other scheduling problems, the structure of the network model is known and all variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus, learning can amount to 'counting' in the case of multinomial distributions. For our problem, we use our rules: Random, Cheapest Cost, Best Cover and Balance of Cost and Cover. In more detail, the steps of our Bayesian optimisation algorithm for nurse scheduling are: 1. Set t = 0, and generate an initial population P(0) at random; 2. Use roulette-wheel selection to choose a set of promising rule strings S(t) from P(t); 3. Compute conditional probabilities of each node according to this set of promising solutions; 4. Assign each nurse using roulette-wheel selection based on the rules' conditional probabilities. A set of new rule strings O(t) will be generated in this way; 5. Create a new population P(t+1) by replacing some rule strings from P(t) with O(t), and set t = t+1; 6. If the termination conditions are not met (we use 2000 generations), go to step 2. Computational results from 52 real data instances demonstrate the success of this approach. They also suggest that the learning mechanism in the proposed approach might be suitable for other scheduling problems. Another direction for further research is to see if there is a good constructing sequence for individual data instances, given a fixed nurse scheduling order. If so, the good patterns could be recognized and then extracted as new domain knowledge. Thus, by using this extracted knowledge, we can assign specific rules to the corresponding nurses beforehand, and only schedule the remaining nurses with all available rules, making it possible to reduce the solution space. Acknowledgements The work was funded by the UK Government's major funding agency, Engineering and Physical Sciences Research Council (EPSRC), under grand GR/R92899/01. References [1] Aickelin U, "An Indirect Genetic Algorithm for Set Covering Problems", Journal of the Operational Research Society, 53(10): 1118-1126,

'Bayesian Optimisation for Nurse Scheduling'

A Bayesian optimisation algorithm for a nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. When a human scheduler works, he normally builds a schedule systematically following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not yet completed, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this paper, we design a more human-like scheduling algorithm, by using a Bayesian optimisation algorithm to implement explicit learning from past solutions. A nurse scheduling problem from a UK hospital is used for testing. Unlike our previous work that used Genetic Algorithms to implement implicit learning [1], the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The Bayesian optimisation algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, new rule strings have been obtained. Sets of rule strings are generated in this way, some of which will replace previous strings based on fitness. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. For clarity, consider the following toy example of scheduling five nurses with two rules (1: random allocation, 2: allocate nurse to low-cost shifts). In the beginning of the search, the probabilities of choosing rule 1 or 2 for each nurse is equal, i.e. 50%. After a few iterations, due to the selection pressure and reinforcement learning, we experience two solution pathways: Because pure low-cost or random allocation produces low quality solutions, either rule 1 is used for the first 2-3 nurses and rule 2 on remainder or vice versa. In essence, Bayesian network learns 'use rule 2 after 2-3x using rule 1' or vice versa. It should be noted that for our and most other scheduling problems, the structure of the network model is known and all variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus, learning can amount to 'counting' in the case of multinomial distributions. For our problem, we use our rules: Random, Cheapest Cost, Best Cover and Balance of Cost and Cover. In more detail, the steps of our Bayesian optimisation algorithm for nurse scheduling are: 1. Set t = 0, and generate an initial population P(0) at random; 2. Use roulette-wheel selection to choose a set of promising rule strings S(t) from P(t); 3. Compute conditional probabilities of each node according to this set of promising solutions; 4. Assign each nurse using roulette-wheel selection based on the rules' conditional probabilities. A set of new rule strings O(t) will be generated in this way; 5. Create a new population P(t+1) by replacing some rule strings from P(t) with O(t), and set t = t+1; 6. If the termination conditions are not met (we use 2000 generations), go to step 2. Computational results from 52 real data instances demonstrate the success of this approach. They also suggest that the learning mechanism in the proposed approach might be suitable for other scheduling problems. Another direction for further research is to see if there is a good constructing sequence for individual data instances, given a fixed nurse scheduling order. If so, the good patterns could be recognized and then extracted as new domain knowledge. Thus, by using this extracted knowledge, we can assign specific rules to the corresponding nurses beforehand, and only schedule the remaining nurses with all available rules, making it possible to reduce the solution space. Acknowledgements The work was funded by the UK Government's major funding agency, Engineering and Physical Sciences Research Council (EPSRC), under grand GR/R92899/01. References [1] Aickelin U, "An Indirect Genetic Algorithm for Set Covering Problems", Journal of the Operational Research Society, 53(10): 1118-1126,

Family and carer smoking control programmes for reducing children's exposure to environmental tobacco smoke

BackgroundChildren's exposure to other people's cigarette smoke (environmental tobacco smoke, or ETS) is associated with a range of adverse health outcomes for children. Parental smoking is a common source of children's exposure to ETS. Older children are also at risk of exposure to ETS in child care or educational settings. Preventing exposure to cigarette smoke in infancy and childhood has significant potential to improve children's health worldwide.ObjectivesTo determine the effectiveness of interventions aiming to reduce exposure of children to ETS.Search methodsWe searched the Cochrane Tobacco Addiction Group Specialized Register and conducted additional searches of the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, PsycINFO, EMBASE, CINAHL, ERIC, and The Social Science Citation Index & Science Citation Index (Web of Knowledge). Date of the most recent search: September 2013.Selection criteriaControlled trials with or without random allocation. Interventions must have addressed participants (parents and other family members, child care workers and teachers) involved with the care and education of infants and young children (aged 0 to 12 years). All mechanisms for reduction of children's ETS exposure, and smoking prevention, cessation, and control programmes were included. These include health promotion, social-behavioural therapies, technology, education, and clinical interventions.Data collection and analysisTwo authors independently assessed studies and extracted data. Due to heterogeneity of methodologies and outcome measures, no summary measures were possible and results were synthesised narratively.Main resultsFifty-seven studies met the inclusion criteria. Seven studies were judged to be at low risk of bias, 27 studies were judged to have unclear overall risk of bias and 23 studies were judged to have high risk of bias. Seven interventions were targeted at populations or community settings, 23 studies were conducted in the 'well child' healthcare setting and 24 in the 'ill child' healthcare setting. Two further studies conducted in paediatric clinics did not make clear whether the visits were to well or ill children, and another included both well and ill child visits. Thirty-six studies were from North America, 14 were in other high income countries and seven studies were from low- or middle-income countries. In only 14 of the 57 studies was there a statistically significant intervention effect for child ETS exposure reduction. Of these 14 studies, six used objective measures of children's ETS exposure. Eight of the studies had a high risk of bias, four had unclear risk of bias and two had a low risk of bias. The studies showing a significant effect used a range of interventions: seven used intensive counselling or motivational interviewing; a further study used telephone counselling; one used a school-based strategy; one used picture books; two used educational home visits; one used brief intervention and one study did not describe the intervention. Of the 42 studies that did not show a significant reduction in child ETS exposure, 14 used more intensive counselling or motivational interviewing, nine used brief advice or counselling, six used feedback of a biological measure of children's ETS exposure, one used feedback of maternal cotinine, two used telephone smoking cessation advice or support, eight used educational home visits, one used group sessions, one used an information kit and letter, one used a booklet and no smoking sign, and one used a school-based policy and health promotion. In 32 of the 57 studies, there was reduction of ETS exposure for children in the study irrespective of assignment to intervention and comparison groups. One study did not aim to reduce children's tobacco smoke exposure, but rather aimed to reduce symptoms of asthma, and found a significant reduction in symptoms in the group exposed to motivational interviewing. We found little evidence of difference in effectiveness of interventions between the well infant, child respiratory illness, and other child illness settings as contexts for parental smoking cessation interventions.Authors' conclusionsWhile brief counselling interventions have been identified as successful for adults when delivered by physicians, this cannot be extrapolated to adults as parents in child health settings. Although several interventions, including parental education and counselling programmes, have been used to try to reduce children's tobacco smoke exposure, their effectiveness has not been clearly demonstrated. The review was unable to determine if any one intervention reduced parental smoking and child exposure more effectively than others, although seven studies were identified that reported motivational interviewing or intensive counselling provided in clinical settings was effective.

QoS-oriented sesource allocation and scheduling of multiple composite web services in a hybrid cloud using a random-key genetic algorithm

In cloud computing resource allocation and scheduling of multiple composite web services is an important challenge. This is especially so in a hybrid cloud where there may be some free resources available from private clouds but some fee-paying resources from public clouds. Meeting this challenge involves two classical computational problems. One is assigning resources to each of the tasks in the composite web service. The other is scheduling the allocated resources when each resource may be used by more than one task and may be needed at different points of time. In addition, we must consider Quality-of-Service issues, such as execution time and running costs. Existing approaches to resource allocation and scheduling in public clouds and grid computing are not applicable to this new problem. This paper presents a random-key genetic algorithm that solves new resource allocation and scheduling problem. Experimental results demonstrate the effectiveness and scalability of the algorithm.

QoS-oriented resource allocation and scheduling of multiple composite Web services in a hybrid cloud using a random-key genetic algorithm

In cloud computing resource allocation and scheduling of multiple composite web services is an important challenge. This is especially so in a hybrid cloud where there may be some free resources available from private clouds but some fee-paying resources from public clouds. Meeting this challenge involves two classical computational problems. One is assigning resources to each of the tasks in the composite web service. The other is scheduling the allocated resources when each resource may be used by more than one task and may be needed at different points of time. In addition, we must consider Quality-of-Service issues, such as execution time and running costs. Existing approaches to resource allocation and scheduling in public clouds and grid computing are not applicable to this new problem. This paper presents a random-key genetic algorithm that solves new resource allocation and scheduling problem. Experimental results demonstrate the effectiveness and scalability of the algorithm.

Intrahousehold allocation in an unintentional random experiment

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