Research question: The current study investigates the income elasticities and socio-economic determinants of direct and indirect sports expenditure categories by means of a log normal hurdle regression. Research methods: The data stem from a representative sample of 3005 Flemish families with school-aged children, gathered through a sports-specific survey. A log normal hurdle regression was used to calculate the determining factors and expenditure elasticities of expenditure on sports participation. Results and findings: The results indicate that income, education and the age of the youngest child are positively related to almost all sports expenditure categories, while the number of family members and degree of urbanisation are significant for only a number of the expenditure categories. The elasticity value of the direct sports expenses is smaller than is the case for indirect sports expenditure. Between the expenditure categories large differences exist, as relatively large elasticities are found for sports holidays, transport and sports food and drinks, as opposed to low values of sports events, sports club membership, entrance fees for sports infrastructure, sports camps, clothing, footwear and equipment. Implications: The fact that income significantly influences all expenditure categories demonstrates that further policy intervention is required to make sports consumption more accessible to lower income groups. Sports enterprises and policymakers need to be aware that negative income shifts have a more profound impact on the indirect expenditure categories, and that certain sports activities (e.g. participation events) are relatively more favoured by low-income groups than is the case for sports club membership
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It has been suggested that physical education (PE) and active transport can make a meaningful contribution to children's physical activity (PA) levels. However, data on the contribution these activities to total PA is scarce, and PE's contribution to total physical activity energy expenditure (PAEE) has to our knowledge never been determined. This is probably explained by the methodological complexity of determining PAEE (Welk, 2002). In this paper, we present the first data of an ongoing study using combined heart rate monitoring and accelerometry, together with activity diaries. Over the six measurement days, PE contributed 5% to total PAEE, and 16% to school-related PAEE, whereas active transportation had a much larger contribution.
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Background: Although the general assumption is that patients with rheumatoid arthritis (RA) have decreased levels of physical activity, no review has addressed whether this assumption is correct. Methods: Our objective was to systematically review the literature for physical activity levels and aerobic capacity (VO2max). in patients with (RA), compared to healthy controls and a reference population. Studies investigating physical activity, energy expenditure or aerobic capacity in patients with RA were included. Twelve studies met our inclusion criteria. Results: In one study that used doubly labeled water, the gold standard measure, physical activity energy expenditure of patients with RA was significantly decreased. Five studies examined aerobic capacity. Contradictory evidence was found that patients with RA have lower VO2max than controls, but when compared to normative values, patients scored below the 10th percentile. In general, it appears that patients with RA spend more time in light and moderate activities and less in vigorous activities than controls. Conclusion: Patients with RA appear to have significantly decreased energy expenditure, very low aerobic capacity compared to normative values and spend less time in vigorous activities than controls
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BACKGROUND: When indirect calorimetry is not available, predictive equations are used to estimate resing energy expenditure (REE). There is no consensus about which equation to use in hospitalized patients. The objective of this study is to examine the validity of REE predictive equations for underweight, normal weight, overweight, and obese inpatients and outpatients by comparison with indirect calorimetry.METHODS: Equations were included when based on weight, height, age, and/or gender. REE was measured with indirect calorimetry. A prediction between 90 and 110% of the measured REE was considered accurate. The bias and root-mean-square error (RMSE) were used to evaluate how well the equations fitted the REE measurement. Subgroup analysis was performed for BMI. A new equation was developed based on regression analysis and tested.RESULTS: 513 general hospital patients were included, (253 F, 260 M), 237 inpatients and 276 outpatients. Fifteen predictive equations were used. The most used fixed factors (25 kcal/kg/day, 30 kcal/kg/day and 2000 kcal for female and 2500 kcal for male) were added. The percentage of accurate predicted REE was low in all equations, ranging from 8 to 49%. Overall the new equation performed equal to the best performing Korth equation and slightly better than the well-known WHO equation based on weight and height (49% vs 45% accurate). Categorized by BMI subgroups, the new equation, Korth and the WHO equation based on weight and height performed best in all categories except from the obese subgroup. The original Harris and Benedict (HB) equation was best for obese patients.CONCLUSIONS: REE predictive equations are only accurate in about half the patients. The WHO equation is advised up to BMI 30, and HB equation is advised for obese (over BMI 30). Measuring REE with indirect calorimetry is preferred, and should be used when available and feasible in order to optimize nutritional support in hospital inpatients and outpatients with different degrees of malnutrition.
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BACKGROUND: Hospital stays are associated with high levels of sedentary behavior and physical inactivity. To objectively investigate physical behavior of hospitalized patients, these is a need for valid measurement instruments. The aim of this study was to assess the criterion validity of three accelerometers to measure lying, sitting, standing and walking. METHODS: This cross-sectional study was performed in a university hospital. Participants carried out several mobility tasks according to a structured protocol while wearing three accelerometers (ActiGraph GT9X Link, Activ8 Professional and Dynaport MoveMonitor). The participants were guided through the protocol by a test leader and were recorded on video to serve as reference. Sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) were determined for the categories lying, sitting, standing and walking. RESULTS: In total 12 subjects were included with a mean age of 49.5 (SD 21.5) years and a mean body mass index of 23.8 kg/m2 (SD 2.4). The ActiGraph GT9X Link showed an excellent sensitivity (90%) and PPV (98%) for walking, but a poor sensitivity for sitting and standing (57% and 53%), and a poor PPV (43%) for sitting. The Activ8 Professional showed an excellent sensitivity for sitting and walking (95% and 93%), excellent PPV (98%) for walking, but no sensitivity (0%) and PPV (0%) for lying. The Dynaport MoveMonitor showed an excellent sensitivity for sitting (94%), excellent PPV for lying and walking (100% and 99%), but a poor sensitivity (13%) and PPV (19%) for standing. CONCLUSIONS: The validity outcomes for the categories lying, sitting, standing and walking vary between the investigated accelerometers. All three accelerometers scored good to excellent in identifying walking. None of the accelerometers were able to identify all categories validly.
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These are hard days for companies: they have to survive in a market that has been hit by a financial crisis. Many countries in Europe have severe problems trying to overcome this financial crisis. The main remedy applied by governments is to cut back on expenditure, but on the other hand it is said that it is important for a country, and especially for companies, to invest in innovation. These innovations should lead to innovative products that will lead to profitability turnovers for these companies and, as a consequence, improve the economic conditions in a country. Universities provide students with engineering competences, like develop innovation, with which they can show a higher degree of ability to answer complex questions such as how to become players in the market again. Teaching students to become more innovative engineers, Fontys University of Applied Sciences, Department of Engineering, has designed a curriculum in which students are educated in the competence innovation. An important element in the process of teaching innovation to students is the approach of inquiring into possibilities of patents. In the second semester of the first year, students can decide to join an innovative project called: ‘The invention project’. The basis of this project is that students are given the opportunity to create their own invention and with their previously acquired knowledge and skills they design, calculate, prototype and present their invention. In a research project, the experiences of students in this Invention Project have been analysed. The goal of this study was to understand what the success factors are for such a project. The basis of this inquiry is a questionnaire to identify the opinions of students. The research was carried out in the spring semester of 2012. In total 31 students were involved in this research. The results show that there was a high degree of student satisfaction about the Invention Project focused on innovation development. Success factors for this project in the first year of the curriculum were seen: 1 to work on own inventions, 2 development of student’s perception of the total product creation process and 3 to make students see the relevance of contacts with real professionals from industry and from the patent office in their own project. Improvements can be made by: 1 helping students more during the creativity stage in the project and 2 to coach them more on the aspect of engineering a successful invention of which they can be proud. This Invention project is a interesting with which collaborations with other universities can be set up.
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This paper reports on the first stage of a research project1) that aims to incorporate objective measures of physical activity into health and lifestyle surveys. Physical activity is typically measured with questionnaires that are known to have measurement issues, and specifically, overestimate the amount of physical activity of the population. In a lab setting, 40 participants wore four different sensors on five different body parts, while performing various activities (sitting, standing, stepping with two intensities, bicycling with two intensities, walking stairs and jumping). During the first four activities, energy expenditure was measured by monitoring heart rate and the gas volume of in‐ and expired O2 and CO2. Participants subsequently wore two sensor systems (the ActivPAL on the thigh and the UKK on the waist) for a week. They also kept a diary keeping track of their physical activities, work and travel hours. Machine learning algorithms were trained with different methods to determine which sensor and which method was best able to differentiate the various activities and the intensity with which they were performed. It was found that the ActivPAL had the highest overall accuracy, possibly because the data generated on the upper tigh seems to be best distinguishing between different types of activities and therefore led to the highest accuracy. Accuracy could be slightly increased by including measures of heartrate. For recognizing intensity, three different measures were compared: allocation of MET values to activities (used by ActivPAL), median absolute deviation, and heart rate. It turns out that each method has merits and disadvantages, but median absolute deviation seems to be the most promishing metric. The search for the best method of gauging intensity is still ongoing. Subsequently, the algorithms developed for the lab data were used to determine physical activity in the week people wore the devices during their everyday activities. It quickly turned out that the models are far from ready to be used on free living data. Two approaches are suggested to remedy this: additional research with meticulously labelled free living data, e.g., by combining a Time Use Survey with accelerometer measurements. The second is to focus on better determining intensity of movement, e.g., with the help of unsupervised pattern recognition techniques. Accuracy was but one of the requirements for choosing a sensor system for subsequent research and ultimate implementation of sensor measurement in health surveys. Sensor position on the body, wearability, costs, usability, flexibility of analysis, response, and adherence to protocol equally determine the choice for a sensor. Also from these additional points of view, the activPAL is our sensor of choice.
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Due to the ageing population, the prevalence of musculoskeletal disorders will continue to rise, as well as healthcare expenditure. To overcome these increasing expenditures, integration of orthopaedic care should be stimulated. The Primary Care Plus (PC+) intervention aimed to achieve this by facilitating collaboration between primary care and the hospital, in which specialised medical care is shifted to a primary care setting. The present study aims to evaluate the referral decision following orthopaedic care in PC+ and in particular to evaluate the influence of diagnostic tests on this decision. Therefore, retrospective monitoring data of patients visiting PC+ for orthopaedic care was used. Data was divided into two periods; P1 and P2. During P2, specialists in PC+ were able to request additional diagnostic tests (such as ultrasounds and MRIs). A total of 2,438 patients visiting PC+ for orthopaedic care were included in the analysis. The primary outcome was the referral decision following PC+ (back to the general practitioner (GP) or referral to outpatient hospital care). Independent variables were consultation- and patient-related predictors. To describe variations in the referral decision, logistic regression modelling was used. Results show that during P2, significantly more patients were referred back to their GP. Moreover, the multivariable analysis show a significant effect of patient age on the referral decision (OR 0.86, 95% CI = 0.81– 0.91) and a significant interaction was found between the treating specialist and the period (p = 0.015) and between patient’s diagnosis and the period (p < 0.001). Despite the significant impact of the possibility of requesting additional diagnostic tests in PC+, it is important to discuss the extent to which the availability of diagnostic tests fits within the vision of PC+. In addition, selecting appropriate profiles for specialists and patients for PC+ are necessary to further optimise the effectiveness and cost of care.
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Background: The purpose of this study was to investigate the cost-effectiveness and budget impact of the Boston University Approach to Psychiatric Rehabilitation (BPR) compared to an active control condition (ACC) to increase the social participation (in competitive employment, unpaid work, education, and meaningful daily activities) of individuals with severe mental illnesses (SMIs). ACC can be described as treatment as usual but with an active component, namely the explicit assignment of providing support with rehabilitation goals in the area of social participation. Method: In a randomized clinical trial with 188 individuals with SMIs, BPR (n = 98) was compared to ACC (n=90). Costs were assessed with the Treatment Inventory of Costs in Patients with psychiatric disorders (TIC-P). Outcome measures for the cost-effectiveness analysis were incremental cost per Quality Adjusted Life Year (QALY) and incremental cost per proportional change in social participation. Budget Impact was investigated using four implementation scenarios and two costing variants. Results: Total costs per participant at 12-month follow-up were e 12,886 in BPR and e 12,012 in ACC, a non-significant difference. There were no differences with regard to social participation or QALYs. Therefore, BPR was not cost-effective compared to ACC. Types of expenditure with the highest costs were in order of magnitude: supported and sheltered housing, inpatient care, outpatient care, and organized activities. Estimated budget impact of wide BPR implementation ranged from cost savings to e190 million, depending on assumptions regarding uptake. There were no differences between the two costing variants meaning that from a health insurer perspective, there would be no additional costs if BPR was implemented on a wider scale in mental health care institutions. Conclusions: This was the first study to investigate BPR cost-effectiveness and budget impact. The results showed that BPR was not cost-effective compared to ACC. When interpreting the results, one must keep in mind that the cost-effectiveness of BPR was investigated in the area of social participation, while BPR was designed to offer support in all rehabilitation areas. Therefore, more studies are needed before definite conclusions can be drawn on the cost-effectiveness of the method as a whole.
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This chapter offers a working definition of social accountability as any citizen-led action beyond elections that aims to enhance the accountability of state actors. We view social accountability as a broad array of predominantly bottom-up initiatives, aimed at improving the quality of governance (especially oversight and responsiveness) through active citizen participation. We also trace the evolution of SA as a concept in the literature over the past decades and, then, discuss some influential theoretic approaches to SAIs, pointing out strengths and weaknesses of each model. Finally, we suggest organising Arab SAIs into one of three categories: (1) transparency; (2) advocacy; or (3) participatory governance and we review each of these existing action formats by discussing their main strengths and flaws.
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