Background: Impaired upper extremity function due to muscle paresis or paralysis has a major impact on independent living and quality of life (QoL). Assistive technology (AT) for upper extremity function (i.e. dynamic arm supports and robotic arms) can increase a client’s independence. Previous studies revealed that clients often use AT not to their full potential, due to suboptimal provision of these devices in usual care. Objective: To optimize the process of providing AT for impaired upper extremity function and to evaluate its (cost-)effectiveness compared with care as usual. Methods: Development of a protocol to guide the AT provision process in an optimized way according to generic Dutch guidelines; a quasi-experimental study with non-randomized, consecutive inclusion of a control group (n = 48) receiving care as usual and of an intervention group (optimized provision process) (n = 48); and a cost-effectiveness and cost-utility analysis from societal perspective will be performed. The primary outcome is clients’ satisfaction with the AT and related services, measured with the Quebec User Evaluation of Satisfaction with AT (Dutch version; D-QUEST). Secondary outcomes comprise complaints of the upper extremity, restrictions in activities, QoL, medical consumption and societal cost. Measurements are taken at baseline and at 3, 6 and 9 months follow-up.
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Abstract Background: Cardiovascular disease is the leading cause of the estimated 11–25 years reduced life expectancy for persons with serious mental illness (SMI). This excess cardiovascular mortality is primarily attributable to obesity, diabetes, hypertension, and dyslipidaemia. Obesity is associated with a sedentary lifestyle, limited physical activity and an unhealthy diet. Lifestyle interventions for persons with SMI seem promising in reducing weight and cardiovascular risk. The aim of this study is to evaluate the effectiveness and cost-effectiveness of a lifestyle intervention among persons with SMI in an outpatient treatment setting. Methods: The Serious Mental Illness Lifestyle Evaluation (SMILE) study is a cluster-randomized controlled trial including an economic evaluation in approximately 18 Flexible Assertive Community Treatment (FACT) teams in the Netherlands. The intervention aims at a healthy diet and increased physical activity. Randomisation takes place at the level of participating FACT-teams. We aim to include 260 outpatients with SMI and a body mass index of 27 or higher who will either receive the lifestyle intervention or usual care. The intervention will last 12 months and consists of weekly 2-h group meetings delivered over the first 6 months. The next 6 months will include monthly group meetings, supplemented with regular individual contacts. Primary outcome is weight loss. Secondary outcomes are metabolic parameters (waist circumference, lipids, blood pressure, glucose), quality of life and health related self-efficacy. Costs will be measured from a societal perspective and include costs of the lifestyle program, health care utilization, medication and lost productivity. Measurements will be performed at baseline and 3, 6 and 12 months. Discussion: The SMILE intervention for persons with SMI will provide important information on the effectiveness, cost-effectiveness, feasibility and delivery of a group-based lifestyle intervention in a Dutch outpatient treatment setting. Trial registration: Dutch Trial Registration NL6660, registration date: 16 November 2017.
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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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Granular materials (GMs) are simply a collection of individual particles, e.g., rice, coffee, iron-ore. Although straightforward in appearance, GMs are key to several processes in chemical-pharmaceutical, high-tech, agri-food and energy industry. Examples include laser sintering in additive manufacturing, tableting in pharma or just mixing of your favourite crunchy muesli mix in food industry. However, these bulk material handling processes are notorious for their inefficiency and ineffectiveness. Thereby, affecting the overall expenses and product quality. To understand and enhance the quality of a process, GMs industries utilise computer-simulations, much like how cars and aeroplanes have been designed and optimised since the 1990s. Just as how engineers utilise advanced computer-models to develop our fuel-efficient vehicle design, energy-saving granular processes are also developed utilising physics-based simulation-models, using a computer. Although physics-based models can effectively optimise large-scale processes, creating and simulating a fully representative virtual prototype of a GMs process is very iterative, computationally expensive and time intensive. On the contrary, given the available data, this is where machine learning (ML) could be of immense value. Like how ML has transformed the healthcare, energy and other top sectors, recent ML-based developments for GMs show serious promise in faster virtual prototyping and reduced computational cost. Enabling industries to rapidly design and optimise, enhancing real-time data-driven decision making. GranML aims to empower the GMs industries with ML. We will do so by (i) performing an in-depth GMs-ML literature review, (ii) developing open-access ML implementation guidelines; and (iii) an open-source proof-of-concept for an industry-relevant use case. Eventually, our follow-up mission is to build upon this vital knowledge by (i) expanding the consortium; (ii) co-developing a unified methodology for efficient computer-prototyping, unifying physics- and ML-based technologies for GMs; (iii) enhancing the existing computer-modelling infrastructure; and (iv) validating through industry focused demonstrators.
Nature areas in North-West Europe (NWE) face an increasing number of visitors (intensified by COVID-19) resulting in an increased pressure on nature, negative environmental impacts, higher management costs, and nuisance for local residents and visitors. The high share of car use exaggerates these impacts, including peak pressures. Furthermore, the almost exclusive access by car excludes disadvantaged people, specifically those without access to a car. At the same time, the urbanised character of NWE, its dense public transport network, well-developed tourism & recreation sector, and presence of shared mobility providers offers ample opportunities for more sustainable tourism. Thus, MONA will stimulate sustainable tourism in and around nature areas in NWE which benefits nature, the environment, visitors, and the local economy. MONA will do so by encouraging a modal shift through facilitating sustainableThe pan-European Innovation Action, funded under the Horizon Europe Framework Programme, aims to promote innovative governance processes ,and help public authorities in shaping their climate mitigation and adaptation policies. To achieve this aim, the GREENGAGE project will leverage citizens’ participation and equip them with innovative digital solutions that will transform citizen’s engagement and cities’ effectiveness in delivering the European Green Deal objectives for carbon neutral cities.Focusing on mobility, air quality and healthy living, citizens will be inspired to observe and co-create their cities by sensing their urban environments. The aim to complement, validate, and enrich information in authoritative data held by the public administrations and public agencies. This will be facilitated by engaging with citizens to co-create green initiatives and to develop Citizen Observatories. In GREENGAGE, Citizen Observatories will be a place where pilot cities will co-examine environmental issues integrating novel bottom-up process with top-down perspectives. This will provide the basis to co-create and co-design innovative solutions to monitor environmental problems at ground level with the help of citizens.With two interrelated project dimensions, the project aims to enhance intelligence applied to city decision-making processes and governance by engaging with citizen observations integrated with Copernicus, GEOSS, in-situ, and socio-economic intelligence, and by delivering innovative governance models based on novel toolboxes of decision-making methodologies and technologies. The envisioned citizens observatory campaigns will be deployed and fully demonstrated in 5 pilot engagements in selected European cities and regions including: Bristol (the United Kingdom), Copenhagen (Denmark), Turano / Gerace (Italy) and the region of Noord Brabant (the Netherlands). These innovation pilots aim to highlight the need for smart city governance by promoting citizen engagement, co-creation, gathering new data which will complement existing datasets and evidence-based decision and policymaking.
This project assists architects and engineers to validate their strategies and methods, respectively, toward a sustainable design practice. The aim is to develop prototype intelligent tools to forecast the carbon footprint of a building in the initial design process given the visual representations of space layout. The prediction of carbon emission (both embodied and operational) in the primary stages of architectural design, can have a long-lasting impact on the carbon footprint of a building. In the current design strategy, emission measures are considered only at the final phase of the design process once major parameters of space configuration such as volume, compactness, envelope, and materials are fixed. The emission assessment only at the final phase of the building design is due to the costly and inefficient interaction between the architect and the consultant. This proposal offers a method to automate the exchange between the designer and the engineer using a computer vision tool that reads the architectural drawings and estimates the carbon emission at each design iteration. The tool is directly used by the designer to track the effectiveness of every design choice on emission score. In turn, the engineering firm adapts the tool to calculate the emission for a future building directly from visual models such as shared Revit documents. The building realization is predominantly visual at the early design stages. Thus, computer vision is a promising technology to infer visual attributes, from architectural drawings, to calculate the carbon footprint of the building. The data collection for training and evaluation of the computer vision model and machine learning framework is the main challenge of the project. Our consortium provides the required resources and expertise to develop trustworthy data for predicting emission scores directly from architectural drawings.
