Serious games foster the acquisition of complex problem-solving skills. Assessment of such skills should be in line with instruction, and within a serious game environment its content validity should equal face-to-face assessment. Research on assessment in serious gaming has remained rather scarce. This article shows how assessment can be implemented in serious gaming in a way that assures content validity. The core of the authors’ validation method entails mapping learning activities (as contained in the game scenario) on performance indicators and outputs (as derived from formal attainment levels). They present how they have elaborated and applied the method for an assessment game for ICT managers in secondary vocational education. They describe the procedure and extent to which this assessment is content-valid compared to face-to-face assessment.
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ObjectiveTo identify malnutrition assessment methods in cancer patients and assess their content validity based on internationally accepted definitions for malnutrition.Study Design and SettingSystematic review of studies in cancer patients that operationalized malnutrition as a variable, published since 1998. Eleven key concepts, within the three domains reflected by the malnutrition definitions acknowledged by European Society for Clinical Nutrition and Metabolism (ESPEN) and the American Society for Parenteral and Enteral Nutrition (ASPEN): A: nutrient balance; B: changes in body shape, body area and body composition; and C: function, were used to classify content validity of methods to assess malnutrition. Content validity indices (M-CVIA–C) were calculated per assessment method. Acceptable content validity was defined as M-CVIA–C ≥ 0.80.
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Within rehabilitation, there is a great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self- or attendant-pushed wheelchair propulsion. The aim of this study was to validate this new detection method by comparison with manual annotation of wheelchair use. Twenty-four amputation and stroke patients completed a semi-structured course of active and passive wheelchair use. Based on a machine learning approach, a method was developed that detected the type of movement. The machine learning method was trained based on the data of a single-wheel sensor as well as a setup using an additional sensor on the frame. The method showed high accuracy (F1 = 0.886, frame and wheel sensor) even if only a single wheel sensor was used (F1 = 0.827). The developed and validated measurement method is ideally suited to easily determine wheelchair use and the corresponding activity level of patients in rehabilitation.
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Environmental nano- and micro-plastics (NMPs) are highly diverse [2]. Accounting for this diversity is one of the main challenges to develop a comprehensive understanding of NMPs detection, quantification, fate, and risks [3]. Two major issues currently limit progresses within this field: (a) validation and broadening the current analytical tools (b) uncertainty with respect to NMPs occurrence and behaviour at small scales (< 20 micron). Tracking NMPs in environmental systems is currently limited to micron size plastics due to the size detection limit of the available analytical techniques. There are currently many uncertainties regarding detecting nanoplastics in real environmental systems, e.g. the inexistence of commercially available NMPs and incompatibility between them and those generated from plastic fragments degradation in the environment. Trying to tackle these problems some research groups synthesized NMPs dopped with metals inside [16]. However, even though elemental analysis techniques (ICP-MS) are rather sensitive, the low volume of these metals encapsulated in the nanoparticles make their detection rather challenging. At the same time, due to Sars-Cov-19 pandemic, nucleic acid identification technologies (LAMP, PCR) experienced a fast evolution and are able to provide detection at very low levels with very compact and reliable equipment. Nuclepar proposes the use of Electrohydrodynamic Atomization (EHDA) to generate NMPs coated with nucleic acids of different polymer types, sizes, and shapes, which can be used as support for detection of such particles using PCR-LAMP technology. If proven possible, Nuclepar might become a first step towards an easy NMPs detection tool. This knowledge will certainly impact current risk assessment tools, efficient interventions to limit emissions and adequate regulations related to NMPs.
To enable circularity new tools are needed. Regulatory compliance with the European Commission has introduced the Digital Product Passport (DPP) as part of the Ecodesign for Sustainable Products Regulation (ESPR). This framework requires traceability across all production tiers, including Tier 4, which covers raw material origins. The textile clothing leather and footwear (TCLF) sector has been identified as priority categories for DPP adoption, with mandatory compliance set between 2027 and 2030. DPP system standardizes lifecycle value chain data and includes information on material origin, manufacturing, assembly, and end-of-life handling. For the Dutch textile sector, comprising of almost 11,000 companies, DPP implementation presents significant challenges due to fragmented data infrastructure and long product lifecycles. Traditional identifiers (e.g., QR-codes, RFID) are often damaged or removed, limiting their effectiveness. Molecular characterization—using established techniques like spectral and chemical analysis—is emerging as the only reliable long-term solution for persistent, product-embedded identification. These molecular methods allow precise validation of fiber content, wear analysis, and recyclability, addressing compliance and end-of-life traceability issues. The Molecular Digital Physical Digital Product Passport (M-DPP) initiative demonstrates a practical application of these techniques for wool and cotton. It employs co-design to ensure regulatory alignment and develops an open-source API to support automated validation, extended producer responsibility (EPR), return and reuse (RE), textile lifecycle recovery (TLR), and material sorting and recycling (MSR). Smart contract functionality enables automated execution within deposit-refund systems, improving traceability and circularity. An iterative, design-thinking methodology underpins system development, ensuring adaptability to evolving standards. Pilot testing in collaboration with fashion and interior partners will validate the molecular sensing and data integration approach. Dissemination and scaling will occur through partnerships with NewTexEco, Circolab, DCTV, and TNO’s Center of Excellence for DPPs, aligning with European standardization efforts and enabling sector-wide adoption.
