BACKGROUND: Limited research has examined what is actually done in the process of care by nursing staff in long-term institutional care. The applied instruments employed different terminologies, and psychometric properties were inadequately described. This study aimed to develop and test an observational instrument to identify and examine the amount of time spent on nursing interventions in long-term institutional care using a standardized language.METHODS: The Groningen Observational instrument for Long-Term Institutional Care (GO-LTIC) is based on the conceptual framework of the Nursing Interventions Classification. Developmental, validation, and reliability stages of the GO-LTIC included: 1) item generation to identify potential setting-specific interventions; 2) examining content validity with a Delphi panel resulting in relevant interventions by calculating the item content validity index; 3) testing feasibility with trained observers observing nursing assistants; and 4) calculating inter-rater reliability using (non) agreement and Cohen's kappa for the identification of interventions and an intraclass correlation coefficient for the amount of time spent on interventions. Bland-Altman plots were applied to visualize the agreement between observers. A one-sample student T-test verified if the difference between observers differed significantly from zero.RESULTS: The final version of the GO-LTIC comprised 116 nursing interventions categorized into six domains. Substantial to almost perfect kappa's were found for interventions in the domains basic (0.67-0.92) and complex (0.70-0.94) physiological care. For the domains of behavioral, family, and health system interventions, the kappa's ranged from fair to almost perfect (0.30-1.00). Intraclass correlation coefficients for the amount of time spent on interventions ranged from fair to excellent for the physiological domains (0.48-0.99) and poor to excellent for the other domains (0.00-1.00). Bland Altman plots indicated that the clinical magnitude of differences in minutes was small. No statistical significant differences between observers (p > 0.05) were found.CONCLUSIONS: The GO-LTIC shows good content validity and acceptable inter-rater reliability to examine the amount of time spent on nursing interventions by nursing staff. This may provide managers with valuable information to make decisions about resource allocation, task allocation of nursing staff, and the examination of the costs of nursing services.
Background Clients facing decision-making for long-term care are in need of support and accessible information. Construction of preferences, including context and calculations, for clients in long-term care is challenging because of the variability in supply and demand. This study considers clients in four different sectors of long-term care: the nursing and care of the elderly, mental health care, care of people with disabilities, and social care. The aim is to understand the construction of preferences in real-life situations. Method Client choices were investigated by qualitative descriptive research. Data were collected from 16 in-depth interviews and 79 client records. Interviews were conducted with clients and relatives or informal caregivers from different care sectors. The original client records were explored, containing texts, letters, and comments of clients and caregivers. All data were analyzed using thematic analysis. Results Four cases showed how preferences were constructed during the decision-making process. Clients discussed a wide range of challenging aspects that have an impact on the construction of preferences, e.g. previous experiences, current treatment or family situation. This study describes two main characteristics of the construction of preferences: context and calculation. Conclusion Clients face diverse challenges during the decision-making process on long-term care and their construction of preferences is variable. A well-designed tool to support the elicitation of preferences seems beneficial.
Behavior change is a topic that is of great interest to many people. People can use apps to exercise more, eat healthier, or learn a new skill, but and digital interventions and games are also used by policy makers and companies to create a safe environment for the general public or to increase sales. Given this interest in behavior change, it is not surprising that this topic has seen a lot of interest from the scientific community. This has resulted in a wide range of theories and techniques to bring about behavior change. However, maintaining behavior change is rarely addressed, and as a result poorly understood. In this paper, we take a first step in the design of digital interventions for long-term behavior change by placing a range of behavior change techniques on a long-term behavior change timeline.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
Nowadays, there is particular attention towards the additive manufacturing of medical devices and instruments. This is because of the unique capability of 3D printing technologies for designing and fabricating complex products like bone implants that can be highly customized for individual patients. NiTi shape memory alloys have gained significant attention in various medical applications due to their exceptional superelastic and shape memory properties, allowing them to recover their original shape after deformation. The integration of additive manufacturing technology has revolutionized the design possibilities for NiTi alloys, enabling the fabrication of intricately designed medical devices with precise geometries and tailored functionalities. The AM-SMART project is focused on exploring the suitability of NiTi architected structures for bone implants fabricated using laser powder bed fusion (LPBF) technology. This is because of the lower stiffness of NiTi alloys compared to Ti alloys, closely aligning with the stiffness of bone. Additionally, their unique functional performance enables them to dissipate energy and recover the original shape, presenting another advantage that makes them well-suited for bone implants. In this investigation, various NiTi-based architected structures will be developed, featuring diverse cellular designs, and their long-term thermo-mechanical performance will be thoroughly evaluated. The findings of this study underscore the significant potential of these structures for application as bone implants, showcasing their adaptability for use also beyond the medical sector.
Creating and testing the first Brand Segmentation Model in Augmented Reality using Microsoft Hololens. Sanoma together with SAMR launched an online brand segmentation tool based on large scale research, The brand model uses several brand values divided over three axes. However they cannot be displayed clearly in a 2D model. The space of BSR Quality Planner can be seen as a 3-dimensional meaningful space that is defined by the terms used to typify the brands. The third axis concerns a behaviour-based dimension: from ‘quirky behaviour’ to ‘standardadjusted behaviour’ (respectful, tolerant, solidarity). ‘Virtual/augmented reality’ does make it possible to clearly display (and experience) 3D. The Academy for Digital Entertainment (ADE) of Breda University of Applied Sciences has created the BSR Quality Planner in Virtual Reality – as a hologram. It’s the world’s first segmentation model in AR. Breda University of Applied Sciences (professorship Digital Media Concepts) has deployed hologram technology in order to use and demonstrate the planning tool in 3D. The Microsoft HoloLens can be used to experience the model in 3D while the user still sees the actual surroundings (unlike VR, with AR the space in which the user is active remains visible). The HoloLens is wireless, so the user can easily walk around the hologram. The device is operated using finger gestures, eye movements or voice commands. On a computer screen, other people who are present can watch along with the user. Research showed the added value of the AR model.Partners:Sanoma MediaMarketResponse (SAMR)
