Background: Successful implementation of multifactorial fall prevention interventions (FPIs) is essential to reduce increasing fall rates in communitydwelling older adults. However, implementation often fails due to the complex context of the community involving multiple stakeholders within and across settings, sectors, and organizations. As there is a need for a better understanding of the occurring context-related challenges, the current scoping review purposes to identify what contextual determinants (i.e., barriers and facilitators) influence the implementation of FPIs in the community. Methods: A scoping reviewwas performed using the Arksey andO’Malley framework. First, electronic databases (Pubmed, CINAHL, SPORTDiscus, PsycINFO) were searched. Studies that identified contextual determinants that influence the implementation of FPIs in the community were included. Second, to both validate the findings from the literature and identify complementary determinants, health and social care professionals were consulted during consensus meetings (CMs) in four districts in the region of Utrecht, the Netherlands. Data were analyzed following a directed qualitative content analysis approach, according to the 39 constructs of the Consolidated Framework for Implementation Research. Results: Fourteen relevant studies were included and 35 health and social care professionals (such as general practitioners, practice nurses, and physical therapists) were consulted during four CMs. Directed qualitative content analysis of the included studies yielded determinants within 35 unique constructs operating as barriers and/or facilitators. The majority of the constructs (n = 21) were identified in both the studies and CMs, such as “networks and communications”, “formally appointed internal implementation leaders”, “available resources” and “patient needs and resources”. The other constructs (n = 14) were identified only in the . Discussion: Findings in this review show that awide array of contextual determinants are essential in achieving successful implementation of FPIs in the community. However, some determinants are considered important to address, regardless of the context where the implementation occurs. Such as accounting for time constraints and financial limitations, and considering the needs of older adults. Also, broad cross-sector collaboration and coordination are required in multifactorial FPIs. Additional context analysis is always an essential part of implementation efforts, as contexts may differ greatly, requiring a locally tailored approach.
BACKGROUND: Secondary prevention of coronary artery disease (CAD) is increasingly provided by nurse-coordinated prevention programs (NCPP). Little is known about nurses' perspectives on these programs.AIM: To investigate nurses' perspectives/experiences in NCPPs in acute coronary syndrome patients.METHODS: Thirteen nurses from NCPPs in 11 medical centers in the RESPONSE trial completed an online survey containing 45 items evaluating 3 outcome categories: (1) conducting NCPP visits; (2) effects of NCPP interventions on risk profiles and (3) process of care.RESULTS: Nurses felt confident in counseling/motivating patients to reduce CAD risk. Interventions targeting LDL, blood pressure and medication adherence were reported as successful, corresponding with significant improvements of these risk factors. Improving weight, smoking and physical activity was reported as less effective. Screening for anxiety/depression was suggested as an improvement.CONCLUSIONS: Nurses acknowledge the importance and effectiveness of NCPPs, and correctly identify which components of the program are the most successful. Our study provides a basis for implementation and quality improvement for NCCPs.
BACKGROUND: Causes of anterior cruciate ligament (ACL) injuries are multifactorial. Anterior cruciate ligament injury prevention should thus be approached from a multifactorial perspective as well. Training to resist fatigue is an underestimated aspect of prevention programs given that the presence of fatigue may play a crucial role in sustaining an ACL injury.OBJECTIVES:The primary objective of this literature review was to summarize research findings relating to the kinematic and kinetic effects of fatigue on single-leg landing tasks through a systematic review and meta-analysis. Other objectives were to critically appraise current approaches to examine the effects of fatigue together with elucidating and proposing an optimized approach for measuring the role of fatigue in ACL injury prevention.METHODS:A systematic literature search was conducted in the databases PubMed (1978-November 2017), CINAHL (1992-November 2017), and EMBASE (1973-November 2017). The inclusion criteria were: (1) full text, (2) published in English, German, or Dutch, (3) healthy subjects, (4) average age ≥ 18 years, (5) single-leg jump landing task, (6) evaluation of the kinematics and/or kinetics of the lower extremities before and after a fatigue protocol, and (7) presentation of numerical kinematic and/or kinetic data. Participants included healthy subjects who underwent a fatigue protocol and in whom the effects of pre- and post-fatigue on three-dimensional lower extremity kinematic and kinetics were compared. Methods of data collection, patient selection, blinding, prevention of verification bias, and study design were independently assessed.RESULTS:Twenty studies were included, in which four types of single-leg tasks were examined: the single-leg drop vertical jump, the single-leg drop landing, the single-leg hop for distance, and sidestep cutting. Fatigue seemed to mostly affect initial contact (decreased angles post-fatigue) and peak (increased angles post-fatigue) hip and knee flexion. Sagittal plane variables at initial contact were mostly affected under the single-leg hop for distance and sidestep cutting conditions whilst peak angles were affected during the single-leg drop jump.CONCLUSIONS:Training to resist fatigue is an underestimated aspect of prevention programs given that the presence of fatigue may play a crucial role in sustaining an ACL injury. Considering the small number of variables affected after fatigue, the question arises whether the same fatigue pathways are affected by the fatigue protocols used in the included laboratory studies as are experienced on the sports field.
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Mattresses for the healthcare sector are designed for robust use with a core foam layer and a polyurethane-coated polyester textile cover. Nurses and surgeons indicate that these mattresses are highly uncomfortable to patients because of poor microclimatic management (air, moisture, temperature, friction, pressure regulation, etc) across the mattress, which can cause pressure ulcers (in less than a day). The problem is severe (e.g., extra recovery time, medication, increased risk, and costs) for patients with wounds, infection, pressure-sensitive decubitus. There are around 180,000 waterproof mattresses in the healthcare sector in the Netherlands, of which yearly 40,000 mattresses are discarded. Owing to the rapidly aging population it is expected to increase the demand for these functional mattresses from 180,000 to 400,000 in the next 10 years in the healthcare sector. To achieve a circular economy, Dutch Government aims for a 50% reduction in the use of primary raw materials by 2030. As of January 1, 2022, mattress manufacturers and importers are obliged to pay a waste management contribution. Within the scope of this project, we will design, develop, and test a circular & functional mattress for the healthcare (cure & care) sector. The team of experts from knowledge institutes, SMEs, hospital(s), branch-organization joins hands to design and develop a functional (microclimate management, including ease of use for nurses and patients) mattress that deals with uncomfortable sleeping and addresses the issue of pressure ulcers thereby overall accelerating the healing process. Such development addresses the core issue of circularity. The systematic research with proper demand articulation leads to V-shape verification and validation research methodology. With design focus and applied R&D at TRL-level (4-6) is expected to deliver the validated prototype(s) offering SMEs an opportunity to innovate and expand their market. The knowledge will be used for dissemination and education at Saxion.
Predictive maintenance, using data of thousands of sensors already available, is key for optimizing the maintenance schedule and further prevention of unexpected failures in industry.Current maintenance concepts (in the maritime industry) are based on a fixed maintenance interval for each piece of equipment with enough safety margin to minimize incidents. This means that maintenance is most of the time carried out too early and sometimes too late. This is in particular true for maintenance on maritime equipment, where onshore maintenance is strongly preferred over offshore maintenance and needs to be aligned with the vessel’s operations schedule. However, state-of-the-art predictive maintenance methods rely on black-box machine learning techniques such as deep neural networks that are difficult to interpret and are difficult to accept and work with for the maintenance engineers. The XAIPre project (pronounce Xyper) aims at developing Explainable Predictive Maintenance algorithms that do not only provide the engineers with a prediction but in addition, with a risk analysis on the components when delaying the maintenance, and what the primary indicators are that the algorithms use to create inference. To use predictive maintenance effectively in Maritime operations, the predictive models and also the optimization of the maintenance schedule using these models, need to be aware of the past and planned vessel activities, since different activities affect the lifetime of the machines differently. For example, the degradation of a hydraulic pump inside a crane depends on the type of operations the crane but also the vessel is performing. Thus the models do not only need to be explainable but they also need to be aware of the context which is in this case the vessel and machinery activity. Using sensor data processing and edge-computing technologies that will be developed and applied by the Hanze University of Applied Sciences in Groningen (Hanze UAS), context information is extracted from the raw sensor data. The XAIPre project combines these Explainable Context Aware Machine Learning models with state-of-the-art optimizers, that are already developed and available from the NWO CIMPLO project at LIACS, in order to develop optimal maintenance schedules for machine components. The resulting XAIPre prototype offers significant competitive advantages for maritime companies such as Heerema, by increasing the longevity of machine components, increasing worker safety and decreasing maintenance costs.
Predictive maintenance, using data of thousands of sensors already available, is key for optimizing the maintenance schedule and further prevention of unexpected failures in industry. Current maintenance concepts (in the maritime industry) are based on a fixed maintenance interval for each piece of equipment with enough safety margin to minimize incidents. This means that maintenance is most of the time carried out too early and sometimes too late. This is in particular true for maintenance on maritime equipment, where onshore maintenance is strongly preferred over offshore maintenance and needs to be aligned with the vessel’s operations schedule. However, state-of-the-art predictive maintenance methods rely on black-box machine learning techniques such as deep neural networks that are difficult to interpret and are difficult to accept and work with for the maintenance engineers. The XAIPre project (pronounce “Xyper”) aims at developing Explainable Predictive Maintenance (XPdM) algorithms that do not only provide the engineers with a prediction but in addition, with 1) a risk analysis on the components when delaying the maintenance, and 2) what the primary indicators are that the algorithms used to create inference. To use predictive maintenance effectively in Maritime operations, the predictive models and the optimization of the maintenance schedule using these models, need to be aware of the past and planned vessel activities, since different activities affect the lifetime of the machines differently. For example, the degradation of a hydraulic pump inside a crane depends on the type of operations the crane performs. Thus, the models do not only need to be explainable but they also need to be aware of the context which is in this case the vessel and machinery activity. Using sensor data processing and edge-computing technologies that will be developed and applied by the Hanze UAS in Groningen, context information is extracted from the raw sensor data. The XAIPre project combines these Explainable Context Aware Machine Learning models with state-of-the-art optimizers, that we already developed in the NWO CIMPLO project at LIACS, in order to develop optimal maintenance schedules for machine components. The optimizers will be adapted to fit within XAIPre. The resulting XAIPre prototype offers significant competitive advantages for companies such as Heerema, by increasing the longevity of machine components, increasing worker safety, and decreasing maintenance costs. XAIPre will focus on the predictive maintenance of thrusters, which is a key sub-system with regards to maintenance as it is a core part of the vessels station keeping capabilities. Periodic maintenance is currently required in fixed intervals of 5 years. XPdM can provide a solid base to deviate from the Periodic Maintenance prescriptions to reduce maintenance costs while maintaining quality. Scaling up to include additional components and systems after XAIPre will be relatively straightforward due to the accumulated knowledge of the predictive maintenance process and the delivered methods. Although the XAIPre system will be evaluated on the use-cases of Heerema, many components of the system can be utilized across industries to save maintenance costs, maximize worker safety and optimize sustainability.
