Objectives Adherence to injury prevention programmes in football remains low, which is thought to drastically reduce the effects of injury prevention programmes. Reasons why (medical) staff and players implement injury prevention programmes, have been investigated, but player’s characteristics and perceptions about these programmes might influence their adherence. Therefore, this study investigated the relationships between player’s characteristics and adherence and between player’s perceptions and adherence following an implemented injury prevention programme. Methods Data from 98 of 221 football players from the intervention group of a cluster randomised controlled trial concerning hamstring injury prevention were analysed. Results Adherence was better among older and more experienced football players, and players considered the programme more useful, less intense, more functional and less time-consuming. Previous hamstring injuries, educational level, the programme’s difficulty and intention to continue the exercises were not significantly associated with adherence. Conclusion These player’s characteristics and perceptions should be considered when implementing injury prevention programmes.
Background: Existing studies have yet to investigate the perspectives of patients and professionals concerning relapse prevention programs for patients with remitted anxiety or depressive disorders in primary care. User opinions should be considered when optimizing the use and implementation of interventions. Objective: This study aimed to evaluate the GET READY relapse prevention programs for patients with remitted anxiety or depressive disorders in general practice. Methods: Semistructured interviews (N=26) and focus group interviews (N=2) with patients and mental health professionals (MHPs) in the Netherlands were performed. Patients with remitted anxiety or depressive disorders and their MHPs who participated in the GET READY study were interviewed individually. Findings from the interviews were tested in focus group interviews with patients and MHPs. Data were analyzed using thematic analysis. Results: Participants were positive about the program because it created awareness of relapse risks. Lack of motivation, lack of recognizability, lack of support from the MHP, and symptom severity (too low or too high) appeared to be limiting factors in the use of the program. MHPs play a crucial role in motivating and supporting patients in relapse prevention. The perspectives of patients and MHPs were largely in accordance, although they had different perspectives concerning responsibilities for taking initiative. Conclusions: The implementation of the GET READY program was challenging. Guidance from MHPs should be offered for relapse prevention programs based on eHealth. Both MHPs and patients should align their expectations concerning responsibilities in advance to ensure optimal usage. Usage of blended relapse prevention programs may be further enhanced by diagnosis-specific programs and easily accessible support from MHPs.
MULTIFILE
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.
