Journal of Medical Engineering and Technology最新文献

Long-term neurological conditions (LTNCs) cause physical and psychological symptoms that have a significant impact on activities of daily living and quality of life. Multidisciplinary teams are effective at providing treatment for people with LTNCs; however, access to such services by people with disabilities can be difficult and as a result, good quality care is not universal. One potential solution is telehealth. This review describes the potential of telehealth to support people with LTNCs, the challenges of designing and implementing these systems, and the key recommendations for those involved in telehealth to facilitate connected services that can benefit patients, carers and healthcare professionals. These recommendations include understanding the problems posed by LTNCs and the needs of the end-user through a person-centred approach. We discuss how to work collaboratively and use shared learning, and consider how to effectively evaluate the intervention at every stage of the development process.

The context of child prosthetics is a complex and important area for research and innovation. Yet, like many areas of paediatric medical technology development, there are several barriers to innovating specifically for the unique needs of children (i.e., a relatively small patient population or 'market'). As such, much child prosthetics technology is developed from the downsizing of adult prosthetics, leading to suboptimal outcomes for children and young people. Since 2016, the Starworks Child Prosthetics Research Network has been exploring this space, bringing children and their families together with key opinion leaders from the NHS, clinical Academia and leading National Research Centres with capabilities in child prosthetics with the aim of increasing research across the system. Above all else, Starworks is centred on the needs of children and their families, ensuring they have an equal voice in driving the ongoing research agenda. This article will share key learnings from the creation and development of the Starworks Network that may be applicable and/or adaptable across a wider paediatric medical technology research and innovation landscape. In particular it will discuss how it addressed three key aims of; (1) Addressing child-specific issues; (2) Building a sustainable network; and (3) Fostering impactful innovation.

NIHR (National Institute for Health Research) Devices for Dignity MedTech Cooperative (D4D) and NIHR Children and Young People MedTech Cooperative (CYPMedTech) have established track records in keeping patient and public involvement (PPI) at the core of medical technology development, evaluation and implementation. The 2020 global COVID-19 pandemic presented significant challenges to maintaining this crucial focus. In this paper we describe prior successful methodologies and share examples of the adaptations made in order to continue to engage with patients and the public throughout the pandemic and beyond. We reflect on learning gained from these experiences, and new areas of scope and focus relating to broadening the reach of engagement and representation, along with associated resource requirements and impact metrics.

Neurocare Knowhow is an online learning platform for care workers who support people with neurological conditions. Care workers often do not receive specialist training around neurological conditions and can experience anxiety and apprehension about caring for this group. Neurocare Knowhow aims to increase care workers' knowledge and confidence. Featuring flexible and personalisable digital features, in combination with documentary video and audio, it offers an alternative to traditional e-learning, moving away from longform didactic courses to flexible on-the-go learning. This co-design project worked closely with people with neurological conditions, their families, care workers and care organisation managers to validate the need and develop a proof of concept pilot. Co-design activity took place across multiple workshops in person and online. These gathered detailed insights into preferred features that support engaging online learning. The platform offers learning across diverse neurological conditions as a whole, with a focus on shared symptoms and challenges encountered by people with neurological conditions. Ongoing development to scale the pilot up to meet an anticipated national audience includes a focus on artificial intelligence to support searches at a moment of need and a range of personalisable features for individual and team learning.

Disposable face masks are among the personal protective equipment (PPE) that highly contribute to protecting people in the context of the current COVID-19 pandemic. Health authorities recommend wearing a mask as a barrier measure to limit the spread of viral respiratory diseases. During the first waves of the pandemic, besides professional high-quality PPE, decontaminated disposable mask reuse and homemade cloth masks were allowed due to scarcities. This work introduces a simple method based on-time history of the differential pressure, and an easy to use the setup for the testing of different kinds of respiratory protective masks for the purposes of quality control and evaluation of air permeability performance. The standard mask testing method and the new proposed approach were then used to evaluate the effect of machine washing on the widely used type of disposable masks; namely the surgical (medical) face masks. The objective is to determine the number of acceptable washing cycles that this kind of mask can withstand before losing its performance in terms of breathability and airflow resistance. Other quality characteristics such as material (fibres) degradation and hydrophobicity are investigated. Degradation mechanisms due to washing cycles for the different mask constituent layers were studied by scanning electron microscopy (SEM) imaging. This work is an attempt to contribute to the determination of the reusability threshold of general-purpose disposable surgical type face masks thereby contributing to the reduction of environmental concerns. Results in terms of the studied above parameters suggest limiting the reuse of standard type surgical masks to only one machine washing cycle.

Incidence of non-union following long bone fracture fixation and spinal fusion procedures is increasing, and very costly for patients and the medical system. Direct current (DC) electrical stimulation has shown success as an adjunct therapy to stimulate bone healing and increase surgery success rates, though drawbacks of current devices and implantable battery packs have limited widespread use. Energy harvesting utilising piezoelectric materials has been widely studied for powering devices without a battery, and a preclinical animal study has shown efficacy of a piezocomposite spinal fusion implant resulting in faster, more robust fusion. Most piezoelectric energy harvesters operate most effectively at high frequencies, limiting power generation from loads experienced by orthopaedic implants during human motion. This work characterises the efficient power generation capability of a novel composite piezoelectric material under simulated walking loads. Building on compliant layer adaptive composite stacks (CLACS), the power generation of mixed-mode CLACS (MMCLACS) is defined. Utilising poling direction to capitalise on in-plane strain generation due to compliant layer expansion, MMCLACS significantly increased power output compared to a standard piezo stack. The combination of radial and through-thickness poled piezoelectric elements within a stack to create MMCLACS significantly increases power generation under low-frequency dynamic loads. This technology can be adapted to a variety of architectures and assembled as a load-bearing energy harvester within current implants. MMCLACS integrated with implants would provide enough power to deliver bone healing electrical stimulation directly to the fusion site, decreasing non-union rates, and also could provide quantitative assessment of healing progression through load sensing.