641 research outputs found
The development, feasibility, and acceptability of a breakfast group intervention for stroke rehabilitation
Background: There are 1.2 million stroke survivors in the UK and the number is projected to increase significantly over the next decade. Research suggests that between 50% and 80% of hospitalised stroke survivors experience difficulties with eating and drinking. Presently, rehabilitation approaches to address these difficulties involve individual rehabilitation sessions led by uni-professionals. Recent national stroke guidance recommends that stroke survivors receive three hours of daily rehabilitation and emphasises the importance of addressing the psychosocial aspects of recovery. Implementing these recommendations presents a challenge to healthcare professionals, who must explore innovative methods to provide the necessary rehabilitation intensity. This study aimed to address these challenges by codesigning a multi-disciplinary breakfast group intervention and implementation toolkit to improve psychosocial outcomes.
Methods: The Hawkins 3-step framework for intervention design was used to develop a multidisciplinary breakfast group intervention and to understand if it was acceptable and feasible for patients and healthcare professionals in an acute stroke ward. The Hawkins 3- steps were 1) evidence review and consultations 2) coproduction 3) prototyping. In collaboration with fifteen stakeholders, a prototype breakfast group intervention and implementation toolkit were codesigned over four months. Experience-based Codesign was used to engage stakeholders.
Results: The literature review is the first to investigate the psychosocial impact of eating and drinking difficulties post stroke. The key finding was the presence of psychological and social impacts which included, the experience of loss, fear, embarrassment shame and humiliation as well as social isolation. Stroke survivors were striving to get back to normality and this included the desire to socially dine with others. Two prototype iterations of the intervention were tested with 16 stroke survivors across three hospital sites. The multidisciplinary breakfast group intervention was designed to offer intensive rehabilitation in a social group context. The codesigned implementation toolkit guided a personalised and tailored approach. A perceived benefit of the intervention was the opportunity to address the psychosocial aspects of eating and drinking rehabilitation as well as providing physical rehabilitation. Stroke survivors highly value the opportunity to socialise and receive support from their peers. The intervention was acceptable to both patients and healthcare professionals, and the workforce model proved practical and feasible to deliver using a collaborative approach in the context of resource-limited healthcare.
Conclusions: The breakfast group interventions, developed through codesign, were positively received by patients and staff and feasible to deliver. They introduce an innovative and novel approach to stroke rehabilitation, personalised to each individual's needs, and offer a comprehensive intervention which addresses both physical and psychosocial aspects which target challenges related to eating and drinking. Unique contributions of this study include a theoretical model for breakfast group interventions, a programme theory and practical tool kit for clinicians to support the translation of research findings and implement breakfast groups in clinical practice
Wearable Sensors as a Preoperative Assessment Tool: A Review
Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool
Exploring the impact of integrated COPD care in general practice
Integrated care is an umbrella term used to describe collaboration across differing healthcare sectors. Integrated care interventions directed towards patients with Chronic Obstructive Pulmonary Disease (COPD) in primary care have been shown to improve patient outcomes, such as quality of life. However, the utilisation of integrated care interventions to improve guideline adherence and reduce the prevalence of COPD misdiagnosis in primary care has not been explored previously.
The mixed methods systematic review demonstrated that misdiagnosis of COPD does occur in primary care and is predominantly due to difficulties utilising spirometry and differentiating COPD from asthma. Integrated care interventions utilising specialist led spirometry were shown to be able to identify misdiagnosed patients and were perceived to be able to reduce the prevalence of COPD misdiagnosis in primary care.
The impact of integrating COPD specialists into GP practices was evaluated through a pragmatic cluster randomised controlled trial (INTEGR COPD). The integration of COPD specialists led to significant improvements in the delivery of guideline adherent care, which was shown to correlate with improvements in quality of life. Integrating COPD specialists into GP practice also led to misdiagnosed patients being identified and having their diagnosis and treatment corrected.
The integration of COPD specialists into GP practices was found to be acceptable to patients and healthcare professionals. The reluctance to challenge historic diagnoses was thought to be the underlying cause of patients remaining misdiagnosed in primary, within this cohort. Specialist involvement was deemed to have a positive impact in reducing the extent of COPD misdiagnosis in primary care.
The findings from this thesis suggest that integrated COPD care has a positive impact on the delivery of optimal patient care as well as the prevalence of COPD misdiagnosis in GP practices
The 2023 wearable photoplethysmography roadmap
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology
Facilitating prosociality through technology: Design to promote digital volunteerism
Volunteerism covers many activities involving no financial rewards for volunteers but which contribute
to the common good. There is existing work in designing technology for volunteerism in HumanComputer Interaction (HCI) and related disciplines that focuses on motivation to improve
performance, but it does not account for volunteer wellbeing. Here, I investigate digital volunteerism
in three case studies with a focus on volunteer motivation, engagement, and wellbeing. My research
involved volunteers and others in the volunteering context to generate recommendations for a
volunteer-centric design for digital volunteerism. The thesis has three aims:
1. To investigate motivational aspects critical for enhancing digital volunteers’ experiences
2. To identify digital platform attributes linked to volunteer wellbeing
3. To create guidelines for effectively supporting volunteer engagement in digital volunteering
platforms
In the first case study I investigate the design of a chat widget for volunteers working in an
organisation with a view to develop a design that improves their workflow and wellbeing. The second
case study investigates the needs, motivations, and wellbeing of volunteers who help medical
students improve their medical communication skills. An initial mixed-methods study was followed by
an experiment comparing two design strategies to improve volunteer relatedness; an important
indicator of wellbeing. The third case study looks into volunteer needs, experiences, motivations, and
wellbeing with a focus on volunteer identity and meaning-making on a science-based research
platform. I then analyse my findings from these case studies using the lens of care ethics to derive
critical insights for design.
The key contributions of this thesis are design strategies and critical insights, and a volunteer-centric
design framework to enhance the motivation, wellbeing and engagement of digital volunteers
Leveraging Simulation to Understand Nursing Student Learning in Technologically Complex Environment
Introduction The adoption of simulation technologies in schools of nursing has increased significantly over the last 5 years. Results regarding the use of simulation technology have been positive. The use of simulation technologies has been developed to include complex nursing situations that resemble real clinical settings. Research has demonstrated that memory recall is better in an environment that is the same or similar to the environment in which the learning took place (Krokos et al., 2018). For this reason, educators have used simulation technologies to create realistic environments where students can learn. However, learning outcomes in simulations are affected by a multidimensional range of factors other than technology, such as cognitive load and anxiety (Josephsen, 2018; Yockey & Henry, 2019). Research has demonstrated that mastery of learning outcomes depends on whether extraneous and intrinsic cognitive load (CL) is maintained at a level at which students experience learning and knowledge transfer (Sweller et al., 2019). Research has also demonstrated that anxiety affects learning (Shearer, 2016). However, little research has investigated how simulation technology as an educational design element affects learning outcomes, CL, and anxiety in nursing students.Methods This quasi-experimental three-group comparison design, based on cognitive load theory examined the effects of technological complexity in simulation on learning outcomes in prelicensure nursing students performing cardiac assessment in a simulated environment. The NLN Jeffries simulation theory was used to construct the intervention and formulate discussion. Students were recruited from two schools of nursing in northern Colorado from a Bachelor of Nursing program, an associate of nursing program, and a second degree Bachelor of Nursing program (N=88). Students were randomly assigned to one of three groups representing three levels of technological complexity in a simulation scenario where students performed a cardiac assessment. Students completed 4 surveys measuring anxiety, cognitive load, technological acceptance, and demographic data. ANOVA, multiple regression, Chi square analysis and MANOVA were used to analyze relationships between variables. Results Three variables were found to be statistically significant; extraneous cognitive load was negatively correlated with CCEI scores (p=.028). Age (22-25 years) was positively correlated with CCEI scores (p=.005) and having a previous associate degree was negatively correlated with CCEI scores (p=.016). Discussion age of participant, and level of degree emerged as influencing factors, and that increased extraneous load decreased learning. Implications for nursing education, and several recommendations for future research were suggested. The findings of this research lay the foundation for a program of research into identifying the effects of increased technology use on learning outcomes
Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions
Sixth-generation (6G) networks anticipate intelligently supporting a wide
range of smart services and innovative applications. Such a context urges a
heavy usage of Machine Learning (ML) techniques, particularly Deep Learning
(DL), to foster innovation and ease the deployment of intelligent network
functions/operations, which are able to fulfill the various requirements of the
envisioned 6G services. Specifically, collaborative ML/DL consists of deploying
a set of distributed agents that collaboratively train learning models without
sharing their data, thus improving data privacy and reducing the
time/communication overhead. This work provides a comprehensive study on how
collaborative learning can be effectively deployed over 6G wireless networks.
In particular, our study focuses on Split Federated Learning (SFL), a technique
recently emerged promising better performance compared with existing
collaborative learning approaches. We first provide an overview of three
emerging collaborative learning paradigms, including federated learning, split
learning, and split federated learning, as well as of 6G networks along with
their main vision and timeline of key developments. We then highlight the need
for split federated learning towards the upcoming 6G networks in every aspect,
including 6G technologies (e.g., intelligent physical layer, intelligent edge
computing, zero-touch network management, intelligent resource management) and
6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous
systems). Furthermore, we review existing datasets along with frameworks that
can help in implementing SFL for 6G networks. We finally identify key technical
challenges, open issues, and future research directions related to SFL-enabled
6G networks
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