Exploring virtual reality and prosthetic training.

Working together with health care professionals and a world leading bionic prosthetic maker we created a prototype that aims to decrease the time it takes for a transradial amputee to train how to use a Myoelectric prosthetic arm. Our research indicates that the Oculus Rift, Microsoft’s Kinect and the Thalmic Labs Myo gesture control armband will allow us to create a unique, cost effective training tool that could be beneficial to amputee patients

Are there three main subgroups within the patellofemoral pain population? A detailed characterisation study of 127 patients to help develop targeted Intervention (TIPPs)

• Background Current multimodal approaches for the management of non-specific patellofemoral pain are not optimal, however, targeted intervention for subgroups could improve patient outcomes. This study explores whether subgrouping of non-specific patellofemoral pain patients, using a series of low cost simple clinical tests, is possible. • Method The exclusivity and clinical importance of potential subgroups was assessed by applying à priori test thresholds (1 SD) from seven clinical tests in a sample of adult patients with non-specific patellofemoral pain. Hierarchical clustering and latent profile analysis, were used to gain additional insights into subgroups using data from the same clinical tests. • Results One hundred and thirty participants were recruited, 127 had complete data: 84 (66%) female, mean age 26 years (SD 5.7) and mean BMI 25.4 (SD 5.83), median (IQR) time between onset of pain and assessment was 24 (7-60) months. Potential subgroups defined by the à priori test thresholds were not mutually exclusive and patients frequently fell into multiple subgroups. Using hierarchical clustering and latent profile analysis three subgroups were identified using 6 of the 7 clinical tests. These subgroups were given the following nomenclature: (i) ‘strong’, (ii) ‘weak and tighter’, and (iii) ‘weak and pronated foot’. • Conclusions We conclude that three subgroups of patellofemoral patients may exist based on the results of six clinical tests which are feasible to perform in routine clinical practice. Further research is needed to validate these findings in other datasets and, if supported by external validation, to see if targeted interventions for these subgroups improve patient outcomes

Randomised controlled feasibility trial of a web-based weight management intervention with nurse support for obese patients in primary care

<b>Background</b><p></p> There is a need for cost-effective weight management interventions that primary care can deliver to reduce the morbidity caused by obesity. Automated web-based interventions might provide a solution, but evidence suggests that they may be ineffective without additional human support. The main aim of this study was to carry out a feasibility trial of a web-based weight management intervention in primary care, comparing different levels of nurse support, to determine the optimal combination of web-based and personal support to be tested in a full trial.<p></p> <b>Methods</b><p></p> This was an individually randomised four arm parallel non-blinded trial, recruiting obese patients in primary care. Following online registration, patients were randomly allocated by the automated intervention to either usual care, the web-based intervention only, or the web-based intervention with either basic nurse support (3 sessions in 3 months) or regular nurse support (7 sessions in 6 months). The main outcome measure (intended as the primary outcome for the main trial) was weight loss in kg at 12 months. As this was a feasibility trial no statistical analyses were carried out, but we present means, confidence intervals and effect sizes for weight loss in each group, uptake and retention, and completion of intervention components and outcome measures.<p></p> <b>Results</b><p></p> All randomised patients were included in the weight loss analyses (using Last Observation Carried Forward). At 12 months mean weight loss was: usual care group (n = 43) 2.44 kg; web-based only group (n = 45) 2.30 kg; basic nurse support group (n = 44) 4.31 kg; regular nurse support group (n = 47) 2.50 kg. Intervention effect sizes compared with usual care were: d = 0.01 web-based; d = 0.34 basic nurse support; d = 0.02 regular nurse support. Two practices deviated from protocol by providing considerable weight management support to their usual care patients.<p></p> <b>Conclusions</b><p></p> This study demonstrated the feasibility of delivering a web-based weight management intervention supported by practice nurses in primary care, and suggests that the combination of the web-based intervention with basic nurse support could provide an effective solution to weight management support in a primary care context

Undertaking multi-centre randomised controlled trials in primary care: learnings and recommendations from the PULsE-AI trial researchers

Background Conducting effective and translational research can be challenging and few trials undertake formal reflection exercises and disseminate learnings from them. Following completion of our multicentre randomised controlled trial, which was impacted by the COVID-19 pandemic, we sought to reflect on our experiences and share our thoughts on challenges, lessons learned, and recommendations for researchers undertaking or considering research in primary care. Methods Researchers involved in the Prediction of Undiagnosed atriaL fibrillation using a machinE learning AlgorIthm (PULsE-AI) trial, conducted in England from June 2019 to February 2021 were invited to participate in a qualitative reflection exercise. Members of the Trial Steering Committee (TSC) were invited to attend a semi-structured focus group session, Principal Investigators and their research teams at practices involved in the trial were invited to participate in a semi-structured interview. Following transcription, reflexive thematic analysis was undertaken based on pre-specified themes of recruitment, challenges, lessons learned, and recommendations that formed the structure of the focus group/interview sessions, whilst also allowing the exploration of new themes that emerged from the data. Results Eight of 14 members of the TSC, and one of six practices involved in the trial participated in the reflection exercise. Recruitment was highlighted as a major challenge encountered by trial researchers, even prior to disruption due to the COVID-19 pandemic. Researchers also commented on themes such as the need to consider incentivisation, and challenges associated with using technology in trials, especially in older age groups. Conclusions Undertaking a formal reflection exercise following the completion of the PULsE-AI trial enabled us to review experiences encountered whilst undertaking a prospective randomised trial in primary care. In sharing our learnings, we hope to support other clinicians undertaking research in primary care to ensure that future trials are of optimal value for furthering knowledge, streamlining pathways, and benefitting patients

Identification of strata for a trial of a targeted multimodal physiotherapy intervention in patellofemoral pain patients

Background Patellofemoral Pain (PFP) is a musculoskeletal disorder causing significant pain and dysfunction around the knee, commonly leading to long term limitations. People with PFP are commonly referred for physiotherapy, although current multimodal physiotherapy approaches tend to be rather ad-hoc and are failing in the long term. Identification of patient strata (or subgroups) who may respond differentially to interventions has been recognised as an international priority. It has been proposed that there are PFP patient strata, classified based on clinical tests, who would respond to modes of interventions targeted at the individual's stratum. Methods Participants with PFP underwent clinical assessment of muscle weakness, muscle length, patellar mobility and foot posture. The presence of strata was explored using two classification techniques: hierarchical cluster analysis and latent profile analysis (LPA); for LPA, the Bayesian Information Criterion was used to guide model selection. Results One hundred and twenty seven (of 130) recruited participants had complete assessment data. A three strata solution appeared optimal from modelling and clinical perspectives. The three suggested strata were characterised as: stronger; weaker and tighter; weaker with pronated feet. However, although membership of the stronger stratum was consistent, there were substantial differences in the other composition of the other two strata between the two classification approaches. Conclusion PFP patients can be classified into clinical strata, although further investigation is needed to identify thresholds upon which stratification should be performed. Interventions are currently being mapped onto the strata and feasibility and evaluation trials of these interventions should then be performed

Evaluation of a web-based intervention to reduce antibiotic prescribing for LRTI in six European countries: quantitative process analysis of the GRACE/INTRO randomised controlled trial.

To reduce the spread of antibiotic resistance, there is a pressing need for worldwide implementation of effective interventions to promote more prudent prescribing of antibiotics for acute LRTI. This study is a process analysis of the GRACE/INTRO trial of a multifactorial intervention that reduced antibiotic prescribing for acute LRTI in six European countries. The aim was to understand how the interventions were implemented and to examine effects of the interventions on general practitioners' (GPs') and patients' attitudes

Safety of disinvestment in mid- to late-term follow-up post primary hip and knee replacement: The UK SAFE evidence synthesis and recommendations

Background: Joint replacement surgery has revolutionised the management of degenerative joint disease. Increasing demand for surgery and post-surgical reviews has overwhelmed orthopaedic services and, consequently, many centres have reduced or stopped follow-up. Such disinvestment is without an evidence base and raises questions regarding the consequences to patients.Objectives: To produce evidence- and consensus-based recommendations as to how, when and on whom follow-up should be conducted. Our research question was ‘Is it safe to disinvest in mid- to late-term follow-up of hip and knee replacement?’.MethodsThe study comprised three complementary evidence synthesis work packages to inform a final consensus process. Work package 1 was a systematic review of the clinical effectiveness and cost-effectiveness literature. Work package 2 used routine national data sets (i.e. the Clinical Practice Research Datalink–Hospital Episode Statistics, Hospital Episode Statistics–National Joint Registry–patient-reported outcome measures) to identify pre, peri and postoperative predictors of mid- to late-term revision, and prospective data from 560 patients to understand how patients present for revision surgery. Work package 3 used a Markov model to simulate the survival, health-related quality of life and NHS costs of patients following hip or knee replacement surgery. Finally, evidence from work packages 1–3 informed a face-to-face consensus panel, which involved 32 stakeholders.Results: Our overarching statements are as follows: (1) these recommendations apply to post primary hip and knee replacement follow-up; (2) the 10-year time point in these recommendations is based on a lack of robust evidence beyond 10 years; and (3) in these recommendations, the term ‘complex cases’ refers to individual patient and surgical factors that may increase the risk of replacement failure. Our recommendations are as follows: for Orthopaedic Data Evaluation Panel 10A* (ODEP-10A*) minimum implants, it is safe to disinvest in routine follow-up from 1 to 10 years post non-complex hip and knee replacement provided that there is rapid access to orthopaedic review; (2) for ODEP-10A* minimum implants in complex cases or non-ODEP-10A* minimum implants, periodic follow-up post hip and knee replacement may be required from 1 to 10 years; (3) at 10 years post hip and knee replacement, clinical and radiographic evaluation is recommended; and (4) after 10 years post hip and knee replacement, frequency of further follow-up should be based on the 10-year assessment (note that ongoing rapid access to orthopaedic review is still required) [Stone M, Smith L, Kingsbury S, Czoski-Murray C, Judge A, Pinedo-Villanueva R, et al. Evidence-based follow-up recommendations following primary hip and knee arthroplasty (UK SAFE). Orthop Proc 2020;102–B:13. https://doi.org/10.1302/1358-992X.2020.5.013].Limitations: The current absence of data beyond 10 years restricted the evidence base.Conclusions: For ODEP-10A* prostheses, the UK SAFE programme demonstrated that it is safe to disinvest in routine follow-up in the 1- to 10-year period after non-complex hip and knee replacement. At 10 years, clinical and radiographic review is recommended. Complex cases, implants not meeting the 10A* criteria and follow-up after revision surgery are not covered by this recommendation.Future work: The evidence base for follow-up after 10 years requires further evaluation. Further work should establish the most clinically effective and cost-effective model of delivering a rapid access service and evaluate alternative models for follow-up services, such as virtual clinics. Finally, the needs and outcomes of patients who are symptomatic but do not have appropriate follow-up should be investigated

Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial

Atrial fibrillation (AF) is associated with an increased risk of stroke, enhanced stroke severity, and other comorbidities. However, AF is often asymptomatic, and frequently remains undiagnosed until complications occur. Current screening approaches for AF lack either cost-effectiveness or diagnostic sensitivity; thus, there is interest in tools that could be used for population screening. An AF risk prediction algorithm, developed using machine learning from a UK dataset of 2,994,837 patients, was found to be more effective than existing models at identifying patients at risk of AF. Therefore, the aim of the trial is to assess the effectiveness of this risk prediction algorithm combined with diagnostic testing for the identification of AF in a real-world primary care setting. Eligible participants (aged =30?years and without an existing AF diagnosis) registered at participating UK general practices will be randomised into intervention and control arms. Intervention arm participants identified at highest risk of developing AF (algorithm risk score?=?7.4%) will be invited for a 12-lead electrocardiogram (ECG) followed by two-weeks of home-based ECG monitoring with a KardiaMobile device. Control arm participants will be used for comparison and will be managed routinely. The primary outcome is the number of AF diagnoses in the intervention arm compared with the control arm during the research window. If the trial is successful, there is potential for the risk prediction algorithm to be implemented throughout primary care for narrowing the population considered at highest risk for AF who could benefit from more intensive screening for AF. Trial Registration: NCT04045639