Bia Ystok, Poland

Short-term Effect of Using Non-immersive Gamified Exercises on Chronic Pain in People With Stroke.

The Effect of Ingesting a Novel Algae Protein Source on Rested and Exercised Muscle Protein Synthesis Rates in Older Adults.

Experience of Biologic Treatments for Severe Asthma: a Survey

A multi-centre cross-sectional survey to retrospectively assess patients' perceptions of their responses to biologic treatments for their severe asthma. Methods A total of 400 patients will be recruited from the severe asthma centres responsible for their clinical care. Leads from each participating site will identify patient's eligible to take part in the study. Each lead for a site will be provided with unique 'Collector IDs' (that are linked with a unique survey web URL). After receiving verbal consent from each patient, the researcher at the site will allocate the patient a 'Collector ID' and unique web URL to a survey hosted on SurveyMonkey. On visiting the survey URL, patients will be provided with study information, provide informed consent and complete the survey information. Participants will complete the survey without any assistance from physicians or the study staff. All survey questions and responses will be provided in the participants' native languages. All processes will be monitored by a study coordinator to assess recruitment and the completion of the survey. Variables Variables from the patient survey will include: - Experience of starting a biologic treatment o What changed, when did the change take place and to what degree was the change experienced. - Side effects experienced after taking a biologic treatment. Demographics - Gender - Age Medication use - Use of OCS - Use of biologic treatment Data analysis 1. To describe the time course and nature of response in people with severe asthma who continue to be prescribed biologics for between 6 - 18 months. - Demographics data and other survey responses will be reported as descriptive statistics including mean (SD). - We will report a frequency distribution of responses for each survey item to describe the samples experience of biologic drugs for their severe asthma. - To assess for the presence of recall differences between those who have been on a biologic for <12 months vs those on a biologic for ≥12 months data from these participants will be stratified and compared. 2. To identify subgroups of response pattern to biologic treatments for severe asthma. Analysis will be exploratory and descriptive, with relationships between survey responses identified through correlations. These correlations will provide insight into which experiences of biologic treatment (response) are related. For example, the literature anticipates that a fast response will be associated with a "better" response to biologic treatments. We will define "better" from a patient perspective, by investigating the relationship between Question 8 (Satisfaction with treatment), and time to first noticed treatment response (Question 6). 3. To describe the characteristics of the different subgroups of response pattern. - Participants demographic data will be reported as a function of the different response patterns we identify in analysis 2. 4. To identify a set of hypotheses driven characteristics which may predict response to biologics in future longitudinal studies for people with severe asthma, including the extra-pulmonary symptoms and side effects of treatment. - Fatigue and other side effects from biologics may be predictors of fast response or high satisfaction with treatment. - Correlations between the different response patterns identified in analysis 2, and questions concerning side effects and extra-pulmonary questions will be calculated. These include questions: 17, 18 and 19. - We will also calculate correlations between the response patterns we identify in analysis 2, and the biologic the patient was prescribed.

Development of Digital Services for Parkinson's Disease

Parkinson's disease (PD) is one of the most common neurodegenerative diseases worldwide, affecting 1% of the population older than 65. Currently, PD diagnosis is based on history, clinical assessments, and neurological examination. The most widely used criteria for diagnosis are the Movement Disorder Society (MDS) criteria and instrument (i.e. The MDS-UPDRS). Further information may be gained from people's subjective description of their symptoms and/or via some short walking tests, such as 3-meter Timed Up and Go (TUG) performed as a snapshot in the clinic. However, people's symptoms vary through and between days and subjective descriptions rely on their memory and observations at home. These recollections can be unreliable or lack enough detail (particularly when the person has cognitive impairment). Therefore, current PD diagnosis criteria are highly dependent on the person and on the diagnosing physician. This subjectivity may lead to a variability in the diagnosis. Furthermore, these clinical assessments are unable to accurately track disease progression over time, making it difficult to provide personalized care. Additionally, manual examinations lack precise measurement instruments, resulting in a low precision of observed measurements and the inability to detect early-stage, subclinical signs. An objective diagnosis based on quantitative data rather than subjective interpretation of clinical findings is important. Therefore, an early and accurate diagnosis of PD, as well as accurate disease progression monitoring, are still important challenges in PD. Several oculo-visual abnormalities have been described in PD. Studies report an abnormal ocular motor function in 75-87.5% of people with PD. These dysfunctions may precede or follow motor symptoms and thus, the evaluation of ocular motor function may provide valuable information regarding early disease detection or disease progression. The most commonly reported ocular motor dysfunctions are impairments in saccades, smooth pursuit, and vergence. Gait impairments are among the most common and disabling symptoms of PD. Gait impairments include freezing of gait (FOG), an inability to initiate or maintain normal walking patterns, often resulting in a stochastic stop/start gait, and festinating gait (FSG), which is a shortening of stride length with elevated step frequency, resulting in fast, shuffling steps. Both FOG and FSG contribute to an increased risk of falls (and fall-related injuries) in people with PD relative to the wider elderly population. Objective, and continuous remote gait monitoring would be highly important in people with PD, to objectively track gait impairments in real-time, and potentially contribute to objectively track disease progression, which may lead to personalized care for individuals with PD. In this project, ocular motor, pupil and gait data in people with Parkinson's disease (PD) will be collected in order to develop machine learning models for the diagnosis and monitoring of PD. With this, the investigators aim to advance the state of the art in PD diagnosis and monitoring. By integrating the principles of machine learning with high-quality sensor data, more accurate and earlier diagnosis could potentially be achieved. Ocular motor and pupil data will be collected with the standard clinical examination and with neos, a medical device approved for objective ocular motor and pupil measurement. Gait will be collected using an IMU sensor and GaitQ senti, a consumer device that allows for an objective and continuous remote gait monitoring. The primary objective of this project is to collect ocular motor, pupil and gait data from people with PD in order to develop and compare machine learning models for diagnosing and monitoring PD. Secondary objectives are: Correlate ocular motor, pupil and gait parameters with several clinical parameters, including the MDS-UPDRS. Collect real-world evidence (RWE) data regarding health economics parameters to address the individual and combined properties, effects, and/or impacts of the deployed health technologies. By analysing the data collected, we also aim to contribute to the scientific understanding of PD, potentially uncovering new insights into disease patterns, progression, and response to treatments.

The Effect of Ketone Esters on Forearm Glucose Metabolism

Impaired skeletal muscle glucose uptake following a meal ("insulin resistance"), is a primary risk factor for developing type 2 diabetes. We and others have consistently shown that ingesting exogenous ketones can reduce blood glucose concentration. Mechanistically, this must arise through reduced glucose release (i.e. from liver), and/or increased uptake (i.e. into skeletal muscle). Our current MRC-funded work is focussing on ketone-liver interactions in patients with type 2 diabetes. Here we aim to investigate how KE influence skeletal muscle glucose metabolism.

Five Lives MED to Improve Cognitive Function in Mild Cognitive Impairment

Mild cognitive impairment is a significant public health concern. Non-pharmacological interventions, specifically multi-domain lifestyle and computerised cognitive training interventions, offer an accessible, scalable, engaging and potentially effective solution to improve cognitive function. Five Lives MED is an interventional digital health app consisting of a physical activity habit forming coaching programme and cognitive training exercises. The purpose of this study is to evaluate the efficacy of the Five Lives MED device; the primary hypothesis is that there is a significant difference in MoCA scores between the intervention group and the control group after the 12-week Five Lives MED intervention, with an effect size of d = 0.50 or greater favouring the intervention group. This is a multi-centre, randomised, single-blind, controlled study in participants aged ≥ 50 years with mild cognitive impairment. Participants will be randomly assigned to the intervention (Five Lives MED) or control group for a 12-week period on a 1:1 allocation ratio.

Knee4Life Project: Empowering Knee Recovery After Total Knee Replacement Through Digital Health

1. Background Total knee replacement (TKR) is a common procedure, with more than 100,000 per year undertaken in England and Wales. Although most patients have a successful outcome following their TKR, approximately 10-20% of patients are dissatisfied, chiefly because of pain and knee stiffness. A method to detect early problems with pain and stiffness could facilitate earlier referral to non-surgical treatments, which are effective in preventing the need for manipulation under anaesthetic [MUA]. Currently, rates of MUA are 2.5% (~2,500 patients per year in England and Wales), costing ~£14k per procedure. Our current understanding of when stiffness develops and the timing and best treatment(s) for stiffness are limited. A recent James Lind Alliance Priority Setting Partnership identified stiffness after TKR as a top-10 research priority to better understand and test interventions. Current measures are not accurate or suitable for use in the home. The investigators need tools to accurately measure early indicators for stiffness. 2. Rationale The investigators currently have no tool to remotely and accurately detect development of early post-surgical knee stiffness. This study aims to develop a cost-effective tool to measure and quantify knee stiffness before and after total knee replacement (TKR) surgery for use across the NHS. The research seeks to understand how knee range of motion (ROM) recovers after TKR and detect early signs of stiffness. It also aims to predict who might develop stiffness after TKR and explore the relationship between pain and stiffness. Current methods for measuring knee range of motion (ROM), such as hand-held tools for measuring angles, have limitations in terms of accuracy and need trained healthcare staff to use them. The ideal tool would be low-cost, easy to use, and provide rapid feedback to patients and clinical teams. The study will involve the development and validation of a computer vision-based approach (using cameras to assess movements) to monitor knee flexion and extension, and a walking pattern assessment. Video-based technology or computer vision (CV) has recently been pioneered in Exeter to measure spine movement in patients with ankylosing spondylitis. Computer vision is an emerging technology that has great potential for monitoring knee flexion in people with knee stiffness. This approach involves the use of cameras and machine learning algorithms to detect and analyse knee joint angles during movement automatically. By providing objective and accurate measurements of knee flexion, computer vision has the potential to improve the assessment of knee stiffness and facilitate targeted treatment interventions. However, as with any new technology, there is a need to validate the method in the context of patients with knee stiffness to ensure its accuracy and reliability. Studies have highlighted the importance of developing machine learning algorithms specifically for this patient population to account for individual differences in movement patterns and limitations due to stiffness. Further research is needed to assess the validity and feasibility of computer vision-based approaches for monitoring knee flexion in people with knee stiffness, which could ultimately improve the diagnosis, monitoring, and management of this condition. Validation of the computer vision-based approach for monitoring knee flexion in people with knee stiffness is essential to ensure its reliability and accuracy. This requires developing and refining machine learning algorithms that can accurately detect and measure knee joint angles in this patient population. This study will evaluate the accuracy and precision of the algorithm against gold-standard measurement methods, such as motion capture or goniometry. Furthermore, this study will examine the sensitivity of the approach to changes in knee flexion due to stiffness and pain and assess its feasibility in a clinical setting. Once validated, the computer vision-based approach has the potential to provide a non-invasive and objective means of monitoring knee flexion in people with knee stiffness, which could inform treatment decisions and improve patient outcomes. Another tool which the investigators will use is the Gaitcapture app which takes advantage of the accelerometer and gyroscope sensor in a mobile phone and acts similarly to an inertial measurement unit (IMU) to provide us with acceleration and rotation data. The validation of the computer vision-based approach will involve comparing it against gold-standard measurement methods (specialist physiotherapy assessment). In addition to the computer vision-based approach, the study will utilise body-worn sensors and mobile apps to monitor the physical activity levels, walking patterns and step counts of participants. This data will provide insights into people with TKR's overall physical activity patterns and help evaluate the usability, acceptability, feasibility, and accuracy of the tools for diagnostics and monitoring. The findings of this research project have the potential to improve the diagnosis, monitoring, and management of knee stiffness after total knee replacement (TKR), with the potential to reducing the need for MUA surgery. By providing accurate measurements and early detection, the tools developed in this study could enable earlier referral to non-surgical treatments and reduce the need for costly and risky procedures to improve knee range of motion (ROM) after total knee replacement (TKR) surgery, like a manipulation of the knee under anaesthesia. Here, the investigators will conduct a validation study of a marker-less motion capture algorithm to determine its accuracy and assess its feasibility and usability for implementation on a large scale in the home. the investigators will also ascertain the test-retest reliability of algorithm outputs such as knee flexion/extension angles.

Laser Speckle Imaging During Breast Reconstruction

Understanding Beta Cell Disorders Through the Study of Rare Genotypes (ENDURE)

The human body needs sugar for energy, but too much or too little sugar in the blood is bad for health. To control the amount of sugar in the blood, a molecule called insulin is made by specialised beta cells in the pancreas. In diabetes, beta cells don't make enough insulin which causes high blood sugar levels. In hyperinsulinism, they make too much insulin leading to very low blood sugar levels. Over time, these disorders can lead to serious health problems. The cause of some cases of diabetes and nearly all cases of hyperinsulinism, is a single spelling mistake in the person's DNA (a variant) that changes how the insulin producing beta cells work. The overarching aim of the ENDURE study is to understand how DNA variants cause beta cell disorders and to improve understanding of how beta cells work. It is hoped that the insights from this research may lead to new ways to treat and/or improve the lives of people living with beta cell disorders. Participants will be selected based on having a confirmed disease-causing genetic change that results in beta cells not working properly, or a suitably matched control (same sex, close in age and weight). Consent: Prospective study participants will be provided with the appropriate participant information sheet (PIS) and Sub-Study Flowchart detailing the study and procedures (specific to the participant's genotype). If interested in participating, the ENDURE study team will contact them to discuss the study in detail and answer any questions and address concerns raised to allow the prospective participant to make an informed decision regarding taking part in the study. For the Imaging Sub-Study, following receipt of verbal consent, participants will be asked to complete a "MRI Safety Checklist Screening Form" that is necessary to screen them prior to booking their MRI scan. Prospective study participants are individuals with a rare genetic mutation that is associated with a monogenic beta cell disorder. The cohort of prospective study participants is diverse in terms of background, primary language, home country. To have an inclusive study set-up, the study team will provide documents that are translated into the participant's (or guardian's) primary language, where English is not the primary language. Additionally, a National Health Service (NHS) appointed interpreter/interpretation service (e.g., Language Line) will be arranged for phone calls and the study visit to ensure clear communication. The participant's clinician may also attend and provide translation. Withdrawal of consent: All participants (and their legal guardians) will be informed of their right to withdraw from the study, without stating a reason, at any time up to and including data and sample analysis, without prejudice or jeopardy to any future clinical care. If a participant permanently withdraws from the study, the reason (if provided) will be recorded. Research visit(s): All study participants will be invited to the National Institute for Health and Care Research (NIHR) Exeter Clinical Research Facility (Exeter CRF), or alternative convenient location, to provide written informed consent/assent and undergo Core Study data collection, measurements, and provide blood samples collected by a nurse or doctor (paediatric, if necessary), fully trained in this procedure. This Core Study visit should take approximately 40 minutes to 1 hour. MRI: Depending on their genotype, participants will be invited to take part in the Imaging Sub-Study to have an MRI (Magnetic Resonance Imaging) scan to measure organ size and body fat distribution. The MRI appointment may be arranged as a separate visit if more convenient for the participant. Patients with genetic variants that result in neonatal diabetes often lack insulin in utero as well as after birth. These patients are typically born at proximately half normal birth weight as the foetus in the womb lack insulin and this is a major growth factor for the foetus. To allow assessment of the impact of lack of insulin in utero on post-natal growth fat distribution and the pancreatic size, participants with genetic subtypes that stop insulin secretion in utero will be invited to have an MRI (optional) during their visit. A CE marked MRI scanner, at the Mirielle Gillings Neuroimaging Centre (MGNC), will be used to acquire multiple standard vendor sequences of the participant's body to look at fat distribution and measure organ size (40-60 minutes). During the procedure the participant can ask to stop the procedure at any time, between and during scans; and can also have breaks between the scans. A priority scan list will be followed to obtain images according to importance to address the research questions. Data Collection and Recording: All participants will be pseudo-anonymised by assigning a unique study identifier (ID) under which all data and samples collected will be recorded and stored. The relevant visit Case Report Form (CRF) will capture all the information required to ensure that all the documented statistical information dictated in the protocol is captured and documented at each visit. This also serves to monitor patient eligibility and safety at Sponsor level. Hard copies will be stored in the Trial Master File (TMF). Sample collection and storage: All samples will be collected and processed by a suitably qualified and trained member of the clinical/research team, whose role is documented on the study delegation log. The amount of blood to be drawn from participants is solely for research purposes and will not exceed the recommended limits in accordance with World Health Organization (WHO) guidelines according to age and/or weight of study participants. Children will be offered use of a topical anaesthetic, as is common practise during phlebotomy of children. Blood collection will be made into specific blood collection tubes according to the study aim. The Exeter Blood Sciences Laboratory will provide analyses from routine biochemistry tests available in the NHS test repertoire. All assays are CE marked, fully validated, and accredited by Clinical Pathology Accreditation (CPA). Clinical results will be available within 21 days of receipt of the sample. The investigators' laboratories, based at the University of Exeter, will receive blood samples to isolate blood fractions according to established protocols according to manufacturers' description. A detailed Standard Operating Procedure (SOP) will be provided detailing clear instruction to the logistics of sample labelling, logging and management of sample processing and storage of isolated blood fractions. Robust procedures, in compliance with the Human Tissue Act 2004, are always followed to monitor and maintain the integrity and traceability of the samples, stored in a licensed area, including their disposal. All samples will be processed, logged and frozen using sample-appropriate storage procedures. Human Tissue Authority (HTA) approved locations for storage are available within the investigators' laboratories at the University of Exeter Medical School. All saved samples will be stored under the study ID, with the file linking the study code to personal identifiable information held securely by the PI, accessible only to personnel with training in data protection who require this information to perform their duties. Those with access to personal identifiable data will be documented on the study Delegation Log. The Study ID will provide a robust pseudo-anonymised system for management and location tracking of all study samples. The research team will monitor consent status via the study database. Where samples are unable to be collected, this should be documented under the participant Study ID with reason for non-collection provided. Transfer of custodianship of stored samples with consent to the Genetic Beta Cell Research Bank (GBCRB), managed by The Royal Devon University Healthcare NHS Foundation Trust's Exeter Genomics Laboratory, may occur during the study or at the end of the study as defined above. Stored samples will then be made available for further separate ethically-approved research. End of Study: The parameter marking the end of the study is the 3 months after the final participant's final visit or 3 months prior to end of the funded period (whichever is later), to allow for final collection of data and analysis. Incidental Findings: Investigations in this study are aimed to answer research questions and not guide clinical care. Therefore, individual results will not be reported routinely to the participant and their clinician(s). However, incidental findings outside the range of the normal population may occur. All incidental findings will be discussed with the Chief Investigator (CI), or medically qualified delegate, to assess whether immediate clinical action is required. If required, the test result(s) would be communicated back to the participant and their healthcare providers to enable initiation of follow-up and/or treatment. Participants will not expect to receive individual results unless clinical action is needed. A statement to this effect is included in the information sheet and consent form. Safety: The timeframe for recording a Serious Adverse Event (SAE) will be from the time of consent to one week following the last visit of a study participant. Any reportable adverse effects noted will be reported within 24 hours to the CI and the Sponsor as per standard NHS R&D protocols. Nominated co-investigators will be authorised to sign the SAE forms in the absence of the CI at the co-ordinating site, or the PI at the participating sites. Indemnity: The lead Sponsor, University of Exeter, provides cover under its No Fault Compensation Insurance, which provides for payment of damages or compensation in respect of any claim made by a research participant for bodily injury arising out of participation in a clinical trial or healthy volunteer study (with certain restrictions). Public liability insurance is provided to cover the design and management of the study. NHS indemnity covers potential legal liability: i) for harm to participants arising from the design of the research; and ii) of investigators/research staff for harm to participants arising from the conduct of the research. Access to Final Study Dataset and Archiving: Where consent is given by the participant/parent/guardian, their remaining samples and data from the project will be gifted to the Genetic Beta Cell Research Bank (GBCRB), an approved tissue bank (REC ref: Wales Research Ethics Committee 5, 22/WA/0268) in Exeter to be used for future research. The GBCRB is managed by The Royal Devon University Healthcare NHS Foundation Trust's Exeter Genomics Laboratory. Access to samples/data is through application to the Genetic Beta Cell Research Bank steering committee. When the research study is complete, it is a requirement of the UK Policy Framework for Health & Social Care and Sponsor Trust Policy that the records are kept for a further 15 years. At the end of the study, the study data will be archived by the CI at the University of Exeter. Dissemination Policy: Results will be written up and submitted for publication in (open-access) peer-reviewed journal(s), and prior to open access preprint servers (e.g., such as medRxiv or bioRxiv). Abstracts will be submitted to national and international conferences. Results will also be presented to colleagues (clinical and research) at regular in-house meetings. Ongoing updates on study progress will also be made to Exeter PPIE group for continued feedback. Some data will also be deposited in electronic archives that are available to other researchers upon request to ensure data is used only to advance scientific and medical understanding. Written information outlining the key findings of the study will be sent to all participants and uploaded to the NIHR Exeter CRF and study websites.

Spirulina Supplementation In Recovery From Damaging Exercise