myDiabetes secures Qismet accreditation renewal until 2028

A new partnership between leading digital health innovators, my mhealth and Patients Know Best (PKB) means shared NHS customers can streamline the delivery of their digital care tools, making it easier to empower patients to manage their health effectively. The collaboration brings together my mhealth’s award-winning self-management platforms with PKB’s personal health record solution, which is already embedded within the NHS App. “At the heart of this partnership is the patient,” said Dr. David Pettigrew, CEO of my mhealth . “By aligning our platforms, we’re enabling people to take greater control of their health while supporting clinicians with joined-up, efficient care pathways. It’s a significant step towards the NHS’s vision of a single ‘front door’ for digital health.” Key Benefits for Patients and the NHS: ● One seamless journey: Patients and clinicians benefit from a more unified experience across apps and services. ● Better outcomes through joined-up care: Shared access to data empowers more personalised and timely interventions. ● Greater access to services: Patients can engage with support tools and resources anytime, anywhere. ● Reduced clinical workload: Digitally enhanced care pathways streamline processes and free up clinical time. ● Scalable long-term condition support: Proven tools for managing COPD, asthma, diabetes, and more, integrated with national systems. ● Patient empowerment: Enabling people to be active participants in their health journey. This partnership also honours the early vision of digital health pioneer Dr Warner Slack, who said in the 1970s: “I hoped that the computer would help the doctor in the care of the patient. And in the back of my mind was the idea that the computer might actually help patients to help themselves with their medical problems.” Today , that vision is becoming reality - placing digital tools directly in the hands of patients and enabling a more connected, compassionate, and sustainable NHS. About my mhealth my mhealth provides evidence-based digital therapeutics for patients with long-term conditions including COPD, asthma, diabetes, and heart disease. Trusted by NHS organisations across the UK, their platforms deliver scalable self-management support and remote monitoring tools that improve outcomes and reduce healthcare burden. About Patients Know Best Patients Know Best is the World’s largest Personal Health Record (PHR) and patient engagement platform, integrating data feeds from over 550 health organisations and providers. The system connects information from GPs, hospitals, social and mental health care providers, to create a single, unified copy of patient data. Everything from appointments and letters to test results, care plans, real-time monitoring data and discharge summaries, as well as the patient’s own data, are all available in one patient record, enabling patients and healthcare professionals to access up-to-date health information anytime, anywhere. In the UK, the platform serves over 5 million patients, registering 100,000+ patients and releasing over 20 million test results a month. PKB integrates with the NHS App to provide a single front door for patients to access their information.

NHS University College London Hospitals NHS Foundation Trust, part of North Central London ICB, is taking a significant step towards enhancing patient empowerment and optimising disease management. Asthma is a chronic condition that affects millions of people worldwide, often leading to severe health complications if not managed properly. Recognising the critical need for effective self-management tools, NHS University College London Hospitals NHS Foundation Trust has chosen the myAsthma app to provide patients with the resources they need to take control of their health. Dr Kay Roy PhD FRCP, Consultant Respiratory Physician University College London Hospitals NHS Foundation Trust, comments “We are thrilled to introduce myAsthma as a self-management tool to our community. It represents a significant step forward in empowering our patients with asthma to take control of their health. By providing them with personalised support, we believe this tool will greatly improve their quality of life. Additionally, the use of myAsthma in outpatient settings will help triage patients more effectively, ensuring they are seen in a timely manner and appropriately referred for the right investigations and services. Our team is excited to see the positive impact this will have on the asthma population across North Central London ICB." The myAsthma app, part of the my mhealth suite of digital health solutions, is designed to empower patients with comprehensive tools and information to manage their asthma more effectively. Key features include: • Personalised Action Plans: Tailored asthma management plans based on individual patient needs. • Inhaler technique training: Contributing to better health outcomes and reduced risk of exacerbations • Medication Tracking: Reminders and logs to ensure patients take their medication as prescribed. • Symptom tracking: Easy-to-use tools for tracking symptoms and triggers. • Educational Resources: Access to a wealth of information on asthma, helping patients understand their condition and how to manage it. As more NHS partners embrace the my mhealth platform, we're thrilled to witness its growing impact and the positive changes it is bringing to long-term condition care. For more information on this article or other my mhealth projects, please get in touch https://mymhealth.com/contact-us

The my mhealth data library is an extensive resource designed to support healthcare providers by offering a wealth of information and tools related to COPD and long-term health conditions.

Permission to use received from Rebecca Fowler View poster

Henry M.G. Glyde1Alison M. Blythin2 Tom M.A. Wilkinson3Ian T. Nabney4 James W. Dodd5 EPSRC Centre for Doctoral Training in Digital Health and Care, University of Bristol, Bristol, UK my mHealth Limited, Bournemouth , UK my mHealth and Clinical and Experimental Science, University of Southampton, Southampton, UK School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK Academic Respiratory Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK Abstract Background Acute exacerbations of COPD (AECOPD) are episodes of breathlessness, cough and sputum which are associated with the risk of hospitalisation, progressive lung function decline and death. They are often missed or diagnosed late . Accurate timely intervention can improve these poor outcomes. Digital tools can be used to capture symptoms and other clinical data in COPD. This study aims to apply machine learning to the largest available real-world digital dataset to identify AECOPD Prediction tool which could be used to support early intervention improve clinical outcomes. Objective To create and validate a machine learning predictive model that forecasts exacerbations of COPD 1-8 days in advance. The model is based on routine patient-entered data from myCOPD self-management app. Method Adaptations of the AdaBoost algorithm were employed as machine learning approaches. The dataset included 506 patients users between 2017-2021. 55,066 app records were available for stable COPD event labels and 1,263 records of AECOPD event labels. The data used for training the model included COPD assessment test (CAT) scores, symptom scores, smoking history, and previous exacerbation frequency. All exacerbation records used in the model were confined to the 1-8 days preceding a self-reported exacerbation event. Results TheEasyEnsemble Classifier resulted in a Sensitivity of 67.0% and a Specificity of 65% with a positive predictive value (PPV) of 5.0% and a negative predictive value (NPV) of 98.9%. An AdaBoost model with a cost-sensitive decision tree resulted in a a Sensitivity of 35.0% and a Specificity of 89.0% with a PPV of 7.08% and NPV of 98.3%. Conclusion This preliminary analysis demonstrates that machine learning approaches to real-world data from a widely deployed digital therapeutic has the potential to predict AECOPD and can be used to confidently exclude the risk of exacerbations of COPD within the next 8 days. Permission to use received from Henry Glyde. Read more on Heliyon website.

Francesca D’angelo 1 Charlotte Smith 2 University Hospital of Derby and Burton, Health and Wellbeing Department, Burton, UK University Hospital of Derby and Burton, Cardiac Rehabilitation Department, Burton Upon Trent, UK. To examine the effectiveness of psychological outcomes using a web-based Cardiac Rehabilitation application compared with a conventional programme or a combination of both. University Hospitals of Derby and Burton NHS Foundation Trust poster presented at the BACPR Annual Conference October 5-6th 2023 Poster presented at the BACPR Annual Conference October 5-6th 2023 Permission to use received from Charlotte Smith

Christopher Duckworth 1 Bethany Cliffe 2. Brian Pickering 1 Ben Ainsworth 2 Alison Blythin 3 Adam Kirk 3 Adam Kirk Thomas M. A. Wilkinson 3,4,5 Michael J. Boniface 1 1 IT Innovation Centre, Digital Health and Biomedical Engineering, University of Southampton, Southampton, UK. 2. School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK my mHealth Limited, London, UK. National Institute for Health Research Applied Research Collaboration Wessex, University of Southampton , Southampton , GB Faculty of Medicine, University of Southampton, Southampton , GB Mobile Health (mHealth) has the potential to be transformative in the management of chronic conditions. Machine learning can leverage self-reported data collected with apps to predict periods of increased health risk, alert users, and signpost interventions. Despite this, mHealth must balance the treatment burden of frequent self-reporting and predictive performance and safety. Here we report how user engagement with a widely used and clinically validated mHealth app, myCOPD (designed for the self-management of Chronic Obstructive Pulmonary Disease), directly impacts the performance of a machine learning model predicting an acute worsening of condition (i.e., exacerbations). We classify how users typically engage with myCOPD, finding that 60.3% of users engage frequently, however, less frequent users can show transitional engagement (18.4%), becoming more engaged immediately ( < 21 days) before exacerbating. Machine learning performed better for users who engaged the most, however, this performance decrease can be mostly offset for less frequent users who engage more near exacerbation. We conduct interviews and focus groups with myCOPD users, highlighting digital diaries and disease acuity as key factors for engagement. Users of mHealth can feel overburdened when self-reporting data necessary for predictive modelling and confidence of recognising exacerbations is a significant barrier to accurate self-reported data. We demonstrate that users of mHealth should be encouraged to engage when they notice changes to their condition (rather than clinically defined symptoms) to achieve data that is still predictive for machine learning, while reducing the likelihood of disengagement through desensitisation. Read more

Christopher Duckworth 1 Michael J Boniface 1 Adam Kirk 2 Thomas M A Wilkinson 2 3 4 IT Innovation Centre, Digital Health and Biomedical Engineering, University of Southampton, Southampton, UK. my mHealth Limited, London, UK. National Institute for Health Research Applied Research Collaboration Wessex, University of Southampton , Southampton , GB Faculty of Medicine, University of Southampton, Southampton , GB Introduction: The GOLD (Global Initiative for Chronic Obstructive Lung Disease) 2023 guidelines proposed important changes to the stratification of disease severity using the "ABCD" assessment tool. The highest risk groups "C" and "D" were combined into a single category "E" based on exacerbation history, no longer considering symptomology. Purpose: We quantify the differential disease progression of individuals initially stratified by the GOLD 2022 "ABCD" scheme to evaluate these proposed changes. Patients and methods: We utilise data collected from 1529 users of the myCOPD mobile app, a widely used and clinically validated app supporting people living with COPD in the UK. For patients in each GOLD group, we quantify symptoms using COPD Assessment Tests (CAT) and rate of exacerbation over a 12-month period post classification. Results: CAT scores for users initially classified into GOLD C and GOLD D remain significantly different after 12 months (Kolmogorov-Smirnov statistic = 0.59, P = 8.2 × 10-23). Users initially classified into GOLD C demonstrate a significantly lower exacerbation rate over the 12 months post classification than those initially in GOLD D (Kolmogorov-Smirnov statistic = 0.26; P = 3.1 × 10-2; all exacerbations). Further, those initially classified as GOLD B have higher CAT scores and exacerbation rates than GOLD C in the following 12 months. Conclusion: CAT scores remain important for stratifying disease progression both in-terms of symptomology and future exacerbation risk. Based on this evidence, the merger of GOLD C and GOLD D should be reconsidered. Read more