my mhealth develop coronavirus app for NHS to support patients remotely.

24 Apr, 2020

COVID-19 Virtual Ward app: a remote coronavirus monitoring service giving clinicians a 360-degree view of patients symptoms.



COVID-19 Virtual Ward is for patients suspected to have or diagnosed with coronavirus. By answering a few questions and recording a few measurements twice a day - cough, wellness, breathing, pulse, temperature and oxygen levels (using an oximeter) - the app uses a clinically recognised scoring algorithm to assist patients being managed in a virtual environment to be cared for remotely by their clinical team.

The app works in parallel with all four apps in the my mhealth eco-system - myCOPD, myAsthma, myDiabetes and myHeart - making it the only coronavirus app in the world that supports patients with co-morbidities.

Clinical teams can monitor hundreds of patients remotely and intervene when necessary, taking pressure off front-line services.

Dr Simon Bourne, founder and CEO of my mhealth and the principal designer of the app talks about the benefits the new COVID-19 Virtual Ward brings to clinicians and patients: "As clinicians ourselves we have experienced first-hand the enormous resource impact that the current pandemic is having at all levels of care. In response to a request from NHS clinicians and providers, we have built a simple yet powerful bolt-on to the current my mhealth platform that will enable a safe, cost-effective, immediately deployable solution to managing the many 1000s of patients that need to be monitored outside of the conventional setting."

The patient dashboard displays all the patients under a clinician's care, their current symptoms, oxygen saturation, pulse rate and wellness.

The patient's profile allows clinical teams to track their patient's symptoms and physiology over time, making it easy to see changes and to decide when a patient needs extra support.


With all the latest official coronavirus advice from the UK governments Chief Medical Officer and Chief Science Officer, the COVID-19 Virtual Ward app has been heralded as an essential tool in the NHS arsenal for fighting coronavirus and supporting the population at scale. Dr Bourne explained why, "In a time when we are all having to run clinical services remotely, there is a realisation that to manage patients at a population scale we need to harness the same devices for healthcare services and monitoring, that many of our patients are already using to manage their day to day lives. Technology has the power to free up clinical resources to enable us to prioritise care for the most vulnerable in the community".

Dr Bourne has invited clinical commissioning groups to join forces with my health, saying "We can help clinical teams to get up and running within 48 hours, please contact us today and we can support, train and activate your teams remotely to start admitting patients to the virtual ward within hours".

If you're a healthcare professional or clinician and want to know more about how COVID-19 Virtual Ward can help your patients, you can find out more at www.mymhealth.com or by calling my mhealth on 0044 (0)1202 299 583


By July 2024 07 Aug, 2024
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
By 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. 07 Aug, 2024
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.
By 2nd July 2024 07 Aug, 2024
Permission to use received from Rebecca Fowler View poster
By 13 May 2024 07 Aug, 2024
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.
By 5th October 2023 07 Aug, 2024
Charlotte Smith 1 Francesca D’angelo 2 University Hospital of Derby and Burton, Cardiac Rehabilitation Department, Burton Upon Trent, UK. University Hospital of Derby and Burton, Health and Wellbeing Department, Burton, UK To examine the effectiveness of physical activity 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 Permission to use received from Charlotte Smith
By 5th October 2023 07 Aug, 2024
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
By 12 March 2024 07 Aug, 2024
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
By 24th October 2023 07 Aug, 2024
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
By 15 May 2023 07 Aug, 2024
Alison M. Blythin 1 Jack Elkes 2 Ronie Walter 3 Amber Bhogal 1 Ian Thompson Thomas van Lindholm 1 Matt Smith 1 Trish Gorely 3 Tom M.A. Wilkinson 1,5 Stephen J. Leslie 3,4 and Adam Kirk 1 my mhealth Limited, London, UK Imperial College London Clinical Trials Unit, London, UK University of the Highlands and Islands, SCOTLAND NHS Highlands Cardiology Department, SCOTLAND University of Southampton Faculty of Medicine, UK COVID-19 significantly impacted cardiac rehabilitation (CR) delivery. Service disruption left numerous patients without treatment access. Many healthcare teams made use of digital apps to support CR delivery and patients remotely. This evaluation aimed to analyse digital CR access from the myHeart interactive, cloud-based self-management app during the pandemic. Five NHS secondary care CR services agreed to combine existing anonymised app data between Mar-Oct 2020 for 12-weeks to align as much as possible with traditional CR models. No statistically significant differences were observed across age groups or gender between users who activated myHeart and those who did not. N=314/350 (89.7%) users accessed 5,469 CR videos with N=313/314 (99.7%) accessing 3,606 within the first 6-weeks of activation. No statistically significant differences were observed across gender or age group for education video views. Users with angina only diagnosis accessed more exercise videos than those with other reported diagnoses. Patient user feedback responses showed a statistically significant increase in self-management confidence following myHeart access. myHeart provided remote timely CR during service disruption. This evaluation is the beginning of a journey to understand app usage however further research is needed to fully understand the role digital health can play in the delivery of CR.f your post goes he re. To edit this text, click on it and delete this default text and start typing your own or paste your own from a different source. Read more.
By 11 March 2023 07 Aug, 2024
A.M. Blythin 1, J. Elkes 2 T. van Lindholm 1 A. Bhogal 1 T.M.A. Wilkinson 1 C. Saville 3 A. Kirk 1 Department of Research & Innovation, my mhealth Limited, Bournemouth, UK. School of Public Health, Imperial College Clinical Trials Unit, London, UK. Faculty of Medicine, University of Southampton, Southampton, Hampshire, UK Digital health interventions provide a potential solution to improve diabetes education delivery at population scale, overcoming barriers identified with traditional approaches. This evaluation analysed usage data for people with type 2 diabetes focusing on digital structured diabetes education. Results showed a positive uptake and usage with myDiabetes, with increases in app activity post-COVID. No statistically significant differences were observed between gender or age for those activated. No statistically significant differences observed in education video views across age groups, gender, diabetes treatment type or smoking status. The findings support the use of digital health in the provision of additional support for the delivery of diabetes education. There is potential for increasing diabetes education rates by offering patients a digital option in combination with traditional service delivery which should be substantiated through future research. Read more
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