How to achieve 2028 NHS England Long-Term Plan Targets for cardiac rehab
27 Apr, 2021
Dorset County Hospital NHS Foundation Trust Cardiac Rehabilitation team have hit the 2028 NHS targets for the past two years by adopting a new approach utilising the my mhealth app, myHeart.
In 2004, the Dorset CR service first introduced a menu-based CR delivery model which provided patients with a choice of either traditional in-person or home-based CR programmes. By 2016-17 the team had achieved a 20% increase in patient participation equating to a 70% annual patient uptake. However, the team "felt that there was still an unmet need, particularly for those who felt too vulnerable to travel to a CR group or could not accommodate a CR session during the day due to work or care-giving responsibilities."
In 2017, the Dorset CR team and my mhealth developed an innovative CR module within myHeart to offer patients a digital component to compliment both traditional in-person and home-based models of CR delivery. The impact of utilising myHeart for CR uptake can be seen in the table below, with the services already exceeding the NHSE targets for 2028:
In March 2020, most traditional in-person CR programmes were suspended due to Covid-19. There was a rapid demand to quickly identify and adopt alternative ways of delivering CR to patients. However, the team in Dorset, due to the increased flexibility and resilience of their service model, simply suspended traditional in-person CR delivery and offered patients one of the other available options. myHeart usage increased by 124 (23.7%) patients during the first Covid-19 wave and continues to be a popular option for patients with a further 32.4% opting to use it in the first three months of year 3. These results show that digital health can contribute to supporting CR services to achieve greater patient uptake and completion rates.
Cardiac Rehab Specialist Nurse Shaun Porter told us...
"The menu-based approach of cardiac rehabilitation, has allowed us to provide an individually appropriate cardiac rehabilitation service to all of our patients, irrespective of physical conditions, ability to travel, IT literacy or socio-economic status. This, coupled with our holistic cardiac event follow-up clinic, ensures that all patients are assessed and treated promptly and holistically following their cardiac event."
The digitally enhanced approach adopted in Dorset has increased uptake to Cardiac Rehabilitation to consistently achieve and surpass the NHS targets for 2028 and increase service resilience.
"The myHeart App has proved really popular with patients and complements the other group-based and individual treatment options the rehab team offer. More cardiac patients than ever before are now receiving some form of rehab thanks to developments such as myHeart, which is having a hugely positive impact on people’s recovery and quality of life."
If you want to find out more about how digitally enhanced cardiac rehab can help you meet the NHS England Long-Term Targets for cardiac rehab please get in touch here or call us on +44 (0)1202 299 583.
Permission to use received from Rebecca Fowler View poster .
Results of a service evaluation using myHeart in a large London-based acute NHS Trust. We are pleased to report the outcomes from this recent service evaluation of myHeart and the potential benefit of using the myHeart app to supplement existing cardiac rehabilitation (CR). Cardiac rehabilitation is an essential evidence-based intervention that supports patient recovery following a cardiac event. It offers patients a structured education and exercise programme to aid recovery and support behavioural changes to help reduce the risk of future cardiac health complications. The myHeart app provides a structure educational and exercise intervention that mirrors current CR service delivery as well as supportive self-management tools. Overall, 721 patients were invited to participate in 1 of 4 groups (class-based CR, class-based CR with myHeart, home-based CR and home-based CR with myHeart). A total of 584 patients opted to use class-based CR and of these 43 chose to include myHeart to support them with this. There were 137 patients in the home-based group, with 54 choosing to include myHeart alongside their CR. This 12-week evaluation involved functional, physical and psychological assessments both before and after CR to explore potential changes. Patients were also asked to complete a rating of perceived exertion Borg RPE scale (Borg 6-20). Those in the home-based groups were contacted mid-way through the study. Results identified three key outcomes: 1. Blood pressure, cholesterol, LDL, BMI, HbA1c and exercise were all very similar across the groups with marginal differences across each measure. 2. Drop-out rates (DOR) of patients being invited to attend CR and attending CR were significantly lower in those groups with access to myHeart. * Class only: DOR = 58.2% Class + App; DOR = 25.6% * Home only: DOR = 73.5% Home + App; DOR = 42.6% 3. Those patients with access to myHeart and in the home-based group saw the greatest improvement in anxiety and depression scores. This real-world evaluation provides an encouraging insight into the potential impact of myHeart to supplement CR services, and is suggestive that, as an adjunct to support both class and home-based programmes, myHeart helps to reduce drop-out rates in CR and can assist in reinforcing continuous engagement with CR programmes.
Results have led to continued QISMET Accreditation for myDiabetes (QIS2015) and have revealed the app could play an important role in supporting structured patient education delivery for type 2 diabetes following initial diagnosis, and as an ongoing resource. Together with a large Health and Care Partnership, we led a multi-centre service evaluation to explore the impact of myDiabetes on education course attendance rates. T2DM is a serious and growing problem worldwide and affects more than 3 million people in the UK. Structured education is a large part of managing T2DM to promote a healthy lifestyle and improve blood sugar control. However, the uptake for education courses has been less than encouraging across the UK. By offering a digital alternative or adjunct to a class-based course those who are unable or prefer not to attend a class-based programme, are able to receive structured education. myDiabetes is an app to support patients and clinicians manage diabetes together, remotely and at scale. Overall, 83 T2DM patients were recruited by the healthcare team, of which 28 chose to use myDiabetes alone, 35 chose only usual care, and 20 chose to use both. Patient education usage was monitored over a 12-week period. During this evaluation we monitored changes in diabetes related clinical health outcomes where possible, including HbA1c, blood pressure and body mass index. Participants in all three sites were asked to complete the Problem Areas In Diabetes (PAID) questionnaire at the beginning and end of the evaluation to explore markers of improvement in diabetes related distress. Results showed the app was acceptable in this care setting with 31 of 42 patients using it alone or as an adjunct to usual care. A total of 586 education videos were watched, on average each patient watched 22.5 (SD 19.6) videos. There was a reduction in PAID scores across all arms, with the app only arm showing the greatest improvement. Patients using myDiabetes showed the greatest improvement in HbA1c (-7.5 vs –4.4 mmol/mol), Systolic Blood Pressure (-12.2 vs +3.3 mmHg) and PAID score (-6.8 vs –5.2). In this real world evaluation myDiabetes performed better than published education course completion rates and resulted in significant improvements in HbA1c and PAID score compared to classed-based programs. This supports the use of myDiabetes to support the delivery of structured based education for patients with type 2 diabetes.
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
Read the myCOPD Data Booklet.
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.
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
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
