A5085867326- Chronic Obstructive Pulmonary Disease (COPD) Research
- Mobile Health and mHealth Applications
- Chronic Disease Management Strategies
- Asthma and respiratory diseases
- Digital Mental Health Interventions
- Artificial Intelligence in Healthcare
- Air Quality Monitoring and Forecasting
- Respiratory and Cough-Related Research
- Diabetes Management and Education
- Family and Patient Care in Intensive Care Units
- Health Literacy and Information Accessibility
Exacerbations of COPD are one the commonest causes admission and readmission to hospital. The role digital interventions support self-management in improving outcomes is uncertain. We conducted an open, randomised controlled trial a health platform application (app) 41 patients recruited following hospital with acute exacerbation. Subjects were either receive usual care, including written plan (n = 21), or myCOPD app 20) for 90 days. primary efficacy outcome was recovery rate symptoms...
Self-reporting digital apps provide a way of remotely monitoring and managing patients with chronic conditions in the community. Leveraging data collected by these prognostic models could increased personalization care reduce burden for people who live conditions. This study evaluated predictive ability prediction acute exacerbation events obstructive pulmonary disease using self-reported to health app.The aim this was evaluate if app can be used predict near future.This is retrospective...
Self-management interventions in COPD aim to improve patients' knowledge, skills and confidence make correct decisions, thus improving health status outcomes. myCOPD is a web-based self-management app known inhaler use exercise capacity individuals with more severe COPD. We explored the impact of patients mild–moderate or recently diagnosed through 12-week, open-label, parallel-group, randomised controlled trial compared usual care. The co-primary outcomes were between-group differences mean...
Abstract Mobile Health (mHealth) has the potential to be transformative in management of chronic conditions. Machine learning can leverage self-reported data collected with apps predict periods increased health risk, alert users, and signpost interventions. Despite this, mHealth must balance treatment burden frequent self-reporting predictive performance safety. Here we report how user engagement a widely used clinically validated app, myCOPD (designed for self-management Chronic Obstructive...
Acute exacerbations of COPD (AECOPD) are episodes breathlessness, cough and sputum which associated with the risk hospitalisation, progressive lung function decline death. They often missed or diagnosed late. Accurate timely intervention can improve these poor outcomes. Digital tools be used to capture symptoms other clinical data in COPD. This study aims apply machine learning largest available real-world digital dataset develop AECOPD Prediction could support early
Structured diabetes education has evidenced benefits yet reported uptake rates for those referred to traditional in-person programmes within 12 months of diagnosis were suboptimal. Digital health interventions provide a potential solution improve delivery at population scale, overcoming barriers identified with approaches. myDiabetes is cloud-based interactive digital self-management app. This evaluation analysed usage data people type 2 focusing on structured education.Descriptive...
Background Self-reporting digital applications provide a way of remotely monitoring and managing patients with chronic conditions in the community. Leveraging data collected by these prognostic models could increased personalisation care reduce burden for people who live conditions. This study evaluated predictive ability prediction acute exacerbation events Chronic Obstructive Pulmonary Disease using self-reported to health application. Methods Retrospective evaluating use symptom...
<b>Background:</b> Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity and a significant burden on people healthcare systems. Identifying self-reported data that has predictive capacity for AECOPD may enable timely intervention improved outcomes. <b>Methods:</b> This Exploratory Data Analysis uses user-entered from the myCOPD app (a digital therapeutic) to identify features capable predicting AECOPD. 1,758 patient users...
<sec> <title>BACKGROUND</title> Self-reporting digital apps provide a way of remotely monitoring and managing patients with chronic conditions in the community. Leveraging data collected by these prognostic models could increased personalization care reduce burden for people who live conditions. This study evaluated predictive ability prediction acute exacerbation events obstructive pulmonary disease using self-reported to health app. </sec> <title>OBJECTIVE</title> The aim this was evaluate...