DIGIPREDICT: Physiological, behavioural and environmental predictors of asthma attacks – a prospective observational study using digital markers and artificial intelligence - study protocol

Introduction Asthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are not always recognised, highlighting a potential role for technology. The aim of this study ‘DIGIPREDICT’ is to identify early digital markers of asthma attacks using sensors embedded in smart devices including watches and inhalers, and leverage health and environmental datasets and artificial intelligence, to develop a risk prediction model to provide an early, personalised warning of asthma attacks.Methods and analysisA prospective sample of 300 people, 12 years or older, with a history of a moderate or severe asthma attack in the last 12 months will be recruited in New Zealand. Each participant will be given a smart watch (to assess physiological measures such as heart and respiratory rate), peak flow meter, smart inhaler (to assess adherence and inhalation) and a cough monitoring application to use regularly over 6 months with fortnightly questionnaires on asthma control and well-being. Data on sociodemographics, asthma control, lung function, dietary intake, medical history and technology acceptance will be collected at baseline and at 6 months. Asthma attacks will be measured by self-report and confirmed with clinical records. The collected data, along with environmental data on weather and air quality, will be analysed using machine learning to develop a risk prediction model for asthma attacks.Ethics and dissemination Ethical approval has been obtained from the New Zealand Health and Disability Ethics Committee (2023 FULL 13541). Enrolment began in August 2023. Results will be presented at local, national and international meetings, including dissemination via community groups, and submission for publication to peer-reviewed journals.<br/

Application of machine learning to support self-management of asthma with mHealth

While there have been several efforts to use mHealth technologies to support asthma management, none so far offer personalised algorithms that can provide real-time feedback and tailored advice to patients based on their monitoring. This work employed a publicly available mHealth dataset, the Asthma Mobile Health Study (AMHS), and applied machine learning techniques to develop early warning algorithms to enhance asthma self-management. The AMHS consisted of longitudinal data from 5,875 patients, including 13,614 weekly surveys and 75,795 daily surveys. We applied several well-known supervised learning algorithms (classification) to differentiate stable and unstable periods and found that both logistic regression and naïve Bayes-based classifiers provided high accuracy (AUC > 0.87). We found features related to the use of quick-relief puffs, night symptoms, frequency of data entry, and day symptoms (in descending order of importance) as the most useful features to detect early evidence of loss of control. We found no additional value of using peak flow readings to improve population level early warning algorithms

Developing theory-based asthma self-management interventions for South Asians and African Americans:A systematic review

PURPOSE: Intervention development guidelines suggest that behavioural interventions benefit from being theory-based. Minority populations typically benefit less from asthma self-management interventions, and the extent to which appropriate theory has been used for culturally tailored interventions has not been addressed. We aimed to determine theory use and theoretical domains targeted in asthma self-management interventions for South Asian and Black populations. METHODS: We systematically searched electronic databases, research registers, manually searched relevant journals and reference lists of reviews for randomised controlled trials of asthma self-management for South Asian and Black populations, and extracted data using the Theory Coding Scheme to inform if/how theory was used and explore its associations with asthma outcomes, and the Theoretical Domains Framework was used to identify targeted theoretical domains and its relationship to effectiveness of asthma outcomes. RESULTS: 20 papers (19 trials) were identified; theory was not extensively used in interventions. It was unclear whether theory use or theoretical domains targeted in interventions improved asthma outcomes. South Asian interventions included 'behavioural regulation', while 'reinforcement' was mostly used in African American interventions. 'Knowledge' was central for all populations, though there were differences related to 'environmental context and resources' e.g., language adaptations for South Asians; asthma resources provided for African Americans. Author descriptions of interventions targeting providers were limited. CONCLUSIONS: There was little evidence of theory-based approaches used in cultural interventions for asthma self-management. Demystifying theoretical concepts (and cultural interpretations of constructs) may provide clarity for 'non-experts', enabling mainstream use of theory-driven approaches in intervention development

General practitioners with a special interest in respiratory medicine:national survey of UK primary care organisations

Non peer reviewedPublisher PD

A woman with asthma: a whole systems approach to supporting self-management

A 35-year-old lady attends for review of her asthma following an acute exacerbation. There is an extensive evidence base for supported self-management for people living with asthma, and international and national guidelines emphasise the importance of providing a written asthma action plan. Effective implementation of this recommendation for the lady in this case study is considered from the perspective of a patient, healthcare professional, and the organisation. The patient emphasises the importance of developing a partnership based on honesty and trust, the need for adherence to monitoring and regular treatment, and involvement of family support. The professional considers the provision of asthma self-management in the context of a structured review, with a focus on a self-management discussion which elicits the patient’s goals and preferences. The organisation has a crucial role in promoting, enabling and providing resources to support professionals to provide self-management. The patient’s asthma control was assessed and management optimised in two structured reviews. Her goal was to avoid disruption to her work and her personalised action plan focused on achieving that goalCase Stud

Implementation of digital health interventions in respiratory medicine:a call to action by the European Respiratory Society m-Health/e-Health Group

An action plan prepared by @EuroRespSoc Group 01.04 (m-health/e-health) concerning the implementation of digital health interventions in respiratory medicine http://bit.ly/2JeEuox