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

IntroductionAsthma 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...

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Bibliographic Details
Published in:	BMJ open respiratory research Vol. 11; no. 1; p. e002275
Main Authors:	Chan, Amy Hai Yan, Te Ao, Braden, Baggott, Christina, Cavadino, Alana, Eikholt, Amber A, Harwood, Matire, Hikaka, Joanna, Gibbs, Dianna, Hudson, Mariana, Mirza, Farhaan, Naeem, Muhammed Asif, Semprini, Ruth, Chang, Catherina L, Tsang, Kevin C H, Shah, Syed Ahmar, Jeremiah, Aron, Abeysinghe, Binu Nisal, Roy, Rajshri, Wall, Clare, Wood, Lisa, Dalziel, Stuart, Pinnock, Hilary, van Boven, Job F M, Roop, Partha, Harrison, Jeff
Format:	Journal Article
Language:	English
Published:	England British Thoracic Society 22-05-2024 BMJ Publishing Group LTD BMJ Publishing Group
Subjects:	Adolescent Adult Artificial Intelligence Asthma Child Clinical Epidemiology Datasets Digital technology Effectiveness Female Hospitals Humans Inhaler devices Inhalers Male Nebulizers and Vaporizers New Zealand Observational Studies as Topic Patients Physiology Population Prospective Studies Surveys and Questionnaires Telemedicine New Zealand Clinical Epidemiology Surveys and Questionnaires Telemedicine Inhaler devices Asthma
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Summary:	IntroductionAsthma 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 disseminationEthical 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.Trial registration numberAustralian New Zealand Clinical Trials Registry ACTRN12623000764639; Australian New Zealand Clinical Trials Registry.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23
ISSN:	2052-4439 2052-4439
DOI:	10.1136/bmjresp-2023-002275