Chan, Amy Hai YanTe Ao, BradenBaggott, ChristinaCavadino, AlanaEikholt, Amber AHarwood, MatireHikaka, JoannaGibbs, DiannaHudson, MarianaMirza, FarhaanNaeem, Muhammed AsifSemprini, RuthChang, Catherina LTsang, Kevin CHShah, Syed AhmarJeremiah, AronAbeysinghe, Binu NisalRoy, RajshriWall, ClareWood, LisaDalziel, StuartPinnock, Hilaryvan Boven, Job FMRoop, ParthaHarrison, Jeff2024-05-282024-05-282024-05-22BMJ Open Respiratory Research, ISSN: 2052-4439 (Print); 2052-4439 (Online), BMJ Publishing Group, 11(1), e002275-. doi: 10.1136/bmjresp-2023-0022752052-44392052-4439http://hdl.handle.net/10292/17604INTRODUCTION: 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 ANALYSIS: A 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. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry ACTRN12623000764639; Australian New Zealand Clinical Trials Registry.© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.http://creativecommons.org/licenses/by-nc/4.0/AsthmaClinical EpidemiologyInhaler devicesSurveys and QuestionnairesTelemedicineAsthmaClinical EpidemiologyInhaler devicesSurveys and QuestionnairesTelemedicine32 Biomedical and Clinical Sciences3201 Cardiovascular Medicine and Haematology3202 Clinical SciencesLungAsthmaRespiratory3 Good Health and Well Being3201 Cardiovascular medicine and haematology3202 Clinical sciencesAdolescentAdultArtificial IntelligenceAsthmaChildFemaleHumansMaleNebulizers and VaporizersNew ZealandObservational Studies as TopicProspective StudiesHumansAsthmaArtificial IntelligenceProspective StudiesNew ZealandMaleAdultFemaleChildObservational Studies as TopicNebulizers and VaporizersAdolescentHumansAsthmaProspective StudiesNebulizers and VaporizersArtificial IntelligenceAdolescentAdultChildNew ZealandFemaleMaleObservational Studies as TopicJournal ArticleOpenAccess10.1136/bmjresp-2023-002275