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DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol.
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.
Affiliation
  • Chan AHY; School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand a.chan@auckland.ac.nz.
  • Te Ao B; School of Population Health, University of Auckland, Auckland, New Zealand.
  • Baggott C; Department of Respiratory Medicine and Respiratory research unit, Waikato Hospital, Hamilton, New Zealand.
  • Cavadino A; School of Population Health, University of Auckland, Auckland, New Zealand.
  • Eikholt AA; University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, Netherlands.
  • Harwood M; Medication Adherence Expertise Center of the northern Netherlands (MAECON), Groningen, Netherlands.
  • Hikaka J; School of Population Health, University of Auckland, Auckland, New Zealand.
  • Gibbs D; Te Kupenga Hauora Maori, University of Auckland, Auckland, New Zealand.
  • Hudson M; Pinnacle Midlands Health Network, Hamilton, New Zealand.
  • Mirza F; School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand.
  • Naeem MA; Department of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand.
  • Semprini R; Department of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand.
  • Chang CL; National University of Computer and Emerging Sciences, Islamabad, Pakistan.
  • Tsang KCH; Medical Research Institute of New Zealand, Wellington, New Zealand.
  • Shah SA; Department of Respiratory Medicine and Respiratory research unit, Waikato Hospital, Hamilton, New Zealand.
  • Jeremiah A; University College London, London, UK.
  • Abeysinghe BN; The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK.
  • Roy R; The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK.
  • Wall C; Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand.
  • Wood L; Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand.
  • Dalziel S; Department of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand.
  • Pinnock H; Department of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand.
  • van Boven JFM; Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, New South Wales, Australia.
  • Roop P; Children's Emergency Department, Starship Children's Hospital, Auckland, New Zealand.
  • Harrison J; The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK.
BMJ Open Respir Res ; 11(1)2024 May 22.
Article in En | MEDLINE | ID: mdl-38777583
ABSTRACT

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

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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Asthma / Artificial Intelligence Limits: Adolescent / Adult / Child / Female / Humans / Male Country/Region as subject: Oceania Language: En Journal: BMJ Open Respir Res Year: 2024 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Asthma / Artificial Intelligence Limits: Adolescent / Adult / Child / Female / Humans / Male Country/Region as subject: Oceania Language: En Journal: BMJ Open Respir Res Year: 2024 Document type: Article Affiliation country: