DIGIPREDICT: Physiological, Behavioural and Environmental Predictors of Asthma Attacks-A Prospective Observational Study Using Digital Markers and Artificial Intelligence - Study Protocol

Date
2024-05-22
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
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
BMJ Publishing Group
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.

Description
Keywords
Asthma , Clinical Epidemiology , Inhaler devices , Surveys and Questionnaires , Telemedicine , Asthma , Clinical Epidemiology , Inhaler devices , Surveys and Questionnaires , Telemedicine , 32 Biomedical and Clinical Sciences , 3201 Cardiovascular Medicine and Haematology , 3202 Clinical Sciences , Lung , Asthma , Respiratory , 3 Good Health and Well Being , 3201 Cardiovascular medicine and haematology , 3202 Clinical sciences
Source
BMJ Open Respiratory Research, ISSN: 2052-4439 (Print); 2052-4439 (Online), BMJ Publishing Group, 11(1), e002275-. doi: 10.1136/bmjresp-2023-002275
Rights statement
© 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/.