Evidence map›Paper›PMID 38777583›Full record

ArticleBMJ open respiratory research2024

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

Amy Hai Yan Chan, Braden Te Ao, Christina Baggott, Alana Cavadino, Amber A Eikholt, Matire Harwood, Joanna Hikaka, Dianna Gibbs, Mariana Hudson, Farhaan Mirza and 15 more

Abstract readClinical Trial Protocol
In one paragraph

Article in BMJ open respiratory research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Amy Hai Yan ChanSchool of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand a.chan@auckland.ac.nz.ORCID http://orcid.org/0000-0002-1291-3902
Braden Te AoSchool of Population Health, University of Auckland, Auckland, New Zealand.
Christina BaggottDepartment of Respiratory Medicine and Respiratory research unit, Waikato Hospital, Hamilton, New Zealand.
Alana CavadinoSchool of Population Health, University of Auckland, Auckland, New Zealand.
Amber A EikholtUniversity Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, Netherlands.
Matire HarwoodSchool of Population Health, University of Auckland, Auckland, New Zealand.
Joanna HikakaTe Kupenga Hauora Māori, University of Auckland, Auckland, New Zealand.
Dianna GibbsPinnacle Midlands Health Network, Hamilton, New Zealand.
Mariana HudsonSchool of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand.
Farhaan MirzaDepartment of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand.
Muhammed Asif NaeemDepartment of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand.
Ruth SempriniMedical Research Institute of New Zealand, Wellington, New Zealand.
Catherina L ChangDepartment of Respiratory Medicine and Respiratory research unit, Waikato Hospital, Hamilton, New Zealand.
Kevin C H TsangUniversity College London, London, UK.
Syed Ahmar ShahThe University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK.
Aron JeremiahDepartment of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand.
Binu Nisal AbeysingheDepartment of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand.
Rajshri RoyDepartment of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand.
Clare WallDepartment of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand.
Lisa WoodBiomedical Sciences and Pharmacy, University of Newcastle, Newcastle, New South Wales, Australia.
Stuart DalzielChildren's Emergency Department, Starship Children's Hospital, Auckland, New Zealand.
Hilary PinnockThe University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK.
Job F M van BovenUniversity Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, Netherlands.ORCID http://orcid.org/0000-0003-2368-2262
Partha RoopDepartment of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand.
Jeff HarrisonSchool of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 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.

Indexed as

Artificial IntelligenceAsthmaAdolescentAdultChildFemaleHumansMaleNebulizers and VaporizersNew ZealandObservational Studies as TopicProspective StudiesAsthmaClinical EpidemiologyInhaler devicesSurveys and QuestionnairesTelemedicine

Identifiers

PMID38777583
PMCPMC11116853

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.