Observational studyBMJ open2017
MyAirCoach: the use of home-monitoring and mHealth systems to predict deterioration in asthma control and the occurrence of asthma exacerbations; study protocol of an observational study.
Observational study in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02774772 (The Use of Home-monitoring and mHealth Systems to Predict Asthma Control and the Occurrence of Asthma Exacerbations), which is not on this map. Cited by 34 papers, 2 of them syntheses that pooled 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.
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.
The Use of Home-monitoring and mHealth Systems to Predict Asthma Control and the Occurrence of Asthma Exacerbations
Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 75 citations in OpenAlex.
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Individual-level interventions to reduce personal exposure to outdoor air pollution and their effects on people with long-term respiratory conditions.The Cochrane database of systematic reviews · 2021Pooled it
- Improving machine-learning development in allergology: bridging the gap between open-access and cohort-based databases.Current opinion in allergy and clinical immunology · 2026Review
- The rise of artificial intelligence in respiratory primary care and pulmonology: a scoping review.NPJ primary care respiratory medicine · 2026Article
- Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review.Interactive journal of medical research · 2024Review
- The Effect of Inhaled Beta-2 Agonists on Heart Rate in Patients With Asthma: Sensor-Based Observational Study.JMIR cardio · 2024Observational
- Clinical Validation of Respiratory Rate Estimation Using Acoustic Signals from a Wearable Device.Journal of clinical medicine · 2024Article
- Enhancing Adult Asthma Management: A Review on the Utility of Remote Home Spirometry and Mobile Applications.Journal of personalized medicine · 2024Review
- iPREDICT: Characterization of Asthma Triggers and Selection of Digital Technology to Predict Changes in Disease Control.Journal of asthma and allergy · 2024Article
- Prediction of Asthma Exacerbations in Children.Journal of personalized medicine · 2023Review
- Article
- Home monitoring with connected mobile devices for asthma attack prediction with machine learning.Scientific data · 2023Observational
- Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations.Scientific reports · 2022Article
- Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol.BMJ open · 2022Article
- Digital tools in allergy and respiratory care.The World Allergy Organization journal · 2022Review
- The Current and Future Role of Technology in Respiratory Care.Pulmonary therapy · 2022Review
- A Wearable Tele-Health System towards Monitoring COVID-19 and Chronic Diseases.IEEE reviews in biomedical engineering · 2022Review
- Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review.Journal of asthma and allergy · 2022Review
- mHealth and telemedicine utility in the monitoring of allergic diseases.Frontiers in allergy · 2022Review
- Mixed methods process evaluation of my breathing matters, a digital intervention to support self-management of asthma.NPJ primary care respiratory medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAsthma is a variable lung condition whereby patients experience periods of controlled and uncontrolled asthma symptoms. Patients who experience prolonged periods of uncontrolled asthma have a higher incidence of exacerbations and increased morbidity and mortality rates. The ability to determine and to predict levels of asthma control and the occurrence of exacerbations is crucial in asthma management. Therefore, we aimed to determine to what extent physiological, behavioural and environmental data, obtained by mobile healthcare (mHealth) and home-monitoring sensors, as well as patient characteristics, can be used to predict episodes of uncontrolled asthma and the onset of asthma exacerbations. METHODS AND ANALYSIS: In an 1-year observational study, patients will be provided with mHealth and home-monitoring systems to record daily measurements for the first-month (phase I) and weekly measurements during a follow-up period of 11 months (phase II). Our study population consists of 150 patients, aged ≥18 years, with a clinician's diagnosis of asthma, currently on controller medication, with uncontrolled asthma and/or minimally one exacerbation in the past 12 months. They will be enrolled over three participating centres, including Leiden, London and Manchester. Our main outcomes are the association between physiological, behavioural and environmental data and (1) the loss of asthma control and (2) the occurrence of asthma exacerbations. ETHICS: This study was approved by the Medical Ethics Committee of the Leiden University Medical Center in the Netherlands and by the NHS ethics service in the UK. TRIAL REGISTRATION NUMBER: NCT02774772.
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Registered trials
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.