Observational studyAmerican journal of respiratory and critical care medicine2018
Passive Nocturnal Physiologic Monitoring Enables Early Detection of Exacerbations in Children with Asthma. A Proof-of-Concept Study.
Observational study in American journal of respiratory and critical care medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05047419 (Using Mobile Health Approaches to Improve Asthma Treatment), which is not on this map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Using Mobile Health Approaches to Improve Asthma Treatment
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Digital health and artificial intelligence in paediatric respiratory medicine: what clinicians need to know and what is coming next.Breathe (Sheffield, England) · 2026Review
- Towards improved asthma control in children by non-invasive home monitoring.Scientific reports · 2025Article
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- From data to medical context: the power of categorization in healthcare.Frontiers in medicine · 2025Article
- Digital markers of asthma exacerbations: a systematic review.ERJ open research · 2024Review
- Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review.JMIR AI · 2024Article
- DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol.BMJ open respiratory research · 2024Article
- Remote digital health technologies for improving the care of people with respiratory disorders.The Lancet. Digital health · 2024Review
- Tele-Monitoring Applications in Respiratory Allergy.Journal of clinical medicine · 2024Review
- Prediction of Asthma Exacerbations in Children.Journal of personalized medicine · 2023Review
- eHealth Technologies for Monitoring Pediatric Asthma at Home: Scoping Review.Journal of medical Internet research · 2023Article
- Remote Patient Monitoring and Teleconsultation to Improve Health Outcomes and Reduce Health Care Utilization of Pediatric Asthma (ALPACA Study): Protocol for a Randomized Controlled Effectiveness Trial.JMIR research protocols · 2023Article
- A digital approach to asthma self-management in adults: Protocol for a pragmatic randomized controlled trial.Contemporary clinical trials · 2022Article
- Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol.BMJ open · 2022Article
- A scoping review of mHealth monitoring of pediatric bronchial asthma before and during COVID-19 pandemic.Paediatric respiratory reviews · 2022Article
- Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review.BMJ open respiratory research · 2022Article
- Smart Speakers: The Next Frontier in mHealth.JMIR mHealth and uHealth · 2022Article
- Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review.Journal of asthma and allergy · 2022Review
- The Incremental Prognostic Value of Hepatocyte Growth Factor in First-Ever Acute Ischemic Stroke: An Early Link Between Growth Factor and Interleukins.Frontiers in neurology · 2021Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
rationaleAsthma management depends on prompt identification of symptoms, which challenges both patients and providers. In asthma, a misapprehension of health between exacerbations can compromise compliance. Thus, there is a need for a tool that permits objective longitudinal monitoring without increasing the burden of patient compliance.
objectivesWe sought to determine whether changes in nocturnal physiology are associated with asthma symptoms in pediatric patients.
methodsUsing a contactless bed sensor, nocturnal heart rate (HR), respiratory rate, relative stroke volume, and movement in children with asthma 5-18 years of age (n = 16) were recorded. Asthma symptoms and asthma control test (ACT) score were reported every 2 weeks. Random forest model was used to identify physiologic parameters associated with asthma symptoms. Elastic net regression was used to identify variables associated with ACT score. MEASUREMENTS AND MAIN
resultsThe model on the full cohort performed with sensitivity of 47.2%, specificity of 96.3%, and accuracy of 87.4%; HR and respiratory parameters were the most important variables in this model. The model predicted asthma symptoms 35% of the time on the day before perception of symptoms, and 100% of the time for a select subject for which the model performed with greater sensitivity. Multivariable and bivariable analyses demonstrated significant association between HR and respiratory rate parameters and ACT score.
conclusionsNocturnal physiologic changes correlate with asthma symptoms, supporting the notion that nocturnal physiologic monitoring represents an objective diagnostic tool capable of longitudinally assessing disease control and predicting asthma exacerbations in children with asthma at home.
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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.