SynthesisChronic respiratory disease2016
Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review.
Synthesis in Chronic respiratory disease, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 citing papers in PubMed, 2 syntheses or guidelines pooled it, 117 citations in OpenAlex.
- AI and Internet of Things for Chronic Obstructive Pulmonary Disease Remote Monitoring: Systematic Review of Exacerbation Prediction and Key Physiological Variables.JMIR medical informatics · 2026Pooled it
- Effectiveness of Mobile Health-Based Self-Management Programs on Health-Related Outcomes in Patients With Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis.JMIR mHealth and uHealth · 2025Pooled it
- Impact of Telemonitoring With Exacerbation Prediction Algorithm Versus Telemonitoring Alone on Hospitalizations and Health-Related Quality of Life in Patients With COPD.Respiratory care · 2025Trial
- Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System.Journal of medical Internet research · 2017Trial
- Artificial Intelligence for Early Detection and Prediction of Chronic Obstructive Pulmonary Disease Exacerbations.Healthcare (Basel, Switzerland) · 2026Review
- Predicting clinical exacerbations using respiratory oscillometry during home telemonitoring of patients taking medium-/high-dose asthma treatment.ERJ open research · 2026Article
- Using wearable and nearable devices in telerehabilitation for COPD: a scoping review of digital endpoints in home-based programs.Frontiers in digital health · 2026Review
- Artificial Intelligence Methods and Digital Intervention Strategies for Predicting and Managing Chronic Obstructive Pulmonary Disease Exacerbations: An Umbrella Review.Healthcare (Basel, Switzerland) · 2025Review
- An mHealth App and System Architecture for Respiratory Disease Management: Design Principles, Tool Development, and Pilot Usability Study.JMIR formative research · 2025Article
- Artificial Intelligence in the Management of Asthma: A Review of a New Frontier in Patient Care.Journal of asthma and allergy · 2025Review
- From data to medical context: the power of categorization in healthcare.Frontiers in medicine · 2025Article
- Home spirometry telemonitoring in pediatric patients with asthma: a mixed study.Frontiers in pediatrics · 2025Article
- Implementation of an algorithm for predicting exacerbations in telemonitoring: A multimethod study of patients' and clinicians' experiences.International journal of nursing studies advances · 2024Article
- Intelligent Clinical Decision Support System for Managing COPD Patients.Journal of personalized medicine · 2023Article
- Digital health in chronic obstructive pulmonary disease.Chronic diseases and translational medicine · 2023Review
- Big Data Analytics to Reduce Preventable Hospitalizations-Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions.International journal of environmental research and public health · 2023Article
- Unleashing the Power of Very Small Data to Predict Acute Exacerbations of Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2023Article
- Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations.Scientific reports · 2022Article
- MRI-assessed diaphragmatic function can predict frequent acute exacerbation of COPD: a prospective observational study based on telehealth-based monitoring system.BMC pulmonary medicine · 2022Observational
- Multi-disciplinary community respiratory team management of patients with chronic respiratory illness during the COVID-19 pandemic.NPJ primary care respiratory medicine · 2022Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Major reported factors associated with the limited effectiveness of home telemonitoring interventions in chronic respiratory conditions include the lack of useful early predictors, poor patient compliance and the poor performance of conventional algorithms for detecting deteriorations. This article provides a systematic review of existing algorithms and the factors associated with their performance in detecting exacerbations and supporting clinical decisions in patients with chronic obstructive pulmonary disease (COPD) or asthma. An electronic literature search in Medline, Scopus, Web of Science and Cochrane library was conducted to identify relevant articles published between 2005 and July 2015. A total of 20 studies (16 COPD, 4 asthma) that included research about the use of algorithms in telemonitoring interventions in asthma and COPD were selected. Differences on the applied definition of exacerbation, telemonitoring duration, acquired physiological signals and symptoms, type of technology deployed and algorithms used were found. Predictive models with good clinically reliability have yet to be defined, and are an important goal for the future development of telehealth in chronic respiratory conditions. New predictive models incorporating both symptoms and physiological signals are being tested in telemonitoring interventions with positive outcomes. However, the underpinning algorithms behind these models need be validated in larger samples of patients, for longer periods of time and with well-established protocols. In addition, further research is needed to identify novel predictors that enable the early detection of deteriorations, especially in COPD. Only then will telemonitoring achieve the aim of preventing hospital admissions, contributing to the reduction of health resource utilization and improving the quality of life of patients.
Indexed as
Identifiers
What OpenQuestion holds
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.