ArticleRespiratory research2022
A proof of concept for continuous, non-invasive, free-living vital signs monitoring to predict readmission following an acute exacerbation of COPD: a prospective cohort study.
Article in Respiratory research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 14 papers, 2 of them syntheses 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.
The Use of Medical Devices to Monitor COPD Patients Study - An Observational Feasibility Study
The Use of Medical Devices to Monitor COPD Patients Study - An Observational Feasibility Study
Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 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
- Differentiating the start of an exacerbation from day-to-day variation in people with COPD: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Development of an artificial intelligence prediction model for moderate-to-severe COPD exacerbations using continuous multiple unobtrusive sensors: protocol of a multicentre prospective observational study.BMJ open respiratory research · 2026Article
- Associations between multidimensional fatigue and wearable-derived cardio-respiratory variables in post-COVID-19 patients: an observational study.Frontiers in digital health · 2026Article
- The BREATH-TRACHER 2 Trial: Protocol for a Retrospective Mixed Methods Study to Establish the Utility of a Wearable Device in the Detection of Chronic Obstructive Pulmonary Disease Exacerbations.JMIR research protocols · 2025Article
- At-home validation of a non-contact, radar-based breathing monitor for long-term care of patients with respiratory diseases: A proof-of-concept study.Pneumologie (Stuttgart, Germany) · 2025Article
- Determining what patients admitted with a chronic obstructive pulmonary disease (COPD) exacerbation will use for remote clinical monitoring: a patient engagement survey.BMJ open respiratory research · 2025Article
- Wearables research for continuous monitoring of patient outcomes: A scoping review.PLOS digital health · 2025Article
- Prognostic value of heart rate variability for risk of serious adverse events in continuously monitored hospital patients.Journal of clinical monitoring and computing · 2024Article
- The Potential Use and Value of a Wearable Monitoring Bracelet for Patients With Chronic Obstructive Pulmonary Disease: Qualitative Study Investigating the Patient and Health Care Professional Perspectives.JMIR formative research · 2024Article
- Artificial Intelligence-Driven Prognosis of Respiratory Mechanics: Forecasting Tissue Hysteresivity Using Long Short-Term Memory and Continuous Sensor Data.Sensors (Basel, Switzerland) · 2024Article
- Feasibility of a wearable self-management application for patients with COPD at home: a pilot study.BMC medical informatics and decision making · 2024Observational
- Validation of the Rome Severity Classification of Chronic Obstructive Pulmonary Disease Exacerbation: A Multicenter Cohort Study.International journal of chronic obstructive pulmonary disease · 2024Article
- Wearable technology interventions in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis.NPJ digital medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThe use of vital signs monitoring in the early recognition of an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) post-hospital discharge is limited. This study investigated whether continuous vital signs monitoring could predict an AECOPD and readmission.
methods35 people were recruited at discharge following hospitalisation for an AECOPD. Participants were asked to wear an Equivital LifeMonitor during waking hours for 6 weeks and to complete the Exacerbations of Chronic Pulmonary Disease Tool (EXACT), a 14-item symptom diary, daily. The Equivital LifeMonitor recorded respiratory rate (RR), heart rate (HR), skin temperature (ST) and physical activity (PA) every 15-s. An AECOPD was classified as mild (by EXACT score), moderate (prescribed oral steroids/antibiotics) or severe (hospitalisation).
resultsOver the 6-week period, 31 participants provided vital signs and symptom data and 14 participants experienced an exacerbation, of which, 11 had sufficient data to predict an AECOPD. HR and PA were associated with EXACT score (p < 0.001). Three days prior to an exacerbation, RR increased by mean ± SD 2.0 ± 0.2 breaths/min for seven out of 11 exacerbations and HR increased by 8.1 ± 0.7 bpm for nine of these 11 exacerbations.
conclusionsIncreased heart rate and reduced physical activity were associated with worsening symptoms. Even with high-resolution data, the variation in vital signs data remains a challenge for predicting AECOPDs. Respiratory rate and heart rate should be further explored as potential predictors of an impending AECOPD.
trial registrationISRCTN registry; ISRCTN12855961. Registered 07 November 2018-Retrospectively registered, https://www.isrctn.com/ISRCTN12855961.
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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.