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Updated: Aug 4, 2025

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
Published on: April 19, 2019
A descriptive study of the clinical impacts on COVID-19 survivors using telemonitoring (The TeleCOVID Study)
Josephine Sau Fan Chow1,2,3,4, Annamarie D'Souza5, Megan Ford6
1Clinical Innovation & Business Unit, South Western Sydney Local Health District, Sydney, NSW, Australia.
Background:
There is increasing evidence that COVID-19 survivors are at increased risk of experiencing a wide range of cardiovascular complications post infection; however, there are no validated models or clear guidelines for remotely monitoring the cardiac health of COVID-19 survivors.
Objective:
This study aims to test a virtual, in-home healthcare monitoring model of care for detection of clinical symptoms and impacts on COVID-19 survivors. It also aims to demonstrate system usability and feasibility.
Methods:
This open label, prospective, descriptive study was conducted in South Western Sydney. Included in the study were patients admitted to the hospital with the diagnosis of COVID-19 between June 2021 and November 2021. Eligible participants after consent were provided with a pulse oximeter to measure oxygen saturation and a S-Patch EX to monitor their electrocardiogram (ECG) for a duration of 3 months. Data was transmitted in real-time to a mobile phone via Bluetooth technology and results were sent to the study team via a cloud-based platform. All the data was reviewed in a timely manner by the investigator team, for post COVID-19 related symptoms, such as reduction in oxygen saturation and arrhythmia.
Outcome Measure:
This study was designed for feasibility in real clinical setting implementation, enabling the study team to develop and utilise a virtual, in-home healthcare monitoring model of care to detect post COVID-19 clinical symptoms and impacts on COVID-19 survivors.
Results:
During the study period, 23 patients provided consent for participation. Out of which 19 patients commenced monitoring. Sixteen patients with 81 (73.6%) valid tests were included in the analysis and amongst them seven patients were detected by artificial intelligence to have cardiac arrhythmias but not clinically symptomatic. The patients with arrhythmias had a higher occurrence of supraventricular ectopy, and most of them took at least 2 tests before detection. Notably, patients with arrhythmia had significantly more tests than those without [t-test, t (13) = 2.29, p < 0.05].
Conclusions:
Preliminary observations have identified cardiac arrhythmias on prolonged cardiac monitoring in 7 out of the first 16 participants who completed their 3 months follow-up. This has allowed early escalation to their treating doctors for further investigations and early interventions.
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