Evidence map›Paper›PMID 41061108›Full record

ArticleJMIR formative research2025

Monitoring People With COVID-19 at Home With the COVIDFree@Home Program: Feasibility Cohort Study.

Andrea S Gershon, Alex Mariakakis, Eyal de Lara, Joseph Munn, Maryann Calligan, Daniyal Liaqat, Salaar Liaqat, Junlin Chen, Teresa To, Philip W Lam and 11 more

Abstract read
In one paragraph

Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Trial
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Andrea S GershonDepartment of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-0246-594X
Alex MariakakisDepartment of Computer Science, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-9986-3345
Eyal de LaraDepartment of Computer Science, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-6730-555X
Joseph MunnDepartment of Medicine, Sunnybrook Health Sciences Centre, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada, 1 4164804758, 1 4164805153.ORCID 0000-0003-1018-917X
Maryann CalliganMedicine, University Health Network, Toronto, ON, Canada.ORCID 0009-0009-7624-2085
Daniyal LiaqatDepartment of Computer Science, University of Toronto, Toronto, ON, Canada.ORCID 0000-0003-0618-2821
Salaar LiaqatDepartment of Computer Science, University of Toronto, Toronto, ON, Canada.ORCID 0009-0009-6193-2614
Junlin ChenDepartment of Computer Science, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-2167-3074
Teresa ToDalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.ORCID 0000-0001-7463-3423
Philip W LamDepartment of Medicine, Sunnybrook Health Sciences Centre, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada, 1 4164804758, 1 4164805153.ORCID 0000-0001-5677-4660
Andrew SimorDepartment of Medicine, Sunnybrook Health Sciences Centre, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada, 1 4164804758, 1 4164805153.ORCID 0000-0002-8184-3987
Adrienne K ChanDepartment of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-9187-9465
Nisha AndanyDepartment of Medicine, Sunnybrook Health Sciences Centre, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada, 1 4164804758, 1 4164805153.ORCID 0009-0005-4851-5067
Sameer MasoodDepartment of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.ORCID 0000-0001-8120-8150
Nick DanemanDepartment of Medicine, Sunnybrook Health Sciences Centre, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada, 1 4164804758, 1 4164805153.ORCID 0000-0001-8827-3764
Tiffany ChanDepartment of Medicine, Sunnybrook Health Sciences Centre, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada, 1 4164804758, 1 4164805153.ORCID 0000-0001-7126-8432
Christopher GrahamDepartment of Medicine, Trillium Health Partners, Toronto, ON, Canada.ORCID 0000-0002-7744-4422
Vikram ComondoreDepartment of Medicine, William Osler Health System, Brampton, ON, Canada.ORCID 0009-0004-5577-166X
Andre de MoulliacIQVIA (United States), Durham, NC, United States.ORCID 0009-0008-7005-0421
Alice Y TuMedicine, University Health Network, Toronto, ON, Canada.ORCID 0009-0005-9817-6593
Robert WuDepartment of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-6895-1107

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: During the COVID-19 pandemic, many with acute infection isolated at home, with a small but significant number requiring hospitalization. At the time, since the pathogen was fairly unknown, clinicians were uncertain about which patients would rapidly deteriorate and need hospitalization. We developed the COVIDFree@Home smartphone app and clinician dashboard to monitor and support people managing at home with acute COVID-19 infection. It was uncertain whether such an app would be used by patients and whether it would support patient care. This knowledge would inform telemedicine and digital health tools being used to deliver care to patients remotely at that time. Objective: This study aimed to determine the feasibility of using a smartphone app and clinician dashboard for remote clinical monitoring of people with COVID-19 at home. Methods: A feasibility study set at 3 hospital sites (University Health Network, Sunnybrook Health Sciences Centre, and Trillium Health Partners) between 2020 and 2022 was conducted. Participants newly diagnosed with COVID-19 were asked to enter data into a smartphone app called COVIDFree@Home twice daily for 10 days while isolating at home. Their data, including symptoms, temperature, and oxygen saturation, were monitored on a clinician-facing dashboard. The primary outcome of feasibility was the number of patients who used the app. We also examined patient satisfaction through a survey questionnaire. Results: A total of 431 patients were recruited, out of which 229 (56.5%) were females and the average age was 38.9 (SD 12.8) years. There were 376 (87.2%) participants who used the app to report symptoms or oxygen saturation at least once. Among these participants, 373 (99.2%) reported symptoms and 363 (96.5%) reported oxygen saturation. Participants reported symptoms an average of 1.7 (SD 1.1) times per day for a median of 5 (IQR 3-8) days. Oxygen saturation levels were reported 1.5 times per day for a median of 6 (IQR 4-9) days. There were 19 hospitalizations (4.4%) among study participants. Most patients felt comfortable using the app, felt reassured their data was being monitored and did not have privacy concerns. Conclusions: Patients with acute COVID-19 infection engaged with a remote home monitoring platform, however, not at the recommended frequency or duration. Remote patient monitoring of acute respiratory infection appears viable and can offer patients reassurance. It has the potential to reduce strain on the health care system during future pandemics, but further evidence is required to demonstrate improved health outcomes.

Indexed as

COVID-19Mobile ApplicationsAdultAgedCohort StudiesFeasibility StudiesFemaleHome Care ServicesHumansMaleMiddle AgedSARS-CoV-2SmartphoneTelemedicineCOVID-19mobile applicationoxygen saturationpandemicremote monitoring

Identifiers

PMID41061108
PMCPMC12507130

What OpenQuestion holds

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Registered trials

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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.