Monitoring People With COVID-19 at Home With the COVIDFree@Home Program: Feasibility Cohort Study
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".