Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jun 29, 2020
Date Accepted: Jul 26, 2020
Date Submitted to PubMed: Aug 13, 2020
App-based tracking of self-reported COVID-19 symptoms
ABSTRACT
Background:
COVID-19 is an infection characterized by various different clinical presentations. Knowledge of possible symptoms and their distribution allows an early identification of infected patients.
Objective:
To determine the distribution pattern and possible unreported symptoms an app-based self-reporting tool was created.
Methods:
The COVID-19 Symptom Tracker study is an app-based daily self-reporting study. Between 08 April and 15 May 2020, a total of 22,327 individuals installed the smartphone app (COVID-19 Symptom Tracker) on their mobile device. An initial questionnaire asks for demographic information (age, gender, post code) and a past medical history with relevant chronic diseases. The participants are notified daily to report whether they are suffering from current symptoms and have been tested for SARS-CoV-2. When seeking healthcare advice additional questions regarding diagnostics and therapy are asked. Participation is open for every adult (minimum age 18 years). The study is completely anonymous.
Results:
11,829 (52.98%) participants completed the symptom questionnaire at least once. 291 of these participants stated that a RT-PCR test for SARS-CoV-2 was performed. 65 reported a positive and 226 a negative test result. The mean average number of reported symptoms in the group of untested participants was 0.81 (SD: 1.85). Participants with a positive test showed a mean average of 5.63 symptoms (SD: 2.82). Most significant risk factors are diabetes (OR: 8.95; CI: 3.30-22.37) and chronic heart disease (OR: 2.85; CI: 1.43-5.69). We identified chills, fever, loss of smell, nausea and vomiting and shortness of breath as the top five of the strongest predictors for a COVID-19 infection. The odds ratio (with 95% confidence interval) for loss of smell was 3.13 (1.76-5.58). Nausea and vomiting (OR: 2.84; CI: 1.61-5.00) has been reported as an uncommon symptom however our data suggest a significant predictive value.
Conclusions:
Self-reported symptom tracking helps to identify novel symptoms of the COVID-19 disease and estimate the predictive value of certain symptoms. This helps to develop reliable screening tools. A clinical screening with a high pre-test probability allows the rapid identification of infections and a cost-effective use of testing resources. Our data stress the necessity for an awareness of loss of smell and taste as a cardinal symptom and suggest that diabetes is a risk factor for a highly symptomatic course of a COVID-19 infection. Clinical Trial: DNA
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