RESUMO
Background: Mobile health (mHealth) offers potential benefits to both patients and healthcare systems. Existing remote technologies to measure respiratory rates have limitations such as cost, accessibility and reliability. Using smartphone sensors to measure respiratory rates may offer a potential solution to these issues. Objective: The aim of this study was to conduct a comprehensive assessment of a novel mHealth smartphone application designed to measure respiratory rates using movement sensors. Methods: In Study 1, 15 participants simultaneously measured their respiratory rates with the app and a Food and Drug Administration-cleared reference device. A novel reference analysis method to allow the app to be evaluated 'in the wild' was also developed. In Study 2, 165 participants measured their respiratory rates using the app, and these measures were compared to the novel reference. The usability of the app was also assessed in both studies. Results: The app, when compared to the Food and Drug Administration-cleared and novel references, respectively, showed a mean absolute error of 1.65 (SD = 1.49) and 1.14 (1.44), relative mean absolute error of 12.2 (9.23) and 9.5 (18.70) and bias of 0.81 (limits of agreement = -3.27 to 4.89) and 0.08 (-3.68 to 3.51). Pearson correlation coefficients were 0.700 and 0.885. Ninety-three percent of participants successfully operated the app on their first use. Conclusions: The accuracy and usability of the app demonstrated here in individuals with a normal respiratory rate range show promise for the use of mHealth solutions employing smartphone sensors to remotely monitor respiratory rates. Further research should validate the benefits that this technology may offer patients and healthcare systems.
RESUMO
The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.
