Sensor-based surveillance for digitising real-time COVID-19 tracking in the USA (DETECT): a multivariable, population-based, modelling study.

Autor: Radin JM; Scripps Research Translational Institute, La Jolla, CA, USA. Electronic address: jmradin@scripps.edu., Quer G; Scripps Research Translational Institute, La Jolla, CA, USA., Pandit JA; Scripps Research Translational Institute, La Jolla, CA, USA., Gadaleta M; Scripps Research Translational Institute, La Jolla, CA, USA., Baca-Motes K; Scripps Research Translational Institute, La Jolla, CA, USA., Ramos E; Scripps Research Translational Institute, La Jolla, CA, USA; CareEvolution, Ann Arbor, MI, USA., Coughlin E; Scripps Research Translational Institute, La Jolla, CA, USA., Quartuccio K; Scripps Research Translational Institute, La Jolla, CA, USA., Kheterpal V; CareEvolution, Ann Arbor, MI, USA., Wolansky LM; The Rockefeller Foundation, New York City, NY, USA., Steinhubl SR; Scripps Research Translational Institute, La Jolla, CA, USA., Topol EJ; Scripps Research Translational Institute, La Jolla, CA, USA.
Jazyk: angličtina
Zdroj: The Lancet. Digital health [Lancet Digit Health] 2022 Nov; Vol. 4 (11), pp. e777-e786. Date of Electronic Publication: 2022 Sep 22.
DOI: 10.1016/S2589-7500(22)00156-X
Abstrakt: Background: Traditional viral illness surveillance relies on in-person clinical or laboratory data, paper-based data collection, and outdated technology for data transfer and aggregation. We aimed to assess whether continuous sensor data can provide an early warning signal for COVID-19 activity as individual physiological and behavioural changes might precede symptom onset, care seeking, and diagnostic testing.
Methods: This multivariable, population-based, modelling study recruited adult (aged ≥18 years) participants living in the USA who had a smartwatch or fitness tracker on any device that connected to Apple HealthKit or Google Fit and had joined the DETECT study by downloading the MyDataHelps app. In the model development cohort, we included people who had participated in DETECT between April 1, 2020, and Jan 14, 2022. In the validation cohort, we included individuals who had participated between Jan 15 and Feb 15, 2022. When a participant joins DETECT, they fill out an intake survey of demographic information, including their ZIP code (postal code), and surveys on symptoms, symptom onset, and viral illness test dates and results, if they become unwell. When a participant connects their device, historical sensor data are collected, if available. Sensor data continue to be collected unless a participant withdraws from the study. Using sensor data, we collected each participant's daily resting heart rate and step count during the entire study period and identified anomalous sensor days, in which resting heart rate was higher than, and step count was lower than, a specified threshold calculated for each individual by use of their baseline data. The proportion of users with anomalous data each day was used to create a 7-day moving average. For the main cohort, a negative binomial model predicting 7-day moving averages for COVID-19 case counts, as reported by the Centers for Disease Control and Prevention (CDC), in real time, 6 days in the future, and 12 days in the future in the USA and California was fitted with CDC-reported data from 3 days before alone (H 0 ) or in combination with anomalous sensor data (H 1 ). We compared the predictions with Pearson correlation. We then validated the model in the validation cohort.
Findings: Between April 1, 2020, and Jan 14, 2022, 35 842 participants enrolled in DETECT, of whom 4006 in California and 28 527 in the USA were included in our main cohort. The H 1 model significantly outperformed the H 0 model in predicting the 7-day moving average COVID-19 case counts in California and the USA. For example, Pearson correlation coefficients for predictions 12 days in the future increased by 32·9% in California (from 0·70 [95% CI 0·65-0·73] to 0·93 [0·92-0·94]) and by 12·2% (from 0·82 [0·79-0·84] to 0·92 [0·91-0·93]) in the USA from the H 0 model to the H 1 model. Our validation model also showed significant correlations for predictions in real time, 6 days in the future, and 12 days in the future.
Interpretation: Our study showed that passively collected sensor data from consenting participants can provide real-time disease tracking and forecasting. With a growing population of wearable technology users, these sensor data could be integrated into viral surveillance programmes.
Funding: The National Center for Advancing Translational Sciences of the US National Institutes of Health, The Rockefeller Foundation, and Amazon Web Services.
Competing Interests: Declaration of interests LMW works for The Rockefeller Foundation, which funded part of this study. VK is the principal and an employee of CareEvolution. ER is the principal science officer and an employee of CareEvolution and Scripps Research. JAP is an adviser for Angiotensin Therapeutics, Precision Health, Cardiosense, and Sense AI. GQ and JMR are supported in part under a grant from The Rockefeller Foundation and the National Center for Advancing Translational Sciences, the US National Institutes of Health. All other authors declare no competing interests.
(Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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