Shortly after the ERC project was granted, I received all the regulatory approvals and conducted a Prospective cohort: a large-scale clinical trial for two years in which >4,600 participants received smartwatches, downloaded a dedicated mobile application we developed, and permitted access to their medical records. The smartwatches continuously monitored several physiological measures, including heart rate. For those interested, we provided home testing kits (to test respiratory or bacterial infections including - Group A streptococcus, influenza (A and B), and Covid-19). The mobile application collected daily self-reported questionnaires on well-being (sleeping habits, general mood, sports activities, etc.), reported symptoms, and home-test results. As of June 2023, 2,430 participants completed two years in the trials, and ~2,300 participants continue in the project. Retrospective cohort: A cohort of 250,000 members for 15 years (2010 - 2025) that serve as a representative sample of the Israeli population demographically. We received access to the electronic medical records of these members in a pseudonymized fashion.
As of June 2023, 2,430 participants completed the two years period, 2,369 are active participants. Overall, we have ~820,000 days in which participants completed the daily questionnaire and ~1,950,000 days of smartwatch data. This big and rich detailed, and labeled data is pivotal to the success of the project. Analyzing the data from the perspective and the retrospective cohorts (or part of it), we used ML models to detect COVID-19 test results in the period before antigen kits were available [1]. We developed a machine-learning model for COVID-19 detection that uses four layers of information: (i) sociodemographic characteristics of the individual, (ii) spatio-temporal patterns of the disease, (iii) medical condition and general health consumption of the individual, and (iv) information reported by the individual during the testing episode. We evaluated our model on 140,682 members of Maccabi Health Services who were tested for COVID-19 at least once between February and October 2020 (period before home kits were available and it took 2-5 days to receive PCR test results). Our ability to predict early on the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be used for a more efficient testing policy.
We also predict COVID-19 deteriorations in hospitals [2], and evaluate the effectiveness of proactive surveillance programs in reducing infections [3]. Moving toward the epidemic stage of COVID-19, we analyze medical records to evaluate the effectiveness of routinely boosting sub-populations at least every 6 months may be warranted, depending on age and immune status. Our study provides evidence for the potential benefit of a routine 6-month cadence for Covid-19 boosters for the highest-risk groups, even during relatively lower Covid-19 prevalence. We also demonstrated the considerably higher sensitivity of wearable sensors in detecting physiological reactions after COVID-19 vaccinations and COVID-19 infection [4–7]. We are now finalizing a paper that introduces a new term to the medical community – the DCUBATION period— the digital incubation period. Analyzing episodes of COVID-19, Group A step., Influenza, and other ILI, we identified a clear anomaly in physiological measures 6-48 hours before the participant identifies a symptom, and 48-72 before the test is conducted.
As of July 2023, we published 7 publications in leading journals in medicine and multidisciplinary fields including the Lancet Respiratory Medicine (Impact factor 102.6) Cell Reports Medicine (IF 17.0) the formal journal of the US CDC -Emerging Infectious Diseases (IF 11.8) publications from the Nature portfolio, including npj digital medicine – (IF 15.3) and a publication from journal of the Journal of the Royal Society Interface (IF 4.3). Furthermore, 3 publications are currently under review in leading journals, and 1 more is finalized before submission.
