We are very excited to share with you our recent preprint for Phase 2 of our COVID-19 wearable study.
Before we dive in, if you are part of our 3,246 study cohort, or have helped us make this study known to the world in any way, we are beyond grateful for your patience and trust. Thank you for helping us making this research study possible within 7 months!
Why do we think this study is important?
- This is the 1st large-scale real-time monitoring and alerting system for detecting abnormal physiological changes, including COVID-19 and other respiratory illnesses, using smartwatch data.
- Our algorithms are agnostic for different smartwatches to send users alerts.
We believe there is a huge potential to scale our research and implement these findings for improving healthcare. Please reach out if you would be interested in supporting our work.
Currently, we are still actively recruiting study participants for the COVID-19 wearable study. If you have a smartwatch, or know someone who may be interested, you can help us grow our study cohort by signing up, or tell your friends about our work!
A quick recap of the COVID-19 wearable study
The recent preprint demonstrates results from Phase 2 of the COVID-19 wearable study. If you are interested in learning more about both Phase 1 and Phase 2 studies, we have covered them in better detail in a previous newsletter:
- Phase 1 study was published in Nature Biomedical Engineering in December 2020. The research team used retrospective wearable data to develop algorithms that could detect COVID-19 infection before symptoms emerge. The study shows 63% of COVID-19 positive cases could have been detected in real-time, with a median of 4 days prior to symptom onset.
- In Phase 2, the research team aimed to validate algorithms to detect pre-symptomatic and asymptomatic COVID-19 cases in real-time.
What are our findings from the Phase 2 study?
- Our system demonstrates a 78% accuracy in alerting study participants who are pre-symptomatic or asymptomatic:
- Among 68 COVID-19 positive study participants who have wearable data, our algorithm (NightSignal) sent alerts to 53 of them at or before symptom onset/ diagnosis or diagnosis date in asymptomatic cases.
- The alert is generated at a median of 3 days prior to symptom onset, to enable effective early self-isolation and testing.
- The algorithms also identify signals resulting from vaccination:
- Red alert is triggered after both vaccination doses or only one dose for Pfizer-BioNTech and Moderna vaccines.
- For the first vaccine dose, the maximum resting heart rate overnight occurs on the first night after receiving Pfizer-BioNTech vaccine.
- For the second vaccine dose, the maximum resting heart rate overnight occurs on the first night or the second night after receiving Pfizer-BioNTech or Moderna vaccine.
- In terms of reported symptoms, participants who received Pfizer-BioNTech vaccine reported fever after either dose; however, participants who received Moderna vaccine reported no fever after the first dose, but close to 60% of them reported fever after the second dose.
- An association between symptoms and signals was investigated:
- Fatigue and poor sleep are common symptoms reported by study participants who received red alerts. However, aches and pains, headaches, coughs, and feeling ill were reported more frequently by COVID-19 positive study participants.
- Stress, intense exercise, and alcohol intake are symptoms most reported by COVID-19 negative participants. Interestingly, there are more red alerts triggered among COVID-19 negative study participants during the winter holiday season. This is a consistent phenomenon observed in our previous study before the pandemic. This observation implies people may experience increased stress, alcohol intake, or travel during the holiday season.
