Pharmageek
medRxiv - The Preprint Server for Health Sciences
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Background: Patients with COVID-19 have increased sleep disturbances and decreased sleep quality during and after the infection. The current published literature focuses mainly on qualitative analyses based on surveys and subjective measurements rather than quantitative data.
Objective: In this paper, we assessed the long-term effects of COVID-19 through sleep patterns from continuous signals collected via wearable wristbands.
Methods: Patients with a history of COVID-19 were compared to a control arm of individuals who never had COVID-19. Baseline demographics were collected for each subject. Linear correlations among the mean duration of each sleep phase and the mean daily biometrics were performed. The average duration for each subject’s total sleep time and sleep phases per night was calculated and compared between the 2 groups.
Results: This study includes 122 patients with COVID-19 and 588 controls (N=710). Total sleep time was positively correlated with respiratory rate (RR) and oxygen saturation (SpO2). Increased awake sleep phase was correlated with increased heart rate, decreased RR, heart rate variability (HRV), and SpO2. Increased light sleep time was correlated with increased RR and SpO2 in the group with COVID-19. Deep sleep duration was correlated with decreased heart rate as well as increased RR and SpO2. When comparing different sleep phases, patients with long COVID-19 had decreased light sleep (244, SD 67 vs 258, SD 67; P=.003) and decreased deep sleep time (123, SD 66 vs 128, SD 58; P=.02).
Conclusions: Regardless of the demographic background and symptom levels, patients with a history of COVID-19 infection demonstrated altered sleep architecture when compared to matched controls. The sleep of patients with COVID-19 was characterized by decreased total sleep and deep sleep.
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The COVID-19 pandemic has led more people to start using wearable technology to track vital signs, physical activity, and sleep. The significant features of these devices include their capability to collect continuous, noninvasive data. We developed a COVID-19 risk stratification model using the Biostrap wearable device which utilizes a baseline-adjusted continuous scale and other escalation points-based on our recent case report, to enhance the National Early Warning Score (NEWS2). Preliminary research has found that our adjusted Early Warning Score (Biostrap-EWS) might be highly specific in identifying early-stage respiratory infections. We present the case of Biostrap CEO Sameer Sontakey, a 35-year-old man, whom the app notified as having a high likelihood of respiratory illness after which the diagnosis SARS-CoV-2 was confirmed with a nasal swab. Our Biostrap-EWS algorithm appears to detect respiratory infections in a real-world environment via passively collected biometric data. To validate the reliability of the algorithm, further research is required.
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Pharma giant Pfizer has shelled out nearly US$120 million to acquire a small Australian company claiming to have developed a smartphone app that can accurately diagnose COVID-19 by analyzing the sound of a cough.
Lire l'article complet sur : newatlas.com