Abstract Background Pathophysiological responses to viral infections such as COVID-19 significantly affect sleep duration, sleep quality, and concomitant cardiorespiratory function. The widespread adoption of consumer smart bed technology presents a unique opportunity for unobtrusive, real-world, longitudinal monitoring of sleep and physiological signals, which may be valuable for infectious illness surveillance and early detection. During the COVID-19 pandemic, scalable and noninvasive methods for identifying subtle early symptoms in naturalistic settings became increasingly important. Existing digital health studies have largely relied on wearables or patient self-reports, with limited adherence and recall bias. In contrast, smart bed–derived signals enable high-frequency objective data capture with minimal user burden. Objective The aim of this study was to leverage objective, longitudinal biometric data captured using ballistocardiography signals from a consumer smart bed platform, along with predictive modeling, to detect and monitor COVID-19 symptoms at an individual level. Methods A retrospective cohort of 1725 US adults with sufficient longitudinal data and completed surveys reporting COVID-19 test outcomes was identified from users of a smart bed system. Smart bed ballistocardiography-derived metrics included nightly pulse rate, respiratory rate, total sleep time, sleep stages, and movement patterns. Participants served as their own controls, comparing reference (baseline) and symptomatic periods. A two-stage analytical pipeline was used: (1) a gradient-boosted decision-tree “symptom detection model” independently classified each sleep session as symptomatic or not, and (2) an “illness-symptom progression model” using a Gaussian Mixture Hidden Markov Model estimated the probability of symptomatic states across contiguous sleep sessions by leveraging the temporal relationship in the data. Statistical analyses evaluated within-subject changes, and the model’s ability to discriminate illness windows was quantified using receiver operating characteristic metrics. Results Out of 122 participants who tested positive for COVID-19, symptoms were detected by the model in 104 cases. Across the cohort, the model captured significant deviations in sleep and cardiorespiratory metrics during symptomatic periods compared to baseline, with an area under the receiver operating characteristic curve of 0.80, indicating high discriminatory performance. Limitations included reliance on self-reported symptoms and test status, as well as the demographic makeup of the smart bed user base. Conclusions Smart beds represent a valuable resource for passively collecting objective, longitudinal sleep and physiological data. The findings support the feasibility of using these data and machine learning models for real-time detection and tracking of COVID-19 and related illnesses. Future directions include model refinement, integration with other health signals, and applications for population-scale surveillance of emerging infectious diseases.
Garcia‐Molina et al. (Wed,) studied this question.