Abstract
Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.
Highlights
The function of public health systems is to understand and respond to health trends affecting populations [1]
Oura Health used Internet of Things (IoT) data gathered from their Oura ring, a wearable sensor that tracks key signals from the human body, delivering critical insights to help an individual harness their body’s potential daily and to monitor vital health indicators [92]
Evidence shows the risk of hospitalization related to COVID-19 can be calculated from self-reported symptoms and predictive physiological signs by combining different health and behavioral data from consumer wearable devices; this may help identify pathological changes weeks before observation using traditional epidemiological monitoring [99, 100]
Summary
The function of public health systems is to understand and respond to health trends affecting populations [1]. Oura Health used IoT data gathered from their Oura ring, a wearable sensor that tracks key signals from the human body (sleep, heart rate, skin temperature, physical activity), delivering critical insights to help an individual harness their body’s potential daily and to monitor vital health indicators [92] Another hurdle is the ability to fuse data from multiple devices to produce a unified result. Evidence shows the risk of hospitalization related to COVID-19 can be calculated from self-reported symptoms and predictive physiological signs by combining different health and behavioral data from consumer wearable devices; this may help identify pathological changes weeks before observation using traditional epidemiological monitoring [99, 100]. This process will help discover new public health indicators and advance our understanding of existing disease risk factors
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