Heatwaves, intensified by climate change, pose significant challenges to the health and well-being of global populations. Among the most vulnerable groups, the elderly is particularly susceptible to the adverse effects of heatwaves, including disruptions in sleep quality. Sleep plays a vital role in overall health and cognitive function, and heatwaves hinder the attainment of restorative deep sleep stages. This vision paper proposes an approach to evaluate the impact of heatwaves on sleep health in older adults using big data from smart thermostats and the novel, non-invasive technology called zero-effort technology. By leveraging this vast dataset, the study aims to provide comprehensive insights into the specific effects of heatwaves on sleep quality, overcoming the limitations of subjective and invasive methods. Preliminary analysis from a non-AC household in British Columbia underscores the potential of this approach. The vision is to expand this research to various elderly living settings, integrating indoor temperature data to discern sleep pattern shifts during heatwaves. This approach sets the stage for devising evidence-based interventions and public health strategies to counteract the detrimental effects of heatwaves on elderly sleep health.

Air pollution, acknowledged as the paramount environmental risk to health by the World Health Organization (WHO), presents a substantial and intricate global public health challenge. This challenge emanates from the emission of toxic particles and gases, inducing severe health and developmental adversities while concurrently serving as a notable driver of climate change. Despite the escalating threats, contemporary surveillance ecosystems encounter limitations in effectively monitoring both indoor and outdoor air pollution levels, particularly in delivering timely alerts for individuals at heightened risk.Existing air pollution alert systems presently rely on ecological data derived from outdoor air quality monitoring stations. However, this methodology constrains the capacity to monitor individual-level exposure and provide personalized recommendations for mitigation or adaptation. The integration of machine learning (ML) emerges as a transformative solution, facilitating advanced projections, monitoring, modeling, and assessment of air quality. Leveraging sensor data, ML empowers informed, evidence-based decision-making, thereby presenting a substantial opportunity for innovation and enhancement in the realm of air pollution management.

Traditionally, public health researchers gathered health data through direct observation and testing, or Electronic Medical Records (EMR). The collected data was often in paper form and stored at the researcher’s own facility. Research objectives and data processing were only discussed with Institutional Review Boards (IRBs) with no negotiation between researchers and data subjects beyond collecting informed consent.

Wearable sensors utilize small, low-cost, noninvasive, and wireless components. These sensors capture vital signs, allowing the monitoring of patients remotely. In this manner, they are efficient tools to enhance patient care and can be used to monitor vulnerable populations, and keep track of the development of chronic diseases, and the transmission of infectious illnesses – such as during pandemics. However, there are many challenges to monitoring patients using wearables, with massive data generation and battery power consumption being significant constraints. Strategies to reduce data generation should be applied taking into account the patient’s clinical status and health risks. Previous studies took advantage of single early-warning scores (EWS) utilized in infirmaries to detect emergencies, reduce transmissions, and be a reference for self-adaptive features embedded in the devices. Our work proposes the use of composite EWS to infer health deterioration risk, minimize data transmissions and power consumption, and reduce excessive alarms through self-adaptive features based on these scores. We also compare our method with previous studies using real patient data. Further, we propose applying self-adaptive features to sampling, processing, and transmission rates. Our method demonstrated enhanced data reduction, 81% fewer readings than the baseline, significant pruning of the number of alarms, and dynamic and automatic inference of patient risk.

Air pollution is a global public health concern. It is responsible for a cascade of adverse health outcomes. However, quantifying the effects and impacts of air pollution is complicated. The advancement of IoT and big data technologies can now allow public health officials and researchers to monitor air pollution levels and take appropriate and rapid actions to mitigate the harms. We propose the development of an agnostic ecosystem that collects big data and from various sensors, analyzes and predicts harm using AI and deep learning.

The Internet of Things (IoT) has enabled novel solutions for monitoring patients’ health through wearable sensors in conditions of both non-communicable and infectious diseases. In this paper, we report work in progress involving the development of an IoT-based COVID-19 health monitoring system that can effectively monitor the essential physiological functions of a patient through wireless sensors, thus supporting the early detection of severe cases and the continuous assessment of the patient status. The work provides several main contributions, as it includes: (i) a brief description of the current IoT-based system for remote monitoring of COVID-19 patients; (ii) a description of embedded characteristics of our device, including its contextual functions, early warning score mechanisms and self-adaptive features; and (iii) a description of our preliminary experiment results. Our proposed solution reduced drastically the amount of redundancy in data and still maintain monitoring accuracy. Given the COVID-19 scenarios, in which human resources are extended to the limit and the number of patients in severe conditions is often high, a system that can support IoT-based continuous monitoring are essential to identify changes in clinical status promptly and accurately and can potentially transform the way patients are monitored.

Public health monitoring methods have limitations that affect the quality of data. To support traditional data collection efforts, personal smart technologies can be used to collect multimodal, real-time and continuous data. Public health agencies can then study and predict the prevalence of conditions in a population using advanced analytics. Apple Health is one of the most popular sources of health data from personal devices, supporting diverse sensors that collect a wide range of information from heart rate to blood pressure and sleep. This paper introduces a system that uses a mobile health platform to extract Apple Health data to support public health monitoring. Development, security and privacy considerations are discussed, and a pilot study is proposed which collects several objective sensor data from Apple Health as well as self-report perceived stress (both using the platform) to create stress prediction models. Ultimately, the system described can provide public health agencies with novel methods to collect multimodal data from consumer devices as well as implement interventions in real-time to minimize the impact of conditions, such as stress, in a population. The system advances the state-of-the-art in health monitoring by being one of the first works to leverage health data from consumer-level personal devices for public health.