Application of a mobile health data platform for public health surveillance: A case study in stress monitoring and prediction.

Background: Public health surveillance involves the collection, analysis and dissemination of data to improve population health. The main sources of data for public health decision-making are surveys, typically comprised of self-report which may be subject to biases, costs and delays. To complement subjective data, objective measures from sensors could potentially be used.

Leveraging mHealth Technologies for Public Health.

Traditional public health surveillance efforts are generally based on self-reported data. Although well validated, these methods may nevertheless be subjected to limitations such as biases, delays, and costs or logistical challenges. An alternative is the use of smart technologies (eg, smartphones and smartwatches) to complement self-report indicators.

Can heart rate variability data from the Apple Watch electrocardiogram quantify stress?

Chronic stress has become an epidemic with negative health risks including cardiovascular disease, hypertension, and diabetes. Traditional methods of stress measurement and monitoring typically relies on self-reporting. However, wearable smart technologies offer a novel strategy to continuously and non-invasively collect objective health data in the real-world.

Cross Dataset Analysis for Generalizability of HRV-Based Stress Detection Models.

Stress is an increasingly prevalent mental health condition across the world. In Europe, for example, stress is considered one of the most common health problems, and over USD 300 billion are spent on stress treatments annually. Therefore, monitoring, identification and prevention of stress are of the utmost importance.

Using apple watch ECG data for heart rate variability monitoring and stress prediction: A pilot study.

Stress is an increasingly prevalent mental health condition that can have serious effects on human health. The development of stress prediction tools would greatly benefit public health by allowing policy initiatives and early stress-reducing interventions. The advent of mobile health technologies including smartphones and smartwatches has made it possible to collect objective, real-time, and continuous health data.

A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home.

Due to the COVID-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person contacts, enabling early-detection of severe cases, and remotely assessing patients' status. Internet of Things (IoT) technologies have been used for monitoring patients' health with wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases.

Blockchain Applications in Health Care and Public Health: Increased Transparency.

Background: Although big data and smart technologies allow for the development of precision medicine and predictive models in health care, there are still several challenges that need to be addressed before the full potential of these data can be realized (eg, data sharing and interoperability issues, lack of massive genomic data sets, data ownership, and security and privacy of health data). Health companies are exploring the use of blockchain, a tamperproof and distributed digital ledger, to address some of these challenges.

Objective: In this viewpoint, we aim to obtain an overview of blockchain solutions that aim to solve challenges in health care from an industry perspective, focusing on solutions developed by health and technology companies.

Convergence of Precision Medicine and Public Health Into Precision Public Health: Toward a Big Data Perspective.

The field of precision medicine explores disease treatments by looking at genetic, socio-environmental, and clinical factors, thus trying to provide a holistic view of a person's health. Public health, on the other hand, is focused on improving the health of populations through preventive strategies and timely interventions. With recent advances in technology, we are able to collect, analyze and store for the first-time large volumes of real-time, diverse and continuous health data.

A Blockchain-Based Consent Platform for Active Assisted Living: Modeling Study and Conceptual Framework.

Background: Recent advancements in active assisted living (AAL) technologies allow older adults to age well in place. However, sensing technologies increase the complexity of data collection points, making it difficult for users to consent to data collection. One possible solution for improving transparency in the consent management process is the use of blockchain, an immutable and timestamped ledger.

Proposed Implementation of Blockchain in British Columbia's Health Care Data Management.

Background: There are several challenges such as information silos and lack of interoperability with the current electronic medical record (EMR) infrastructure in the Canadian health care system. These challenges can be alleviated by implementing a blockchain-based health care data management solution.

Objective: This study aims to provide a detailed overview of the current health data management infrastructure in British Columbia for identifying some of the gaps and inefficiencies in the Canadian health care data management system.