The traditional healthcare industry is undergoing a major paradigm shift due to the rapid advances and developments in mobile, wearable, and other wireless technologies. These Mobile Health (mHealth) technologies promise to bring tremendous benefits and opportunities to the diagnosis, prognosis, treatment, and prevention of human diseases for a better quality of life. In the meantime, mHealth also presents unprecedented performance and security challenges in the entire process of data collection, processing, analysis, synthesis, and visualization. For example, the advent of wearable technology has now made it possible to constantly monitor sophisticated biometrics for many people ranging from home athletes to chronic healthcare patients. A wide spectrum of devices is being designed as either a replacement of an existing healthcare monitor or a proposition for a new multifunction one. These devices typically require communication with a central healthcare system via cell phones or tablets and thus threats to the data at rest and in transit still exist, apart from a potential risk of misuse via patient profiling. Therefore, it is crucial to design and implement new mHealth technologies to build reliable, accurate, efficient, and secure healthcare environments for optimal patient care. The wide deployments of such wearable monitoring devices have also raised a critical issue to health informatics caused by the sheer volume and high complexity of health data collected anywhere and anytime. Machine learning and big data-oriented algorithms, models, systems, and platforms are needed to support the analysis, use, interpretation, and integration of diverse health data.

The purpose of this special issue is to publish original and high-quality research papers as well as review articles addressing recent mHealth advances and developments that use mobile and wireless devices to improve health outcomes, healthcare services, and health research.

Potential topics include but are not limited to the following:

• Advances in sensor devices for biomedical monitoring
• Infrastructures and architecture for mHealth
• mHealth informatics and information/management systems
• Security, confidentiality, and privacy in mHealth
• Energy management and optimization issues
• Big data analytics, machine learning, and visualization techniques for mHealth
• Crowdsourcing for public health surveillance, prevention, and emergencies using mHealth
• Communications between patients and physicians through mHealth
• Mobile and wearable healthcare applications
• Decision support systems for mHealth
• Pervasive and ubiquitous computing technologies for health and well-being
• Remote diagnosis and patient management