Apps, wearables, and sensors can unobtrusively gauge physiological and emotional states and collect, quantify, and monitor data regarding a user’s day-to-day behaviors, can provide timely and patient – centered care to those living at a distance with chronic disease, and can be used to coordinate care when multiple providers are involved, reducing costs to the health care system. In all cases, merely tracking data is not enough to maintain health behaviors long-term, and the software must also incorporate motivational constructs important for the adoption and habituation of health-related behaviors. Even though all these technologies are promising, further research is needed to validate their use and long-term impact and to identify possible risks, from physiological harm to breaching of privacy and confidentiality with insecure devices, and how to mitigate any risks.
Industries such as the healthcare industry are embracing mobile technology to support and integrate with these technologies to provide secured and efficient services demanded by other networks. This has resulted in new applications such as mobile health (mHealth) converged with Internet of Things (IoT). Furthermore, health service network operations will want to manage their customers’ devices to provide better managed services as well as physicians, who will want to access their patients’ device for real time monitoring or actuation when needed. Beyond detecting and diagnosing the disease, healthcare providers want to use mHealth to help patients deal with various diseases like diabetes, epileptic seizures, insomnia, schizophrenia, even Parkinson’s disease. (1) (2)
Let’s have a Look at diabetes as an example. Nearly 30 million Americans are now living with diabetes, and another 86 million have prediabetes, a higher-than-normal blood sugar level that can lead to diabetes. This disease accounts for unnecessary loss of vision, amputations, heart disease, kidney damage, and premature death. It also costs Americans $245 billion a year. But chronic diseases like diabetes need not take such huge personal or economic tolls. Easily implemented changes that digitize components of health and health care can lighten the load for people, their doctors, and the country at large. Millions of Americans currently use devices to monitor their health and fitness. These include scales, activity monitors (Fitbit, Apple Watch, Microsoft Band, and the like), heart rate and blood sugar monitors, and more. The data they record can help people take more control over their health and lifestyles. They can also help doctors keep track of their patients’ health, as information from these devices can be uploaded into electronic health records. Data from such devices could also alert doctors or first-aid workers to a problem that requires immediate attention, like a stroke or heart attack. (3)
A research team from Univeristy of Pittsburgh released a study that aims to develop and assess the usability of a Just in Time Adaptive Intervention application platform called iREST (“interactive Resilience Enhancing Sleep Tactics”) for use in behavioral insomnia interventions. iREST can be used by both patients and clinicians. The iREST app was developed from the mobile logical architecture of Just in Time Adaptive Intervention. It consists of a cross-platform smartphone app, a clinician portal, and secure 2-way communications platform between the app and the portal. The usability study comprised 19 Active Duty Service Members and Veterans between the ages of 18 and 60. iREST provides a feasible platform for the implementation of Just in Time Adaptive Intervention in mHealth-based and remote intervention settings. The system was rated highly usable and its crossplatformness made it readily implemented within the heavily segregated smartphone market. The use of wearables to track sleep is promising; yet the accuracy of this technology needs further improvement. Ultimately, iREST demonstrates that mHealth-based Just in Time Adaptive Intervention is not only feasible, but also works effectively. (4)

Also, it’s important to mention the fact that the FDA has approved an mHealth wearable for people living with epilepsy that’s designed to detect convulsive seizures and immediately notify care team members. The Embrace smart device, developed by Cambridge, Mass.- based Empatica, uses machine learning technology to measure electrodermal activity and identify when a user experiences the most dangerous kinds of seizures, known as “grand mal” or “generalized tonic – clonic” seizures, then sends alerts by text message and e-mail to designated caregivers. (5)

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Furthermore, diabetics need to frequently monitor their blood sugar or blood glucose levels to prevent the disease from causing greater harm. Wearable wireless devices are currently on the market though that ease this process for diabetics. Continuous glucose monitors (CGM) and insulin pumps help measure glucose levels from fluid under the skin. The readings from CGMs can then be used to advise the diabetic what course of action to take, or in the case of emergencies, can be sent immediately to healthcare providers. Currently, mHealth CGM devices are being primarily used in the North American and European markets. There will be increasing opportunities in emerging markets as the number of diabetics throughout Asia, Africa, and the Middle East continues to rise and places a rising burden on regional healthcare systems. This presents an enormous market opportunity for companies to create more efficient treatment devices, in terms of both ease of use and cost, that will help diabetics to properly manage their condition. (6)

Technology is constantly and rapidly changing. What was a “new” technology a few years ago (or even a few months ago) can likely be considered commonplace, outdated or even obsolete today. The Internet is constantly and rapidly changing. This is particularly true for how people use the Internet to find information, services and products—which has a dynamic influence in how technologies are used, perceived and innovated.

Although this article demonstrates that even if a substantial amount of progress has been made in the field of mHealth research, large gaps remain that need to be addressed. Many issues relate to how research has been conducted in this area (eg, poor reporting) whereas others are specific to mHealth applications (eg, lack of knowledge of effectiveness of specific mHealth intervention components).