Abstract-Among the panoply of applications enabled by the Internet of Things (IoT), smart and connected health care is a particularly important one. Networked sensors, either worn on the body or embedded in our living environments, make possible the gathering of rich information indicative of our physical and mental health. Captured on a continual basis, aggregated, and effectively mined, such information can bring about a positive transformative change in the health care landscape. In particular, the availability of data at hitherto unimagined scales and temporal longitudes coupled with a new generation of intelligent processing algorithms can: (a) facilitate an evolution in the practice of medicine, from the current post facto diagnose-andtreat reactive paradigm, to a proactive framework for prognosis of diseases at an incipient stage, coupled with prevention, cure, and overall management of health instead of disease, (b) enable personalization of treatment and management options targeted particularly to the specific circumstances and needs of the individual, and (c) help reduce the cost of health care while simultaneously improving outcomes. In this paper, we highlight the opportunities and challenges for IoT in realizing this vision of the future of health care.

Background The QT interval is a risk marker for cardiac events such as TdP. However, QT measurements obtained from a 12-lead ECG during clinic hours may not capture the full extent of a patient’s daily QT range. Objective We evaluated the utility of 24-hour Holter ECG recording in patients with long QT syndrome to identify dynamic changes in the heart-rate corrected QT interval, and we investigated methods of visualizing the resulting data sets. Methods Beat-to-beat QTc (Bazett) intervals were automatically measured across 24-hour Holters from 202 LQT1, 89 LQT2, 14 LQT3 genotyped patients and a reference group of 200 healthy individuals. We measured the percentage of beats with QTc greater than the gender-specific threshold (QTc>470ms in women and QTc>450ms in men). The percentage of beats with QTc prolongation was determined across the 24-hour recordings. Results Based on the median percentage of heart beats per patient with QTc prolongation, LQT1 patients showed more frequent QTc prolongation during the day (~3PM) than they did at night (~3AM): 97% vs. 48%, p~10−4 for men, 68% vs. 30%, p~10−5 for women. LQT2 patients showed less frequent QTc prolongation during the day, compared to nighttime: 87% vs. 100%, p~10−4 for men, 62% vs. 100%, p~10−3 for women. Conclusion In patients with genotype positive LQTS, significant differences exist in the degree of daytime and nocturnal QTc prolongation. Holter monitoring using the “QT clock” concept may provide an easy, fast, and accurate method to assess the true personalized burden of QTc prolongation.

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society describes the current status of mobile health technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mobile health. The promises of predictive analytics but also operational challenges in embedding mobile health into routine clinical care are explored.

No abstract

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society describes the current status of mobile health (“mHealth”) technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self‐management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.