Wearables and the Medical Revolution

Abstract

Wearable sensors are already impacting healthcare and medicine by enabling health monitoring outside of the clinic and prediction of health events. This paper reviews current and prospective wearable technologies and their progress toward clinical application. We describe technologies underlying common, commercially available wearable sensors and early-stage devices and outline research, when available, to support the use of these devices in healthcare. We cover applications in the following health areas: metabolic, cardiovascular and gastrointestinal monitoring; sleep, neurology, movement disorders and mental health; maternal, pre- and neo-natal care; and pulmonary health and environmental exposures. Finally, we discuss challenges associated with the adoption of wearable sensors in the current healthcare ecosystem and discuss areas for future research and development.

Developing an mHealth app for post-stroke upper limb rehabilitation: Feedback from US and Ethiopian rehabilitation clinicians

“…In doing so, barriers at the technology, user, organizational, and regulatory level can be identified. 51 As with all studies, this research had some limitations. First, although our sample of Ethiopian rehabilitation clinicians is large enough to be representative of the larger population of Ethiopian rehabilitation clinicians (n = 69, ~350 practicing rehabilitation clinicians), our sample of 75 US rehabilitation clinicians may not necessarily be reflective of the broader group of 210,900 practicing PTs 52 and 130,400 OTs 53 in the United States.…”

Big data and health

“…Many of these findings were highly impactful, such as early detection of cardiomyopathy, lymphoma, and two precancerous conditions (monoclonal gammopathy of undetermined significance and smoldering myeloma). 2 However, the health data revolution is just beginning as more direct-to-consumer devices that measure health information become available (eg, electrocardiogram monitors for smart watches and blood pressure cuffs), [3][4][5] omics measurements become more sensitive and facile, and new imaging technologies emerge.…”

The Need for Artificial Intelligence in Digital Therapeutics

“…The improvements in machine learning, and specifically deep learning, over recent years have allowed new discoveries of many potential digital biomarkers of health and disease states [12]. These algorithms have demonstrated potential in identifying and predicting risk for patients with a variety of diseases and disorders, including diabetes, cardiovascular and gastrointestinal disease, neurological and movement disorders, mental health, and sleep disorders [12].…”

“…The improvements in machine learning, and specifically deep learning, over recent years have allowed new discoveries of many potential digital biomarkers of health and disease states [12]. These algorithms have demonstrated potential in identifying and predicting risk for patients with a variety of diseases and disorders, including diabetes, cardiovascular and gastrointestinal disease, neurological and movement disorders, mental health, and sleep disorders [12]. Consequently, research from the individual-patient level can be applied to Given that the field of adaptable digital therapeutics is so new, very few examples of therapeutic technologies exist using advanced artificial intelligence that differ beyond diagnosing or monitoring tools (e.g., artificially intelligent radiology scanners).…”

“…In conclusion, digital therapeutic systems have the potential to be a unique and efficient way to monitor symptoms, disease progression, and medication adherence, and provide relevant feedback interventions for patients and their clinical teams in various patient populations and disease states [7,8,12]. However, if digital therapeutics have a chance of being the "next best thing in healthcare," they need to offer something of significant value to patients and healthcare providers, besides simply encompassing digitized technology.…”