Wearable devices and predicting heart disease

In this study, authors aimed to explore whether 6-minute walk distances can be predicted by wrist-worn devices in patients with different stages of mitral and aortic valve disease. 

Browse Articles

  • Comment
    | Open Access

    It has been proposed that telehealth may help to combat the epidemic of diabetes and other chronic diseases in the US. As a result of rapid technological advancement over the past decade, there has been an explosion in virtual diabetes management program offerings rooted in smartphone technology, connected devices for blood glucose monitoring, and remote coaching or support. Such offerings take many forms with unique features. We provide a care team-based classification system for connected diabetes care programs and highlight their strengths and limitations. We also include a framework for how the different classes of connected diabetes care may be deployed in a health system to promote improved population health.

    • Brian J. Levine
    • , Kelly L. Close
    •  & Robert A. Gabbay
  • Perspective
    | Open Access

    • O. T. Inan
    • , P. Tenaerts
    • , S. A. Prindiville
    • , H. R. Reynolds
    • , D. S. Dizon
    • , K. Cooper-Arnold
    • , M. Turakhia
    • , M. J. Pletcher
    • , K. L. Preston
    • , H. M. Krumholz
    • , B. M. Marlin
    • , K. D. Mandl
    • , P. Klasnja
    • , B. Spring
    • , E. Iturriaga
    • , R. Campo
    • , P. Desvigne-Nickens
    • , Y. Rosenberg
    • , S. R. Steinhubl
    •  & R. M. Califf
  • Comment
    | Open Access

    Digital health technology tools (DHTT) are technologies such as apps, smartphones, and wearables that remotely acquire health-related information from individuals. They have the potential advantages of objectivity and sensitivity of measurement, richness of high-frequency sensor data, and opportunity for passive collection of health-related data. Thus, DHTTs promise to provide patient phenotyping at an order of granularity several times greater than is possible with traditional clinical research tools. While the conceptual development of novel DHTTs is keeping pace with technological and analytical advancements, an as yet unaddressed gap is how to develop robust and meaningful outcome measures based on sensor data. Here, we describe two roadmaps which were developed to generate outcome measures based on DHTT data: one using a data-centric approach and the second a patient-centric approach. The data-centric approach to develop digital outcome measures summarizes those sensor features maximally sensitive to the concept of interest, exemplified with the quantification of disease progression. The patient-centric approach summarizes those sensor features that are optimally relevant to patients’ functioning in everyday life. Both roadmaps are exemplified for use in tracking disease progression in observational and clinical interventional studies, and with a DHTT designed to evaluate motor symptom severity and symptom experience in Parkinson’s disease. Use cases other than disease progression (e.g., case-finding) are considered summarily. DHTT research requires methods to summarize sensor data into meaningful outcome measures. It is hoped that the concepts outlined here will encourage a scientific discourse and eventual consensus on the creation of novel digital outcome measures for both basic clinical research and clinical drug development.

    • Kirsten I. Taylor
    • , Hannah Staunton
    • , Florian Lipsmeier
    • , David Nobbs
    •  & Michael Lindemann

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