Miroslav Muzny1, Andre Henriksen2, Alain Giordanengo3, Jan Muzik4, Astrid Grøttland5, Håvard Blixgård5, Gunnar Hartvigsen6, Eirik Årsand5. 1. Spin-Off Company and Research Results Commercialization Center, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic; Norwegian Centre for E-Health Research, University Hospital of North Norway, Tromsø, Norway. Electronic address: mmuzny@gmail.com. 2. Department of Community Medicine, University of Tromsø - The Arctic University of Norway, Tromsø, Norway. 3. Norwegian Centre for E-Health Research, University Hospital of North Norway, Tromsø, Norway; Department of Computer Science, University of Tromsø - The Arctic University of Norway, Tromsø, Norway. 4. Spin-Off Company and Research Results Commercialization Center, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic. 5. Norwegian Centre for E-Health Research, University Hospital of North Norway, Tromsø, Norway. 6. Department of Computer Science, University of Tromsø - The Arctic University of Norway, Tromsø, Norway.
Abstract
BACKGROUND: Wearable devices with an ability to collect various type of physiological data are increasingly becoming seamlessly integrated into everyday life of people. In the area of electronic health (eHealth), many of these devices provide remote transfer of health data, as a result of the increasing need for ambulatory monitoring of patients. This has a potential to reduce the cost of care due to prevention and early detection. OBJECTIVE: The objective of this study was to provide an overview of available wearable sensor systems with data exchange possibilities. Due to the heterogeneous capabilities these systems possess today, we aimed to systematize this in terms of usage, where there is a need of, or users benefit from, transferring self-collected data to health care actors. METHODS: We searched for and reviewed relevant sensor systems (i.e., devices) and mapped these into 13 selected attributes related to data-exchange capabilities. We collected data from the Vandrico database of wearable devices, and complemented the information with an additional internet search. We classified the following attributes of devices: type, communication interfaces, data protocols, smartphone/PC integration, connection to smartphone health platforms, 3rd party integration with health platforms, connection to health care system/middleware, type of gathered health data, integrated sensors, medical device certification, access to user data, developer-access to device, and market status. Devices from the same manufacturer with similar functionalities/characteristics were identified under the same device family. Furthermore, we classified the systems in three subgroups of relevance for different actors in mobile health monitoring systems: EHR providers, software developers, and patient users. RESULTS: We identified 362 different mobile health monitoring devices belonging to 193 device families. Based on an analysis of these systems, we identified the following general challenges: CONCLUSIONS: Few of the identified mobile health monitoring systems use standardized, open communication protocols, which would allow the user to directly acquire sensor data. Use of open protocols can provide mobile health (mHealth) application developers an alternative to proprietary cloud services and communication tools, which are often closely integrated with the devices. Emerging new types of sensors, often intended for everyday use, have a potential to supplement health records systems with data that can enrich patient care.
BACKGROUND: Wearable devices with an ability to collect various type of physiological data are increasingly becoming seamlessly integrated into everyday life of people. In the area of electronic health (eHealth), many of these devices provide remote transfer of health data, as a result of the increasing need for ambulatory monitoring of patients. This has a potential to reduce the cost of care due to prevention and early detection. OBJECTIVE: The objective of this study was to provide an overview of available wearable sensor systems with data exchange possibilities. Due to the heterogeneous capabilities these systems possess today, we aimed to systematize this in terms of usage, where there is a need of, or users benefit from, transferring self-collected data to health care actors. METHODS: We searched for and reviewed relevant sensor systems (i.e., devices) and mapped these into 13 selected attributes related to data-exchange capabilities. We collected data from the Vandrico database of wearable devices, and complemented the information with an additional internet search. We classified the following attributes of devices: type, communication interfaces, data protocols, smartphone/PC integration, connection to smartphone health platforms, 3rd party integration with health platforms, connection to health care system/middleware, type of gathered health data, integrated sensors, medical device certification, access to user data, developer-access to device, and market status. Devices from the same manufacturer with similar functionalities/characteristics were identified under the same device family. Furthermore, we classified the systems in three subgroups of relevance for different actors in mobile health monitoring systems: EHR providers, software developers, and patient users. RESULTS: We identified 362 different mobile health monitoring devices belonging to 193 device families. Based on an analysis of these systems, we identified the following general challenges: CONCLUSIONS: Few of the identified mobile health monitoring systems use standardized, open communication protocols, which would allow the user to directly acquire sensor data. Use of open protocols can provide mobile health (mHealth) application developers an alternative to proprietary cloud services and communication tools, which are often closely integrated with the devices. Emerging new types of sensors, often intended for everyday use, have a potential to supplement health records systems with data that can enrich patient care.
Authors: Amy Abernethy; Laura Adams; Meredith Barrett; Christine Bechtel; Patricia Brennan; Atul Butte; Judith Faulkner; Elaine Fontaine; Stephen Friedhoff; John Halamka; Michael Howell; Kevin Johnson; Peter Long; Deven McGraw; Redonda Miller; Peter Lee; Jonathan Perlin; Donald Rucker; Lew Sandy; Lucia Savage; Lisa Stump; Paul Tang; Eric Topol; Reed Tuckson; Kristen Valdes Journal: NAM Perspect Date: 2022-06-27
Authors: Miroslav Muzny; André Henriksen; Alain Giordanengo; Jan Muzik; Astrid Grøttland; Håvard Blixgård; Gunnar Hartvigsen; Eirik Årsand Journal: Data Brief Date: 2019-12-12