We performed a search to identify available wearable sensors systems that can collect patient health data and have data sharing capabilities. Findings available in "Wearable sensors with possibilities for data exchange: Analyzing status and needs of different actors in mobile health monitoring systems" [1]. We performed an initial search of the Vandrico wearable database, and supplemented the resulting device list with an internet search. In addition to relevant meta-data (i.e. name, description, manufacturer, web-link, etc.) for each device, we also collected data on 13 attributes related to data exchange. I.e. device type, communication interface, data transfer protocol, smartphone and/or PC integration, direct integration to open health platform, 3rd platform integration with open health platform, support for health care system/middleware connection, recorded health data types, integrated sensors, medical device certification, whether or not the use can access collected data, device developer access, and device availability on the market. In addition, we grouped each device into three groups of actors that these devices are relevant for: electronic health record providers, software developers, and patients. The collected data can be used as an overview of available devices for future researchers with interest in the mobile health (mHealth) area.

• Publikační typ
• časopisecké články MeSH

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.

• MeSH
• lidé MeSH
• mobilní aplikace MeSH
• nositelná elektronika * MeSH
• poskytování zdravotní péče MeSH
• srdeční arytmie MeSH
• telemedicína MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH

BACKGROUND: Type 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key parameters of blood glucose dynamics to support the effort toward developing a digital infectious disease detection system. OBJECTIVE: The study aims to retrospectively analyze the effect of infection incidence and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm and to provide a general framework regarding how a digital infectious disease detection system can be designed and developed using self-recorded data from people with type 1 diabetes as a secondary source of information. METHODS: We retrospectively analyzed high precision self-recorded data of 10 patient-years captured within the longitudinal records of three people with type 1 diabetes. Obtaining such a rich and large data set from a large number of participants is extremely expensive and difficult to acquire, if not impossible. The data set incorporates blood glucose, insulin, carbohydrate, and self-reported events of infections. We investigated the temporal evolution and probability distribution of the key blood glucose parameters within a specified timeframe (weekly, daily, and hourly). RESULTS: Our analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively. CONCLUSIONS: We presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.

• MeSH
• diabetes mellitus 1. typu komplikace   MeSH
• dospělí MeSH
• incidence MeSH
• individualizovaná medicína metody   MeSH
• infekční nemoci etiologie   patologie   MeSH
• komplikace diabetu diagnóza   MeSH
• lidé MeSH
• retrospektivní studie MeSH
• telemedicína metody   MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

BACKGROUND: New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects. OBJECTIVE: The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research. METHODS: We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands. RESULTS: We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands. CONCLUSIONS: The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated.

• MeSH
• cvičení fyziologie   MeSH
• fitness náramky trendy   MeSH
• fotopletysmografie metody   MeSH
• lidé MeSH
• mobilní aplikace trendy   MeSH
• nositelná elektronika trendy   MeSH
• srdeční frekvence fyziologie   MeSH
• zápěstí MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

BACKGROUND: Wearable computing has long been described as the solution to many health challenges. However, the use of this technology as a diabetes patient self-management tool has not been fully explored. A promising platform for this use is the smartwatch-a wrist-worn device that not only tells time but also provides internet connection and ability to communicate information to and from a mobile phone. METHOD: Over 9 months, the design of a diabetes diary application for a smartwatch was completed using agile development methods. The system, including a two-way communication between the applications on the smartwatch and mobile phone, was tested with 6 people with type 1 diabetes. A small number of participants was deliberately chosen due to ensure an efficient use of resources on a novel system. RESULTS: The designed smartwatch system displays the time, day, date, and remaining battery time. It also allows for the entry of carbohydrates, insulin, and blood glucose (BG), with the option to view previously recorded data. Users were able to record specific physical activities, program reminders, and automatically record and transfer data, including step counts, to the mobile phone version of the diabetes diary. The smartwatch system can also be used as a stand-alone tool. Users reported usefulness, responded positively toward its functionalities, and also provided specific suggestions for further development. Suggestions were implemented after the feasibility study. CONCLUSIONS: The presented system and study demonstrate that smartwatches have opened up new possibilities within the diabetes self-management field by providing easier ways of monitoring BG, insulin injections, physical activity and dietary information directly from the wrist.

• MeSH
• ambulantní monitorování krevního tlaku MeSH
• chytrý telefon * MeSH
• diabetes mellitus 1. typu farmakoterapie   MeSH
• dietní sacharidy MeSH
• dietní záznamy * MeSH
• dospělí MeSH
• hypoglykemika aplikace a dávkování   terapeutické užití   MeSH
• inzulin aplikace a dávkování   terapeutické užití   MeSH
• krevní glukóza MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mobilní aplikace MeSH
• péče o sebe MeSH
• průzkumy a dotazníky MeSH
• studie proveditelnosti MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH