BACKGROUND: Today's diabetes-oriented telemedicine systems can gather and analyze many parameters like blood glucose levels, carbohydrate intake, insulin doses, and physical activity levels (steps). Information collected can be presented to patients in a variety of graphical outputs. Despite the availability of several technical means, a large percentage of patients do not reach the goals established in their diabetes treatment. OBJECTIVE: The objective of the study was to evaluate the benefits of the Diani telemedicine system for the treatment of patients with type 1 diabetes mellitus. METHODS: Data were collected during a 24-week feasibility study. Patients responded to the World Health Organization Quality of Life - BREF (WHOQOL-BREF) questionnaire and a system evaluation questionnaire. The level of glycated hemoglobin (HbA1c) and the patient's body weight were measured, and the patient's use of the telemedicine system and their daily physical activity level were monitored. All data were sent from the patient's device to the Diani server using a real-time diabetes diary app. Wilcoxon and Friedman tests and the linear mixed effects method were used for data analysis. RESULTS: This study involved 10 patients (men: n=5; women: n=5), with a mean age of 47.7 (SD 19.3) years, a mean duration of diabetes of 10.5 (SD 8.6) years, and a mean HbA1c value of 59.5 (SD 6.7) mmol/mol. The median number of days the patients used the system was 84. After the intervention, the mean HbA1c decreased by 4.35 mmol/mol (P=.01). The patients spent 18.6 (SD 6.8) minutes on average using the app daily. After the intervention, the number of patients who measured their blood glucose level at least 3 times a day increased by 30%. The graphical visualization of the monitored parameters, automatic transmission of measured data from the glucometer, compatibility, and interconnection of individual devices when entering data were positively evaluated by patients. CONCLUSIONS: The Diani system was found to be beneficial for patients with type 1 diabetes mellitus in terms of managing their disease. Patients perceived it positively; it strengthened their knowledge of diabetes and their understanding of the influences of the measured values on the management of their disease. Its use had a positive effect on the HbA1c level.
- Publikační typ
- časopisecké články MeSH
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
Mobilní a nositelná elektronika nabízí pacientům s diabetes mellitus nové možnosti sběru dat a jejich efektivnější analýzu. Aplikace pro chytré telefony Diabetesdagboka a webový portál Diani umožňují shromažďování a analýzu hodnot glykemie, dávek sacharidů a inzulinu a míry pohybové aktivity. Hodnoty jsou dostupné v příslušném mobilním telefonu, ale jsou též automatizovaně přenášeny do internetového portálu, kde mohou být doplněny o záznam z elektronického krokoměru a kontinuálního monitoru glykemie. Lze je též zobrazit v různých typech grafických výstupů a jsou dostupné nejenom pacientovi, ale i jeho lékaři. Kazuistika pacienta, který systém téměř 2 roky využíval, prokazuje významné zlepšení metabolické kompenzace (pokles průměrné hodnoty HbA1c o 18,6 mmol/mol v porovnání s předchozím obdobím).
Mobile and wearable technologies offer patients with diabetes mellitus new possibilities for data collection and their more effective analysis. The Diabesdagboga smartphone application and the Diani web portal enable to collect and analyze glycaemia values, carbohydrates intake, insulin doses and the level of physical activity. The data are not only accessible in the corresponding smartphone but also automatically transferred to an Internet portal, where they may be completed by the records from an electronic pedometer and continuous glucose monitor. All these data may then be displayed in various types of graphical outputs and are available to both the patient and the physician. The case report of a patient who has used the system for almost two years shows a significant improvement in metabolic compensation (a decrease in the mean HbA1c value by 18.6 mmol/mol as compared with the previous period).
- Klíčová slova
- diabetický deník, telemonitoring, webová aplikace, webový portál,
- MeSH
- deníky jako téma MeSH
- diabetes mellitus 1. typu * diagnóza terapie MeSH
- diabetes mellitus diagnóza terapie MeSH
- dospělí MeSH
- glykovaný hemoglobin analýza MeSH
- hypoglykemie prevence a kontrola MeSH
- lidé MeSH
- mobilní aplikace MeSH
- selfmonitoring glykemie metody MeSH
- telemedicína metody MeSH
- výsledek terapie MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mužské pohlaví MeSH
- Publikační typ
- kazuistiky MeSH
- práce podpořená grantem 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: The prevalence of diabetes and the use of electronic health (eHealth) are increasing. People with diabetes need frequent monitoring and follow-up of health parameters, and eHealth services can be highly valuable. However, little is known about the use of eHealth in different socioeconomic groups among people with diabetes. OBJECTIVE: The aim of this study was to investigate the use of 4 different eHealth platforms (apps, search engines, video services, and social media sites) and the association with socioeconomic status (SES) among people diagnosed with type 1 and type 2 diabetes mellitus (T1D and T2D, respectively). METHODS: We used email survey data from 1250 members of the Norwegian Diabetes Association (aged 18-89 years), collected in 2018. Eligible for analyses were the 1063 respondents having T1D (n=523) and T2D (n=545). 5 respondents reported having both diabetes types and thus entered into both groups. Using descriptive statistics, we estimated the use of the different types of eHealth. By logistic regressions, we studied the associations between the use of these types of eHealth and SES (education and household income), adjusted for gender, age, and self-rated health. RESULTS: We found that 87.0% (447/514) of people with T1D and 77.7% (421/542) of people with T2D had used 1 or more forms of eHealth sometimes or often during the previous year. The proportion of people using search engines was the largest in both diagnostic groups, followed by apps, social media, and video services. We found a strong association between a high level of education and the use of search engines, whereas there were no educational differences for the use of apps, social media, or video services. In both diagnostic groups, high income was associated with the use of apps. In people with T1D, lower income was associated with the use of video services. CONCLUSIONS: This paper indicates a digital divide among people with diabetes in Norway, with consequences that may contribute to sustaining and shaping inequalities in health outcomes. The strong relationship between higher education and the use of search engines, along with the finding that the use of apps, social media, and video services was not associated with education, indicates that adequate communication strategies for audiences with varying education levels should be a focus in future efforts to reduce inequalities in health outcomes.
- MeSH
- diabetes mellitus 2. typu terapie MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- prevalence MeSH
- průřezové studie MeSH
- průzkumy a dotazníky MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- socioekonomické faktory MeSH
- společenská třída * MeSH
- telemedicína statistika a číselné údaje MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
BACKGROUND: The prevalence of diabetes and the use of electronic health (eHealth) are increasing. People with diabetes need frequent monitoring and follow-up of health parameters, and eHealth services can be of great value. However, little is known about the association between the use of eHealth and provider-based health care services among people with diabetes. OBJECTIVE: The objective of this study was to investigate the use of 4 different eHealth platforms (apps, search engines, video services, and social media sites) and associations with the use of provider-based health care visits among people diagnosed with type 1 diabetes mellitus (T1DM). METHODS: We used email survey data collected from 1250 members of the Norwegian Diabetes Association (aged 18 to 89 years) in 2018. Eligible for analyses were the 523 respondents with T1DM. Using descriptive statistics, we estimated the use of eHealth and the use of general practitioners (GPs) and somatic specialist outpatient services. By logistic regressions, we studied the associations between the use of these provider-based health services and the use of eHealth, adjusted for gender, age, education, and self-rated health. RESULTS: Of the sample of 523 people with T1DM, 90.7% (441/486) had visited a GP once or more, and 61.0% (289/474) had visited specialist services during the previous year. Internet search engines (such as Google) were used for health purposes sometimes or often by 84.0% (431/513), apps by 55.4% (285/514), social media (such as Facebook) by 45.2% (232/513), and video services (such as YouTube) by 23.3% (118/506). Participants aged from 18 to 39 years used all forms of eHealth more than people aged 40 years and older, with the exception of social media. The use of search engines was positively associated with the use of somatic specialist services (odds ratio 2.43, 95% CI 1.33-4.45). GP visits were not associated with any kind of eHealth use. CONCLUSIONS: eHealth services are now widely used for health support and health information by people with T1DM, primarily in the form of search engines but often in the form of apps and social media as well. We found a positive association between the use of search engines and specialist visits and that people with T1DM are frequent users of eHealth, GPs, and specialist services. We found no evidence that eHealth reduces the use of provider-based health care; these services seem to be additional rather than alternative. Future research should focus on how health care services can meet and adapt to the high prevalence of eHealth use. Our results also indicate that many patients with T1DM do not visit specialist clinics once a year as recommended. This raises questions about collaboration in health care services and needs to be followed up in future research.
- MeSH
- diabetes mellitus 1. typu terapie MeSH
- dospělí MeSH
- internet trendy MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- praktičtí lékaři MeSH
- průřezové studie MeSH
- průzkumy a dotazníky MeSH
- specializace MeSH
- telemedicína metody MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Publikační typ
- abstrakt z konference MeSH
- Publikační typ
- abstrakt z konference MeSH
- Publikační typ
- abstrakt z konference MeSH
