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PubMed | Washington University in St. Louis, Jaeb Center for Health Research, Ford Motor Company, Park Nicollet Institute and 5 more.
Type: Journal Article | Journal: JAMA | Year: 2017

Previous clinical trials showing the benefit of continuous glucose monitoring (CGM) in the management of type 1 diabetes predominantly have included adults using insulin pumps, even though the majority of adults with type 1 diabetes administer insulin by injection.To determine the effectiveness of CGM in adults with type 1 diabetes treated with insulin injections.Randomized clinical trial conducted between October 2014 and May 2016 at 24 endocrinology practices in the United States that included 158 adults with type 1 diabetes who were using multiple daily insulin injections and had hemoglobin A1c (HbA1c) levels of 7.5% to 9.9%.Random assignment 2:1 to CGM (n=105) or usual care (control group; n=53).Primary outcome measure was the difference in change in central-laboratory-measured HbA1c level from baseline to 24 weeks. There were 18 secondary or exploratory end points, of which 15 are reported in this article, including duration of hypoglycemia at less than 70 mg/dL, measured with CGM for 7 days at 12 and 24 weeks.Among the 158 randomized participants (mean age, 48years [SD, 13]; 44% women; mean baseline HbA1c level, 8.6%[SD, 0.6%]; and median diabetes duration, 19 years [interquartile range, 10-31 years]), 155 (98%) completed the study. In the CGM group, 93% used CGM 6 d/wk or more in month 6. Mean HbA1c reduction from baseline was 1.1% at 12 weeks and 1.0% at 24 weeks in the CGM group and 0.5% and 0.4%, respectively, in the control group (repeated-measures model P<.001). At 24 weeks, the adjusted treatment-group difference in mean change in HbA1c level from baseline was -0.6% (95% CI, -0.8% to -0.3%; P<.001). Median duration of hypoglycemia at less than <70 mg/dL was 43 min/d (IQR, 27-69) in the CGM group vs 80 min/d (IQR, 36-111) in the control group (P=.002). Severe hypoglycemia events occurred in 2 participants in each group.Among adults with type 1 diabetes who used multiple daily insulin injections, the use of CGM compared with usual care resulted in a greater decrease in HbA1c level during 24 weeks. Further research is needed to assess longer-term effectiveness, as well as clinical outcomes and adverse effects.clinicaltrials.gov Identifier: NCT02282397.


Connected health device are tools of digital technology to provide access and to share information as well as data across healthcare system. These healthcare systems are nothing but are the hospitals, clinics, medical stores etc. As per the current market trends people are trying to raise their living standard. Due to this, they are unable to dedicate the required attention towards their personal health. Hence there is a significant increase for the health related minor and major issues.. Connected health devices help to connect patient and doctors remotely. Connected healthcare devices are also known by terms like telecare, telehealth, telemedicine, mhealth, digital health and e-health. These devices are available in various forms and sizes according as per the customer’s requirement. However, such devices there has long way to establish in the market as companies have to build strong brand image in the market. UK, with more than 100,000 health apps, rapid growth in wearable and 70 % of the UK population who now owes a smartphone and increase in use of digital technology will revolutionize the future of health and social care.” The prominent market players exist in global connected healthcare market are: A&D Medical, Aerotel, Animas Corporation, BL Healthcare, Body Media Inc., Boston Scientific, DexCom Inc., Docobo, eHIT Ltd, eDevice, FitSense Technology, iMetrikus,  Instromedix (Card Guard), Masimo Corporation, Medtronic, Microlife, Nellcor, Nonin Medical, Omron, PHD Medical, Philips, St. Jude Medical, Tunstall, ViTelNet, and WebVMC. For the purpose of this study, market research future has segmented the digital healthcare market by digital healthcare types, digital healthcare devices, and its applications. Digital healthcare are categorized into four subgroups mainly: Telehealth, Telecare, wellness/Fitness and e-health which have their own subgroups. Digital Healthcare devices are like dental examination cameras, Digital ECG and Holters, Quell Relief, pedometer fitness apps, online healthcare insurance, wearable band and many more. The Global e-Healthcare market growth is growing with the rapid growth rate. This high growth is due to the increasing usage of smartphones is increasing. With the increased usage of digital technology, number of health apps on IOS and Android has more than doubled in 2.5 years to mover 1,00,000 Furthermore, this growth rate is also enhance by the ability of providing the quick service is required by consumers to save theirs time from his busy schedule. In this study, market research future has found that more than 25% of users having health issues would first try a device with usability to track their health condition and progress. Also, 27 percent of mobile phone users would prefer a personalized plan to help guide them through their journey to better health. Increasing disposable incomes, growing of usage of technology and growing rate of production of technical equipment like Asthma management kit, Valedo Management kit, Health patch MD Smart medical devices, Digital Pill, Google’s Smart Contact lenses, and Qardiocore (Connected ECG monitor) will drive the growth in medical field in future. Significant mhealth market growth predicted global revenue to be 50-55% per year. In 2013 the mHealth value recorded was $2.4 billion and thus forecasted that this will reach to approximate $21.5 billion after 2018. In European market also the predicted highest annual growth rate is 50-60%. The factors which will drive the increase in use of health apps would be easy to use, simple in design and give guarantee of data security. Ask for Table of Contents of this Report @ https://www.marketresearchfuture.com/ask-toc-request/us-europe-connected-health-devices-market-information-analysis-market-data-major-player-forecast-2023 At Market Research Future (MRFR), we enable our customers to unravel the complexity of various industries through our Cooked Research Report (CRR), Half-Cooked Research Reports (HCRR), Raw Research Reports (3R), Continuous-Feed Research (CFR), and Market Research & Consulting Services. MRFR team have supreme objective to provide the optimum quality market research and intelligence services to our clients. Our market research studies by products, services, technologies, applications, end users, and market players for global, regional, and country level market segments, enable our clients to see more, know more, and do more, which help to answer all their most important questions. In order to stay updated with technology and work process of the industry, MRFR often plans & conducts meet with the industry experts and industrial visits for its research analyst members.


Connected health device are tools of digital technology to provide access and to share information as well as data across healthcare system. These healthcare systems are nothing but are the hospitals, clinics, medical stores etc. As per the current market trends people are trying to raise their living standard. Due to this, they are unable to dedicate the required attention towards their personal health. Hence there is a significant increase for the health related minor and major issues. Connected health devices help to connect patient and doctors remotely. Connected healthcare devices are also known by terms like telecare, telehealth, telemedicine, mhealth, digital health and e-health. These devices are available in various forms and sizes according as per the customer’s requirement. However, such devices there has long way to establish in the market as companies have to build strong brand image in the market. UK, with more than 100,000 health apps, rapid growth in wearable and 70 % of the UK population who now owes a smartphone and increase in use of digital technology will revolutionize the future of health and social care.” The prominent market players exist in global connected healthcare market are: A&D Medical, Aerotel, Animas Corporation, BL Healthcare, Body Media Inc., Boston Scientific, DexCom Inc., Docobo, eHIT Ltd, eDevice, FitSense Technology, iMetrikus, Instromedix (Card Guard), Masimo Corporation, Medtronic, Microlife, Nellcor, Nonin Medical, Omron, PHD Medical, Philips, St. Jude Medical, Tunstall, ViTelNet, and WebVMC. For the purpose of this study, market research future has segmented the digital healthcare market by digital healthcare types, digital healthcare devices, and its applications. Digital healthcare are categorized into four subgroups mainly: Telehealth, Telecare, wellness/Fitness and e-health which have their own subgroups. Digital Healthcare devices are like dental examination cameras, Digital ECG and Holters, Quell Relief, pedometer fitness apps, online healthcare insurance, wearable band and many more. The Global e-Healthcare market growth is growing with the rapid growth rate. This high growth is due to the increasing usage of smartphones is increasing. With the increased usage of digital technology, number of health apps on IOS and Android has more than doubled in 2.5 years to mover 1,00,000 Furthermore, this growth rate is also enhance by the ability of providing the quick service is required by consumers to save theirs time from his busy schedule. In this study, market research future has found that more than 25% of users having health issues would first try a device with usability to track their health condition and progress. Also, 27 percent of mobile phone users would prefer a personalized plan to help guide them through their journey to better health. Increasing disposable incomes, growing of usage of technology and growing rate of production of technical equipment like Asthma management kit, Valedo Management kit, Health patch MD Smart medical devices, Digital Pill, Google’s Smart Contact lenses, and Qardiocore (Connected ECG monitor) will drive the growth in medical field in future. Significant mhealth market growth predicted global revenue to be 50-55% per year. In 2013 the mHealth value recorded was $2.4 billion and thus forecasted that this will reach to approximate $21.5 billion after 2018. In European market also the predicted highest annual growth rate is 50-60%. The factors which will drive the increase in use of health apps would be easy to use, simple in design and give guarantee of data security. Ask for Table of Contents of this Report @ https://www.marketresearchfuture.com/ask-toc-request/us-europe-connected-health-devices-market-information-analysis-market-data-major-player-forecast-2023 At Market Research Future (MRFR), we enable our customers to unravel the complexity of various industries through our Cooked Research Report (CRR), Half-Cooked Research Reports (HCRR), Raw Research Reports (3R), Continuous-Feed Research (CFR), and Market Research & Consulting Services. MRFR team have supreme objective to provide the optimum quality market research and intelligence services to our clients. Our market research studies by products, services, technologies, applications, end users, and market players for global, regional, and country level market segments, enable our clients to see more, know more, and do more, which help to answer all their most important questions. In order to stay updated with technology and work process of the industry, MRFR often plans & conducts meet with the industry experts and industrial visits for its research analyst members. For more information, please visit https://www.marketresearchfuture.com/


Heintzman N.,DexCom Inc. | Kleinberg S.,Stevens Institute of Technology
Journal of Biomedical Informatics | Year: 2016

The amount of observational data available for research is growing rapidly with the rise of electronic health records and patient-generated data. However, these data bring new challenges, as data collected outside controlled environments and generated for purposes other than research may be error-prone, biased, or systematically missing. Analysis of these data requires methods that are robust to such challenges, yet methods for causal inference currently only handle uncertainty at the level of causal relationships – rather than variables or specific observations. In contrast, we develop a new approach for causal inference from time series data that allows uncertainty at the level of individual data points, so that inferences depend more strongly on variables and individual observations that are more certain. In the limit, a completely uncertain variable will be treated as if it were not measured. Using simulated data we demonstrate that the approach is more accurate than the state of the art, making substantially fewer false discoveries. Finally, we apply the method to a unique set of data collected from 17 individuals with type 1 diabetes mellitus (T1DM) in free-living conditions over 72 h where glucose levels, insulin dosing, physical activity and sleep are measured using body-worn sensors. These data often have high rates of error that vary across time, but we are able to uncover the relationships such as that between anaerobic activity and hyperglycemia. Ultimately, better modeling of uncertainty may enable better translation of methods to free-living conditions, as well as better use of noisy and uncertain EHR data. © 2016 The Author(s)


Jacobs P.G.,Oregon Health And Science University | Youssef J.E.,Oregon Health And Science University | Castle J.R.,Oregon Health And Science University | Engle J.M.,Legacy Health System | And 5 more authors.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2011

Patients with diabetes have difficulty controlling their blood sugar and suffer from acute effects of hypoglycemia and long-term effects of hyperglycemia, which include disease of the eyes, kidneys and nerves/feet. In this paper, we describe a new system that is used to automatically control blood sugar in people with diabetes through the fully automated measurement of blood glucose levels and the delivery of insulin and glucagon via the subcutaneous route. When a patient's blood sugar goes too high, insulin is given to the patient to bring his/her blood sugar back to a normal level. To prevent a patient's blood sugar from going too low, the patient is given a hormone called glucagon which raises the patient's blood sugar. While other groups have described methods for automatically delivering insulin and glucagon, many of these systems still require human interaction to enter the venous blood sugar levels into the control system. This paper describes the development of a fully automated closed-loop dual sensor bi-hormonal artificial pancreas system that does not require human interaction. The system described in this paper is comprised of two sensors for measuring glucose, two pumps for independent delivery of insulin and glucagon, and a laptop computer running a custom software application that controls the sensor acquisition and insulin and glucagon delivery based on the glucose values recorded. Two control algorithms are designed into the software: (1) an algorithm that delivers insulin and glucagon according to their proportional and derivative errors and proportional and derivative gains and (2) an adaptive algorithm that adjusts the gain factors based on the patient's current insulin sensitivity as determined using a mathematical model. Results from this work may ultimately lead to development of a portable, easy to use, artificial pancreas device that can enable far better glycemic control in patients with diabetes. © 2011 IEEE.


PubMed | DexCom Inc., Stevens Institute of Technology and Columbia University
Type: | Journal: Journal of biomedical informatics | Year: 2015

Most clinical and biomedical data contain missing values. A patients record may be split across multiple institutions, devices may fail, and sensors may not be worn at all times. While these missing values are often ignored, this can lead to bias and error when the data are mined. Further, the data are not simply missing at random. Instead the measurement of a variable such as blood glucose may depend on its prior values as well as that of other variables. These dependencies exist across time as well, but current methods have yet to incorporate these temporal relationships as well as multiple types of missingness. To address this, we propose an imputation method (FLk-NN) that incorporates time lagged correlations both within and across variables by combining two imputation methods, based on an extension to k-NN and the Fourier transform. This enables imputation of missing values even when all data at a time point is missing and when there are different types of missingness both within and across variables. In comparison to other approaches on three biological datasets (simulated and actual Type 1 diabetes datasets, and multi-modality neurological ICU monitoring) the proposed method has the highest imputation accuracy. This was true for up to half the data being missing and when consecutive missing values are a significant fraction of the overall time series length.


PubMed | DexCom Inc. and Stevens Institute of Technology
Type: | Journal: Journal of biomedical informatics | Year: 2016

The amount of observational data available for research is growing rapidly with the rise of electronic health records and patient-generated data. However, these data bring new challenges, as data collected outside controlled environments and generated for purposes other than research may be error-prone, biased, or systematically missing. Analysis of these data requires methods that are robust to such challenges, yet methods for causal inference currently only handle uncertainty at the level of causal relationships - rather than variables or specific observations. In contrast, we develop a new approach for causal inference from time series data that allows uncertainty at the level of individual data points, so that inferences depend more strongly on variables and individual observations that are more certain. In the limit, a completely uncertain variable will be treated as if it were not measured. Using simulated data we demonstrate that the approach is more accurate than the state of the art, making substantially fewer false discoveries. Finally, we apply the method to a unique set of data collected from 17 individuals with type 1 diabetes mellitus (T1DM) in free-living conditions over 72h where glucose levels, insulin dosing, physical activity and sleep are measured using body-worn sensors. These data often have high rates of error that vary across time, but we are able to uncover the relationships such as that between anaerobic activity and hyperglycemia. Ultimately, better modeling of uncertainty may enable better translation of methods to free-living conditions, as well as better use of noisy and uncertain EHR data.


Basu A.,Endocrine Research Unit | Dube S.,Endocrine Research Unit | Slama M.,Endocrine Research Unit | Errazuriz I.,Endocrine Research Unit | And 6 more authors.
Diabetes | Year: 2013

The accuracy of continuous interstitial fluid (ISF) glucose sensing is an essential component of current and emerging open-and closed-loop systems for type 1 diabetes. An important determinant of sensor accuracy is the physiological time lag of glucose transport from the vascular to the interstitial space. We performed the first direct measurement of this phenomenon to our knowledge in eight healthy subjects under an overnight fasted condition. Microdialysis catheters were inserted into the abdominal subcutaneous space. After intravenous bolus administrations of glucose tracers, timed samples of plasma and ISF were collected sequentially and analyzed for tracer enrichments. After accounting for catheter dead space and assay noise, the mean time lag of tracer appearance in the interstitial space was 5.3-6.2 min. We conclude that in the overnight fasted state in healthy adults, the physiological delay of glucose transport from the vascular to the interstitial space is 5-6 min. Physiological delay between blood glucose and ISF glucose, therefore, should not be an obstacle to sensor accuracy in overnight or fasting-state closedloop systems of insulin delivery or open-loop therapy assessment for type 1 diabetes. © 2013 by the American Diabetes Association.


Kamath A.,DexCom Inc. | Mahalingam A.,DexCom Inc. | Brauker J.,DexCom Inc.
Journal of Diabetes Science and Technology | Year: 2010

Background: The evaluation of continuous glucose monitor (CGM) alert performance should reflect patient use in real time. By evaluating alerts as real-time events, their ability to both detect and predict low and high blood glucose (BG) events can be examined. Method: True alerts (TA) were defined as a CGM alert occurring within ± 30 minutes from the beginning of a low or a high BG event. The TA time to detection was calculated as [time of CGM alert] - [beginning of event]. False alerts (FA) were defined as a BG event outside of the alert zone within ± 30 minutes from a CGM alert. Analysis was performed comparing DexCom™ SEVEN® PLUS CGM data to BG measured with a laboratory analyzer. Results: Of 49 low glucose events (BG ≤70 mg/dl), with the CGM alert set to 90 mg/dl, the TA rate was 91.8%. For 50% of TAs, the CGM alert preceded the event by at least 21 minutes. The FA rate was 25.0%. Similar results were found for high alerts. Conclusion: Continuous glucose monitor alerts are capable of both detecting and predicting low and high BG events. The setting of alerts entails a trade-off between predictive ability and FA rate. Realistic analysis of this trade-off will guide patients in the effective utilization of CGM. © Diabetes Technology Society.


PubMed | DexCom Inc.
Type: Journal Article | Journal: Journal of diabetes science and technology | Year: 2015

The management of type 1 diabetes (T1D) ideally involves regimented measurement of various health signals; constant interpretation of diverse kinds of data; and consistent cohesion between patients, caregivers, and health care professionals (HCPs). In the context of myriad factors that influence blood glucose dynamics for each individual patient (eg, medication, activity, diet, stress, sleep quality, hormones, environment), such coordination of self-management and clinical care is a great challenge, amplified by the routine unavailability of many types of data thought to be useful in diabetes decision-making. While much remains to be understood about the physiology of diabetes and blood glucose dynamics at the level of the individual, recent and emerging medical and consumer technologies are helping the diabetes community to take great strides toward truly personalized, real-time, data-driven management of this chronic disease. This review describes connected technologies--such as smartphone apps, and wearable devices and sensors--which comprise part of a new digital ecosystem of data-driven tools that can link patients and their care teams for precision management of diabetes. These connected technologies are rich sources of physiologic, behavioral, and contextual data that can be integrated and analyzed in the cloud for research into personal models of glycemic dynamics, and employed in a multitude of applications for mobile health (mHealth) and telemedicine in diabetes care.

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