Search tips
Search criteria

Results 1-25 (1723)

Clipboard (0)

Select a Filter Below

Year of Publication
1.  Corrigendum 
PMCID: PMC5038551  PMID: 27460626
2.  Telemedicine for Diabetes 
PMCID: PMC4738230  PMID: 26682960
accountable care organization; big data; diabetes; future; mHealth; sensor; telemedicine
3.  CSII 
PMCID: PMC4928211  PMID: 26682959
insulin infusion set; CSII; adverse event; diabetes
4.  Smartwatch’s Application Algorithm to Prevent Sudden Death and Brain Damage in Hypoglycemic Coma 
PMCID: PMC5038535  PMID: 26659660
hypoglycemic coma; sensor; software algorithm; wearable computers; smartwatches
5.  Integrated Personalized Diabetes Management (PDM) 
Collaborative use of structured self-monitoring of blood glucose (SMBG) data and data management software, utilized within a 6-step cycle enables integrated Personalized Diabetes Management (PDM). The 2 PDM-ProValue studies shall assess the effectiveness of this approach in improving patient outcomes and practice efficiencies in outpatient settings.
The PDM-ProValue studies are 12-month, prospective, cluster-randomized, multicenter, trials to determine if use of integrated PDM in daily life improves glycemic control in insulin-treated type 2 diabetes patients. Fifty-four general medical practices (GPs) and 36 diabetes-specialized practices (DSPs) across Germany will be recruited. The practices will be randomly assigned to the control groups (CNL) or the intervention groups (INT) via cluster-randomization. CNL practices will continue with their usual care; INT practices will utilize integrated PDM. The sample size is 1,014 patients (n = 540 DSP patients, n = 474 GP patients). Each study is designed to detect a between-group difference in HbA1c change of at least 0.4% at 12 months with a power of 90% and 2-sided significance level of .05. Differences in timing and degree of treatment adaptions, treatment decisions, blood glucose target ranges, hypoglycemia, self-management behaviors, quality of life, patients attitudes, clinician satisfaction, practice processes, and resource consumption will be assessed. Study endpoints will be analyzed for the modified intent-to-treat and per protocol populations. Trial results are expected to be available in late 2016.
Effective and efficient strategies to optimize diabetes management are needed. These randomized studies will help determine if PDM is beneficial.
PMCID: PMC5038529  PMID: 26645793
personalized diabetes management; SMBG; self-monitoring of blood glucose; diabetes data management; type 2 diabetes
6.  Toward Development of Psychosocial Measures for Automated Insulin Delivery 
The INSPIRE study working group launched its initial workshop in February 2015 to facilitate collaboration among key stakeholders interested in automated insulin delivery (AID) systems and the psychosocial outcomes of individuals who may use these new technologies. Specifically, the INSPIRE team’s goal is to facilitate measure development assessing the psychosocial factors associated with AID systems. A second working group was held to foster exchange among key stakeholders in AID system development. Patient, health care provider, engineering, industry, academic, regulatory and payer perspectives were presented. The INSPIRE working group will continue to serve as a platform to encourage open dialogue among all stakeholders with the aim of facilitating technology that offers minimal user burden and maximum benefit from both a psychological and physiologic perspective.
PMCID: PMC5038533  PMID: 26645792
automated insulin delivery; artificial pancreas; psychosocial; human factors; diabetes technology
7.  Integration of Administrative, Clinical, and Environmental Data to Support the Management of Type 2 Diabetes Mellitus 
A very interesting perspective of “big data” in diabetes management stands in the integration of environmental information with data gathered for clinical and administrative purposes, to increase the capability of understanding spatial and temporal patterns of diseases. Within the MOSAIC project, funded by the European Union with the goal to design new diabetes analytics, we have jointly analyzed a clinical-administrative dataset of nearly 1.000 type 2 diabetes patients with environmental information derived from air quality maps acquired from remote sensing (satellite) data. Within this context we have adopted a general analysis framework able to deal with a large variety of temporal, geo-localized data. Thanks to the exploitation of time series analysis and satellite images processing, we studied whether glycemic control showed seasonal variations and if they have a spatiotemporal correlation with air pollution maps. We observed a link between the seasonal trends of glycated hemoglobin and air pollution in some of the considered geographic areas. Such findings will need future investigations for further confirmation. This work shows that it is possible to successfully deal with big data by implementing new analytics and how their exploration may provide new scenarios to better understand clinical phenomena.
PMCID: PMC4738227  PMID: 26630915
big data; data analytics; data integration; diabetes mellitus; environmental data; remote sensing
8.  Impact of Chronic Sleep Disturbance for People Living With T1 Diabetes 
The aim was to explore personal experiences and to determine the impact of impaired sleep on well-being and diabetes-related activities/decision making among a cohort of people living with T1D.
Adults with T1D over the age of 18 and parents/carers of children with T1D were invited to complete an online questionnaire about their quality and quantity of sleep. Questions included impact of sleep on diabetes-related decision making, effective calculation of bolus doses, important aspects of psychosocial functioning, and frequency of waking. Diasend download data were used to objectively determine frequency of nocturnal blood glucose testing in children.
A total of 258 parent/carer participants (n = 221 female, 85.6%) and 192 adults with T1D (n = 145, 75.5% female, age range 19 to 89 years) took part. In all, 239 parents/carers and 160 adults believed waking in the night has an impact on their usual daily functioning. Of these, 236 parents/carers and 151 (64%) adults reported the impact as negative. Chronic sleep interruption was associated with detrimental impact on mood, work, family relationships, ability to exercise regularly, ability to eat healthily, and happiness.
Chronic sleep interruption is highly prevalent in adults with T1D and parents/carers of children with T1D with negative effects on daily functioning and well-being. Appropriate interventions are required to alleviate this burden of T1D, address modifiable risk factors for nocturnal hypoglycemia, and reduce the (perceived) need for nocturnal waking.
PMCID: PMC5038531  PMID: 26630914
sleep disturbance; type 1 diabetes; health status
9.  Advances in Patient Self-Monitoring of Blood Glucose 
In 2 articles of the present issue, Bendini et al report about performance results obtained with 2 blood glucose monitoring systems of the Contour Next platform. Using several analysis methods, the authors demonstrate a very high accuracy, which meets all actual regulatory performance criteria. With consistent MARD results < 5% under daily routine conditions, this meter platform is finally fulfilling the accuracy request as set forth by the American Diabetes Association already in the late 1980s. This meter platform is representative for the successful effort of the device manufacturers who were consequently improving the analytical performance of blood glucose meters during the Past 2 decades, starting with an MARD of 12-15% at the end of the past century and reaching an excellent accuracy < 5% today.
PMCID: PMC4738228  PMID: 26621051
blood glucose monitoring; accuracy; technology advances; mean absolute relative deviation
10.  The Contemporary Role of Masked Continuous Glucose Monitoring in a Real-Time World 
Real-time continuous glucose monitoring (RT-CGM) has, in the span of just a few years, established an essential role in the contemporary management of type 1 diabetes. Nonetheless, masked CGM retains an important place in the management of diabetes including assisting with hypoglycemia detection and avoidance, optimizing glycemic control, and acting as a teaching tool for people living with diabetes.
PMCID: PMC5038532  PMID: 26612249
diabetes; continuous glucose monitoring; real-time; masked; professional
11.  Artificial Pancreas Device Systems for the Closed-Loop Control of Type 1 Diabetes 
Closed-loop artificial pancreas device (APD) systems are externally worn medical devices that are being developed to enable people with type 1 diabetes to regulate their blood glucose levels in a more automated way. The innovative concept of this emerging technology is that hands-free, continuous, glycemic control can be achieved by using digital communication technology and advanced computer algorithms.
A horizon scanning review of this field was conducted using online sources of intelligence to identify systems in development. The systems were classified into subtypes according to their level of automation, the hormonal and glycemic control approaches used, and their research setting.
Eighteen closed-loop APD systems were identified. All were being tested in clinical trials prior to potential commercialization. Six were being studied in the home setting, 5 in outpatient settings, and 7 in inpatient settings. It is estimated that 2 systems may become commercially available in the EU by the end of 2016, 1 during 2017, and 2 more in 2018.
There are around 18 closed-loop APD systems progressing through early stages of clinical development. Only a few of these are currently in phase 3 trials and in settings that replicate real life.
PMCID: PMC5038530  PMID: 26589628
algorithm; artificial pancreas; closed-loop control; device system; glycemic control; type 1 diabetes
12.  Metabolic Control With the Bio-inspired Artificial Pancreas in Adults With Type 1 Diabetes 
The Bio-inspired Artificial Pancreas (BiAP) is a closed-loop insulin delivery system based on a mathematical model of beta-cell physiology and implemented in a microchip within a low-powered handheld device. We aimed to evaluate the safety and efficacy of the BiAP over 24 hours, followed by a substudy assessing the safety of the algorithm without and with partial meal announcement. Changes in lactate and 3-hydroxybutyrate concentrations were investigated for the first time during closed-loop.
This is a prospective randomized controlled open-label crossover study. Participants were randomly assigned to attend either a 24-hour closed-loop visit connected to the BiAP system or a 24-hour open-loop visit (standard insulin pump therapy). The primary outcome was percentage time spent in target range (3.9-10 mmol/l) measured by sensor glucose. Secondary outcomes included percentage time in hypoglycemia (<3.9 mmol/l) and hyperglycemia (>10 mmol/l). Participants were invited to attend for an additional visit to assess the BiAP without and with partial meal announcements.
A total of 12 adults with type 1 diabetes completed the study (58% female, mean [SD] age 45 [10] years, BMI 25 [4] kg/m2, duration of diabetes 22 [12] years and HbA1c 7.4 [0.7]% [58 (8) mmol/mol]). The median (IQR) percentage time in target did not differ between closed-loop and open-loop (71% vs 66.9%, P = .9). Closed-loop reduced time spent in hypoglycemia from 17.9% to 3.0% (P < .01), but increased time was spent in hyperglycemia (10% vs 28.9%, P = .01). The percentage time in target was higher when all meals were announced during closed-loop compared to no or partial meal announcement (65.7% [53.6-80.5] vs 45.5% [38.2-68.3], P = .12).
The BiAP is safe and achieved equivalent time in target as measured by sensor glucose, with improvement in hypoglycemia, when compared to standard pump therapy.
PMCID: PMC4773972  PMID: 26581881
closed-loop insulin delivery; Bio-inspired Artificial Pancreas; type 1 diabetes; diabetes technology
14.  Improving Accuracy of Grading and Referral of Diabetic Macular Edema Using Location and Extent of Hard Exudates in Retinal Photography 
Hard exudates (HE) are used as a surrogate marker for sight-threatening diabetic macular edema (DME) in most telemedicine-based screening programs in the world. This study investigates whether proximity of HE to the center of the macula, and extent of HE are associated with greater clinically significant macular edema (CSME) severity. A novel method for associating optical coherence tomography (OCT) scans with CSME was developed.
Eligible subjects were recruited from a DRS program in a community clinic in Oakland, California. Ocular fundus of each subject was imaged using 3-field 45-degree digital retinal photography and scanned using central 7-line spectral domain OCT. Two certified graders separated subjects into 2 groups, those with and without HE within 500 microns from the center of the macula. A modified DME severity scale, developed from Early Treatment Diabetic Retinopathy Study data and adapted to OCT thickness measurements, was used to stratify CSME into severe and nonsevere levels for all subjects.
The probabilities of severe CSME in groups 1 and 2 were 14.4% (95% CI: 8.2%-23.8%) and 9% (95% CI: 2.4%-25.5%), respectively (P = .556). In post hoc analysis, increase in the number of sectors affected by HE within the central zone of the macula was associated with the increase in the probability of being diagnosed with severe CSME.
We have proposed OCT-based classification of DME into severe and nonsevere CSME. Based on this limited analysis, severity of CSME is related more to extent of HE rather than proximity to the center of the macula.
PMCID: PMC4773974  PMID: 26581880
diabetic macular edema; hard exudates; optical coherence tomography; screening
15.  Options for the Development of Noninvasive Glucose Monitoring 
Nowadays nanotechnology has many applications in products used in various areas of daily life; however, this technology has also an option in modern medicine and pharmacy. Therefore, this technology is also an attractive option for the field of diagnosis and treatment of diabetes. Many people with diabetes measure their blood glucose levels regularly to determine the insulin dose. Ideally glucose values would be measured noninvasively (NI). However, none of all the NI approaches studied in the past decades enabled reliable NI measurements under all daily life conditions. Particularly an unfavorable signal-to-noise ratio turned out to be problematic. Based on the known physical possibilities for NI glucose monitoring the focus of this review is on nanotechnology approaches. Functional prototypes exist for some of these that showed promising results under defined laboratory conditions, indicating a good sensitivity and selectivity for glucose. On the second hand is to optimize the technological process of manufacturing. In view of the rapid progress in micro- and nanoelectronics hopefully NI glucose monitoring systems can be developed in the near future.
PMCID: PMC5038528  PMID: 26581879
noninvasive glucose monitoring; physical measurement methods; continuous glucose monitoring; nanotechnology; nanotubes; quantum dots
16.  Evaluation of System Accuracy of the GlucoMen LX Plus Blood Glucose Monitoring System With Reference to ISO 15197:2013 
PMCID: PMC4773971  PMID: 26553022
self-monitoring of blood glucose; ISO 15197:2013; accuracy; blood glucose; SMBG system
17.  Quality of HbA1c Measurement in Trinidad and Tobago 
Monitoring of HbA1c is the standard of care to assess diabetes control. In Trinidad & Tobago (T&T) there are no existing data on the quality of HbA1c measurement. Our study examined the precision and accuracy of HbA1c testing in T&T.
Sets of 10 samples containing blinded duplicates were shipped to laboratories in T&T. This exercise was repeated 6 months later. Precision and accuracy were estimated for each laboratory/method.
T&T methods included immunoassay, capillary electrophoresis, and boronate affinity binding. Most, but not all, laboratories demonstrated acceptable precision and accuracy.
Continuous oversight of HbA1c testing (eg, through proficiency testing) in T&T is recommended. These results highlight the lack of oversight of HbA1c testing in some developing countries.
PMCID: PMC5038524  PMID: 26553021
comparison; glycated hemoglobin; HbA1c; proficiency testing
18.  What Do Your Fingernails Say About You? Can They Indicate That You Have Diabetes? 
PMCID: PMC4667320  PMID: 26518486
advanced glycation end product; diabetes; diagnosis; fingernail; noninvasive
19.  The Transformation of Diabetes Care Through the Use of Person-Centered Data 
The health care industry is undergoing a major transformation. Despite spending more on health care than any other country, the United States has not seen a commensurate improvement in the quality of care. Chronic disease management puts the greatest burden on the health care system with estimates suggesting that 3 of 4 health care dollars are spent on managing chronic disease. Moreover, the number of older patients with chronic conditions, like diabetes, is rising as expected, which only serves to worsen the physician shortage problem we are currently experiencing, and further increase health care costs. Unless new models of health care are established for these patients, they simply will not be served. Consistent with the message above, there are generally 3 universal health care needs, (1) improved outcomes, (2) expanded access, and (3) optimized cost and efficiency. It is likely the future state will involve value-based health care, with payment based on outcomes, not services rendered, and incentives tied more directly to the value delivered. Medical device providers will be held more accountable for positive outcomes, and to ensure success, they will need to create better solutions with their therapies. Instead of the touch point with patients being solely at the time of a procedure or sale of the device, it is likely companies will need to drive toward a more comprehensive partnership with patients, providers, and payers, extending the scope of services and interactions to provide a continuum of care. In general, companies will need to start to think of their most important customers as people living with a condition, as opposed to patients needing immediate medical devices. In this article, I discuss the challenges of health care today and present some of the opportunities to revamp health care delivery in diabetes by leveraging the pervasive use of mobile technologies and digital data.
PMCID: PMC4738226  PMID: 26490217
continuum of care; diabetes; disease management; remote monitoring; telemedicine
20.  Physical Activity Capture Technology With Potential for Incorporation Into Closed-Loop Control for Type 1 Diabetes 
Physical activity is an important determinant of glucose variability in type 1 diabetes (T1D). It has been incorporated as a nonglucose input into closed-loop control (CLC) protocols for T1D during the last 4 years mainly by 3 research groups in single center based controlled clinical trials involving a maximum of 18 subjects in any 1 study. Although physical activity data capture may have clinical benefit in patients with T1D by impacting cardiovascular fitness and optimal body weight achievement and maintenance, limited number of such studies have been conducted to date. Clinical trial registries provide information about a single small sample size 2 center prospective study incorporating physical activity data input to modulate closed-loop control in T1D that are seeking to build on prior studies. We expect an increase in such studies especially since the NIH has expanded support of this type of research with additional grants starting in the second half of 2015. Studies (1) involving patients with other disorders that have lasted 12 weeks or longer and tracked physical activity and (2) including both aerobic and resistance activity may offer insights about the user experience and device optimization even as single input CLC heads into real-world clinical trials over the next few years and nonglucose input is introduced as the next advance.
PMCID: PMC4667300  PMID: 26481641
closed loop; glucose insulin; physical activity capture devices; type 1 diabetes
21.  Exercise and the Development of the Artificial Pancreas 
Regular physical activity (PA) promotes numerous health benefits for people living with type 1 diabetes (T1D). However, PA also complicates blood glucose control. Factors affecting blood glucose fluctuations during PA include activity type, intensity and duration as well as the amount of insulin and food in the body at the time of the activity. To maintain equilibrium with blood glucose concentrations during PA, the rate of glucose appearance (Ra) to disappearance (Rd) in the bloodstream must be balanced. In nondiabetics, there is a rise in glucagon and a reduction in insulin release at the onset of mild to moderate aerobic PA. During intense aerobic -anaerobic work, insulin release first decreases and then rises rapidly in early recovery to offset a more dramatic increase in counterregulatory hormones and metabolites. An “exercise smart” artificial pancreas (AP) must be capable of sensing glucose and perhaps other physiological responses to various types and intensities of PA. The emergence of this new technology may benefit active persons with T1D who are prone to hypo and hyperglycemia.
PMCID: PMC4667314  PMID: 26428933
exercise; artificial pancreas; metabolism; insulin
22.  The Glucose Measurement Industry and Hemoglobin A1c 
The MyStar Extra self-monitoring blood glucose (SMBG) system provides moving estimates of the patient’s hemoglobin A1c (HbA1c). There is a treasure trove of highly accurate glucose data available from highly accurate SMBG, CGM and FGM along with highly accurate HPLC HbA1c. If Nathan’s criteria are used to select subjects whose glucoses can be correlated to the HbA1c, then algorithms can be developed for robustly transforming glucose into HbA1c. These algorithms can then be implemented in any SMBG or with the CGM and FGM software. This calculated HbA1c would even be accurate with Nathan’s excluded population thus reducing the use of fructosamine and glycated protein. Finally, the developer of these new algorithms is advised to use a specific approach for testing her algorithm.
PMCID: PMC4738224  PMID: 26481643
hemoglobin A1c; glucose; calculated HbA1c; anemia
23.  Development of the Diabetes Technology Society Blood Glucose Monitor System Surveillance Protocol 
Inaccurate blood glucsoe monitoring systems (BGMSs) can lead to adverse health effects. The Diabetes Technology Society (DTS) Surveillance Program for cleared BGMSs is intended to protect people with diabetes from inaccurate, unreliable BGMS products that are currently on the market in the United States. The Surveillance Program will provide an independent assessment of the analytical performance of cleared BGMSs.
The DTS BGMS Surveillance Program Steering Committee included experts in glucose monitoring, surveillance testing, and regulatory science. Over one year, the committee engaged in meetings and teleconferences aiming to describe how to conduct BGMS surveillance studies in a scientifically sound manner that is in compliance with good clinical practice and all relevant regulations.
A clinical surveillance protocol was created that contains performance targets and analytical accuracy-testing studies with marketed BGMS products conducted by qualified clinical and laboratory sites. This protocol entitled “Protocol for the Diabetes Technology Society Blood Glucose Monitor System Surveillance Program” is attached as supplementary material.
This program is needed because currently once a BGMS product has been cleared for use by the FDA, no systematic postmarket Surveillance Program exists that can monitor analytical performance and detect potential problems. This protocol will allow identification of inaccurate and unreliable BGMSs currently available on the US market. The DTS Surveillance Program will provide BGMS manufacturers a benchmark to understand the postmarket analytical performance of their products. Furthermore, patients, health care professionals, payers, and regulatory agencies will be able to use the results of the study to make informed decisions to, respectively, select, prescribe, finance, and regulate BGMSs on the market.
PMCID: PMC5038526  PMID: 26481642
accuracy; blood glucose monitor; FDA; meter; protocol; surveillance
24.  Usability and Training Differences Between Two Personal Insulin Pumps 
The purpose of this study was to determine if there were usability and training differences between the Medtronic MiniMed Paradigm Revel™ Insulin Pump and the Tandem Diabetes Care t:slim® Insulin Pump during use by representative users, performing representative tasks, in a simulated use environment.
This study utilized a between-subjects experimental design with a total of 72 participants from 5 sites across the United States. Study participants were randomized to either the Revel pump group or the t:slim Pump group. Participants were 18 years of age or older and managed their diabetes using multiple daily insulin injections. Dependent variables included training time, training satisfaction, time on task, task failures, System Usability Scale (SUS) ratings, perceived task difficulty, and a pump survey that measured different aspects of the pumps and training sessions.
There was a statistically significant difference in training times and error rates between the t:slim and Revel groups. The training time difference represented a 27% reduction in time to train on the t:slim versus the Revel pump. There was a 65% reduction in participants’ use error rates between the t:slim and the Revel group. The t:slim Pump had statistically significant training and usability advantages over the Revel pump.
The reduction in training time may have been a result of an optimized information architecture, an intuitive navigational layout, and an easy-to-read screen. The reduction in use errors with the t:slim may have been a result of dynamic error handling and active confirmation screens, which may have prevented programming errors.
PMCID: PMC4604581  PMID: 25316715
human factors; psychology; research; engineering psychology; industrial psychology; user experience research; user-centered design; insulin pump; medical device safety; use error
25.  Toward Big Data Analytics 
Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies.
PMCID: PMC4738225  PMID: 26468133
diabetes management; diabetes complications; predictive models; machine learning

Results 1-25 (1723)