Mobile health: the power of wearables, sensors, and apps to transform clinical trials.

Mobile technology has become a ubiquitous part of everyday life, and the practical utility of mobile devices for improving human health is only now being realized. Wireless medical sensors, or mobile biosensors, are one such technology that is allowing the accumulation of real-time biometric data that may hold valuable clues for treating even some of the most devastating human diseases. From wearable gadgets to sophisticated implantable medical devices, the information retrieved from mobile technology has the potential to revolutionize how clinical research is conducted and how disease therapies are delivered in the coming years. Encompassing the fields of science and engineering, analytics, health care, business, and government, this report explores the promise that wearable biosensors, along with integrated mobile apps, hold for improving the quality of patient care and clinical outcomes. The discussion focuses on groundbreaking device innovation, data optimization and validation, commercial platform integration, clinical implementation and regulation, and the broad societal implications of using mobile health technologies.

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.

Model-based sensor-augmented pump therapy.

BACKGROUND: In insulin pump therapy, optimization of bolus and basal insulin dose settings is a challenge. We introduce a new algorithm that provides individualized basal rates and new carbohydrate ratio and correction factor recommendations. The algorithm utilizes a mathematical model of blood glucose (BG) as a function of carbohydrate intake and delivered insulin, which includes individualized parameters derived from sensor BG and insulin delivery data downloaded from a patient's pump. METHODS: A mathematical model of BG as a function of carbohydrate intake and delivered insulin was developed. The model includes fixed parameters and several individualized parameters derived from the subject's BG measurements and pump data. Performance of the new algorithm was assessed using n = 4 diabetic canine experiments over a 32 h duration. In addition, 10 in silico adults from the University of Virginia/Padova type 1 diabetes mellitus metabolic simulator were tested. RESULTS: The percentage of time in glucose range 80-180 mg/dl was 86%, 85%, 61%, and 30% using model-based therapy and [78%, 100%] (brackets denote multiple experiments conducted under the same therapy and animal model), [75%, 67%], 47%, and 86% for the control experiments for dogs 1 to 4, respectively. The BG measurements obtained in the simulation using our individualized algorithm were in 61-231 mg/dl min-max envelope, whereas use of the simulator's default treatment resulted in BG measurements 90-210 mg/dl min-max envelope. CONCLUSIONS: The study results demonstrate the potential of this method, which could serve as a platform for improving, facilitating, and standardizing insulin pump therapy based on a single download of data.

Continuous glucose monitoring considerations for the development of a closed-loop artificial pancreas system.

BACKGROUND: Commercialization of a closed-loop artificial pancreas system that employs continuous subcutaneous insulin infusion and interstitial fluid glucose sensing has been encumbered by state-of-the-art technology. Continuous glucose monitoring (CGM) devices with improved accuracy could significantly advance development efforts. However, the current accuracy of CGM devices might be adequate for closed-loop control. METHODS: The influence that known CGM limitations have on closed-loop control was investigated by integrating sources of sensor inaccuracy with the University of Virginia Padova Diabetes simulator. Non-glucose interference, physiological time lag and sensor error measurements, selected from 83 Enlite™ glucose sensor recordings with the Guardian® REAL-Time system, were used to modulate simulated plasma glucose signals. The effect of sensor accuracy on closed-loop controller performance was evaluated in silico, and contrasted with closed-loop clinical studies during the nocturnal control period. RESULTS: Based on n = 2472 reference points, a mean sensor error of 14% with physiological time lags of 3.28 ± 4.62 min (max 13.2 min) was calculated for simulation. Sensor bias reduced time in target for both simulation and clinical experiments. In simulation, additive error increased time <70 mg/dl and >180 mg/dl by 0.2% and 5.6%, respectively. In-clinic, the greatest low blood glucose index values (max = 5.9) corresponded to sensor performance. CONCLUSION: Sensors have sufficient accuracy for closed-loop control, however, algorithms are necessary to effectively calibrate and detect erroneous calibrations and failing sensors. Clinical closed-loop data suggest that control with a higher target of 140 mg/dl during the nocturnal period could significantly reduce the risk for hypoglycemia.

The pathway to the closed-loop artificial pancreas: research and commercial perspectives.

BACKGROUND: Over the past decades, insulin pumps (CSII) and continuous glucose monitoring (CGM) systems have been combined in sensor augmented pump therapy. In addition, artificial pancreas (AP) research has progressed to clinical studies, using combinations of commercially available devices. OBJECTIVE: Sensor augmented pump therapy has been evaluated in a number of clinical trials. These studies have been designed to show glycemic outcomes. The low glucose suspend (LGS) feature of the Medtronic Paradigm Veo (Medtronic MiniMed, Northridge, CA) pump allows CGM to suspend insulin delivery if preset hypoglycemic thresholds are achieved. Evaluation of the AP has been conducted to show effect on time in target, and has involved a variety of algorithms, pumps and CGM systems. RESULTS: Multiple studies have shown that more time is spent in the target range for glucose levels and that there is less hypoglycemia with sensor augmented pump, LGS and early AP platforms. CONCLUSIONS: Current research shows that progress has been made in the iterative steps required to develop the AP.

Accuracy of a new real-time continuous glucose monitoring algorithm.

BACKGROUND: Through minimally invasive sensor-based continuous glucose monitoring (CGM), individuals can manage their blood glucose (BG) levels more aggressively, thereby improving their hemoglobin A1c level, while reducing the risk of hypoglycemia. Tighter glycemic control through CGM, however, requires an accurate glucose sensor and calibration algorithm with increased performance at lower BG levels. METHODS: Sensor and BG measurements for 72 adult and adolescent subjects were obtained during the course of a 26-week multicenter study evaluating the efficacy of the Paradigm REAL-Time (PRT) sensor-augmented pump system (Medtronic Diabetes, Northridge, CA) in an outpatient setting. Subjects in the study arm performed at least four daily finger stick measurements. A retrospective analysis of the data set was performed to evaluate a new calibration algorithm utilized in the Paradigm Veo insulin pump (Medtronic Diabetes) and to compare these results to performance metrics calculated for the PRT. RESULTS: A total of N = 7193 PRT sensor downloads for 3 days of use, as well as 90,472 temporally and nonuniformly paired data points (sensor and meter values), were evaluated, with 5841 hypoglycemic and 15,851 hyperglycemic events detected through finger stick measurements. The Veo calibration algorithm decreased the overall mean absolute relative difference by greater than 0.25 to 15.89%, with hypoglycemia sensitivity increased from 54.9% in the PRT to 82.3% in the Veo (90.5% with predictive alerts); however, hyperglycemia sensitivity was decreased only marginally from 86% in the PRT to 81.7% in the Veo. CONCLUSIONS: The Veo calibration algorithm, with sensor error reduced significantly in the 40- to 120-mg/dl range, improves hypoglycemia detection, while retaining accuracy at high glucose levels.

Delays in minimally invasive continuous glucose monitoring devices: a review of current technology.

Through the use of enzymatic sensors-inserted subcutaneously in the abdomen or ex vivo by means of microdialysis fluid extraction-real-time minimally invasive continuous glucose monitoring (CGM) devices estimate blood glucose by measuring a patient's interstitial fluid (ISF) glucose concentration. Signals acquired from the interstitial space are subsequently calibrated with capillary blood glucose samples, a method that has raised certain questions regarding the effects of physiological time lags and of the duration of processing delays built into these devices. The time delay between a blood glucose reading and the value displayed by a continuous glucose monitor consists of the sum of the time lag between ISF and plasma glucose, in addition to the inherent electrochemical sensor delay due to the reaction process and any front-end signal processing delays required to produce smooth traces. Presented is a review of commercially available, minimally invasive continuous glucose monitors with manufacturer reported device delays. The data acquisition process for the Medtronic MiniMed (Northridge, CA) continuous glucose monitoring system-CGMS Gold-and the Guardian RT monitor is described with associated delays incurred for each processing step. Filter responses for each algorithm are examined using in vitro hypoglycemic and hyperglycemic clamps, as well as with an analysis of fast glucose excursions from a typical meal response. Results demonstrate that the digital filters used by each algorithm do not cause adverse effects to fast physiologic glucose excursions, although nonphysiologic signal characteristics can produce greater delays.

Putative delays in interstitial fluid (ISF) glucose kinetics can be attributed to the glucose sensing systems used to measure them rather than the delay in ISF glucose itself.

BACKGROUND: Since the advent of subcutaneous glucose sensors, there has been intense focus on characterizing the delay in the interstitial fluid (ISF) glucose response and the effect of insulin to alter the plasma-to-ISF glucose gradient. The Medtronic MiniMed continuous glucose monitoring system (CGMS) has often been used for this purpose; however, many of the studies have used experimental conditions that fall outside its intended use, for example, studies that have assessed the delay during rapid glucose excursions brought about by intravenous infusion of glucose or insulin. Under these conditions, it is possible that the rate of glucose change may exceed that allowed by CGMS filtering routines. If so, the estimated delay may be because of the filter rather than the ISF. Also, sensor characteristics, such as nonspecific offset current or stability, may have been inadvertently attributed to changes in the plasma-to-ISF gradient. The potential for these issues to have confounded the understanding of ISF glucose delay and gradient is investigated. METHODS: An in vitro preparation in which no delay or gradient exists between sensor and measurement solution was used to recreate a rapidly changing glucose profile from a previously published in vivo study. The CGMS system (N = 6 sensors) was then used to estimate any artifactual delay and gradient introduced by the system per se. RESULTS: One-point calibration resulted in an apparent change in gradient as glucose was lowered from approximately 100 to 50 mg/dl. After a two-point calibration, sensor glucose followed the glucose profile as it was decreased slowly from approximately 100 to approximately 60 mg/dl; however, when the glucose level was subsequently increased rapidly to approximately 150 mg/dl, CGMS filtering routines limited the rate of change of sensor glucose and introduced a delay similar to that previously attributed to ISF glucose equilibration delay. CONCLUSIONS: Studies that have previously used the Medtronic MiniMed CGMS system to assess changes in the plasma-to-ISF glucose gradient may need to be reassessed to ensure that the offset current was estimated accurately. Studies that have used the system to assess ISF glucose delay during rapid, unphysiologic changes in glucose and did not remove the CGMS smoothing filters may have attributed CGMS filter delay to ISF glucose equilibration.

Metabolic modelling and the closed-loop insulin delivery problem.

The approach used by Medtronic MiniMed to close the insulin delivery loop using the subcutaneous site for both glucose sensing and insulin delivery relies on modeling insulin action and beta-cell insulin secretion. This approach is contrasted with traditional control systems engineering.

Clinical experience with an integrated continuous glucose sensor/insulin pump platform: a feasibility study.

The recent US Food and Drug Administration approval of an integrated real-time continuous glucose monitoring (CGM) system and insulin pump (Medtronic Mini-Med Paradigm REAL-Time System; Medtronic MiniMed, Inc., Northridge, Calif) is the most recent breakthrough paving the way toward the development of a closedloop insulin delivery system that could revolutionize diabetes care. An early prototype of the MiniMed Paradigm REAL-Time System--which provided both realtime CGM and insulin pump therapy but was not yet fully integrated--was tested in 20 volunteer subjects with type 1 diabetes who wore the device for up to 2 y. Subjects were instructed on the technical use of the device but were provided no additional support, aside from their usual diabetes care. Participation in the trial averaged 317 d (minimum, 88 d; maximum, 618 d). Five participants prematurely dropped out of the study (2 because of the inconvenience of carrying 2 devices). Data from all subjects were analyzed. Subjects reduced their A1C by a mean of 1.1% (standard deviation, 0.8). After 3 mo of device use, the number of participants who achieved A1C <7% increased by more than 3-fold. Individuals with baseline A1C >or= 7% had a 67% likelihood of achieving an A1C <7% by the first follow-up assessment. This observational study of combined CGM and insulin pump therapy suggests that this innovative device can help people achieve improved glycemic control. Given that only about one third of individuals with diabetes are achieving glycemic control targets, more effective methods to help patients avoid the devastating consequences of uncontrolled diabetes are desperately needed.

The use of a continuous glucose monitoring system in hypoglycemic disorders.

OBJECTIVE: To evaluate the use of a continuous glucose monitoring system (CGMS) in the evaluation and treatment of infants and children with hypoglycemic disorders. METHODS: Patients with hypoglycemic disorders wore the CGMS device in the Pediatric Clinic Research Center during their evaluation and treatment. Capillary blood glucose (CBG) values were obtained at least 3 times each day and entered into the device for calibration purposes. We evaluated the number of hypoglycemic episodes below 3.3 mmol/l (60 mg/dl) detected by CGMS compared to CBG values and characterized episodes by their duration and intensity. RESULTS: Five patients with hypoglycemic disorders were included in the study. There were a total of 13,369 sensor points, 343 paired sensor and CBG data points, and 57 days included. A total of 180 episodes of hypoglycemia occurred in these five patients, with an average duration of 55 +/- 13 minutes. Using a cut-off of 3.3 mmol/l (60 mg/dl) for hypoglycemia, the sensor had a sensitivity of 65.4%, specificity of 90.6%, and false positive rate of 42.9%. The positive and negative predictive values were 57.1% and 93.2%, respectively. CONCLUSION: CGMS is a useful adjunct in the diagnosis and evaluation of hypoglycemia, and for documentation of euglycemia in these patients following therapy.