11th Annual Symposium on Self-Monitoring of Blood Glucose: April 12–14,2018,Oslo,Norway

State of type 1 diabetes

Despite the introduction of new technologies and therapies, recent data from the type 1 diabetes (T1D) Exchange study shows a worsening in glycated hemoglobin (HbA1c) across all age groups during the past 5 years among ∼30,000 T1D patients treated at 80 diabetes centers of excellence throughout the United States. Less than 30% of these patients are not achieving the American Diabetes Association (ADA) target of <7.0%, and two-thirds of patients are overweight/obese. Data also show a fivefold increase in the use of continuous glucose monitoring (CGM) in the United States. Within the T1D Exchange registry, use of CGM has shown improvements in glycemic control and reductions in severe hypoglycemia, regardless of insulin delivery method, insulin pump, or multiple daily insulin injections (MDI).

Going beyond HbA1c

A new focus on the utility of HbA1c is emerging. Although HbA1c provides important information for population health management and assesses risk for long-term complications, many believe we need to go beyond HbA1c when assessing individual patient's daily glucose control and therapy adjustments. Because HbA1c values can be misleading due to hematological conditions, disease states, physiological states, ethnic difference, and other factors, the U.S. Food and Drug Administration (FDA) and many medical organizations are now looking at other measurements of glycemic control, such as time in range (TIR), time below range (TBR) and time above range. ¹

Interest in using TIR is increasing due to the availability of CGM data. Recent data from Beck and colleagues shows that every 10% increase in TIR (70–180 mg/dL) improves HbA1c by 0.5%. In addition, we know that it only requires 14 days of CGM data to accurately estimate 3 months of glycemic control.

Insulin-requiring patients worldwide

Today, the number of insulin-requiring patients worldwide is estimated to be >100 million, with ∼30 million T1D patients and >70 million insulin-requiring type 2 diabetes (T2D) patients. This number is growing at an annual rate of 4%. Only ∼1% of individuals with diabetes are using an insulin pump due to a number of factors: cost, affordability, availability, knowledge, and implementation challenges. Uptake of closed-loop systems is even lower.

How accurate does CGM need to be for nonadjunctive therapy?

In silico simulations have shown that a mean absolute relative difference (MARD) from laboratory reference values is accurate enough to safely dose insulin without confirmatory finger-stick testing. ² The FDA recently created a 510(k) pathway that authorized the new Dexcom G6 CGM system (Dexcom, Inc., San Diego) for integration with insulin pumps and digital technologies (e.g., Smartphone apps). It has been predicted that, during the next 5 to 10 years, the majority of diabetes patients in Western Europe and the United States will be using CGM instead of self-monitoring of blood glucose (SMBG).

Reducing severe hypoglycemia

The recent DEVOTE study, which investigated use of insulin degludec in T2D, showed noninferiority to insulin glargine in cardiovascular (CV) death, with a significant 46% reduction in severe and nocturnal hypoglycemia in patients randomized to degludec compared with insulin glargine. ³ These findings were consistent with other studies and led to an important label change in the United States; for the first time, an insulin label includes safety data, stating that use of degludec reduces severe hypoglycemia.

Adjunctive therapy for T1D

Because the majority of people with T1D do not achieve target HbA1c, and many are now overweight/obese, it is important to find adjunctive therapies that help these individuals achieve a target HbA1c without any increase in weight. Sodium glucose cotransporter 2 (SGLT2) and sodium glucose cotransporter 1 (SGLT1) inhibitors, a relatively new class of oral medication approved for T2D, are now being studied in T1D patients. SGLT2 inhibitors decrease concentrations of plasma glucose by inhibiting proximal tubular reabsorption of glucose in the kidney. SGLT1 inhibitors improve glucose homeostasis by reducing dietary glucose absorption in the intestine and by increasing the release of gastrointestinal incretins, such as glucagon-like peptide-1 (GLP1), which blunts postprandial excursions.

The Tandem3 trial, a multicenter, randomized, double-blind, placebo-controlled trial, evaluated the safety and efficacy of sotagliflozin (SGLT2 inhibitor) in combination with insulin therapy (pump or injections) in patients with T1D. ⁴ Results showed almost a twofold increase in the number of patients that achieved the HbA1c target of <7.0%, without severe hypoglycemia or diabetic ketoacidosis (DKA). There were also significant reductions in weight, systolic blood pressure, and mean daily bolus dose insulin. Another study, DEPICT 1, investigated treatment with dapagliflozin (another SGLT2 inhibitor) and showed similar results. ⁵ However, in both studies, an increased risk of diabetic ketoacidosis in intervention patients was observed, with higher risks among patients using insulin pump therapy.

Inhaled insulin

Another therapeutic approach to improving glycemic control is inhaled insulin. Although earlier attempts to commercialize inhaled insulin (Exubera) were unsuccessful, the newest formulation, Technosphere insulin (Afrezza, Mannkind Corporation, Westlake Village) addresses many of the limitations of previous inhaled insulins. With its more rapid onset of action and faster return of action to baseline levels, treatment with Technosphere insulin has demonstrated significant reductions in postprandial glucose (PPG) excursions, with less hypoglycemia and reduced glycemic variability (GV). The effect is more significant when patients administer the insulin more than once at meals.

Summary

The prevalence and associated costs of diabetes are increasing. Nearly 500 million people, worldwide, have diabetes, and the total direct and indirect costs now exceed $1.25 trillion, annually. Moreover, recent data show that glycemic control in both T1D and T2D is worsening. On the positive side, new insulins, such as degludec, have been shown to reduce severe hypoglycemia, and improvements in PPG excursions with reduced hypoglycemia have been demonstrated with Technosphere insulin. Use of new oral medications, such as the SGLT2 inhibitors as adjunctive therapy are also promising, showing improvements in glycemic control, weight loss, and lowering of blood pressure. On the technology side, we are seeing progress in artificial pancreas (AP) development, with the launch of the first commercialized hybrid closed-loop system.

Advances in CGM technologies have led to a new generation of CGM systems, featuring greater accuracy, reliability, and convenience. Although many challenges must still be addressed, our expanding array of new therapies and technologies has the potential to improve diabetes control and reduce the burden of diabetes for patients, clinicians, and payers.

Background

Insulin resistance is a condition that is characterized by decreased tissue sensitivity to the insulin action, leading to a compensatory increase in insulin secretion. This metabolic dysfunction leads to a cluster of clinical syndromes, including: T2D, essential hypertension, polycystic ovary syndrome, nonalcoholic fatty liver disease, certain forms of cancer, sleep apnea, and cardiovascular disease (CVD).

Natural history of T2D

When individuals have insulin resistance, the body's normal response is to increase insulin secretion to maintain normal glucose tolerance. When the beta cell begins to fail, a slow increase in glucose levels begins, appearing first as postprandial hyperglycemia and subsequently as fasting hyperglycemia. The onset of diabetes occurs only after a significant decline in beta-cell function. Because the progression of diabetes is inevitable, most T2D patients will require treatment with insulin and/or oral medications to achieve and maintain an acceptable level of glycemic control. The risk of developing T2D is associated with lifestyle factors (e.g., sedentary, overweight/obesity) as well as genetic factors. If one parent has T2D, the risk for developing the disease is 30%. If both parents have T2D, the risk is 70%.

Methods for measuring insulin resistance

The gold standard for measuring insulin resistance is use of a hyperinsulinemic–euglycemic clamp. In this procedure, insulin is infused at a constant rate, resulting in a decrease in blood glucose levels. Exogenous glucose is infused into the venous circulation to maintain blood glucose at a constant level. The amount of glucose infused to maintain homeostasis is indicative of insulin sensitivity. Mathematical models (e.g., homeostatic model assessment-insulin resistance) and clinical proxies are also used for detection/diagnosis of insulin resistance. Clinical proxies include impaired fasting glucose (IFG), impaired glucose tolerance (IGT), prediabetes, and metabolic syndrome.

Diagnosis of T2D and prediabetes

T2D is diagnosed according to defined glycemic thresholds: fasting plasma glucose (≥126 mg/dL), 2-h plasma glucose (≥200 mg/dL) during an oral glucose tolerance test (OGTT), random glucose (≥200 mg/dL), or, most recently, elevated HbA1c (≥6.5%). These metrics are considered clinical cutpoints for T2D diagnosis. The thresholds for T2D diagnosis are based on findings from early studies showing a relationship between these cutpoints with diabetic retinopathy. However, a more recent study showed that diabetic retinopathy develops at lower glycemic levels. ⁶ Elevated fasting and PPG levels below the diabetes threshold are also associated with development of CVD, ⁷ and many diabetes complications are present at the time of diagnosis. Importantly, many people diagnosed with diabetes using HbA1c will not be diagnosed using traditional glucose criteria, and vice versa.

Prediabetes is also diagnosed according to defined glycemic thresholds: fasting plasma glucose (100–125 mg/dL), 2-h plasma glucose (140–199 mg/dL) during an OGTT, or elevated HbA1c (5.7%–6.4%). However, prediction of developing diabetes varies according to the type of test used. For example, an 11-year follow-up of patients with IGT showed that 46% developed T2D; whereas, only 38% patients with IFG developed the disease. ⁸ Among patients with HbA1c levels 5.5%–6.5%, 25%–50% developed T2D at 5 years. ⁹

Diabetes prevention

The goal of diabetes prevention is twofold: (1) prevent progression to diabetes; and (2) prevent development of complications in prediabetic patients. Two large trials showed that diabetes can be prevented through lifestyle modification (diet, exercise) and/or medication. The Finnish Diabetes Prevention Study (DPS) showed a 58% reduction in development of T2D in overweight IGT individuals treated with dietary intervention. ¹⁰

In the U.S. Diabetes Prevention Program, investigators reported a 58% risk reduction in overweight IGT patients treated with intensive lifestyle interventions, and a 31% risk reduction in patients treated with metformin. The mean weight loss in the lifestyle and metformin groups was ∼6.0 and ∼2.0 kg, respectively. ¹¹ However, the long-term follow-up (up to 15 years) showed that 50% of the intensive lifestyle group progressed to T2D, with no difference in the development of macrovascular and microvascular complications compared with the control group. ¹²

Treatment of prediabetes

Use of thiazolidinediones (pioglitazone) has been shown to reduce the incidence of progression to T2D compared with placebo; however, treatment was associated with weight gain, edema, and no reduction in CVD. The ORIGIN showed that early treatment with once-daily insulin glargine in patients with IFG, IGT, and early diabetes resulted in near-normal glycemic control, slowed progression of dysglycemia, with modest increases in hypoglycemia and weight, and neutral effects on (CV) outcomes and cancer. ¹³ A recent study of treatment with GLP1 receptor agonists for weight loss in 2487 overweight individuals without diabetes (40% normoglycemic, 60% prediabetes) showed significant reductions in weight regardless of glycemic status, with improvements in PPG excursions compared with placebo. ¹⁴

The Steno-2 studies showed that intensive, multifactorial treatment (glucose, lipids, blood pressure) in T2D with persistent microalbuminuria showed a 20% absolute risk reduction in CV events and mortality, with a prolonged lifespan. ¹⁵ Other studies have shown similar effects on CVD and other complications in patients with long-duration and early diabetes.

Recommendations for prevention or delay of T2D

Patients with prediabetes should be referred to an intensive diet and physical activity behavioral counseling program, targeting a loss of 7% of body weight, and increase their moderate physical activity to at least 150 min/week. Clinicians should offer follow-up counseling and maintenance programs for long-term success in preventing diabetes. Clinicians should also consider metformin therapy in those with prediabetes, especially for those with body mass index >35 kg/m ² , <60 years of age, and women with prior gestational diabetes. Patients with prediabetes should be monitored at least annually for the development of diabetes. Screening for and treatment of modifiable risk factors for CVD is suggested.

Conclusions

Prediabetes is associated with a number of clinical conditions, including T2D and CVD. Prediabetes and metabolic syndrome are both indicators of insulin resistance and serves as tools for early intervention. In the clinical setting, fasting glucose measurement or an OGTT can identify individuals with IFG or IGT. Treatment with metformin, GLP1 receptor agonists, pioglitazone, and insulin is effective in reducing the risk of progression to T2D in subjects with IGT/IFG, and can reduce features of the metabolic syndrome. However, because no specific treatment of insulin resistance is available, treatment should be tailored to address all presenting clinical features.

Background

The prevalence of obesity and metabolic disease is growing. Although data from 2014 show a leveling off of overweight adults, stark rises in obese and extremely obese adults have been observed. Current thinking in the area of public health attributes the rise in obesity to an energy imbalance between calories consumed and energy expended. As cited by the World Health Organization (WHO), there has been an increase in intake of energy-dense foods that are high in fat, and an increase in physical inactivity due to the increasingly sedentary nature of many forms of work, changing modes of transportation, and increasing urbanization.

One longstanding hypothesis to explain the obesity epidemic is that people in developed countries expend less energy each day than humans did in our “hunter–gatherer” past. However, this hypothesis conflicts with early studies that looked at the association between weight loss and exercise. Although exercise is critical to good health, the evidence supporting exercise as an intervention for weight loss is not promising. Whereas, short-duration studies of exercise interventions (e.g., 7 weeks) have shown weight loss, long-duration studies have found that the effect diminishes over time. Thus, we are left with the question: Do modern lifestyles burn less energy, and, if so, is this why obesity is on the rise?

Hadza energetics project

We investigated daily energy expenditure (DEE) and physical activity among traditional hunter–gatherers (Hadza tribe) in northern Tanzania, a population with high levels of daily physical activity. ¹⁶ Hadza men hunt game and gather honey, while Hadza women gather plant foods. Over 95% of their calories came from wild foods, including tubers, berries, small and large game, baobab fruit, and honey. It is important to note that hunter gatherer populations are extremely healthy, with no obesity, significantly lower body fat percentage, lower fasting glucose, and less incidence of the noncommunicable diseases (e.g., CVD, hypertension) affecting industrialized populations.

In the study, total DEE was measured using the doubly labeled water (DLW) method. DLW is water in which both the hydrogen and the oxygen have been partly or completely replaced with an uncommon isotope for tracing purposes. Daily walking distances and body composition were also measured.

We then compared energy expenditure and body composition among the Hadza to similar data from other populations taken from previous studies and new measurements of U.S. adults. Although we expected the Hadza to have lower body fat than individuals in Western populations, we found no difference between the daily expenditures of Hadza adults and those of adults in the United States and Europe. In other words, DEE did not correspond with daily physical activity. Subsequent studies of mixed farming–hunter–gatherer groups in the Amazon basin confirm this finding.

Implications

These findings suggest a new model for understanding the relationship between daily physical activity and energy expenditure. Our hypothesis is that the human body adapts to increased physical activity to keep the DEE in check, making trade-offs with other expenditures. Interestingly, energy expenditure in people with no physical activity is much higher than expected. We are currently working on a model—Constrained DEE—to begin to explain the mechanisms of metabolic adaptation.

Conclusions

Our findings do nothing to diminish the importance of exercise in promoting health. However, the adaptive nature of our metabolism, resisting increased expenditure even as activity is increased, means that diet (specifically calorie intake) is a more promising target for weight loss than is exercise. Increased daily activity will have only transient effects on total daily expenditure (and weight loss) until the body adapts to the new workload. Rather than treating them as interchangeable, we should promote diet and exercise as two different tools for two different jobs: exercise for health, diet for weight.

Background

Automation and control are integral parts of our day-to-day life. Smart devices can be found in home appliances, automobiles, aviation, and other areas with different degrees of automation to improve the safety and quality of life (QoL). As in other aspects of life, the principles of automation and control can be used in medical devices and in the management of T1D.

The dream of an automated glucose control system has been the focus of research and development for the last four decades. In 2006, the Juvenile Diabetes Research Foundation (JDRF) launched the Artificial Pancreas Project. Over the past decade, improvements in glucose sensing technology, insulin delivery, and communication, together with advances in control and systems engineering, have made the dream of an AP possible. Today, we have the first commercial hybrid closed-loop system—Medtronic 670G.

Goals and expectations

Current medical guidelines define ranges of blood glucose values that minimize long-term complications of diabetes; however, additional glycemic goals (e.g., time below, within and above target range) are now being discussed. Although the primary expectations for AP are reduced time in hypoglycemia and improved glycemic control over time, we understand that with current insulins and devices, AP will not restore perfect glucose control. The hope is that patients will be able to spend less time on diabetes care.

Current AP algorithms

There are three main types of AP algorithms being used in research and industry development. Proportional Integral Derivative algorithms try to minimize errors above the set point. Fuzzy Logic Controllers utilize “if-then” rule-based systems to achieve glycemic control. Model Predictive Controllers use models that predict the future based on past decisions, and then make changes based on predictive computations.

Efficacy and safety

One of the first AP studies looked at pediatric/adolescent patients at a diabetes camp who were treated with an AP system. ¹⁷ Use of the AP system resulted in less nocturnal hypoglycemia and tighter glucose control compared with use of a sensor-augmented insulin pump. Two 12-week, multicenter, crossover, randomized studies that assessed home use of an AP system by adults and pediatric/adolescent T1D patients showed improved control and reduced hypoglycemia among all AP users, and, in adults, resulted in lower HbA1c. ¹⁸ In a study by Garg et al., which assessed the safety and effectiveness of a commercial hybrid closed-loop system (Medtronic 670G system), investigators reported significant improvements in HbA1c (from 7.4% to 6.9%), a 44% reduction in time spent with low blood glucose (<70 mg/dL), and a 40% decline in time spent in dangerous hypoglycemia (<50 mg/dL). ¹⁹

A more recent trial utilized cloud-based, algorithmic adaptation of basal rate and carbohydrate ratio profiles in 30 adults with well-controlled T1D (7.0% HbA1c at baseline) to assess long-term improvement in glycemic control by change in HbA1c and time in euglycemic target range. ²⁰ After 12 weeks, HbA1c was reduced to 6.7% with reduced time in hypoglycemia.

Analysis of recent AP studies suggest that current AP devices can achieve significantly improved glycemic control with ∼2% of time spent at <70 mg/dL and ∼75% of time spent in target range. In studies of newer systems now under review, we believe we can achieve 88%–90% TIR with even greater reductions in TBR. Future improvements will involve transitioning AP development out of academia and into commercial development. Several companies are now involved in these efforts.

Future trajectory of AP

Several unresolved challenges in current AP systems remain: addressing unannounced meal and physical activity; reducing the on-body area footprint; power management; and patient-centric precision medicine. One way to address this is “thinking out of the box.” In a recent pilot study, we compared intraperitoneal (IP) to subcutaneous (SC) insulin delivery in an AP system and found that glycemic regulation with fully automated AP delivering IP insulin was superior to that with SC insulin delivery. ²¹ Fully implanted AP systems may also be an option.

Other modalities may include integration with different devices and software systems (e.g., diabetes management apps, activity trackers), which interact with the AP device. These “context-aware” systems would lead to a “resource-aware” design of algorithms and, in turn, continuous monitoring and “anytime” intervention. The paradigm of diabetes management is shifting from classical healthcare to modern healthcare, utilizing an on-body ecosystem, a collection of wearable sensors and actuators that exploit connectively to promote health and wellness.

Background

Exercise is a pillar of diabetes management. Routine exercise confers significant benefits in terms of improved glycemic control, CV and metabolic health, and psychological wellbeing. Current ADA guidelines recommend that individuals with diabetes exercise at least 150 min/week. However, many of these individuals are unable to achieve the current recommendations for weekly exercise and physical activity.

Barriers to exercise

An important barrier for individuals with T1D is the impact of exercise on blood glucose levels. The unpredictability of glycemia during exercise can lead to fear of hypoglycemia and hyperglycemia, resulting in a reluctance to engage in physical activity. High-level athletes are concerned about the effects of blood glucose levels on performance. Lack of knowledge among patients, teachers, coaches, and healthcare professionals about how to exercise safely also creates barriers. Psychosocial factors not related to T1D (e.g., time, resources, motivation) can also inhibit exercise.

Effects of exercise on blood glucose levels

To help patients implement effective strategies for safe exercise, healthcare professionals must first understand the basic physiology and effect of exercise on the body. When a person starts to exercise, insulin levels drop and the glucagon levels rise. As the glucagon–insulin ratio changes, the liver releases glucose to match muscle glucose consumption, which may prompt an increase in catecholamine levels. In nondiabetic individuals or those with well-managed diabetes, the insulin level allows glucose to exit the liver at a rate that matches muscle glucose consumption. However, when insulin levels do not drop, the liver fails to produce adequate glucose and the muscle consumes more glucose, resulting in hypoglycemia. In situations where insulin levels are too low or catecholamine levels are elevated at the start of exercise, the liver produces too much glucose, resulting in hyperglycemia.

It is also important to consider that different forms of exercise have different effects on glucose in individuals with T1D. Continuous light-to-moderate aerobic exercise generally lowers glucose during the activity. With moderate-to-vigorous intermittent aerobic exercise or resistance training, glucose levels can either rise or fall. Anaerobic exercise, such as sprinting or other types of interval training, can cause glucose to rise and stay elevated for an extended period of time.

Strategies for managing diabetes during exercise

Exercise management for people with T1D is complex, and one approach does not fit all. Therefore, healthcare professionals need to obtain and address some basic information and issues when counseling patients about their exercise regimens. First, it is important to understand the individual's specific goal for exercise. Is he/she exercising to maintain weight or to improve athletic performance? If maintaining weight is the goal, then increasing insulin may not be the best approach. One also needs to know the type, duration, and intensity of the exercise. Healthcare professionals need to make sure that the individual understands the effects of these modalities.

It is also important to know under what conditions the exercise will take place. Will it occur when basal insulin is dominant (basal condition) or within 3 h of a meal bolus (bolus condition)? Finally, one needs to know whether the exercise is planned or unplanned. If it is planned, the individual can take appropriate steps before exercising (e.g., monitor glucose, adjust basal or bolus dosages, modify carbohydrate intake). If it is unplanned, the individual will need to increase his/her monitoring and be prepared to address rising or falling glucose.

Resources

When counseling patients, the goal is to formulate a person-specific exercise management plan. The key is to ensure that the recommendations are individualized and dynamic, with the use of feedback plans that are revisited and refined as needed. Recent consensus guidelines from Riddell et al. provide an up-to-date consensus on exercise management for individuals with T1D who exercise regularly, including glucose targets for safe and effective exercise, and nutritional and insulin dose adjustments. ²² Earlier recommendations by Pivovarov et al. present a new exercise management algorithm for insulin and carbohydrate intake strategies for active youth with T1D. ²³

Summary

Regular physical activity is generally recommended because of the numerous clinical, physical, and psychological benefits it provides. However, many individuals with T1D do not achieve recommendations for activity for a variety of reasons. Healthcare professionals can play a key role in assisting individuals to overcome the barriers to exercise and achieve the benefits of an active lifestyle.

Background

An ideal insulin replacement therapy should mimic the body's physiologic secretion of prandial and basal insulin. Although insulin analogs introduced in the 1990s provided significant improvements over the earlier human regular and animal-source insulins, there are limitations to their efficacy and safety. Even with the faster onset of the rapid-acting analogs, many patients still experience significant postprandial excursions, and excessive hypoglycemia remains an issue. This presentation reviews the new-generation prandial and basal insulin, and discusses the clinical implications of these formulations.

Improving prandial insulin delivery

New ultrafast insulins are approaching a more physiologic profile in managing PPG excursions in T1D, and in replacing early insulin secretion in T2D. We also have now novel insulin formulations, new ways of delivering insulin, such as use of a local heating at the injection site, intradermal insulin or inhaled insulin, and maybe in the future injectable “smart” insulins.

Faster-acting insulin aspart (FIAsp) is one of the newest insulin formulations. The onset of FIAsp appearance in the blood stream has been shown to be twice as fast as insulin aspart, with twofold higher insulin exposure and 74% greater insulin action within the first 30 min after injection. ²⁴

The Onset clinical program explored the benefits of FIAsp therapy in T1D and T2D compared with insulin aspart. In the 52-week Onset1 trial, T1D patients randomized to FIAsp showed improved overall glycemic control with significantly lower 1- and 2-h postprandial excursions. ²⁵ It should be noted that bolus dosage calculations were based on meal carbohydrate count or with a simple titration algorithm based on the next preprandial glucose target (71–108 mg/dL). Additional analysis showed that the largest HbA1c improvement was seen among FIAsp-treated patients who utilized carbohydrate counting in their prandial dosage calculations. The Onset2 trial demonstrated that FIAsp was noninferior to insulin aspart in reducing HbA1c in poorly controlled T2D patients. ²⁶ FIAsp treatment showed improved 1-h PPG excursions with no differences in 2- to 4-h PPG versus insulin aspart.

Advances in basal insulin replacement

Key characteristics of an optimal basal insulin include: longer duration of action (≥24 h); flat action-time profile to lower the risk of hypoglycemia; and less day-to-day variability, which provides the potential to lower fasting glucose targets without hypoglycemia. Two new long-acting analogs are commercially available: insulin degludec and insulin glargine U300.

Clamp studies of insulin degludec pharmacokinetics at various dosages show a mean half-life of 25.4 h, more than twice that of insulin glargine, with a completely flat profile over 24 h. Large clinical trials have shown significantly lower rates of hypoglycemia with insulin degludec compared with insulin glargine. Importantly, use of extreme dosing intervals of 8–40 h demonstrates that the daily injection time of insulin degludec can be varied without compromising glycemic control or safety.

Insulin glargine U300 is three times more concentrated than the U100 formulation. Studies with CGM in T1D patients have shown improved glycemic control, less variability, and reduced nocturnal and/or severe hypoglycemia with insulin glargine U300 versus U100. Similar reductions in hypoglycemia have been demonstrated in T2D patients regardless of dosing regimen (fixed or flexible dosing).

Clinical implications in current clinical practice

Regarding prandial insulin, it has been shown that the optimal time for injection/infusion of prandial insulin is 15–20 min before the meal. However, a recent study of ∼900 T1D and T2D patients found that only 50%–60% follow this recommendation. Therefore, it is important that clinicians provide appropriate counseling to patients regarding the timing of insulin administration. Moreover, titration of ultra-rapid-acting insulin analogs should be based more in PPG values rather than preprandial targets. The increasing use of CGM (real-time and intermittent) will help patients adjust their dosages; however, both the current glucose and trend arrows should be considered. For MDI-treated patients, new mechanical, wearable, bolus-only insulin delivery devices (e.g., Calibra Finesse^®) have the potential to alleviate the burden of repeated insulin injections; insulin pumps are not necessary for every patient.

For the new basal insulins, the titration algorithm should be adapted considering the long duration of these insulins and based on fasting plasma glucose. A once-daily injection schedule should improve compliance. It is important to adjust dosages when transferring patients from old analogs (glargine U100) to new basal insulin analogs: use less insulin with degludec (∼↓10%–15%) and more with glargine U300 (∼↑10%–15%).

Conclusions

New technologies, such as real-time or intermittent CGM, have shown that current rapid-acting insulin analogs are not as quick as originally thought to control excessive glucose excursions. Ultrafast-acting insulin analogs have been developed to fill the gap and can be used optimally in MDI, with insulin pumps, and in future, in closed-loop systems. However, we still need to learn how to better use the new generation of prandial insulins to translate their pharmacological advantages into clinical benefits. We need to pay more attention to the PPG excursions and teach our patients to improve them without increasing hypoglycemia risk. The use of new-generation basal insulins will reduce hypoglycemia risk and increase compliance, especially in T2DM. Finally, despite our traditional consideration of insulin pump therapy as the “gold standard,” not all patients benefit from an insulin pump; even less from sensor-augmented pump therapy.

Background

Many companies now incorporate blood glucose value interpretation tools into their monitoring systems. Some systems utilize simple two- or three-color bar configurations to indicate within-range and out-of-range glucose values; others utilize basic interfaces, such as “smiley” icons, to inform users. The utility of these support tools in helping patients accurately interpret that their blood glucose testing result has not been well studied.

Use-Lab GmbH is an international, independent consultant for medical device manufacturers, and is active in the field of development and optimization of usability concepts for medical products. We evaluated the utility and perceived benefits of the target range indicator (TRI), integrated in the Accu-Chek^® Instant Blood Glucose Meter, compared with two different support tool configurations featured in other blood glucose monitoring systems. ²⁷

Accu-Chek instant blood glucose meter with integrated TRI

The TRI is a proprietary, on-meter support tool that assists users in interpreting their blood glucose test results. The TRI assesses blood glucose values against a nine-point scale with three categories of high glucose (high, very high, extremely high) and low glucose (low, very low, extremely low) results.

Study design/methods

This single-center, three-arm, randomized, simulation study assessed data sets from 140 individuals with T1D (n = 17) and T2D (n = 123). The T1D and T2D cohorts were well balanced with regard to age, gender, and insulin therapy.

In round 1 of the first part of the study, participants were asked to categorize 50 blood glucose values on a seven-point scale (extremely low, very low, low, in range, high, very high, and extremely high). Participants were asked to rate their confidence in their responses after every 15th, 30th, and 50th blood glucose value. In round 2, participants were asked to categorize 50 additional blood glucose values, this time using one of three support tools—TRI, a three-step color bar scale (Colors) or smiley icon (Smileys). The Colors tool presented glucose assessments as red for high values, blue for low values, and green for in-range values. The Smileys tool presented glucose assessments as “happy face” for in-range values and “sad face” for high or low values. Participant confidence was again assessed every 15th, 30th, and 50th glucose value.

During the second part of their sessions, participants were shown the two support tools they had not worked with and were interviewed about their impressions of the support and how they perceived the utility of the tools. Participants were asked to rate statements about how they perceived each tool on a five-point scale (disagree, somewhat disagree, neither agree nor disagree, somewhat agree, agree); responses of somewhat agree and agree were grouped as agreement.

Results

Conclusions

Our study showed that current support tools can help individuals with diabetes interpret their blood glucose test results with greater accuracy and confidence. However, accuracy and confidence was notably higher among participants who used the TRI tool. Moreover, a larger percentage of TRI users indicated positive perceptions regarding the benefits and utility of the tool as it relates to their own diabetes management. This finding is important because persons who believe that a support tool will help them may be more likely to use that tool and, thus, achieve better outcomes.

Background

The Eversense^® CGM system is the first insertable, long-term continuous monitoring system. The system is currently approved in Europe for up to 180 days of use. In the United States, the system was recently approved by the FDA with an indication for adjunctive use with blood glucose confirmatory testing and up to 90 days wear time.

Eversense system

The Eversense CGM system consists of a small, fully implantable glucose sensor, a battery-powered transmitter that is worn externally over the sensor, and a Mobile Medical Application (MMA) or app that displays real-time readings—current glucose, glucose trend, and trend arrows—every 5 min on a mobile device (e.g., iOS or Android devices). The transmitter also provides on-body vibrations that alert users to immediate and impending hypoglycemia and hyperglycemia. Data are continuously transferred to the MMA or app through secured low-energy Bluetooth transmission, which allows users to review current and historical glucose data in real-time and enter insulin dosages, meals, and other relevant information.

The system includes several other unique features, such as on-body vibratory notifications that function independently of the MMA, a gentle silicone-based adhesive patch to secure the transmitter (changed daily) and no acetaminophen interference. The Eversense NOW companion application allows patients to share their data with up to five individuals providing an additional layer of security.

Technology

Unlike other CGM systems that utilize an enzymatic reaction to measure glucose levels in the interstitial fluid, the Eversense system uses a novel fluorescence-based glucose measuring technology that provides accurate glucose values between 40 and 400 mg/dL in interstitial fluid.

Glucose is measured through fluorescence from the glucose-indicating hydrogel, which is polymerized onto the sensor capsule surface over the optical cavity. The indicator reversibly binds to glucose in an equilibrium reaction; no chemicals are consumed, and no by-products are formed.

The optical system within the capsule consists of a light-emitting diode; two spectrally filtered photodiodes, which measure the glucose-dependent fluorescence and reference intensities; and an antenna, which receives power from and communicates with the smart transmitter. The rechargeable smart transmitter remotely powers and communicates with the inserted sensor to initiate and receive the measurements, which are sent to the MMA every 5 min.

Insertion procedure

The sensor is inserted into the SC space in the upper arm using an aseptic technique. After the skin is cleaned and anesthetized, a small incision is made in the upper arm. A blunt dissector is used to create a SC pocket, the sensor is transferred to the pocket, and the incision site is closed using sutures or adhesive strips. In clinical trials, the timeframe for insertion was ∼2.5 min; ∼4.5 min for removal. The procedure for removal is similar.

Evidence from clinical trials

Multicenter, pivotal trial data have demonstrated highly acceptable safety and accuracy results. In the PRECISE study (71 adults, 180 days unblinded use), accuracy results showed overall MARD of 11.6% with no serious adverse events, two minor infections, and two mild skin reactions. ²⁸ For the PRECISE II trial (90 adults, 90 days blinded use), both the sensor and algorithm were modified, resulting in much greater accuracy with an overall MARD of 8.8%, one serious adverse event, and no infections or skin irritation. ²⁹

Real-world data from the European postmarket registry continue to reinforce that endocrinologists can be readily taught the insertion and removal procedures. The safety profile is maintained with an infection rate of <1%, and all other adverse events are minor and self-limited. Adherence to CGM with wear times of 80% or more for the entire 90 days have been correlated to improved glycemic control and meaningful decreases in the percentage of time spent in hypoglycemia.

Future clinical plans

Senseonics is currently conducting a ≥180-day wear time trial in the United States. After obtaining the 180-day indication, the company will initiate a pediatric clinical trial. A longitudinal study, involving 324 adults, is now being conducted in France for reimbursement purposes. Investigator-initiated studies are also under discussion, and the sensor will be included in two AP studies.

Background

Tobias Schulte is 37 years old, married with a young son, and has been working for a German airline for the past 19 years. His hobbies are long-distance running and Triathlon competitions. To date, he has participated in 90 marathons in 31 countries and 3 Iron Man events. Mr. Schulte was diagnosed with T1D in 2016 and uses MDI therapy to manage his diabetes. He is now on his fifth Eversense CGM sensor. In this presentation, Mr. Schulte shared his experiences with the system.

Initial T1D diagnosis

I first became aware of my diabetes during an upcoming Iron Man competition. I was shedding weight and exhibiting common symptoms of the disease. On January 11, 2016, I was diagnosed at the University Hospital in Frankfurt, Germany, with an HbA1c of 12.8%. At that time, even walking from my hospital bed to the cafeteria was a challenge. Concerned about my ability to continue my competitions, I consulted my physician, who encouraged me to begin training for an Iron Man event that would take place in 6 months. Although I was uncertain about how I could do this, my diabetes team was extremely supportive and worked me to make it happen. Six months later, after having gained 29 pounds, and with an HbA1c value of 5.8%, I completed the Iron Man in 12 h and 3 min.

My first Eversense: the beginning of a beautiful friendship

On December 19, 2016, I received my first Eversense sensor implantation. In January 2017, I ran my first marathon with the Eversense sensor, which guided me throughout the course of the race. Four hours later, on the trip home, I wrote a long report to Roche Diabetes Care. I quickly obtained the new Eversense transmitter (which is waterproof), and, 2 months later, ran another marathon in Seoul, South Korea, where I achieved a new T1D marathon personal best. Using the Eversense sensor was no longer about simply competing safely and in good health, but about aspiring to attain ambitious running times.

What differentiates Eversense from others

An important feature of the Eversense system is the alarm function. Knowing that the alarm will wake me to low glucose during the night allows me to tightly manage my glucose levels and maintain an optimal HbA1c. The ability to easily monitor my glucose levels while driving and during other daily activities by simply glancing at my watch or receiver (mounted on my bicycle) adds even greater convenience to my diabetes management. The ability to quickly detach and reattach the transmitter for recharging provides additional flexibility and freedom.

Running a marathon is an exceptional situation

Although I carry a blood glucose meter during marathons, I have yet to use it. With the Eversense system, I can monitor my glucose, speed, mileage, and pulse on my iWatch as I run. The system allows me to set warning levels in the Eversense app to receive alerts when my glucose levels are changing rapidly. This allows me to take corrective action (e.g., eat, drink) before a low glucose occurs without interrupting the run. When comparing my marathon performance before my T1D diagnosis and today, I perceive little difference.

Key attributes

The Eversense app as is user friendly and includes all the essentials; entries can be made easily, reports are available quickly, and users can individualize basic settings. Calibration (done twice daily) is straightforward and allows me to select calibration times that are convenient. The sensor is for 90-day wear time in the United States, and 180-day wear time outside the United States.

Summary

Wearing the Eversense sensor has changed the way people perceive me. My running group and business colleagues have told me that they feel more comfortable with my diabetes. At home, it has helped minimize my mood swings due to hypoglycemia, which improves our family life. In summary, the Eversense system has given me the highest level of security, comfort, and freedom, both in everyday life and in competitive sports. It has had a positive effect on my mood and my environment. The days living without the system between sensor changes truly brings home what is missing.

Background

CGM is transforming diabetes management, step by step. The technology has advanced dramatically in the last decade, and we now have CGM systems that are very accurate, can be inserted every few weeks subcutaneously or implanted for multiple months, and they come with or without alarms. The data can be viewed real-time or retrospectively. Both of these forms of data use can assist significantly in diabetes management and patient peace of mind.

Standardizing CGM metrics

International standardization for key CGM metrics and glucose profile visualization is an important component of effective CGM data utilization in clinical decision making. There have been ∼10 expert consensus meetings on standardizing CGM metrics/visualization since 2013. The last three consensus groups agreed on a core set of 14 key metrics and a suggestion of how to visualize CGM data. ^1,30,31

Among the identified metrics were: TIRs (within, above, below), GV, and adequate data collection period. Although there are no studies from which to draw specific cutpoints for TIRs, data from closed-loop studies suggest the following: <1% time spent <54 mg/dL; <2% time spent <70 mg/dL; >70% time within 70–180 mg/dL; <20% time >180 mg/dL; and <5% time >250 mg/dL. The suggested cutpoint for GV was 36%; ≤ 36% is considered stable, >36% is considered unstable. Two weeks of CGM data are considered adequate to establish a reliable glucose profile.

The consensus recommendations also include use of the ambulatory glucose profile (AGP) to visualize glucose data. This approach was previously endorsed by an expert panel of clinicians in 2012. ³² Many of the CGM device companies now include variations of the AGP format in their download software.

Professional versus personal use

CGM data can be utilized two ways: professional analysis of retrospective data; and personal CGM for daily decision making. Use of retrospective data facilitates more informed treatment decision making (e.g., insulin adjustments, additional medications). Moreover, visualization of the data supports greater patient understanding and engagement, leading to more collaborative patient–clinician interactions. Patients should be encouraged to periodically analyze their data.

Personal CGM use involves patients actively using their CGM data (current glucose, trend arrows) in combination with their insulin parameters to make insulin dosing decisions to achieve desired glycemic control and prevent or mitigate acute events, such as hypoglycemia and hyperglycemia. However, when patients become proficient in reacting to their immediate data, they seldom think to look back on their previous data and analyze the patterns, which may indicate a need to adjust their basal dosages and/or insulin parameters. Patients should be encouraged to perform their own retrospective analyses, periodically.

Clinical care versus research

For clinical purposes, the new metrics may not be as important as the visualized glucose profile. The patterns revealed in the profiles often provide adequate information for glycemic assessment and treatment adjustments. However, now that standardized metrics are available, it is important that researchers begin to use these metrics in their studies. This will allow us to more definitively assess the efficacy and safety of medications. Currently, most studies only report severe hypoglycemia—events requiring medical intervention; however, they do not tell us how much time is spent <54 mg/dL, an important indicator of risk. The challenge now is to get regulatory agencies comfortable with these new metrics and include them in their protocol requirements.

Conclusions

CGM has the potential to transform diabetes management. With accurate and reliable CGM systems and a standardized way to describe and present the data, we can now continue to demonstrate how CGM can improve T1D and T2D management.

Background

CVD places a heavy burden on people with diabetes, and these diseases are among the most frequent causes of morbidity and premature morality among patients with T2D. Although it remains to be proven beyond doubt that good glycemic control can reduce this burden, new glucose-lowering medications are now being evaluated in CV outcome trials to assess their potential benefits.

Effect of intensive glycemic control

The United Kingdom Prospective Diabetes Study (UKPDS) demonstrated that intensive glucose control reduced microvascular complications of T2D, but reductions in macrovascular events were insignificant. A 10-year, observational follow-up analysis later showed a significant reduction in both total mortality and the incidence of myocardial infarction in the group that had been intensively treated; however, these observational data did not provide a final proof of the glucose-lowering effects on CV outcomes. Subsequent trials (ACCORD, ADVANCE, and VADT) also failed to prove a CV benefit of intensive glucose lowering.

Class effects of diabetes newer medications on CVD

Several randomized controlled studies have evaluated new medications from the pharmacological groups of SGLT2 inhibitors, GLP1 agonists, and dipeptidyl peptidase-4 (DPP4) inhibitors on CV safety and efficacy. Although most of these studies were designed to demonstrate safety and noninferiority compared with conventional agents, two of the trials (EMPA-REG, LEADER) showed a CVD benefit. The EMPA-REG study showed that T2D patients at high risk for CV events, who received treatment with empagliflozin (SGLT2-inhibitor), had a lower rate of a combined endpoint of death from CV causes, nonfatal myocardial infarction, or nonfatal stroke than those treated with placebo.

In studies of high-risk patients treated with GLP1 agonists, similar reductions in CVD events were observed with some agents (liraglutide, semaglutide), but only noninferiority with others. Because all of the earlier studies mainly included patients with established CVD, two questions remain: are the results valid for patients without CVD, and are the results valid for other pharmacological agents in their respective group (DPP4, GLP1, and SGLT2)?

CVD-REAL study

The CVD-REAL study compared hospitalization for heart failure (HHF) and death in patients newly initiated on any SGLT2-inhibitor versus other glucose-lowering drugs (oGLD) in six countries to determine if these benefits are seen in real-world practice, and across SGLT2 inhibitor class. ³³ After propensity score matching (1:1), 309,056 patients newly initiated on either SGLT2 inhibitor or oGLD were identified, canagliflozin, dapagliflozin, and empagliflozin accounted for 53%, 42%, and 5% of the total exposure time in the SGLT2 inhibitor treatment group. Investigators reported significant reductions in HHF and all-cause death among patients treated with an SGLT2 inhibitor compared with oGLD therapy. Importantly, there were no signs of significant heterogeneity across the countries, suggesting that the CV benefits observed are likely class related.

The CVD-REAL Nordic study was an observational analysis of individual patient-level data from patient registries in Denmark, Norway, and Sweden. ³⁴ Patients were propensity score matched (1:3) and divided into new users of SGLT2-inhibitors (n = 22,830) and new users of oGLD (n = 68,490). Analyses showed that SGLT2 inhibitor treatment was associated with reduced CVD and CV mortality with a trend toward reduced severe hypoglycemia compared with use of oGLD among T2D patients across a broad CV risk profile.

Further analyses were conducted to compare dapagliflozin (SGLT2 inhibitor) with DPP4 inhibitors regarding risk associations with major adverse CV events in 40,908 T2D patients. ³⁵ These analyses showed that dapagliflozin was associated with lower risks of CV events and all‐cause mortality compared with DPP4-inhibitors within a broad T2D population.

Conclusion

Recent CV outcome trials and observational studies have provided important new knowledge to the practicing physician about the treatment of T2D. Whereas, SGLT2 inhibitors seem to confer similar CV benefits among all T2D patients, and there appears to be heterogeneity within the GLP1 agonist class of drugs. Furthermore, it remains to be seen if the combination of SGLT2 inhibitor and GLP1 agonist is superior to monotherapy with each. Additional findings from ongoing trials that will enable further important improvements in the treatment of T2D are expected in the years to come.

Background

The modern era is marked by an unprecedented epidemic of obesity and diabetes, and it is widely accepted that nutrition is one of its key drivers. Our group has taken a unique approach to studying nutrition, by looking at postprandial glycemic responses to different foods. These responses affect fat storage, hunger, and weight gain, and are associated with obesity, diabetes, CVD, and other chronic metabolic disorders. Our approach therefore provides us with direct measurement and immediate feedback regarding meal effects, contrary to typical primary outcomes in nutritional studies, such as body weight. Maintaining normal blood glucose levels is key to fighting the rise in metabolic disease, but this may not be easy to achieve, as food that induces a high postprandial glucose response (PPGR) in one person may induce a low response in another.

Microbiome

A major source of variability across people is the human microbiome, the collection of over 100 trillion microbes with unique and diverse metabolic capabilities that each person carries in the gut and other body locations. The microbiome is affected by what we eat and, in turn, affects our responses to food. It is also associated with multiple chronic and complex diseases.

Personalized nutrition by prediction of glycemic responses

We conducted a large-scale study to quantitatively measure individualized PPGRs, characterize their variability across people, and identify factors associated with this variability within a cohort of 800 healthy and prediabetic individuals, 20–70 years of age, who were representative of the Israeli population. ³⁶ To measure PPGRs, participants were fitted with a CGM device for 1 week and provided a cell phone app for recording lifestyle events (meal times and components, exercise, sleep, etc.). Potential determinants of interpersonal variability were also assessed, using various measurements: blood tests, lifestyle/medical questionnaires, anthropometrics, and stool samples to profile gut microbiome.

Overall, we obtained more than 1.5 million glucose measurements, data for over 50,000 meals, and metagenomics sequencing for over 1000 stool samples. Our results showed low intrapersonal variability but high interpersonal variability in the glycemic responses of different people to identical meals, suggesting that universal dietary recommendations may have limited efficacy. ^36,37

We then investigated whether personalized PPGRs could be predicted, using a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota. Utilizing a two-phase approach, we first developed the algorithm on the main cohort of 800 participants. Performance was evaluated using a standard leave-one-out crossvalidation scheme, whereby PPGRs of each participant were predicted, using a model trained on the data of all other participants. In the validation phase, we assessed the accuracy of our model on an additional independent cohort of 100 participants. Their PPGRs were predicted using the model trained only on the main cohort. Our results showed that the algorithm accurately predicts personalized PPGR to real-life meals. We further showed that using solely microbiome data can potentially inform choices between different foods, such as white and whole-wheat bread. ³⁷

As a final part of our analysis, we recruited 26 new participants into a short-term prospective dietary intervention trial. Our findings showed that dietary interventions based on our predictor resulted in significant improvements in multiple aspects of glucose metabolism, including lower PPGRs and lower fluctuations in blood glucose levels and consistent alterations to gut microbiota configuration within the intervention period.

Summary

Altogether, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences, and thus provide direct treatment for prediabetes and assist in the worldwide battle against obesity and the diabetes epidemic.

Dual-system model of decision making

The Dual-System model refers to the cognitive process by which daily decisions are made. According to the model, there are two nearly independent systems that humans use for various decisions.

The first system (System 1) is an intuitive, fast approach to decision making. It is automatic, emotional, requires low effort, functions in parallel with numerous decision-making tasks at the same time. Although the system is heuristic, it is also stable and takes a long time to learn.

The second system (System 2) utilizes a more deliberate approach to decision making. This approach is based on knowledge and reasoning. As such, it is a much slower process and requires a person's complete concentration; cognitive multitasking is not possible. However, unlike System 1, the outcome is flexible in that the decision is not simply “automatic.”

Why does eating healthy fail?

Although both systems can be used for making food decisions, a person's immediate situation and cognitive/emotional status dictates the system that is used. System 1 is used when a person does not have the time, knowledge, and/or mental energy to rationally process a particular food decision; whereas, System 2 is generally employed when a person has the time and energy to focus and consider the health consequences of their choices.

Given today's fast-paced, high-stress society in which people are overloaded with distractions and a continuous stream of digital information, it is not surprising that the vast majority of food choices are generated by “default” through System 1. Unfortunately, most of these default food choices are unhealthy.

How can consumers be enabled to use System 2 more frequently for food choices?

The most effective strategy for transitioning individuals from System 1 to System 2 processing is self-monitoring of eating behaviors. Making a conscious decision to pay attention to food choices creates the opportunity to stop and think which foods are healthier. New electronic devices and apps may be useful in supporting self-monitoring.

Another strategy is “nudging.” Nudges are liberty-preserving approaches that steer people into desired directions, but that also allow them the freedom to go their own way. There are several approaches and key principles that businesses and communities can use to effectively nudge people into making healthier food choices.

Summary

Ubiquitous time pressure, distress, and distraction urge people to use System 1 for most food choices unconsciously. Although transitioning people to using System 2 when making food choices is the ideal, nudging provides an alternative approach to helping people make healthier food choices.

Diabetes prevention: reality or myth?

The Diabetes Prevention Program (DPP) and Finnish DPS both demonstrated that lifestyle modification (healthy diet and exercise) results in a 58% relative risk reduction (RRR) in the development of diabetes after ∼3 years treatment. ^10,11 Many have interpreted these findings as evidence that modest weight loss (5%–7% body weight) “prevents” T2D.

The conclusion that we can actually prevent over half the potential cases of diabetes is a myth. Follow-up studies in both trials showed that the RRR of T2D is dependent on the time point at which the outcome (prevention) is measured. For example, if the DPP trial had been terminated at 2 years, the RRR in diabetes incidence would have been about 65%; if terminated at 7 years, the RRR would have been only about 25%. ¹² Similar relationships were also observed in the DPS follow-up analysis. ³⁸ Moreover, the DPP investigators acknowledged that most cases were not truly prevented by lifestyle modification, only delayed, and for about 4 years. The delay was 2 years when metformin was given.

Although the China Da Qing DPS showed significant RRR after 20 years of treatment with lifestyle modification, the results from this study are highly questionable. The intervention group lost no weight over the course of the study. Also, the control group started the study doing significantly more exercise than the intervention group, thereby raising the question whether there was true randomization, and even the investigators explicitly stated that they could not provide an explanation for their results. ³⁹

Insofar as the translation of the above clinical trials into practice, the results of community-based programs have been uniformly disappointing. No community program has shown they can prevent diabetes; virtually none has achieved the weight loss obtained in the DPP, even in year 1 of that intervention, and dropout rates have been high. For example, the large CDC National Prevention Program recently reported the impact of DPP-like lifestyle interventions on weight loss among 14,747 participants, who were at risk for developing T2D. ⁴⁰ The intervention involved 22 individual coaching sessions over 1 year, with a goal of 5%–7% body weight reduction.

Despite the intensity of the intervention, results were modest; average weight loss was 3.6%. Among those who completed ≥4 coaching sessions, only ∼26% achieved ≥5.5%, ∼7% achieved ≥7%, and ∼0.1% achieved >9%. Importantly, 85% of participants never completed the program. These results compare to an average 1-year weight loss in the DPP of about 7.4%, and the 2-year average was about 5.5%. Thus, achieving and sustaining the level of weight reduction required to delay the onset of diabetes appears to be unattainable outside of a clinical trial setting. Of note, in numerous community-based prevention studies, only ∼2% weight loss was achieved. ⁴¹

Lessons learned

The most important lesson we can derive from the large diabetes prevention trials is that neither lifestyle changes nor pharmacological treatment in a real-life setting can truly prevent T2D in a population. Such an intervention will work for some individuals, but we are unable to know ahead of time who will succeed, and giving an expensive intervention to many for the benefit of very few is not cost effective.

Through well-validated modeling, we have learned that individuals need to lose ∼4.5% of body weight, and sustain that reduction for life to achieve a clinically meaningful impact on the prevention of diabetes-related complications. The latter is, of course, what we hope to achieve by delaying the onset of diabetes.

The DPP and DPS also highlight essential criteria for patient success in delaying T2D. That is, participants must receive considerable attention from health professionals and almost unlimited resources to promote and maintain substantial weight loss. Imperviousness to today's obesogenic environment and having enormous self-control are also critical to success. It is very, very difficult to lose a lot of weight (5%–10%) and keep it off for more than a year or 2.

Potential benefits of weight loss

Analysis of HbA1c change in the DPP study showed that, even in the control group, HbA1c levels never rose above 6.5%, and those in the lifestyle group were not significantly lower. There were no differences in any CVD risk factor by year 5 of the study, which was associated with a 2% weight loss.

In the more recent Look AHEAD study, which examined the impact of weight loss in T2D patients with high CVD risk, participants lost ∼9% body weight at 1 year, but the weight regain started soon thereafter. At 4 years, there were no differences from control patients in CVD risk factors. There was no between-group difference in CVD events throughout the study.

It should be noted, however, that sustained weight loss and healthy lifestyle are associated with fewer hospitalizations, lower healthcare costs, less sleep apnea, and an enhanced QoL. Therefore, clinicians should encourage weight loss and healthy behaviors in patients, and hope they succeed. Importantly, a healthy lifestyle is optimized when there is no smoking, no obesity, increased physical activity, and healthy eating.

The challenge of adherence

Despite an increasing number of new medications and devices, a significant percentage of individuals with diabetes are not achieving their treatment goals. Many blame clinicians, citing lack of knowledge, poor communication skills, and an unawareness of clinical guidelines. However, the blame may be misplaced. Even in clinical trials with expert, highly incentivized physicians and motivated, well-trained patients, who are treated with the newest therapies and devices free of charge, ∼25% of patients fail to achieve the HbA1c goals of the study. It is likely, therefore, that the primary problem is a lack of sufficient motivation to follow the prescribed therapeutic regimen.

Suboptimal adherence to prescribe therapy is the key driver of poor diabetes control. It is well known that 30%–40% do not take their medication as prescribed. Although many reasons for suboptimal adherence have been reported, we have no clear demographic picture of who will respond and who will not, and there is no way to predict adherence in advance. This highlights the importance of additional research to better understand patient motivations and disincentives regarding therapy.

Conclusions

Awareness and knowledge of the benefits of lifestyle interventions are essential, but insufficient. For lifestyle changes to achieve a meaningful impact, we need a much better understanding of the factors that influence behavior and how behavior change happens.

Recognizing that some people will change their behavior permanently, we should work to identify those individuals for whom support and encouragement are most likely to result in a beneficial change.

Background

Gamification refers to designing information systems to afford similar experiences and motivations as games do, and consequently, attempting to affect user behavior. It has been employed in various contexts that include education, government services, commerce, intraorganizational communication, and, specifically, in the context of health and wellbeing.

Benefits of gaming

Games are increasingly perceived as a beneficial technology that may have the capacity to enhance our cognitive capabilities, teach new problem-solving skills, improve hand–eye coordination, instill positive motivational styles, and increase our sense of belonging. Contrary to common perceptions, gaming can be a very social activity, teaching collaborative strategies and different ways to think about competition. Moreover, more complex games can enhance our organizational and leadership skills. Importantly, games instill a sense of optimism toward difficult challenges, and provide feedback not only on how we are performing, but in how much effort we are expending. This, in turn, encourages persistence and creates a greater sense of autonomy and self-efficacy.

Evidence supporting use of gamification

Early studies of gamification have shown positive effects on behavior. An extensive meta-analysis of the literature is now underway. In this working article, 819 articles have been reviewed. A total of 273 studies involved empirical studies, out of which 66 were controlled experiments. ⁴² Our initial analyses have shown that the majority of the empirical research on gamification is conducted in the domains of education/learning and health/exercise, and a predominantly positive effect of gaming was observed within these domains. Game mechanics, involving points/scores and badges/achievements tended to show the most positive responses. A recent literature review of gamification studies within the health and wellbeing domain reported positive effects in 59% of the studies assessed.

Design and features

One of the key problems in gamification design has been determining whether it is better to use competition-based or cooperation-based designs. To address this question, we collaborated with a large German car company in a field experiment within a gamified crowdsourcing system, through a parking-related reality/gamified crowdsourcing app (submitted for publication). Three versions of the gamification app were used: cooperative (fully collaborative); competitive (individuals competed with each other), and interteam (participants collaborated in teams and competed with other teams) to investigate the effects of these approaches on motivation and behavior. We found that people within the interteam group reported higher levels of intrinsic motivation and activity, and they were more likely to recommend the app to others.

Because different classes of motivational design may have a differential fit for users, we used survey data from users of HeiaHeia (an exercise encouragement app) to investigate how different goal foci (outcome, mastery, proving, avoiding), and goal attributes (specificity, difficulty) are associated with participants' perceived importance of gamification, social networking, and quantified-self features. ⁴³ Our results showed that being “outcome-focused” is associated with positive evaluations of gamification and quantified-self design classes.

Users with a higher “proving-orientation” perceived gamification and social networking design classes as more important, and users with lower “goal-avoidance” orientation perceived social networking designs as more important. Users with a higher “mastery” orientation perceived quantified-self design as more important. Users with difficult goals were less likely to perceive gamification and social networking design important; whereas, for users with high goal specificity, quantified-self features were important.

Summary

Today, our reality and lives are increasingly game-like, not only because games have become a pervasive part of our lives, but also because activities, systems, and services are increasingly gamified. In recent years, the popularity of gamification has skyrocketed and manifested in growing numbers of gamified applications, as well as a rapidly increasing amount of research.

T2D risk factors

Risk factors for the development of T2D are often categorized as unmodifiable and modifiable. Unmodifiable factors include family history (genetics), ethnicity, and age. Modifiable risk factors include sedentary lifestyle, lack of physical activity, unhealthy eating, and overweight/obesity.

The combination of sedentary lifestyle and lack of physical activity can severely impact QoL because it leads to susceptibility to insulin resistance, overweight/obesity, metabolic syndrome, diabetes that is difficult to treat and, ultimately, the debilitating complications of T2D.

Sedentary lifestyle, lack of physical activity, and T2D

Sedentary is defined as sitting, reclining, or engaged in activities with intensity less than 1.5 Mets for the majority of each day. Mets is a measure of the intensity of physical activity, and is expressed as oxygen consumption per kilogram of body weight. Physically inactive is defined as routine engagement in moderate-intensity physical activity for <30 min, 5 days/week.

Although individuals may change their status from physically inactive to active through routine moderate-intensity exercise, they could still be considered sedentary based on the proportion of time spent with energy expenditure. Numerous studies suggest that sedentary time has an impact on gene expression and molecular and metabolic processes involved in the etiology of obesity, T2D, and coronary artery disease, independent of physical activity.

An important question is whether physical activity alone impacts the effects of sedentary lifestyles on all-cause mortality. A recent systematic review of more than 1 million men and women showed that extended sedentary time and physical inactivity is associated with a higher risk for mortality. ⁴⁴ However, for individuals who are mostly sedentary, simply increasing the time spent per day in moderate-intensity physical activity only mitigates mortality risk. For an individual who is sedentary >hours per day, eliminating the risk would require 60 min of physical activity per day, a level that is unsustainable for most people. To improve outcomes, an individual must address the problem on two fronts: reduce sedentary time and increase daily physical activity.

The Italian experience

The Italian Diabetes and Exercise Study (IDES) was a 12-month, randomized, controlled trial involving 606 T2D patients in 22 outpatient diabetes clinics throughout Italy. ⁴⁵ The aim of the study was to assess efficacy of an intensive exercise intervention strategy in promoting physical activity. The primary endpoint was change in HbA1c; secondary endpoints included changes in CV risk factors, coronary heart disease risk score, and QoL measures. All patients received usual care (medications, nutrition counseling) and theoretical exercise counseling sessions. Patients were then randomized to the control or intervention arm.

The intervention included a supervised, mixed aerobic–resistance exercise program for 150 min/week, which was conducted in two weekly sessions. The results showed a statistically significant decrease in HbA1c and CV risk factors in the intervention group but not control group. Significant improvements in the coronary heart disease risk scores within the intervention group were also observed. Subsequent analyses revealed improvements in QoL as well.

The IDES showed that a strategy combining a supervised, mixed exercise training program with structured exercise counseling was more effective than counseling, alone, in improving physical fitness and QoL, ameliorating HbA1c and improving other modifiable CV risk factors, reducing coronary heart disease risk. The intervention also promoted physical activity outside the supervised sessions, which was likely due to improving patients' knowledge and confidence in their ability to independently perform physical activity effectively and safely.

A follow-up randomized study (IDES-2) assessed the efficacy of a similar behavioral intervention in reducing sedentary time and increasing total daily physical activity in 300 T2D patients who were sedentary and physically inactive. ⁴⁶ Patients in the intervention group received theoretical and practical exercise counseling plus eight, twice-weekly individual exercise counseling once a year for 3 years. Patients in the control group received standard care with physician recommendations for daily physical activity. The primary endpoint was change in total daily physical activity and sedentary time. Changes in physical fitness, modifiable CV risk factors, and health-related QoL were also assessed. At 4 months, significant improvements in HbA1c were observed, as well as reductions in sedentary time and increases in time spent in daily activity. Continuous improvement in all outcomes persisted over the 36-month trial duration.

Take-home messages

When counseling patients, clinicians should consider the following recommendations for increasing physical activity and reducing sedentary time: (1) take frequent breaks from sitting times (e.g., stand, engage in light-intensity walking, or other activities); (2) reduce overall daily sedentary time by, instead, engaging in a wide range of light-intensity physical activities; (3) engage in moderate-to-vigorous physical activity at least 150 min/week; and (4) participate in supervised, combined aerobic–resistance–flexibility activities at least twice per week.

Background

Sub-Saharan Africa (SSA) comprises ∼45 countries, with a population of ∼1 billion. The most populated nations are Nigeria, Ethiopia, Kenya, South Africa, and Sudan. By the year 2035, we will have an estimated population of more than 1.5 billion people, and may eventually become the most populated continent on the planet.

Diabetes prevalence

Diabetes was virtually unknown in SSA 40 years ago. However, in 2014, it was estimated that 20 million people had diabetes, and that 523,000 people died because of diabetes or its complications; 76% of mortality was among individuals <60 years. It is predicted that 41.5 million people in SSA will have diabetes by 2035. This increase is much higher than in any other region in the world. Importantly, ∼69.2% of diabetic patients are undiagnosed. Although the current prevalence of T1D is not known because of high mortality, the most recent International Diabetes Federation (IDF) report shows that Nigeria has one of the highest rates of new T1D cases in Africa.

Key challenges in SSA

Currently, there is very little focus on diabetes in SSA due to other health priorities; 80% of health budgets and 95.5% of research funding go toward infectious disease, such as human immunodeficiency virus (HIV), tuberculosis, and malaria. Fewer than 30% of countries have national policies to address diabetes. This has led to a deficit of awareness and knowledge at all levels. A second challenge is lack of information. Despite published estimates by the IDF and others, the true burden of diabetes and its comorbidities is unknown.

Access to medication poses one of the most significant problems. Insulin, metformin, and glibenclamide are not available in ∼25%–30% of SSA countries. Even when insulin is available, proper storage is a problem. Moreover, the affordability of medications is a significant barrier for most patients. On average, it requires ∼1 week's wages to afford 1 month of treatment.

Lack of healthcare provider knowledge and definitive guidelines has severely limited accurate diagnosis and proper treatment of diabetes. Even with accurate diagnosis, proper treatments are not being prescribed. Poor provision of care for diabetes by government further exacerbates the problem. Lack of specialized clinicians and other trained professionals, poor laboratory infrastructure, and inequitable access to accredited laboratories severely limits diabetes diagnosis and treatment monitoring. Only one third of individuals with diagnosed diabetes have ever had a glucose test.

Point-of-care testing may provide a solution

Point-of-care testing (POCT) is well suited to SSA countries, where health centers are located in sparse, rural areas with poor transportation facilities. Improvements in precision and accuracy of point-of-care instruments, combined with connectivity (through text messaging through Smartphones), allows for management of diabetes from centralized locations. Because POCT for HIV is now being used in many countries, many believe that this approach is clearly feasible for diabetes diagnosis and management. The National Health Laboratory Service from South Africa recently developed an educational program to train clinicians and support staff to use POCT.

Moving forward

A growing number of SSA countries have developed national strategies to address diabetes. These strategies include population-based screening, task shifting through training primary healthcare workers, provision of free medicines or at a subsidized price, and empowerment of patients. Partnerships have also assisted in these efforts. Novo Nordisk currently offers insulin at a lower price to lower-income countries, and several nongovernmental organizations, such as Medecines sans Frontieres, are including diabetes care in their facilities. The Mozambique–United Kingdom partnership program has also achieved important advances in diabetes care for that country.

Despite these initiatives, it is clear that SSA governments need to wake up to the growing burden of diabetes. Diabetes awareness initiatives and screening programs are needed, and diabetes detection/treatment should be integrated with other successful chronic disease initiatives (e.g., HIV). Utilization of nurse-led healthcare teams and decentralized care should be considered to address the shortage of specialized healthcare professionals. Additional research is needed to fully elucidate the burden of diabetes and its risk factors in SSA countries.

The Italian National Health Service

Since 1943 until 1978, Italy had a Bismarck-type health service, with several insurance companies for the different types of workers. In 1978, the Parliament chose to transform this system into the National Health Service (NHS), using a Beveridge Universalistic single-payer model. However, this reform did not include an annual budget for the expenses. Within a few years, the costs of public healthcare became unsustainable. In 1992 a new reform introduced the National Healthcare Fund, instituted by the central government at the beginning of each year. The fund warranted the provision of essential levels of assistance for all the Italian citizens in all regions.

In 2001, a constitutional law legislated a separation of powers between state and regions, creating 21 regional health services. This was important because Italy's regions differ substantially. Gross domestic product per capita varies more than twofold and unemployment rates more than fourfold. With the Italian healthcare services being fully regionalized, this heterogeneity is reflected.

NHS performance

Italy's indicators of health system outcomes, quality, and efficiency are uniformly impressive. Life expectancy, at 82.3 years, is the fifth highest in the Organization for Economic Cooperation and Development (OECD). Admission rates for asthma, chronic pulmonary disease, and diabetes (markers of the quality of primary care) are among the very best in the OECD. Case fatality after stroke or heart attack (markers of the quality of hospital care) are also well below OECD averages. Good healthcare is achieved at low cost. ⁴⁷

Addressing the diabetes epidemic

In 1987, Italy became the first country in the world to pass a law for the care of individuals with diabetes. The law stated that a person with diabetes had the right to be cared by a specialist team, and not be discriminated against at work or school. In support of the law, Italy created a diabetes network made up of ∼600 outpatient clinics, 3 scientific societies, a national federation, and patient associations.

The diabetes clinics provide only outpatient care, and almost every region has a center for T1D pediatric patients. The minimum staff at each clinic is a diabetologist, dedicated nurse, and, at most clinics, a dietician. The larger clinics (∼30% of clinics) provide a broader range of specialists, including a cardiologist, ophthalmologist, podiatrist, diabetes educator, psychologist, and nephrologist. Each clinic provides care to ∼2000 patients; ∼100 children receive care at the pediatric clinics. Almost all clinics utilize electronic medical records (EMRs), and ∼50% provide structured therapeutic education programs.

Outcomes

Clinic outcomes are recorded in a registry by the Italian College of Diabetologists (AMD). From 2004 to 2011, data show a slight increase in percentage of patients with HbA1c ≤7.0% and a notable decrease in those with HbA1c levels ≥8.0%. Moreover, we have observed significant improvements in the “Q” score, a validated tool for measuring the quality of diabetes care related to the incidence of CV events. ⁴⁸

Pros and cons

As with any system, there are advantages and disadvantages. A key advantage of our approach is that the degree of quality of care provided by the diabetes clinics is quite good; glycemic outcomes and reduction of risk factors are satisfactory in comparison with other countries. However, we are limited in the number of patients for whom we provide care. The clinics follow only about half of the Italian diabetes population (∼1,500,000 of more than 3,000,000 individuals with diabetes). Patients who are not seen at the clinics are treated by 60,000 primary care physicians. Additionally, within the network, there remain notable differences either in quality of care or outcomes.

The national diabetes plan

In 2013, the Italian Ministry of Health issued a national diabetes plan. The plan is designed according to the chronic care model, and draws upon an integrated system, involving the primary care and specialist services. All regions have implemented the plan, and significant results have already been demonstrated, especially in the northern regions. Tuscany, for example, has significantly reduced the rate of lower limb amputations in people with diabetes within the past 5 years.

Challenges

Despite the improvements in diabetes care we have observed over the past few years, many challenges remain. All new antidiabetic drugs can be prescribed only by specialists, which create barriers for system integration. We also have several barriers to effective use of devices. Every region has different rules and prices for blood glucose testing strips and meters, and several regions offer limited or no reimbursement for technologies, such as insulin pumps and CGM.

Conclusions

In Italy, individuals with diabetes enjoy several advantages. A favorable legislation and an organized diabetes network facilitate easy access to quality care that is delivered free of charge. However, at the same time, additional improvements are needed. Specifically, we must find ways to integrate primary care into the network, and we must reduce the differences between the regions to create a more homogenous system of care.

Background

Many individuals with diabetes are not achieving their therapy goals despite a growing range of diagnostic and therapeutic options. ^49,50 Although patient adherence to therapy is a key factor, in many cases, goals are not met because clinicians provide inadequate initiation and/or intensification of therapy due to insufficient time and no decision support. Integrated Personalized Diabetes Management (iPDM) may address factors of clinical inertia.

Integrated personalized diabetes management

iPDM is an iterative, six-step iPDM process: (1) an initial assessment of the patient status is conducted and individualized education/training prescribed; (2) blood glucose data are collected according to a structured, therapy-adapted regimen; (3) electronic documentation of glucose data and other relevant diabetes information; (4) systematic data analysis by clinicians in collaboration with their patients; (5) review of current treatment and therapy adjustments when indicated; and (6) assessment of treatment effectiveness at the patient's next visit. The cycle is then repeated.

PDM-ProValue study program

Conclusions

The results of the PDM-ProValue study program suggest that use of an integrated, structured, and personalized approach to the evaluation of diagnostic data and therapeutic decision making provides tangible benefits for patients with insulin-treated T2D and their physicians. For the diabetes team, the iPDM process improves the quality and effectiveness of the communication with their patients. This has the potential to streamline the delivery of patient care and to optimize clinical workflow. Although the program focused on the specific needs of patients with insulin-treated T2D utilizing SMBG, our findings have positive implications for use of iPDM in other patient populations.

Background

Patients with diabetes face the daily burden of managing their disease. The ongoing responsibility of administering medication, testing their glucose, and adhering to prescribed lifestyle behaviors can be overwhelming, and patients often do not document their self-management activities. Inadequate documentation makes it difficult for patients and their clinicians to identify problems and adjust therapy. Although accurate and complete documentation is essential to effective diabetes management, when viewing diabetes as a “data-driven ” disease, the psychological burden and its impact on individuals may be overlooked.

Software solutions aiming to help in diabetes management have been around for decades, and are now going mainstream. The impact of using mobile apps is now becoming clear and accepted among both individuals with diabetes and their healthcare professionals.

mySugr

The mySugr mobile app was launched in 2014. Utilization of the app is growing by more than 1000 per day, and it has one of the highest usage ratings as a medical offering in the App Store and Play Store. The app was designed to cover all diabetes self-management areas included in the American Association of Diabetes Educators (AADE) 7 Healthy Behaviors curriculum: healthy eating; being active; monitoring; taking medication; risk reduction; problem solving; and healthy coping. However, unlike other approaches, which focus primarily on data and long-term planning, the mySugr app provides users with a mix of positive psychology, games and challenges, and assistance in their day-to-day diabetes management. To ensure quality, we registered the app as a medical device. As such, the app is Conformité Européene (CE) marked, with a 1 and 2B risk classification.

User-centric medical solution

The philosophy behind the app is that life with diabetes can and should be active and meaningful. Our goal was to design an app that is visually pleasing and provides positive feedback to the user about his/her daily management successes. The app uses games and challenges to keep the user engaged. But at the same time, it covers all of the diabetes data and functionalities needed for effective diabetes management. A CE-approved bolus advisor is included to assist users with their insulin dosage calculations.

Data from the app are automatically transferred to other devices, and users have easy access to their real-time records. Certified diabetes educators (CDEs), assisted by algorithms, monitor patients who are at risk and reach out to them with coaching and online support. A helpful feature of the app is automatic calculation of test strip consumption. When the app detects that the supply of test strips and lancets is running low, it automatically places an order for delivery of supplies to the user; there is no need for a prescription. This is the mySugr Bundle, which is rolling out across the United States and in parts of Germany; more markets will follow.

Real-world experience

Analyses of the mySugr database, which includes data from more than 1 million users, have shown reductions in estimated HbA1c (eHbA1c) and GV without increased hypoglycemia risk in users with both well-controlled and poorly controlled diabetes. ^52,53 Among high-risk patients who were poorly controlled, we have seen significant reductions in the high blood glucose index and HbA1c over a period of 6 months. ⁵⁴

How the app is used

On a basic level, the app provides feedback regarding what and how the user is doing. The app “jokes” with the user, which keeps the experience entertaining and the user engaged. When users want to make an entry into the app, they can add pictures and other information to document meals, medications, and activities. Other information (e.g., times, locations) and physical activity levels (imported from other apps) are entered automatically. Once the user has added enough data, he/she is rewarded with points.

The dashboard shows the basic statistics of the day. Historical information can be obtained by swiping down to see the current day's history; swiping left accesses data from the past week, month, or quarter. Users can access and download a PDF report that presents basic therapy statistics and detailed logs.

Ability to learn from past experiences

The app allows users to search their historical data regarding past events (e.g., meals, activities, moods, locations) and the impact of those events on glucose and other metrics. With this information, users can make more appropriate therapy decisions based on past experiences in similar situations. For example, if a user wants to know how to calculate an insulin dose for a specific food, he/she enters the food name into the app, which provides a list of all the times he/she has eaten that food, along with the locations and times of day. The user can then access a graph that indicates the effect of the food on glucose.

Insights

The mySugr group has recently formed a new team to enhance the app's capabilities in using the data to more fully individualize the user's experience. The team first focused on finding a simple way to create a personalized, data-driven feature, which looks for patterns in the user's data, personalizes the content according to his/her problem spectrum, adds educational material, and then “hints” at what may be going on with the user, and possible steps to address the situation. It does not provide therapeutic device; it simply helps the user focus on the right area(s).

The algorithms used for the feature were developed in collaboration with 700 current users, 2 diabetes educators, and 3 clinicians. Within 3 months, we had identified and deployed eight distinct problematic patterns for inclusion in the solution, and more are being developed. However, perhaps the most significant aspect of the development process was the tremendous feedback we received from our e-mails and online surveys. Unlike other e-mail campaigns, which usually yield a 3%–5% open rate, we had a 70% open rate. Importantly, 90% of respondents reported that they found the feature helpful, and 40% read the educational materials.

Summary

Effective diabetes management is essential to achieving positive health outcomes. Mobile health applications that address both the clinical and psychological burden of diabetes have the potential to reduce the burden and enhance patient self-management. The combination of positive feedback, useful advice, and convenience is what creates impact.

Clinical inertia

Clinical inertia has emerged as a key contributor to suboptimal diabetes control. ⁵⁵ Low literacy, poor clinician–patient communications, and medication-related issues often cause patients to resist treatment intensification. Patient resistance, in combination with time constraints, a paradigm of reactive care, and insufficient patient data, reinforce clinician reluctance to titrate therapy to reach diabetes goals. These factors are exacerbated by healthcare systems that lack decision support tools, disease registries, and adequate modes and channels of communication between all stakeholders.

The consequences of clinical inertia are significant in terms of diminished QoL, poor glycemic control, and subsequent mortality and morbidity, which, in turn, negatively impact healthcare systems with the high costs associated with uncontrolled diabetes. ^56,57 Conversely, patients who spend more than 80% of the time within their target range show a significant improvement in clinical outcomes, specifically, a reduction in hypoglycemic events. ⁵⁸ Therefore, solutions that effectively address clinical inertia are needed.

Open diabetes management ecosystem

From the Roche Diabetes Care perspective, integrated solutions are needed for all stakeholders. This is the rationale for creating an open diabetes management ecosystem. The ecosystem needs to include patients and family members, clinicians, hospital systems, and payers, and it must offer the opportunity for all of these stakeholders to contribute jointly to optimizing care. To do this effectively, we need to look beyond glucose data and medications. We need to incorporate all data that are relevant to daily diabetes management—nutrition factors, physical activity—and it needs to be done in ways that are interactive and engaging.

Integrated personalized diabetes management

iPDM is a continuous, six-step process that involves structured training, data generation/collection, documentation, structured analysis, individualized therapy adjustment, and then assessment of the efficacy of the adjustment. This process provides a dynamic, personalized approach to care, engaging and collaborating with the patient in therapy decisions. Fundamental to the process are tools and technologies that support the gathering, interpretation, decision support, and sharing of all relevant patient information.

From a patient perspective, there are already a number of devices for monitoring glucose. However, we are now seeing an emergence of mobile technology. Innovative tools, such as the mySugr app, fill the information gaps and, at the same time, keep patients engaged with their daily diabetes management. For clinicians, we are seeing new decision support tools (e.g., Accu-Chek SmartPix, emminens^®), which present data in meaningful ways and provide guidance for appropriate therapy adjustments. Open connectivity between these tools and technologies make it possible to “bundle” the various inputs from patients and clinicians in ways that make it economically feasible to implement connected solutions in daily practice. In turn, connectivity and data sharing with insurers create opportunities for more effective population health management and development of healthcare delivery/payment models that improve health outcomes and reduce overall costs.

True relief for patients

From a patient perspective, diabetes exerts considerable burden on their lives and the lives of their families. Relieving burden is a key priority within the open ecosystem. One example of how we are pushing this priority forward is the mySugr app.

The mySugr app provides essential data in meaningful ways that both engage and entertain patients to support their daily self-management activity. Glucose data are automatically synced, and patients can access their eHbA1c values between visits. A bolus advice feature is available in Europe to support safe insulin dose calculation. In addition, the device is “agnostic”; the open app platform allows connectivity with a variety of devices (even competitive devices).

Importantly, we know that this approach is effective in improving diabetes control. Analysis of data from over 400 mySugr users revealed significant reductions in mean blood glucose levels, eHbA1c, and high blood glucose risk. ^{52 –54}

Patients who desire comprehensive support can subscribe to the mySugr Bundle, which automatically tracks testing supplies (and replenishes when needed). Personalized, one-on-one coaching from CDEs is also part of the bundle. Importantly, the data can be made available to clinicians and insurers. The bundle is offered to health insurers at a fixed price, and six insurers in Germany currently pay for this solution. In essence, this solution addresses the needs of all stakeholders in addressing clinical inertia.

Healthcare professional solutions

Access to accurate and complete glucose data, supported by decision support tools, is critical for effective patient management. The new-generation Accu-Chek Smart Pix diabetes management system facilitates data assessment and therapy adjustment by providing automated pattern and meal detection. Optimized and adaptable visualization of data makes it easier to detect hypoglycemia and other potential risks. As shown in the PDM ProValue study, use of the Accu-Chek Smart Pix diabetes management system within the iPDM process resulted in reductions in HbA1c, improved overall glycemic control, earlier and more frequent therapy adjustments, greater patient adherence, enhanced physician–patient interactions, and increased treatment satisfaction.

Future solutions

Later this year, Roche Diabetes Care plans to introduce a new healthcare professional platform that provides an enhanced level of pattern detection, utilizing more than 20 different patterns derived from clinical evaluations and behavioral device usage. The clinical algorithms can be configured to address the needs and guidelines of individual practices. Additionally, the platform will subsequently provide customizable graphs, specific graphs for CGM and insulin pump users (e.g., AGP), and the ability to include clinical data and notes. As a fully open system, data can be downloaded from different devices and, at a later point, integrated with EMR and electronic health record systems.

Conclusions

Achieving integrated diabetes management confers benefits to all stakeholders through patient relief, better outcomes, and greater cost effectiveness. However, creating and implementing digital solutions that address clinical inertia requires a combination of approaches and involves making fundamental changes in how care is provided.

Age of “datafication”

By the end of 2017, it is estimated that global networks will deliver ∼14 petabytes (14 million gigabytes) of data every 5 min. Big data have become so big and generated so quickly that it cannot be handled by existing database technologies and infrastructures.

Within the healthcare sector, it is estimated that more than 2 million petabytes will be generated each year by 2020. Driving this growth are numerous data sources, including: sensors and connected devices, medical libraries, EMRs, Smartphones, laboratory/POCT, and others. Technologies that facilitate interoperability with other devices/systems, cloud computing, widespread connectivity, medical advancements, and cheap hardware and storage costs enable the use of information generated in Big Data sets.

Utilization of Big Data requires artificial intelligence (AI) technologies that can produce actionable insights. This involves four steps: (1) collect the data; (2) comprehend the information through advanced analytics (e.g., machine learning, deep learning); (3) create predictions; and (4) assess the accuracy of those predictions.

Commercialization of Big Data

Since 2013, 106 healthcare-related data science and AI startups were funded. These ventures are developing products, services, and technologies to cover all phases of product/service development and commercialization. Several of these companies are participating in the healthcare sector, fueling the development of innovative medications, devices, and services.

OWKIN Socrates is a clinical research platform empowered by machine learning and deep learning algorithms for optimizing drug development. Veeva CRM MyInsights is a suite of predefined analytics dashboard and ecosystem partners to create customer visualizations. Ayasdi offers a population risk manager, which automatically identifies nuanced subpopulations, predicts future risk trajectories, and informs the most effective interventions. Chronicled™, a new startup company, leverages blockchain, Internet of Things (IoT), and AI to enforce crossorganization business rules and automate business process, using smart contracts.

Blockchain is a new internet technology that allows digital information to be shared but not copied. IoT refers to a network of physical devices embedded with electronics, software, sensors, and actuators, which enables connectivity and data exchange with other devices.

Big Data and AI across the entire life sciences value chain

Potential impact of Big Data on clinical trials

The impact of Big Data and AI on clinical trials is growing. Designing clinical trials using AI solutions enables researchers to optimize study designs and more efficiently recruit patients while eliminating unnecessary clinical operation burdens. A recent Mayo Clinic study reported an 80% increase in enrollment of clinical trials for breast cancer when using IBM's Watson for clinical trial matching to patient records. Another example is Trials.ai, which facilitates optimization of clinical trials through real-time insights on site performance, retention statistics, and other factors. Meditata's eConnect Partner Program facilitates seamless integration of EMR and clinical trial electronic source data into their “clinical cloud” solution. AI solutions will also provide continual improvements in trial efficacy.

Concerns and considerations

As adoption of Big Data and AI continues, certain issues and limitations must be considered. First, it is important to recognize that AI is costly, both in terms of technical and human resources development, but also in terms of maintenance and upscaling. Second, interoperability with third party platforms is essential. Third, the ability to collect Big Data and share the scientific burden and costs with third parties is critical moving forward. Importantly, because certain AI applications require access to a wide range of data, this could conflict with existing security and privacy standards, such as General Data Protection Regulation. Finally, appropriate business governance is required to oversee all AI initiatives to ensure that control can never be lost over business decisions and strategy.

Background

The global private health insurance market is experiencing major growth and expansion across all regions. The estimated global private health insurance gross written premium income in 2018 is €1.5 trillion and projected to double by 2025. Some markets are expanding more rapidly than others (e.g., Latin America, Asia-Pacific region) due to a number of factors, such as an increasing demand for private healthcare coverage, an aging population, the global disease burden, global population income growth, and pressure on public finances.

Healthcare market dynamics

Healthcare is a dynamic industry with significant opportunity due to the unsustainability of current healthcare economics and regulatory uncertainty. Many large companies are now self-insuring their employees, and others are contracting directly with healthcare providers. We are seeing a trend toward risk reallocation across the healthcare value chain and technical excellence in underwriting, pricing, product design, and claims handling have become major strategic management areas for health insurers. A key strategic driver in the health insurance market today is the shift from “volume” to “value.” As care delivery models make this transition, health insurance leaders are now focused on leveraging outcome-based business models to provide value and stay relevant in an everchanging dynamic industry.

Stakeholder incentives are misaligned

Many of the problems we see in current healthcare systems stem from a misalignment of stakeholder incentives. Payers are trying to control costs through onerous risk management interventions and by limiting patient choice. Moreover, the current fee-for-service reimbursement system, which promotes volume over value, continues to fuel healthcare inflation. Although health prevention strategies have been shown to be highly effective in improving health outcomes and controlling costs, many payers still do not offer wellness and health promotion programs or services. Overall, the complexity of the current healthcare systems is leading to a fragmented patient experience with asymmetry of information preventing patients to manage their own health more effectively. To change behavior, we must also change the system.

Value-based healthcare system

The fundamental goal of healthcare is maximizing value for patients. Within this context, value is expressed in terms of health outcomes that matter to patients relative to the costs of delivering those outcomes over the full cycle of care. A value-based healthcare system includes three key components: (1) a patient-centric care delivery model; (2) enablers in adopting value-based care; and (3) public policy. ⁵⁹

Value-based care delivery models must focus on patient-centric care. They must be designed to facilitate standardized measurement of health outcomes and costs throughout the entire care cycle. Standardized outcome measures are critical to changing payer policies regarding coverage decisions and reimbursement. As with outcomes, cost measurements must involve the entire care cycle. A measurement technique being explored in many hospital systems is “time-driven, activity-based costing” (TDABC), which identifies and measures detailed process maps to improve care pathways. This approach also allows for identification of waste, areas for efficiency improvements, as well as treatment and cost variations. Although TDABC is quite an involved measurement process, hospitals, clinics, and provider organizations are starting to adopt this cost measurement approach to optimize care and cost efficiencies for patients.

Enabling the adoption of value-based care requires value-based reimbursement/payments and business model innovation. We are seeing reimbursement models shifting from low-risk, low-complexity models (e.g., fee-for-service) to more complex risk-based reimbursement models, such as bundle payments and capitation models, which are viewed as value-based reimbursement models. These models involve a single risk-adjusted payment for the care of a condition, covering the full set of services and facilities needed to treat the condition over the full care cycle.

Public policy is perhaps the most difficult issue to deal with in terms of enabling value-based care because of existing healthcare regulations not enabling or encouraging new innovative healthcare delivery and reimbursement models. Therefore, it is important that policy frameworks create an environment in which all actors in the healthcare value chain are enabled to become accountable for healthcare value–improved patient outcomes and greater cost efficiency.

Business model innovation

A fragmented inefficient healthcare system will persist as long as we continue to view the demand side and supply side healthcare structures as separate and independent. Instead, we need to create a healthcare innovation ecosystem, including both the supply and demand-side structures collaborating in creating value-enhancing, win–win relationships between stakeholders in the healthcare value chain. Creating this ecosystem requires a new business model optimization strategy to establish the required collaboration, trust, and transparency among parties. This methodology (Vested^® business model) is based on five core concepts: (1) make outcomes, not transactions, the focus and priority of the business model; (2) focus on what needs to be done, not how it should be done; (3) establish clearly defined and measurable desired outcomes; (4) utilize pricing models that include incentives to optimize the system/business; and (5) establish a governance structure, which provides insight rather than just oversight in managing the model.

Key learnings

Value-based concepts are increasingly accepted and understood by all participants in the healthcare value chain and payers need business model innovation to stay relevant. Data, data science, advanced analytics, and decision tools are essential in enabling value-based care. Complete integration between value-based reimbursement models and health plan/benefit design is critical in optimizing care and reducing claims costs. Payers must invest upfront in value-based models that drive integration and move to a pricing/reimbursement model with incentives that optimize value. Pilot studies and experiments should be used to introduce the concept of value-based reimbursement and contracting, and the outcomes need to be published. Trust, transparency, focused collaboration, and joint innovation between payers, providers, and suppliers are essential in enabling value-based care and impacting rampant healthcare inflation. Finally, organizations should consider formalizing value-based care by establishing new leadership roles (e.g., Chief Value Officer) to champion these critical efforts.