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Continuous Glucose Monitors as Wearable Lifestyle Behavior Change Tools in Obesity and Diabetes
Abstract
Recent advancements in continuous glucose monitoring (CGM) represent a novel and untapped resource to optimize behavior change interventions for the prevention and treatment of type 2 diabetes and obesity. In this chapter, we provide a brief history about CGM and evidence supporting its use, including nontraditional indications (people with type 2 diabetes and nondiabetic populations). We then discuss current applications for CGM as a tool for dietary modification, physical activity behavior change, and weight control as well as insights on the theoretical basis for using CGM as biological feedback to motivate lifestyle behavior change. The chapter concludes with a discussion on the future opportunities for CGM as a wearable lifestyle behavior change tool for the treatment of obesity and diabetes.
In 2023, childhood hypoglycaemia remains a major public health problem and significant risk factor for consequent adverse neurodevelopment. Irrespective of the underlying cause, key elements of clinical management include the detection, prediction and prevention of episodes of hypoglycaemia. These tasks are increasingly served by Continuous Glucose Monitoring (CGM) devices that measure subcutaneous glucose at near-continuous frequency. While the use of CGM in type 1 diabetes is well established, the evidence for widespread use in rare hypoglycaemia disorders is less than convincing. However, in the few years since our last review there have been multiple developments and increased user feedback, requiring a review of clinical application. Despite advances in device technology, point accuracy of CGM remains low for children with non-diabetes hypoglycaemia. Simple provision of CGM devices has not replicated the efficacy seen in those with diabetes and is yet to show benefit. Machine learning techniques for hypoglycaemia prevention have so far failed to demonstrate sufficient prediction accuracy for real world use even in those with diabetes. Furthermore, access to CGM globally is restricted by costs kept high by the commercially-driven speed of technical innovation. Nonetheless, the ability of CGM to digitally phenotype disease groups has led to a better understanding of natural history of disease, facilitated diagnoses and informed changes in clinical management. Large CGM datasets have prompted re-evaluation of hypoglycaemia incidence and facilitated improved trial design. Importantly, an individualised approach and focus on the behavioural determinants of hypoglycaemia has led to real world reduction in hypoglycaemia. In this state of the art review, we critically analyse the updated evidence for use of CGM in non-diabetic childhood hypoglycaemia disorders since 2020 and provide suggestions for qualified use.
Background
Continuous glucose monitor (CGM) systems were originally intended only for people with diabetes. Recently, there has been interest in monitoring glucose concentrations in a variety of other situations. As data accumulate to support the use of CGM systems in additional states unrelated to diabetes, the use of CGM systems is likely to increase accordingly.
Methods
PubMed and Google Scholar were searched for articles about the use of CGM in individuals without diabetes. Relevant articles that included sufficient details were queried to identify what cohorts of individuals were adopting CGM use and to define trends of use.
Results
Four clinical user cases were identified: (1) metabolic diseases related to diabetes with a primary dysregulation of the insulin-glucose axis, (2) metabolic diseases without a primary pathophysiologic derangement of the insulin-glucose axis, (3) health and wellness, and (4) elite athletics. Seven trends in the use of CGM systems in people without diabetes were idenfitied which pertained to both FDA-cleared medical grade products as well as anticipated future products, which may be regulated differently based on intended populations and indications for use.
Conclusions
Wearing a CGM has been used not only for diabetes, but with a goal of improving glucose patterns to avoid diabetes, improving mental or physical performance, and promoting motivate healthy behavioral changes. We expect that clinicians will become increasingly aware of (1) glycemic patterns from CGM tracings that predict an increased risk of diabetes, (2) specific metabolic glucotypes from CGM tracings that predict an increased risk of diabetes, and (3) new genetic and genomic biomarkers in the future.
Our ability to understand and intervene on eating in the absence of hunger (EAH) as it occurs in peoples' natural environments is hindered by biased methods that lack ecological validity. One promising indicator of EAH that does not rely on self-report and is easily assessed in free-living individuals is glucose. Here, we hypothesize that elevated pre-prandial blood glucose concentrations (PPBG), which reflect a source of readily-available, short-term energy, are a biological indicator of EAH. This was a 7-day observational study of N = 41, 18–24 year old men and women with BMI < 25 kg/m² (60%) or BMI ≥ 25 kg/m² (40%). We collected data using ecological momentary assessment from people in their natural environments. We defined EAH by self-report (perceived EAH) and by PPBG thresholds using two methods (standardized, PPBG < 85 mg/dl; personalized, PPBG<individual fasting levels). Multilevel modeling was used to analyze the data. N = 963 eating events were reported. There were significantly (p < .05) fewer perceived EAH events (25%) as compared to standardized (62%) and personalized PPBG-defined EAH events (51%). Consistent with published literature, perceived EAH was more likely to occur at a higher PPBG (p < .01), particularly among participants with a BMI ≥ 25 kg/m² (pint < .01). Additionally, discordance between perceived EAH and PPBG-defined EAH, indicating a perception of hunger at an eating event when PPBS was elevated, was less likely among participants with a BMI < 25 kg/m² vs. those with a BMI ≥ 25 kg/m² (pint < .01) as well as at snacks vs. meals (pint < .01). These findings provide preliminary support for using PPBG as a biological indicator of EAH in free-living individuals.
Introduction:
Diabetes mellitus is a chronic endocrine-metabolic disease, the evolution of which is closely related to people´s self-control of glycemic levels through nutrition, exercise, and medicines.
Aim:
To determine whether smartphone apps can help persons with diabetes to improve their % levels of glycosylated hemoglobin.
Method:
A systematic review and meta-analysis were done. ProQuest, Pubmed/Medline, and Scopus databases were used. The search equation used was "(Prevention and Control) AND Diabetes Mellitus AND Smartphones". The inclusion criteria applied were clinical trials, conducted in 2014-2019.
Results:
n = 18 studies were included in the review. The studies tried different applications to monitor glycemia and support patients to improve glycosylated hemoglobin (HbA1c) levels. More than half of the studies found statistically significant differences in HbA1c in the intervention group compared with the control group. Eleven studies were included in the meta-analysis and the study sample was n = 545 for the experimental group and n = 454 for the control group. The meta-analytic estimation of the HbA1c % level means differences between intervention and control group was statistically significant in favour of the intervention group with a mean difference of -0.37 (-0.58, -0.15. 95% confidence interval).
Conclusion:
Smartphone apps can help people with diabetes to improve their level of HbA1c, but the clinical impact is low.
Background
Regular physical activity (PA) is associated with a lower risk of several types of cancers. However, two-thirds of overweight/obese adults are not sufficiently active; this, in combination with the unfavorable effect of excess body weight, puts them at a greater risk for cancer. One reason that these individuals do not engage in enough PA may be their lack of motivation to change their current behavior due to the perception of putting in effort for possible future gain without obvious short-term benefits. There is a need for innovative ways to help individuals recognize the immediate health benefits of PA and thus increase their motivation.
Methods
This pilot intervention tested a PA education module that included a one-on-one counseling session highlighting the acute effects of PA on glucose patterns, followed by a 10-day self-monitoring period with a continuous glucose monitor (CGM) and a Fitbit tracker. Participants rated the acceptability of the education module on a 5-point Likert scale and completed surveys assessing stages of change for motivational readiness.
Results
Nineteen overweight/obese adults (84% female) completed the study. Participants gave high ratings to the counseling session for improving their PA-related knowledge (mean = 4.22), increasing motivation (mean = 4.29), and providing personally relevant information (mean = 4.35). The summary acceptability scores for the self-monitoring period were 4.46 for CGM and 4.51 for Fitbit. Participants reported a significant decrease in the precontemplation stage and an increase in the action stage (P < 0.05).
Conclusions
CGM is a feasible tool for PA interventions.
Impact
Information from CGM could be used as biological-based feedback to motivate PA.
See all articles in this CEBP Focus section, “Modernizing Population Science.”
Background
Overweight and obesity have become a significant public health concern in both developing and developed countries. Due to the health implications of weight-reduction behaviors, it is important to explore the factors that predict their occurrence. Therefore, the present study was performed to examine factors affecting the behavioral intention of weight management as well as assess the predictive power of the Health Belief Model (HBM) for body mass index (BMI).
Methods
This cross-sectional study was conducted among 336 female students recruited from dormitories of Tabriz University of Medical Sciences, using quota sampling technique. Data were collected by a structured questionnaire in seven parts (including perceived severity, perceived susceptibility, perceived benefit, perceived barrier, cue to action, self-efficacy in dieting and physical activity, and behavioral intention of weight management), based on the HBM. Structural equation modeling (SEM) was conducted to identify the relationship between HBM constructs and behavioral intention of weight management. Linear regression model was performed to test the ability of the HBM to predict students’ BMIs.
Results
Higher level of perceived threats (sum of perceived susceptibility and severity) (β = 0.41, P<0.001), perceived benefits (β = 0.19, P = 0.009), self-efficacy in exercise (β = 0.17, P = 0.001), and self-efficacy in dieting (β = 0.16, P = 0.025) scales was significantly related to greater behavioral intention of weight management. Moreover, perceived threat mediated the relationships between perceived cue to action, perceived benefits, self-efficacy in exercise, and weight management practices. The fit indices of the SEM model seemed acceptable. The final regression model explained approximately 40% of variance in BMI (P<0.001). Additionally, perceived severity, barrier, and self-efficacy in dietary life were the significant variables to predict students’ BMIs.
Conclusions
These findings suggest that health education programs based on the HBM needs to be integrated in preventive health programs and health interventions strategies to ensure adherence and well-being of the participants.
Objectives
Hunger training (HT) is an intervention designed to teach people to eat according to their hunger by connecting physical symptoms of appetite with glucose levels. HT is most effective for weight loss, and improving eating behaviours when adherence is high. However, adherence is a challenge that should be explored prior to wider dissemination. The aim of this study was to explore participants’ experience and self-reported adherence and behaviour change related to HT.
Design
A qualitative study, nested within a randomised controlled pilot study of two different methods of monitoring glucose during HT. Semistructured interviews were audio-recorded, transcribed verbatim and analysed thematically using a phenomenological approach.
Setting
Single-centre study with participants recruited from the local area.
Participants
40 participants began the pilot study and 38 participants (52.6% women) remained at 1 month and completed interviews.
Results
Most participants felt they were able to match their hunger to their glucose levels by the end of the intervention. The main adherence barriers were the social pressure to eat, lack of time and lack of flexibility in participants’ meal schedules. Common adherence enablers were having a set routine, social support and accountability. Participants described increased awareness of hungry versus non-hungry eating and better cognition of feelings of hunger and satiety as a result of the intervention, which in turn led to changes of food choice, portion size and adjusted meal timing and frequency.
Conclusions
Findings show that HT is acceptable from a patient perspective, and results can be used to inform the translation of HT programme to healthcare settings.
Trial registration number
ACTRN12618001257257.
Background:
Self-monitoring of behavior (namely, diet and physical activity) and physiology (namely, glucose) has been shown to be effective in type 2 diabetes (T2D) and prediabetes prevention. By combining self-monitoring technologies, the acute physiological consequences of behaviors could be shown, prompting greater consideration to physical activity levels today, which impact the risk of developing diabetes years or decades later. However, until recently, commercially available technologies have not been able to show individuals the health benefits of being physically active.
Objective:
The objective of this study was to examine the usage, feasibility, and acceptability of behavioral and physiological self-monitoring technologies in individuals at risk of developing T2D.
Methods:
A total of 45 adults aged ≥40 years and at moderate to high risk of T2D were recruited to take part in a 3-arm feasibility trial. Each participant was provided with a behavioral (Fitbit Charge 2) and physiological (FreeStyle Libre flash glucose monitor) monitor for 6 weeks, masked according to group allocation. Participants were allocated to glucose feedback (4 weeks) followed by glucose and physical activity (biobehavioral) feedback (2 weeks; group 1), physical activity feedback (4 weeks) followed by biobehavioral feedback (2 weeks; group 2), or biobehavioral feedback (6 weeks; group 3). Participant usage (including time spent on the apps and number of glucose scans) was the primary outcome. Secondary outcomes were the feasibility (including recruitment and number of sensor displacements) and acceptability (including monitor wear time) of the intervention. Semistructured qualitative interviews were conducted at the 6-week follow-up appointment.
Results:
For usage, time spent on the Fitbit and FreeStyle Libre apps declined over the 6 weeks for all groups. Of the FreeStyle Libre sensor scans conducted by participants, 17% (1798/10,582) recorded rising or falling trends in glucose, and 24% (13/45) of participants changed ≥1 of the physical activity goals. For feasibility, 49% (22/45) of participants completed the study using the minimum number of FreeStyle Libre sensors, and a total of 41 sensors were declared faulty or displaced. For acceptability, participants wore the Fitbit for 40.1 (SD 3.2) days, and 20% (9/45) of participants and 53% (24/45) of participants were prompted by email to charge or sync the Fitbit, respectively. Interviews unearthed participant perceptions on the study design by suggesting refinements to the eligibility criteria and highlighting important issues about the usability, wearability, and features of the technologies.
Conclusions:
Individuals at risk of developing T2D engaged with wearable digital health technologies providing behavioral and physiological feedback. Modifications are required to both the study and to commercially available technologies to maximize the chances of sustained usage and behavior change. The study and intervention were feasible to conduct and acceptable to most participants.
Trial registration:
International Standard Randomized Controlled Trial Number (ISRCTN) 17545949; isrctn.com/ISRCTN17545949.
Every eleventh adult has diabetes, and every third has prediabetes. Over 95% of diabetics are of type 2. It is well established that diabetes doubles the risk of heart disease and stroke apart from increasing the risk of microvascular complications. Hence, strict glycemic control is necessary. However, it increases the risk of hypoglycemia, especially in patients with longstanding diabetes. Continuous glucose monitors (CGM) use a sensor to continuously measure the glucose levels in the interstitial fluid every 10 seconds and gives out mean values every five minutes. CGMs are emerging tools in the management of type 2 diabetes. The prime objective of this review is to find out if there is enough supporting evidence, suggesting that continuous glucose monitoring is more effective than self-monitoring of blood glucose (SMBG) in type 2 diabetes. We conducted a systematic literature search in Medline (PubMed) looking for any studies addressing our objective. It is observed that there is a varying level of evidence supporting that employing a CGM can reduce glycated hemoglobin (HbA1c), hypoglycemic events, and increase patient satisfaction. However, some studies reported no significant benefits. This systematic review with meta-analysis concludes that the use of CGM in type 2 diabetes mellitus (T2DM) is beneficial, as it significantly reduces HbA1c compared to the usual method of SMBG. The pooled mean difference in HbA1c was -0.25 (-0.45, -0.06) and statistically significant (at p = 0.01) when comparing CGM to SMBG.
This study investigated the effect of applying a customized diabetes education program through pattern management (PM), using continuous glucose monitoring system (CGMS) results, on individual self-care behaviors and self-efficacy in patients with type 2 diabetes mellitus. Patients with type 2 diabetes who had never received diabetes education, enrolled from March to September 2017, were sequentially assigned to either PM education or control groups. In the PM education group, the CGMS test was first conducted one week before diabetes education and repeated three times by PM in order to obtain data on self-care behaviors and self-efficacy. These results were then compared before and after education at three and six months. The control group received the traditional diabetes education. Self-efficacy showed statistically significant interactions between the two groups over time, indicating a significant difference in the degree of self-efficacy between the PM education and control groups. Diabetes education by PM using CGMS result analysis improved life habits with a positive influence on self-care behaviors and self-efficacy for diabetes management. Further studies are needed to further develop and apply individual diabetes education programs in order to sustain the effects of self-care behaviors and self-efficacy in patients with diabetes who experience a decrease in self-efficacy after three months of education.
Insulin antibody (IA) may potentially affect a patient’s glycemic control due to its variability in both binding and/or releasing insulin. However, the association between IA titer and daily glycemic variability (GV) is still unknown. We thus performed this cross-sectional, retrospective case-control study to assess the relationship between IA titer and mean amplitude glycemic excursion (MAGE) in type 2 diabetes mellitus (T2DM) patients using a continuous glucose monitoring (CGM) system. We recruited 100 eligible patients ( IA>5% , IA positive) and divided them into two groups—a low (L) group and a high (H) group—based on their IA titer. The control (C) group consisted of 47 patients ( IA≤5% , IA negative) matched for age, BMI, gender, and glycosylated hemoglobin A1c (HbA1c). The CGM determined the GV of enrolled patients. The primary outcome was the relationship between the IA titer and the MAGE, and the secondary outcome was the differences of GV among the three groups. We found that patients in the H group had higher levels of blood glucose fluctuation parameters than those in the L and C groups. The Ln(IA) was positively correlated with Ln(MAGE) even after adjusting for age, gender, BMI, HbA1c, and fasting and postprandial C-peptide( r=0.423 , p<0.001 ). Multiple linear stepwise regression analysis revealed that Ln(IA) was an independent factor of Ln(MAGE) ( beta=0.405 , p<0.001 ). In conclusion, the higher circulating IA titer was associated with increased MAGE in T2DM patients, indicating that those patients with elevated IA titer should receive GV assessment and individualized treatment.
In the present study, we aimed to investigate the effects of continuous glucose monitoring (CGM) on blood glucose levels, body weight, blood pressure, and hypoglycaemia in patients with type 2 diabetes mellitus using a meta-analysis of randomized controlled trials (RCTs). A literature search was performed using MEDLINE, Cochrane Controlled Trials Registry, and ClinicalTrials.gov. RCTs using CGM in patients with type 2 diabetes mellitus were then selected. Statistical analysis included calculation of the standardized mean difference (SMD) or risk ratio and 95% confidence intervals (CIs) using a random effects model. After literature search, seven RCTs (669 patients) satisfied the eligibility criteria established herein and were included into the meta-analysis. Compared with the self-monitoring blood glucose group, the CGM group exhibited significantly lower HbA1c levels (SMD, −0.35; 95% CI, −0.59–−0.10; P=0.006 ) and shorter time spent with hypoglycaemia (SMD, −0.42; 95% CI, −0.70–−0.13; P=0.004 ). Conversely, no differences in body weight and blood pressure were observed between the groups. CGM in patients with type 2 diabetes mellitus could reduce HbA1c levels and time spent with hypoglycaemia. However, because few RCTs were included in this present study and heterogeneity was also noted, care should be taken when interpreting the results.
Background:
Wearable sensors have been increasingly used in behavioral research for real-time assessment and intervention purposes. The rapid advancement of biomedical technology typically used in clinical settings has made wearable sensors more accessible to a wider population. Yet the acceptability of this technology for nonclinical purposes has not been examined.
Objective:
The aim was to assess the acceptability of wearing a continuous glucose monitor (CGM) device among a sample of nondiabetic individuals, and to compare the acceptability of a CGM between a mobile diet tracking app (MyFitnessPal) and an accelerometer.
Methods:
A total of 30 nondiabetic adults went through a 7-day observational study. They wore a CGM sensor, tracked their diet and physical activity using the CGM receiver and MyFitnessPal, and wore an accelerometer on their waist. After the monitoring period, they completed a 10-item survey regarding acceptability of each of the study tools. Two-tailed paired-sample t tests were conducted to examine whether the summary acceptability scores were comparable between the CGM sensor/receiver and MyFitnessPal/accelerometer.
Results:
More than 90% of the study participants agreed that the CGM sensor and receiver were easy to use (28/30 and 27/30, respectively), useful (28/30 and 29/30, respectively), and provided relevant information that was of interest to them (27/30 and 28/30, respectively). The summary acceptability scores (out of a 5-point Likert scale) were mean 4.06 (SD 0.55) for the CGM sensor, mean 4.05 (SD 0.58) for the CGM receiver, mean 4.10 (SD 0.68) for MyFitnessPal, and mean 3.73 (SD 0.76) for the accelerometer.
Conclusions:
The high acceptability of using a CGM from this study suggests a great potential for using CGMs in nondiabetic adults in research settings. Although potential selection bias might contribute to the high acceptability in this study, the continued advancements in wearable sensor technology will make the barriers to tracking and collecting personal physiological data more and more minimal.
The American Diabetes Association and the European Association for the Study of Diabetes convened a panel to update the prior position statements, published in 2012 and 2015, on the management of type 2 diabetes in adults. A systematic evaluation of the literature since 2014 informed new recommendations. These include additional focus on lifestyle management and diabetes self-management education and support. For those with obesity, efforts targeting weight loss, including lifestyle, medication, and surgical interventions, are recommended. With regards to medication management, for patients with clinical cardiovascular disease, a sodium-glucose cotransporter 2 (SGLT2) inhibitor or a glucagon-like peptide 1 (GLP-1) receptor agonist with proven cardiovascular benefit is recommended. For patients with chronic kidney disease or clinical heart failure and atherosclerotic cardiovascular disease, an SGLT2 inhibitor with proven benefit is recommended. GLP-1 receptor agonists are generally recommended as the first injectable medication.
Diabetes is an increasing problem worldwide; almost 30 million people, nearly 10% of the population, in the United States are diagnosed with diabetes. Another 84 million are prediabetic, and without intervention, up to 70% of these individuals may progress to type 2 diabetes. Current methods for quantifying blood glucose dysregulation in diabetes and prediabetes are limited by reliance on single-time-point measurements or on average measures of overall glycemia and neglect glucose dynamics. We have used continuous glucose monitoring (CGM) to evaluate the frequency with which individuals demonstrate elevations in postprandial glucose, the types of patterns, and how patterns vary between individuals given an identical nutrient challenge. Measurement of insulin resistance and secretion highlights the fact that the physiology underlying dysglycemia is highly variable between individuals. We developed an analytical framework that can group individuals according to specific patterns of glycemic responses called “glucotypes” that reveal heterogeneity, or subphenotypes, within traditional diagnostic categories of glucose regulation. Importantly, we found that even individuals considered normoglycemic by standard measures exhibit high glucose variability using CGM, with glucose levels reaching prediabetic and diabetic ranges 15% and 2% of the time, respectively. We thus show that glucose dysregulation, as characterized by CGM, is more prevalent and heterogeneous than previously thought and can affect individuals considered normoglycemic by standard measures, and specific patterns of glycemic responses reflect variable underlying physiology. The interindividual variability in glycemic responses to standardized meals also highlights the personal nature of glucose regulation. Through extensive phenotyping, we developed a model for identifying potential mechanisms of personal glucose dysregulation and built a webtool for visualizing a user-uploaded CGM profile and classifying individualized glucose patterns into glucotypes.
Objective:
This study updates previous estimates of the economic burden of diagnosed diabetes and quantifies the increased health resource use and lost productivity associated with diabetes in 2017.
Research design and methods:
We use a prevalence-based approach that combines the demographics of the U.S. population in 2017 with diabetes prevalence, epidemiological data, health care cost, and economic data into a Cost of Diabetes Model. Health resource use and associated medical costs are analyzed by age, sex, race/ethnicity, insurance coverage, medical condition, and health service category. Data sources include national surveys, Medicare standard analytical files, and one of the largest claims databases for the commercially insured population in the U.S.
Results:
The total estimated cost of diagnosed diabetes in 2017 is 237 billion in direct medical costs and 16,750 per year, of which ∼3.3 billion) and reduced productivity while at work (2.3 billion), inability to work because of disease-related disability (19.9 billion).
Conclusions:
After adjusting for inflation, economic costs of diabetes increased by 26% from 2012 to 2017 due to the increased prevalence of diabetes and the increased cost per person with diabetes. The growth in diabetes prevalence and medical costs is primarily among the population aged 65 years and older, contributing to a growing economic cost to the Medicare program. The estimates in this article highlight the substantial financial burden that diabetes imposes on society, in addition to intangible costs from pain and suffering, resources from care provided by nonpaid caregivers, and costs associated with undiagnosed diabetes.
Background
Acute reductions in postprandial glucose excursions because of movement behaviors have been demonstrated in experimental studies but less so in free-living settings.
Objective
The objective of this study was to explore the nature of the acute stimulus-response model between accelerometer-assessed physical activity, sedentary time, and glucose variability over 13 days in nondiabetic adults.
Methods
This study measured physical activity, sedentary time, and interstitial glucose continuously over 13 days in 29 participants (mean age in years: 44.9 [SD 9.1]; female: 59%, 17/29; white: 90%, 26/29; mean body mass index: 25.3 [SD 4.1]) as part of the Sensing Interstitial Glucose to Nudge Active Lifestyles (SIGNAL) research program. Daily minutes spent sedentary, in light activity, and moderate to vigorous physical activity were associated with daily mean glucose, SD of glucose, and mean amplitude of glycemic excursions (MAGE) using generalized estimating equations.
ResultsAfter adjustment for covariates, sedentary time in minutes was positively associated with a higher daily mean glucose (mmol/L; beta=0.0007; 95% CI 0.00030-0.00103; P
Behavior change interventions typically contain multiple potentially active components: behavior change techniques (BCTs). Identifying which specific BCTs or BCT combinations have the potential to be effective for a given behavior in a given context presents a major challenge. The aim of this study was to review the methods that have been used to identify effective BCTs for given behaviors in given contexts and evaluate their strengths and limitations. A scoping review was conducted of studies that had sought to identify effective BCTs. Articles referring to “behavio(u)r change technique(s)” in the abstract/text were located, and ones that involved identification of effective BCTs were selected. The methods reported were coded. The methods were analyzed in general terms using “PASS” criteria: Practicability (facility to apply the method appropriately), Applicability (facility to generalize from findings to contexts and populations of interest), Sensitivity (facility to identify effective BCTs), and Specificity (facility to rule out ineffective BCTs). A sample of 10% of the studies reviewed was then evaluated using these criteria to assess how far the strengths and limitations identified in principle were borne out in practice. One hundred and thirty-five studies were identified. The methods used in those studies were experimental manipulation of BCTs, observational studies comparing outcomes in the presence or absence of BCTs, meta-analyses of BCT comparisons, meta-regressions evaluating effect sizes with and without specific BCTs, reviews of BCTs found in effective interventions, and meta-classification and regression trees. The limitations of each method meant that only weak conclusions could be drawn regarding the effectiveness of specific BCTs or BCT combinations. Methods for identifying effective BCTs linked to target behavior and context all have important inherent limitations. A strategy needs to be developed that can systematically combine the strengths of the different methods and that can link these constructs in an ontology of behavior change interventions.
The first systems for continuous glucose monitoring (CGM) became available over 15 years ago. Many then believed CGM would revolutionize the use of intensive insulin therapy in diabetes; however, progress toward that vision has been gradual. Although increasing, the proportion of individuals using CGM rather than conventional systems for self-monitoring of blood glucose on a daily basis is still low in most parts of the world. Barriers to uptake include cost, measurement reliability (particularly with earlier-generation systems), human factors issues, lack of a standardized format for displaying results, and uncertainty on how best to use CGM data to make therapeutic decisions. This Scientific Statement makes recommendations for systemic improvements in clinical use and regulatory (pre- and postmarketing) handling of CGM devices. The aim is to improve safety and efficacy in order to support the advancement of the technology in achieving its potential to improve quality of life and health outcomes for more people with diabetes.
Background: Hemoglobin A1c is an excellent population health measure for the risk of vascular complications in diabetes, while continuous glucose monitoring (CGM) is a tool to help personalize a diabetes treatment plan. The value of CGM in individuals with type 1 diabetes (T1D) has been well demonstrated when compared with utilizing self-monitoring of blood glucose (SMBG) to guide treatment decisions.
CGM is a tool for patients and clinicians to visualize the important role that diet, exercise, stress management, and the appropriate selection of diabetes medications can have in managing type 2 diabetes (T2D). Several diabetes organizations have recently reviewed the literature on the appropriate use of CGM in diabetes management and concluded CGM may be a useful educational and management tool particularly for patients on insulin therapy. The indications for using CGM either as a clinic-based loaner distribution model for intermittent use (professional CGM) or a CGM system owned by the patient and used at home with real-time glucose reading (personal CGM) are only beginning to be addressed in T2D. Most summaries of CGM studies conclude that having a standardized glucose pattern report, such as the ambulatory glucose profile (AGP) report, should help facilitate effective shared decision-making sessions.
The future of CGM indications for the use of CGM is evolving rapidly. In some instances, CGM is now approved for making medication adjustments without SMBG confirmation and it appears that some forms of CGM will be approved for use in the Medicare population in the United States in the near future. Many individuals with T1D and T2D and their care teams will come to depend on CGM as a key tool for diabetes management.
Background:
Women with gestational diabetes mellitus (GDM) are required to control their blood glucose shortly after GDM diagnosis to minimize adverse pregnancy outcomes. A real time-continuous glucose monitoring system (RT-CGMS) provides the patient with continuous information about the alterations in levels of the blood glucose. This visibility may empower the patient to modify her lifestyle and engage in therapeutic management. The aim of this study was to determine whether a single application of RT-CGMS to pregnant women shortly after GDM diagnosis is useful as an educational and motivational tool.
Methods:
This study was a prospective open label randomized controlled study conducted at Maternity and Children Hospital, Medina, Saudi Arabia. A total of 130 pregnant women with GDM were randomised to either blood glucose self-monitor alone (SMBG group) (n = 62) or in addition to SMBG, patients wore a Guardian(®) REAL-Time Continuous Glucose Monitoring System (Medtronic MiniMed) once for 3-7 days, within 2 weeks of GDM diagnosis (RT-CGMS group) (n = 68). The primary outcomes were maternal glycemic control and pregnancy outcomes. Secondary outcomes were the changes in parameters of glucose variability, which includes mean sensor readings, standard deviation (SD) of blood glucose, and area under the curve for hyper and hypoglycaemia at the end of the RT-CGMS application.
Results:
HbA1c, mean fasting and postprandial glucose levels were similar in both groups at the end of the pregnancy. Pregnancy outcomes were comparable. However, there was significant improvement in the parameters of glucose variability on the last day of sensor application; both mean glucose and the SD of mean glycaemia were reduced significantly; P = 0.016 and P = 0.034, respectively. The area under the curve for hyper and hypoglycaemia were improved, however, the results were not statistically significant.
Conclusion:
Although a single application of RT-CGMS shortly after GDM diagnosis is helpful as an educational tool, it was not associated with improvement in glycemic control or pregnancy outcomes.
To forecast future trends in diabetes prevalence, morbidity, and costs in the United States, the Institute for Alternative Futures has updated its diabetes forecasting model and extended its projections to 2030 for the nation, all states, and several metropolitan areas. This paper describes the methodology and data sources for these diabetes forecasts and discusses key implications. In short, diabetes will remain a major health crisis in America, in spite of medical advances and prevention efforts. The prevalence of diabetes (type 2 diabetes and type 1 diabetes) will increase by 54% to more than 54.9 million Americans between 2015 and 2030; annual deaths attributed to diabetes will climb by 38% to 385,800; and total annual medical and societal costs related to diabetes will increase 53% to more than $622 billion by 2030. Improvements in management reducing the annual incidence of morbidities and premature deaths related to diabetes over this time period will result in diabetes patients living longer, but requiring many years of comprehensive management of multiple chronic diseases, resulting in dramatically increased costs. Aggressive population health measures, including increased availability of diabetes prevention programs, could help millions of adults prevent or delay the progression to type 2 diabetes, thereby helping turn around these dire projections. (Population Health Management 20xx;xx:xxx-xxx).
Background:
Retrospective continuous glucose monitoring (CGM) studies may provide healthcare professionals (HCPs) with better understanding of glycemic patterns in patients with type 2 diabetes (T2D) and thereby support patient education and appropriate therapeutic interventions.
Methods:
Adults with T2D and A1C values between 8% and 10% were eligible for this 3-month study. Patients were scheduled for 5 visits that included baseline and a month-2 retrospective CGM study (iPro2, Medtronic) followed by data review and therapy modifications. A1C values were determined at baseline and at study end. Questionnaires were completed at each visit. HCP questionnaires assessed perception of the utility of studies; patient questionnaires assessed understanding of the importance of compliance with HCP recommendations. Indices of glycemic variability and control were calculated from CGM data retrospectively.
Results:
A total of 181 subjects enrolled and 148 completed the study (81.8%). There were no serious adverse device effects. Most subjects (91.2%) had > 1 therapy change after review of the first iPro2 test. Mean A1C decreased from 8.6% at baseline to 8.0% at month 3 (p<0.001). Questionnaire results from patients and HCPs indicated that both groups viewed the iPro2 studies and results as acceptable and useful. CGM-based glycemic variability metrics were similar in the two iPro2 tests.
Conclusions:
iPro2 studies provided HCPs with insights and opportunities for initiating changes to treatment regimens and to diet and exercise behaviors, and provided patients with improved knowledge of the importance of therapy compliance. Favorable reductions in A1C suggest that iPro2 tests can facilitate optimal management of T2D.
Aberrant brain reward responses to food-related cues are an implied characteristic of human obesity; yet, findings are inconsistent.
To explain these inconsistencies, we aimed to uncover endophenotypes associated with heterogeneity in attributing incentive
salience to food cues in the context of other emotionally salient cues; a phenomenon described as sign- vs goal tracking in preclinical models. Data from 64 lean and 88 obese adults who were 35.5 ± 9.4 years old and predominantly
women (79%) were analyzed. Participants viewed food-related, pleasant, neutral and unpleasant images while recording electroencephalograph.
Late positive potentials were used to assess incentive salience attributed to the visual stimuli. Eating and affective traits
were also assessed. Findings demonstrated that obese individuals, in general, do not demonstrate aberrant brain reward responses
to food-related cues. As hypothesized, latent profile analysis of the late positive potential uncovered two distinct groups.
‘Sign-trackers’ showed greater responses to food-related cues (P < 0.001) but lower responses to pleasant stimuli (P < 0.001) compared with ‘goal-trackers’. There were proportionally more obese than lean ‘sign-trackers’ (P = 0.03). Obese ‘sign-trackers’ reported significantly higher levels of emotional eating and food craving (P < 0.001). By examining the heterogeneity in brain reactivity to various emotional stimuli, this translational study highlights
the need to consider important neurobehavioral endophenotypes of obesity.
International experts in the fields of diabetes, diabetes technology, endocrinology, mobile health, sport science, and regulatory issues gathered for the 8(th) Annual Symposium on Self-Monitoring of Blood Glucose (SMBG) with a focus on personalized diabetes management. The aim of this meeting was to facilitate new collaborations and research projects to improve the lives of people with diabetes. The 2015 meeting comprised a comprehensive scientific program, parallel interactive workshops, and two keynote lectures.
Background:
Behavioral programs may improve outcomes for individuals with type 2 diabetes, but there is a large diversity of behavioral interventions and uncertainty about how to optimize the effectiveness of these programs.
Purpose:
To identify factors moderating the effectiveness of behavioral programs for adults with type 2 diabetes.
Data sources:
6 databases (1993 to January 2015), conference proceedings (2011-2014), and reference lists.
Study selection:
Duplicate screening and selection of 132 randomized, controlled trials evaluating behavioral programs compared with usual care, active controls, or other behavioral programs.
Data extraction:
One reviewer extracted and another verified data. Two reviewers independently assessed risk of bias.
Data synthesis:
Behavioral programs were grouped on the basis of program content and delivery methods. A Bayesian network meta-analysis showed that most lifestyle and diabetes self-management education and support programs (usually offering ≥11 contact hours) led to clinically important improvements in glycemic control (≥0.4% reduction in hemoglobin [Hb] A1c), whereas most diabetes self-management education programs without added support-especially those offering 10 or fewer contact hours-provided little benefit. Programs with higher effect sizes were more often delivered in person than via technology. Lifestyle programs led to the greatest reductions in body mass index. Reductions in HbA1c seemed to be greater for participants with a baseline HbA1c level of 7.0% or greater, adults younger than 65 years, and minority persons (subgroups with ≥75% nonwhite participants).
Limitations:
All trials had medium or high risk of bias. Subgroup analyses were indirect, and therefore exploratory. Most outcomes were reported immediately after the interventions.
Conclusion:
Diabetes self-management education offering 10 or fewer hours of contact with delivery personnel provided little benefit. Behavioral programs seem to benefit persons with suboptimal or poor glycemic control more than those with good control.
Primary funding source:
Agency for Healthcare Research and Quality. (PROSPERO registration number: CRD42014010515).
Background:
Physical activity plays a critical role in health, including for effective weight maintenance, but adherence to guidelines is often poor. Similarly, although debate continues over whether a "best" diet exists for weight control, meta-analyses suggest little difference in outcomes between diets differing markedly in macronutrient composition, particularly over the longer-term. Thus a more important question is how best to encourage adherence to appropriate lifestyle change. While brief support is effective, it has on-going cost implications. While self-monitoring (weight, diet, physical activity) is a cornerstone of effective weight management, little formal evaluation of the role that self-monitoring technology can play in enhancing adherence to change has occurred to date. People who eat in response to hunger have improved weight control, yet how best to train individuals to recognise when true physical hunger occurs and to limit consumption to those times, requires further study.
Methods/design:
SWIFT (Support strategies for Whole-food diets, Intermittent Fasting, and Training) is a two-year randomised controlled trial in 250 overweight (body mass index of 27 or greater) adults that will examine different ways of supporting people to make appropriate changes to diet and exercise habits for long-term weight control. Participants will be randomised to one of five intervention groups: control, brief support (monthly weigh-ins and meeting), app (use of MyFitnessPal with limited support), daily self-weighing (with brief monthly feedback), or hunger training (four-week programme which trains individuals to only eat when physically hungry) for 24 months. Outcome assessments include weight, waist circumference, body composition (dual-energy x-ray absorptiometry), inflammatory markers, blood lipids, adiponectin and ghrelin, blood pressure, diet (3-day diet records), physical activity (accelerometry) and aerobic fitness, and eating behaviour. SWIFT is powered to detect clinically important differences of 4 kg in body weight and 5 cm in waist circumference. Our pragmatic trial also allows participants to choose one of several dietary (Mediterranean, modified Paleo, intermittent fasting) and exercise (current recommendations, high-intensity interval training) approaches before being randomised to a support strategy.
Discussion:
SWIFT will compare four different ways of supporting overweight adults to lose weight while following a diet and exercise plan of their choice, an aspect we believe will enhance adherence and thus success with weight management.
Trial registration:
Australian and New Zealand Clinical Trials Registry ACTRN12615000010594 . Registered 8(th) January 2015.
Obesity research conducted to date has focused on identifying and educating the public on “what” and “how much” to eat. Despite our efforts, the prevalence of obesity continues to rise. Some of the more promising obesity research is focused on identifying and assessing cognitive and psychological aspects of eating behavior with the intent of understanding “how” we make food-related decisions and “why” we initiate eating episodes if not for physiological reasons. Three theories emerged over the past 50 years that have enhanced our understanding of the cognitive/psychological aspects of eating that influence the relationship between energy intake and obesity: the psychosomatic theory, the theory of externality, and the theory of restraint. These theories have served as the foundation for at least seven psychometric instruments that assess eating behavior in nonclinical, adult populations. With recent changes in the production and marketing of food in developed and developing countries, there is an increased interest among behavioral scientists and psychologists as well as neurobiologists to utilize these psychometric instruments to assess and understand patterns of eating that promote weight gain. We are now exposed to an overabundance of food-related cues encouraging the consumption low-cost, energy-dense foods. Decisions prompting energy intake in this “obesogenic” environment are believed to be guided more frequently by the desire to eat and less frequently by the physiological need for energy. Gaining a greater understanding of the behavioral and neurobiological mechanisms that influence our decision-making processes will aid in the design of more effective interventions aimed to reduce the prevalence of obesity. In this chapter, the three cognitive/psychological eating behavior theories will be reviewed, and a summarized description of the development and validation of select psychometric instruments will be provided. Recent research of the neurobiological mechanisms linking theory-based eating behavior constructs with obesity will also be presented. To conclude, guidelines for the selection of the most suitable ins?trument for research are offered.
"Hunger training", which aims to teach people to eat only when blood glucose is below a set target, appears promising as a weight loss strategy. As the ability of participants to adhere to the rigorous protocol has been insufficiently described, we sought to determine the feasibility of hunger training, in terms of retention in the study, adherence to measuring blood glucose, and eating only when blood glucose concentrations are below a set level of 4.7 mmol/L.
We undertook a two-week feasibility study, utilising an adaptive design approach where the specific blood glucose cut-off was the adaptive feature. A blood glucose cut-off of 4.7 mmol/L (protocol A) was used for the first 20 participants. A priori we decided that if interim analysis revealed that this cut-off did not meet our feasibility criteria, the remaining ten participants would use an individualised cut-off based on their fasting glucose concentrations (protocol B).
Retention of the participants in the study was 97 % (28/29 participants), achieving our criterion of 85 %. Participants measured their blood glucose before 94 % (95 % CI 91, 98) of eating occasions (criterion 80 %). However, participants following protocol A, which used a standard blood glucose cut-off of 4.7 mmol/L, were only able to adhere to eating when blood glucose was below the prescribed level 66 % of the time, below our within-person criterion of 75 %. By contrast, those participants following protocol B (individualised cut-off) adhered to the eating protocol 84 % of the time, a significant (p = 0.010) improvement over protocol A.
Hunger training appears to be a feasible method, at least in the short-term, when an individualised fasting blood glucose is used to indicate that a meal can begin.
This article is a summary of the symposium "Modifying Eating Behavior: Novel Approaches for Reducing Body Weight, Preventing Weight Regain, and Reducing Chronic Disease Risk" held 29 April 2014 at the ASN Scientific Sessions and Annual Meeting at Experimental Biology 2014 in San Diego, CA. In this symposium, novel approaches to modifying eating behavior were highlighted, including 1) alteration of meal timing and macronutrient composition and 2) retraining and provision of feedback about eating behavior. Dr. Ciampolini discussed a method for teaching individuals to recognize a decrease in blood glucose concentration, and therefore the need for energy, by learning the associated physical sensations (signifying hunger). Dr. Madar and Sigal Sofer presented their work on reducing hunger during energy reduction by feeding carbohydrate only in the evening. Dr. Hamilton-Shield reviewed studies on the Mandometer (Mikrodidakt), a device for training individuals to slow eating rate. Finally, Dr. Sazonov presented information on a wearable device, the Automatic Ingestion Monitor, which senses jaw motion and/or hand-to-mouth gestures to detect and characterize food intake. His goal is to use the instrument to prevent overeating by providing feedback to the user to stop ingestion at a predetermined limit.
Background
Hunger training teaches people to eat according to their appetite using pre-prandial glucose measurement. Previous hunger training interventions used fingerprick blood glucose, however continuous glucose monitoring (CGM) offers a painless and convenient form of glucose monitoring. The aim of this randomised feasibility trial was to compare hunger training using CGM with fingerprick glucose monitoring in terms of adherence to the protocol, acceptability, weight, body composition, HbA1c, psychosocial variables, and the relationship between adherence measures and weight loss.
Methods
40 adults with obesity were randomised to either fingerpricking or scanning with a CGM and followed identical interventions for 6 months, which included 1 month of only eating when glucose was under their individualised glucose cut-off. For months 2–6 participants relied on their sensations of hunger to guide their eating and filled in a booklet.
Results
90% of the fingerpricking group and 85% of the scanning group completed the study. Those using the scanner measured their glucose an extra 1.9 times per day (95% CI 0.9, 2.8, p < 0.001) compared with those testing by fingerprick. Both groups lost similar amounts of weight over 6 months (on average 4 kg), were satisfied with the hunger training program and wanted to measure their glucose again within the next year. There were no differences between groups in terms of intervention acceptability, weight, body composition, HbA1c, eating behaviours, or psychological health. Frequency of glucose testing and booklet entry both predicted a clinically meaningful amount of weight loss.
Conclusions
Either method of measuring glucose is effective for learning to eat according to hunger using the hunger training program. As scanning with a CGM encouraged better adherence to the protocol without sacrificing outcome results, future interventions should consider using this new technology in hunger training programs.
The concept of implantable glucose sensors has been promulgated for more than 40 years. It is now accepted that continuous glucose monitoring (CGM) increases quality of life by allowing informed diabetes management decisions as a result of more optimized glucose control. The focus of this article is to provide a brief overview of the CGM market history, emerging technologies, and the foreseeable challenges for the next CGM generations as well as proposing possible solutions in an effort to advance the next generation of implantable sensor.
Background:
Continuous glucose monitoring (CGM) has potential to address challenges of type 1 diabetes (T1D) management for young children. CGM use is increasing, yet remains underutilized. Characterizing parents' experiences with CGM can inform clinical strategies to help parents make decisions about diabetes management, overcome obstacles to initiating and sustaining CGM use, and maximize benefits of CGM use in their children's diabetes care.
Methods:
Transcripts from semistructured qualitative interviews with 55 parents of children aged 1 to <8 years, with T1D duration ≥6 months, and whose child currently or previously used CGM were coded and analyzed to derive themes about their experiences with CGM.
Results:
Participants were 88% mothers and the mean child age was 5.0 ± 1.5 years. Parents described benefits of CGM use: decreased worry about glucose excursions, improved sleep, increased sense of safety with children who cannot recognize or express symptoms of hypo- or hyperglycemia, and greater comfort with other caregivers, especially using remote monitoring functionality when away from children. Challenges included painful insertions, wearing multiple devices on small bodies, disruptive alerts, data gaps due to lost signals, skin/adhesive problems, and difficulty interpreting the amount of information generated by CGM. For some, the challenges outweighed potential benefits and they stopped CGM use.
Conclusions:
CGM may address unique challenges of T1D in young children and increase parental comfort with diabetes management, yet there are multiple barriers to initiating or maintaining CGM use. Education and behavioral support to address these benefits and barriers may equip caregivers with skills to address challenges of CGM use.
Abbreviations:
A1C = hemoglobin A1C; AACE = American Association of Clinical Endocrinologists; ACCORD = Action to Control Cardiovascular Risk in Diabetes; ACCORD BP = Action to Control Cardiovascular Risk in Diabetes Blood Pressure; ACE = American College of Endocrinology; ACEI = angiotensin-converting enzyme inhibitor; AGI = alpha-glucosidase inhibitor; apo B = apolipoprotein B; ARB = angiotensin II receptor blocker; ASCVD = atherosclerotic cardiovascular disease; BAS = bile acid sequestrant; BMI = body mass index; BP = blood pressure; CCB = calcium channel blocker; CGM = continuous glucose monitoring; CHD = coronary heart disease; CKD = chronic kidney disease; DKA = diabetic ketoacidosis; DPP4 = dipeptidyl peptidase 4; eGFR = estimated glomerular filtration rate; EPA = eicosapentaenoic acid; ER = extended release; FDA = Food and Drug Administration; GLP1 = glucagon-like peptide 1; HDL-C = high-density-lipoprotein cholesterol; HeFH = heterozygous familial hypercholesterolemia; LDL-C = low-density-lipoprotein cholesterol; LDL-P = low-density-lipoprotein particle; Look AHEAD = Look Action for Health in Diabetes; NPH = neutral protamine Hagedorn; OSA = obstructive sleep apnea; PCSK9 = proprotein convertase subtilisin-kexin type 9 serine protease; RCT = randomized controlled trial; SU = sulfonylurea; SGLT2 = sodium-glucose cotransporter 2; SMBG = self-monitoring of blood glucose; T2D = type 2 diabetes; TZD = thiazolidinedione.
Purpose:
Large randomized trials have demonstrated the efficacy of continuous glucose monitoring (CGM) in persons with type 1 diabetes and insulin-treated type 2 diabetes. The purpose of this article is to provide basic knowledge about CGM technology, discuss the use of CGM data in clinical practice, and direct clinicians to online resources that provide comprehensive information and tools relevant to patient selection, education/training, and reimbursement.
Conclusions:
Effective use of CGM requires all members of the health care team to become knowledgeable and skilled in integrating CGM into their practices and in teaching their patients how to safely incorporate CGM use into their daily diabetes self-management.
Background:
The Centers for Medicare and Medicaid Services recently issued final rules for the Medicare Diabetes Prevention Program (MDPP), offering an unprecedented opportunity to provide lifestyle intervention to Medicare beneficiaries with prediabetes via a pay-for-performance model. The MDPP is based on the widely disseminated, yearlong National Diabetes Prevention Program (NDPP), which has lesser but still beneficial risk-reduction outcomes among minority and low-income participants.
Objectives:
We compare projected payments based on outcomes of a diverse sample of Medicare beneficiaries to service delivery costs, and explore resulting implications for MDPP access and sustainability.
Methods:
We delivered NDPP in a safety-net health care system from 2013 to 2017 and conducted an analysis of service cost, beneficiary performance, and projected MDPP reimbursement.
Results:
Among 1165 total participants, 213 (18.3%) were Medicare beneficiaries. Participating beneficiaries were 40.6% Hispanic, 31.6% non-Hispanic black, and 26.9% non-Hispanic white and 69.5% low-income. Overall beneficiary performance would result in an average reimbursement of 800 per participant, leaving an average gap of $661 per beneficiary.
Conclusions:
Findings from delivering the NDPP to diverse and undeserved patients show a large gap between service costs and projected reimbursement. Although many MDPP suppliers are needed to reach all Medicare beneficiaries with prediabetes, insufficient reimbursement may be a deterrent. Health disparities may also widen as suppliers serving diverse and low-income populations will likely receive especially low payments, threatening access. Higher payments are supported by strong return-on-investment findings and seem needed to reduce diabetes prevalence and related disparities.
Aims
The effect of intensive glycaemic control, blood pressure control and lipid levels control alone or as part of a multifactorial intervention has not been fully evaluated. We aimed to estimate the effects of more intensive interventions, compared with standard care, on risk factor control and cardiovascular outcomes in people with type 2 diabetes and microalbuminuria.
Methods
We searched MEDLINE, Embase and the Cochrane library without language restrictions from inception to August 10, 2018. We included randomised controlled trials that evaluated intensive interventions in adults with type 2 diabetes and microalbuminuria. The review was registered on PROSPERO (registration number 42017055208). We used random effects meta-analysis to calculate overall pooled effect estimates across studies.
Results
A total of seven (n = 1210) randomised controlled trials were included, four studies (n = 758) reported HbA1c, six studies (n = 950) reported blood pressure measurements, and three studies (n = 896) examined non-fatal MI, non-fatal stroke, cardiovascular mortality, and all-cause mortality. Intensive interventions indicated statistically significant reductions in both systolic and diastolic blood pressure, and a nonsignificant trend for reduction in HbA1c, total cholesterol, LDL, triglycerides and urinary albumin excretion rate. There was no evidence to suggest that compared with standard care, intensive interventions reduced the risk of non-fatal MI [risk ratio (RR) 0·50; 95% CI 0·20, 1·22; P = 0·127], non-fatal stroke (RR 0·44; 95% CI 0·10, 1·91; P = 0·275), CV mortality (RR 0·95; 95% CI 0·48, 1·86; P = 0·874) or all-cause mortality (RR 0·80; 95% CI 0·51, 1·25; P = 0·324).
Conclusions
Apart from blood pressure outcomes, there was no evidence that intensive interventions improve or worsen HbA1c, total cholesterol, LDL, triglycerides, urinary albumin excretion rate, risk of cardiovascular or mortality outcomes in people with type 2 diabetes and microalbuminuria. Results of this review are mainly influenced by one small trial, hence uncertainty surrounding the effect of intensive interventions in people with type 2 diabetes and microalbuminuria still exists. Large studies are urgently required in this high risk cardiovascular group of patients.
Real-time continuous glucose monitoring (RT-CGM) provides real time glucose readings to participants wearing the device. The ability to see changes in glucose has the potential to provide immediate feedback to users on food choices and physical activity. The National Diabetes Prevention Program is currently the only reimbursable intervention for diabetes prevention and weight loss. The purpose of this article is to review the CGM literature on measurements other than Hemoglobin A1c (HbA1c) changes and hypoglycemia and discuss RT-CGM potential use as a behavior modification tool for lifestyle changes and weight reduction in people with prediabetes and type 2 diabetes (T2D).
Eating beyond physiological need contributes to obesity onset. Measuring this behavior could help identify those at risk for weight gain. This study measured eating in the absence of hunger (EAH) and its relationship with weight change and self-report measures related to appetite and eating behavior. EAH was assessed in 46 lean young women (69% pre-selected for weight gain proneness) after lunch and defined as the number of calories subsequently consumed from snacks. Participants also completed questionnaires, and their body weights were measured regularly over the next year. Participants consumed a mean 188 calories (±140) during the EAH test. Caloric intake during the EAH test was associated with hedonic hunger (p < .01, R2 = 0.18), loss of control eating (p < .001, R2 = 0.29), and weight gain over two months (p < .01, R2 = 0.19), controlling for baseline body mass index. All were large effect sizes. In contrast, EAH was unrelated to emotional eating, disinhibition, and longer-term weight change. Amount of the test meal eaten in a hungry state was unrelated to these variables. While EAH has mainly been examined in children, these results expand its utility to adults. EAH seems to reflect naturalistic eating behavior, as shown by its relationship with short-term weight gain, drive to overconsume foods, and loss of control over eating. EAH may be a useful test to identify young adults at risk for weight gain and/or disordered eating, and may be a target for intervention.
Objective:
To determine the effectiveness of various monitoring strategies on weight loss, body composition, blood markers, exercise, and psychosocial indices in adults with overweight and obesity following a 12-month weight loss program.
Methods:
Two hundred fifty adults with BMI ≥ 27 were randomized to brief, monthly, individual consults, daily self-monitoring of weight, self-monitoring of diet using MyFitnessPal, self-monitoring of hunger, or control over 12 months. All groups received diet and exercise advice, and 171 participants (68.4%) remained at 12 months.
Results:
No significant differences in weight, body composition, blood markers, exercise, or eating behavior were apparent between those in the four monitoring groups and the control condition at 12 months (all P ≥ 0.053). Weight differences between groups ranged from -1.1 kg (-3.8 to 1.6) to 2.2 kg (-1.0 to 5.3). However, brief support and hunger training groups reported significantly lower scores for depression (difference [95% CI]: -3.16 [-5.70 to -0.62] and -3.05 [-5.61 to -0.50], respectively) and anxiety (-1.84, [-3.67 to -0.02]) scores than control participants.
Conclusions:
Although adding a monitoring strategy to diet and exercise advice did not further increase weight loss, no adverse effects on eating behavior were observed, and some monitoring strategies may even benefit mental health.
Background: Magnesium acts as a cofactor in many intracellular reactions including phosphorylation of the insulin receptor; therefore, its imbalance can potentially cause insulin resistance. Low serum magnesium concentration has been associated with the development of metabolic syndrome and type 2 diabetes mellitus. Objective: To study the association between the daily dietary magnesium intake and insulin resistance estimated by the homeostatic model assessment of insulin resistance and homeostatic model assessment 2, as well as insulin sensitivity estimated by the Matsuda index. Methods: In a university affiliated medical center, 32 participants (22 women, 10 men) that had an indication for testing for type 2 diabetes mellitus with an oral glucose tolerance test were enrolled in this cross-sectional, comparative study. Clinical and biochemical evaluations were carried out including an oral glucose tolerance test. Hepatic insulin resistance index, homeostatic model assessment 2, homeostatic model assessment of insulin resistance, and Matsuda insulin sensitivity were calculated for each participant. They were asked to recall their food ingestion (24 hours) of three days of the past week, including a weekend day; magnesium intake was calculated according to the food nutritional information. Results: The low dietary magnesium intake group (< 4.5 mg/kg/day) had a higher two-hour insulin concentration after an oral glucose tolerance test compared to those with high dietary magnesium (119.5 [73.0-190.6] vs. 63.5 [25.4-114.2]; p = 0.008), and insulin sensitivity assessed by the Matsuda index was higher in the high dietary magnesium intake group (4.3 ± 3.1 vs. 2.4 ± 1.5; p = 0.042). In multiple linear regression analysis a higher dietary magnesium intake was independently associated (p = 4.93; p = 0.05) with a better insulin sensitivity estimated by the Matsuda index. Conclusions: Our results suggest that higher magnesium intake is independently associated with better insulin sensitivity in patients at risk for type 2 diabetes mellitus.
Importance
Between 1980 and 2000, the prevalence of obesity increased significantly among adult men and women in the United States; further significant increases were observed through 2003-2004 for men but not women. Subsequent comparisons of data from 2003-2004 with data through 2011-2012 showed no significant increases for men or women.
Objective
To examine obesity prevalence for 2013-2014 and trends over the decade from 2005 through 2014 adjusting for sex, age, race/Hispanic origin, smoking status, and education.
Design, Setting, and Participants
Analysis of data obtained from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional, nationally representative health examination survey of the US civilian noninstitutionalized population that includes measured weight and height.
Exposures
Survey period.
Main Outcomes and Measures
Prevalence of obesity (body mass index ≥30) and class 3 obesity (body mass index ≥40).
Results
This report is based on data from 2638 adult men (mean age, 46.8 years) and 2817 women (mean age, 48.4 years) from the most recent 2 years (2013-2014) of NHANES and data from 21 013 participants in previous NHANES surveys from 2005 through 2012. For the years 2013-2014, the overall age-adjusted prevalence of obesity was 37.7% (95% CI, 35.8%-39.7%); among men, it was 35.0% (95% CI, 32.8%-37.3%); and among women, it was 40.4% (95% CI, 37.6%-43.3%). The corresponding prevalence of class 3 obesity overall was 7.7% (95% CI, 6.2%-9.3%); among men, it was 5.5% (95% CI, 4.0%-7.2%); and among women, it was 9.9% (95% CI, 7.5%-12.3%). Analyses of changes over the decade from 2005 through 2014, adjusted for age, race/Hispanic origin, smoking status, and education, showed significant increasing linear trends among women for overall obesity (P = .004) and for class 3 obesity (P = .01) but not among men (P = .30 for overall obesity; P = .14 for class 3 obesity).
Conclusions and Relevance
In this nationally representative survey of adults in the United States, the age-adjusted prevalence of obesity in 2013-2014 was 35.0% among men and 40.4% among women. The corresponding values for class 3 obesity were 5.5% for men and 9.9% for women. For women, the prevalence of overall obesity and of class 3 obesity showed significant linear trends for increase between 2005 and 2014; there were no significant trends for men. Other studies are needed to determine the reasons for these trends.
Objective:
To determine whether adding mindfulness-based eating and stress management practices to a diet-exercise program improves weight loss and metabolic syndrome components.
Methods:
In this study 194 adults with obesity were randomized to a 5.5-month program with or without mindfulness training and identical diet-exercise guidelines. Intention-to-treat analyses with multiple imputation were used for missing data. The primary outcome was 18-month weight change.
Results:
Estimated effects comparing the mindfulness to control arm favored the mindfulness arm in (a) weight loss at 12 months, -1.9 kg (95% CI: -4.5, 0.8; P = 0.17), and 18 months, -1.7 kg (95% CI: -4.7, 1.2; P = 0.24), though not statistically significant; (b) changes in fasting glucose at 12 months, -3.1 mg/dl (95% CI: -6.3, 0.1; P = 0.06), and 18 months, -4.1 mg/dl (95% CI: -7.3, -0.9; P = 0.01); and (c) changes in triglyceride/HDL ratio at 12 months, -0.57 (95% CI: -0.95, -0.18; P = 0.004), and 18 months, -0.36 (95% CI: -0.74, 0.03; P = 0.07). Estimates for other metabolic risk factors were not statistically significant, including waist circumference, blood pressure, and C-reactive protein.
Conclusions:
Mindfulness enhancements to a diet-exercise program did not show substantial weight loss benefit but may promote long-term improvement in some aspects of metabolic health in obesity that requires further study.
Diabetes, a prevalent disease in the United States, is greatly impacted by lifestyle choices, notably nutrition. The goal of this research was to determine which of the nutritional tracking applications (apps) available for Apple (Cupertino, CA) iOS, Android(®) (Google, Mountain View, CA), and Windows (Microsoft, Redmond, WA) platforms should be a first recommendation to diabetes patients searching for a smartphone app to aid in dietary logging and, for some apps, other varying lifestyle and health data. This project did so by identifying the smartphone apps available on the iTunes(®) (Apple), Google Play, and Microsoft stores that have nutritional tracking capabilities and are of potential benefit to a patient with diabetes based on certain criteria. Each of the individual apps was then evaluated to determine which would be of most benefit to a diabetes patient. The apps were assessed based on several parameters, such as their food databases, logging options, additional tracking options, interoperability with other devices and apps, and diabetes-specific resources. This information was then compiled and evaluated to determine which apps would be of most benefit for diabetes patients. This research provides valuable information for both patients and healthcare providers because the results of this study can be used as a reference for practitioners wishing to make app recommendations for diabetes patients who are implementing lifestyle change as an aspect of therapy.
This chapter discusses the treatment for eating disorders. The Diagnostic and Statistical Manual of Mental Disorders recognizes two primary eating disorders: anorexia nervosa (AN) and bulimia nervosa (BN). It also includes binge eating disorder (BED), sub threshold versions of AN and BN, and other disordered eating patterns. The most widely researched treatments for eating disorders are based on cognitive-behavioral procedures and have focused on BN and BED. Acceptance-based methods for treating eating disorders deserve increased attention, and several interventions that incorporate mindfulness training and acceptance-related procedures. Some of these are adaptations of previously developed interventions. For example, dialectical behavior therapy (DBT) has been adapted for BED and BN; mindfulness-based cognitive therapy (MBCT) has been adapted for BED; and acceptance and commitment therapy (ACT) has been applied to AN. In addition, mindfulness-based eating awareness training (MB-EAT) is developed specifically for BED. MB-EAT is developed by integrating elements from MBSR and CBT with guided eating meditations. The program draws on traditional mindfulness meditation techniques, as well as guided meditation, to address specific issues pertaining to shape, weight, and eating-related self regulatory processes such as appetite and both gastric and taste-specific satiety.
Over 380 million adults worldwide are currently living with diabetes and the number has been projected to reach 590 million by 2035. Uncontrolled diabetes often lead to complications, disability, and early death. In the management of diabetes, dietary intervention to control carbohydrate intake is essential to help manage daily blood glucose level within a recommended range. The intervention traditionally relies on a self-report to estimate carbohydrate intake through a paper based diary. The traditional approach is known to be inaccurate, inconvenient, and resource intensive. Additionally, patients often require a long term of learning or training to achieve a certain level of accuracy and reliability. To address these issues, we propose a design of a smartphone application that automatically estimates carbohydrate intake from food images. The application uses imaging processing techniques to classify food type, estimate food volume, and accordingly calculate the amount of carbohydrates. To examine the proof of concept, a small fruit database was created to train a classification algorithm implemented in the application. Consequently, a set of fruit photos (n=6) from a real smartphone were applied to evaluate the accuracy of the carbohydrate estimation. This study demonstrates the potential to use smartphones to improve dietary intervention, although further studies are needed to improve the accuracy, and extend the capability of the smartphone application to analyse broader food contents.
Some apps have the potential to encourage healthier habits and are accessible to most people, writes Iltifat Husain, but Des Spence notes the lack of any evidence of effectiveness and the potential for encouraging unnecessary anxiety
This systematic review and meta-analysis consolidates recent evidence on the effectiveness of lifestyle-based, weight loss interventions for adults with type 2 diabetes.
A literature search from January 2003 to July 2013 was conducted (PubMed, Embase, CINAHL and Web of Science). Eligible studies were randomized controlled trials evaluating weight loss interventions (diet and physical activity, with or without behavioral strategies) of ≥12-weeks duration, compared to usual care or other comparison intervention. Ten studies were included for review. Some heterogeneity was present in the sample, thus random-effects models were used to calculate pooled effects.
Intervention duration ranged from 16-weeks to nine years, with all but one delivered via individual or group face-to-face sessions. From six studies comparing lifestyle intervention to usual care the pooled effect on weight (n = 5,795) was -3.33 kg (95% CI: -5.06, -1.60 kg), and on HbA1c (n = 5,784) was -0.29% (95% CI: -0.61, 0.03%), with both attenuated in sensitivity analyses. The pooled within-group effect on weight (n = 3,063) from all ten lifestyle intervention groups was -5.33 kg (95% CI: -7.33, -3.34 kg), also attenuated in sensitivity analyses. No participant or intervention characteristic examined explained the heterogeneity. Only one study assessed whether intervention effects were maintained following the end-of-intervention.
Lifestyle-based weight loss intervention trials in type 2 diabetes achieve, on average, modest reductions in weight and HbA1c, but results were heavily influenced by one trial. Evidence-based approaches for improving the effectiveness of lifestyle-based interventions in type 2 diabetes are needed along with future studies reporting on maintenance and cost-effectiveness.
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