New research directions on disparities in obesity and type 2 diabetes

Social and Structural Determinants of Health and Social Injustices Contributing to Obesity Disparities

PURPOSE OF REVIEW: To analyze how social and structural determinants of health and social injustice impact the risk of obesity, its treatment and treatment outcomes, and to explore the implications for prevention and future treatment interventions. RECENT FINDINGS: Racial and ethnic minorities, such as non-Hispanic Black adults and Hispanic adults, and adults with a low socioeconomic status have a greater risk of obesity than non-Hispanic white adults and adults with a high socioeconomic status. The underlying causes of obesity disparities include obesogenic neighborhood environments, inequities in access to obesity treatment, and lack of access to affordable nutrient-dense foods. Experts have called for interventions that address the social and structural determinants of obesity disparities. Population-based interventions that focus on improving neighborhood conditions, discouraging the consumption of unhealthy foods and beverages, expanding access to obesity treatment, and ensuring equitable access to fruits and vegetables have been proven to be effective. There is a growing body of evidence that shows the relationship between social and structural determinants of health and injustice on disparities in obesity among racial and ethnic minorities and individuals with a low SES. Population-based, equity-focused interventions that address the underlying causes of obesity disparities are needed to reduce obesity disparities and improve the health outcomes of minoritized and marginalized groups.

Social Determinants of Health Phenotypes and Cardiometabolic Condition Prevalence Among Patients in a Large Academic Health System: Latent Class Analysis

Background

Adverse social determinants of health (SDoH) have been associated with cardiometabolic disease; however, disparities in cardiometabolic outcomes are rarely the result of a single risk factor.

Objective

This study aimed to identify and characterize SDoH phenotypes based on patient-reported and neighborhood-level data from the institutional electronic medical record and evaluate the prevalence of diabetes, obesity, and other cardiometabolic diseases by phenotype status.

Methods

Patient-reported SDoH were collected (January to December 2020) and neighborhood-level social vulnerability, neighborhood socioeconomic status, and rurality were linked via census tract to geocoded patient addresses. Diabetes status was coded in the electronic medical record using International Classification of Diseases codes; obesity was defined using measured BMI ≥30 kg/m2. Latent class analysis was used to identify clusters of SDoH (eg, phenotypes); we then examined differences in the prevalence of cardiometabolic conditions based on phenotype status using prevalence ratios (PRs).

Results

Complete data were available for analysis for 2380 patients (mean age 53, SD 16 years; n=1405, 59% female; n=1198, 50% non-White). Roughly 8% (n=179) reported housing insecurity, 30% (n=710) reported resource needs (food, health care, or utilities), and 49% (n=1158) lived in a high-vulnerability census tract. We identified 3 patient SDoH phenotypes: (1) high social risk, defined largely by self-reported SDoH (n=217, 9%); (2) adverse neighborhood SDoH (n=1353, 56%), defined largely by adverse neighborhood-level measures; and (3) low social risk (n=810, 34%), defined as low individual- and neighborhood-level risks. Patients with an adverse neighborhood SDoH phenotype had higher prevalence of diagnosed type 2 diabetes (PR 1.19, 95% CI 1.06-1.33), hypertension (PR 1.14, 95% CI 1.02-1.27), peripheral vascular disease (PR 1.46, 95% CI 1.09-1.97), and heart failure (PR 1.46, 95% CI 1.20-1.79).

Conclusions

Patients with the adverse neighborhood SDoH phenotype had higher prevalence of poor cardiometabolic conditions compared to phenotypes determined by individual-level characteristics, suggesting that neighborhood environment plays a role, even if individual measures of socioeconomic status are not suboptimal.

Real-Time Continuous Glucose Monitoring in Adolescents and Young Adults With Type 2 Diabetes Can Improve Quality of Life

OBJECTIVE

Real-time continuous glucose monitoring (CGM) is effective for diabetes management in cases of type 1 diabetes and adults with type 2 diabetes (T2D) but has not been assessed in adolescents and young adults (AYAs) with T2D. The objective of this pilot interventional study was to assess the feasibility and acceptability of real-time CGM use in AYAs with T2D.

METHODS

Adolescents and young adults (13-21 years old) with T2D for six months or more and hemoglobin A1c (A1c) greater than 7%, on any Food and Drug Administration-approved treatment regimen, were included. After a blinded run-in period, participants were given access to a real-time CGM system for 12 weeks. The use and acceptability of the real-time CGM were evaluated by sensor usage, surveys, and focus group qualitative data.

RESULTS

Participants' (n = 9) median age was 19.1 (interquartile range [IQR] 16.8-20.5) years, 78% were female, 100% were people of color, and 67% were publicly insured. Baseline A1c was 11.9% (standard deviation ±2.8%), with median diabetes duration of 2.5 (IQR 1.4-6) years, and 67% were using insulin. Seven participants completed the study and demonstrated statistically significant improvement in diabetes-related quality of life, with the mean Pediatric Quality of Life inventory (PedsQL) diabetes score increasing from 70 to 75 after using CGM (P = .026). Focus group results supported survey results that CGM use among AYAs with T2D is feasible, can improve quality of life, and has the potential to modify behavior.

CONCLUSION

Real-time CGM is feasible and acceptable for AYAs with T2D and may improve the quality of life of patients with diabetes. Larger randomized controlled trials are needed to assess the effects on glycemic control and healthy lifestyle changes.

Neuroelectric indices of pre-motor planning and adiposity are selectively related to balance in children

BACKGROUND

Motor function and weight status are components of physical fitness that have been implicated in childhood motor and cognitive development. The lateralized readiness potential (LRP), an index of motor planning and action, can provide context surrounding relationships between fitness and brain activity underlying cognitive and motor functions. This study evaluated the relationship between the LRP and motor skills, as well as associations between weight status and neural and behavioral motor functions.

METHODS

Children aged 7-13 (n = 35) participated in a cross-sectional study, using the Movement Assessment Battery for Children 2nd edition (MABC-2) to assess balance, manual dexterity, and aiming/catching. The stimulus- (LRP-S) and response-locked (LRP-R) LRPs were elicited from a modified flanker task. Stepwise regressions tested the association between LRPs and MABC-2 components. Linear regressions were conducted to examine BMI and %Fat in relation to LRPs and MABC-2 components.

RESULTS

Analyses revealed that LRP-S mean amplitude difference (β = 0.401, P = 0.042) and reaction time interference scores (β = 0.545, P = 0.004) were positively associated with balance, after adjusting for covariates. The LRP-S and interference scores did not predict other MABC-2 outcomes and LRP-R did not predict any MABC-2 components. Further, %Fat (β = -0.439, P = 0.044), not BMI (β = -0.364, P = 0.082), only predicted balance.

CONCLUSION

We found that changes in the LRP-S amplitude were positively associated with balance, and %Fat was negatively related to balance. This evidence is that fitness components such as weight status and coordination are related to neural markers of motor function which may be useful in intervention designs aimed to improve brain function via improvements in physical fitness and health behaviors.

Interactive visualization tool to understand and monitor health disparities in diabetes care and outcomes

Objective

Type 2 diabetes (T2DM) poses a significant public health challenge, with pronounced disparities in control and outcomes. Social determinants of health (SDoH) significantly contribute to these disparities, affecting healthcare access, neighborhood environments, and social context. We discuss the design, development, and use of an innovative web-based application integrating real-world data (electronic health record and geospatial files), to enhance comprehension of the impact of SDoH on T2 DM health disparities.

Methods

We identified a patient cohort with diabetes from the institutional Diabetes Registry (N = 67,699) within the Duke University Health System. Patient-level information (demographics, comorbidities, service utilization, laboratory results, and medications) was extracted to Tableau. Neighborhood-level socioeconomic status was assessed via the Area Deprivation Index (ADI), and geospatial files incorporated additional data related to points of interest (i.e., parks/green space). Interactive Tableau dashboards were developed to understand risk and contextual factors affecting diabetes management at the individual, group, neighborhood, and population levels.

Results

The Tableau-powered digital health tool offers dynamic visualizations, identifying T2DM-related disparities. The dashboard allows for the exploration of contextual factors affecting diabetes management (e.g., food insecurity, built environment) and possesses capabilities to generate targeted patient lists for personalized diabetes care planning.

Conclusion

As part of a broader health equity initiative, this application meets the needs of a diverse range of users. The interactive dashboard, incorporating clinical, sociodemographic, and environmental factors, enhances understanding at various levels and facilitates targeted interventions to address disparities in diabetes care and outcomes. Ultimately, this transformative approach aims to manage SDoH and improve patient care.

Mapping Lifestyle Interventions for Gestational Diabetes Prevention: A Scoping Review

PURPOSE OF REVIEW

The purpose of this study was to conduct a scoping review to map intervention, sample, and physiologic measurement characteristics of lifestyle interventions for gestational diabetes mellitus (GDM) prevention.

RECENT FINDINGS

A total of 19 studies met selection criteria from 405 articles screened (PubMed, Web of Science). No studies were US-based (47% multi-site), and all were delivered in clinical settings. The most targeted nutrition components were low carbohydrate intake (sugar rich foods/added sugars, low glycemic index), low fat intake (mainly low-fat meat, dairy, and saturated fat), and increased fruits and vegetables. Many studies promoted 150 min/week moderate-intensity physical activity. Only two studies provided supervised physical activity sessions. Dietitians and nurses were the most common implementers. Samples were characterized as adults with obesity (mean age 31 yr, BMI 31 kg/m2). Asian populations were predominantly studied. Four studies used theoretical frameworks (75% of which used Social Cognitive Theory). GDM diagnostic criteria set forth by the American Diabetes Association were the most widely used. Insulin sensitivity was commonly assessed via fasting indices. There was a lack of multi-disciplinary, multi-level, and theory-based lifestyle interventions for reducing GDM risk. Addressing these gaps and prioritizing high-risk populations in the US with measurement of traditional and novel biomarkers will advance the field.

Perspective: Nutrition Health Disparities Framework: A Model to Advance Health Equity

Disparities in nutrition, such as poor diet quality and inadequate nutrient intake, arise from multiple factors and are related to adverse health outcomes such as obesity, diabetes, cardiovascular disease, and some cancers. The aim of the current perspective is to present a nutrition-centric socioecological framework that delineates determinants and factors that contribute to diet and nutrition-related disparities among disadvantaged populations. The Nutrition Health Disparities Framework (NHDF) describes the domains (biological, behavioral, physical/built environment, sociocultural environment, and healthcare system) that influence nutrition-related health disparities through the lens of each level of influence (that is, individual, interpersonal, community, and societal). On the basis of the scientific literature, the authors engaged in consensus decision making in selecting nutrition-related determinants of health within each domain and socioecological level when creating the NHDF. The framework identifies how neighborhood food availability and access (individual/built environment) intersect with cultural norms and practices (interpersonal/sociocultural environment) to influence dietary behaviors, exposures, and risk of diet-related diseases. In addition, the NHDF shows how factors such as genetic predisposition (individual/biology), family dietary practices (interpersonal/behavioral), and food marketing policies (societal) may impact the consumption of unhealthy foods and beverages and increase chronic disease risk. Family and peer norms (interpersonal/behavior) related to breastfeeding and early childhood nutrition interact with resource-poor environments such as lack of access to preventive healthcare settings (societal/healthcare system) and low usage of federal nutrition programs (societal/behavioral), which may increase risk of poor nutrition during childhood and food insecurity. The NHDF describes the synergistic interrelationships among factors at different levels of the socioecological model that influence nutrition-related outcomes and exacerbate health disparities. The framework is a useful resource for nutrition researchers, practitioners, food industry leaders, and policymakers interested in improving diet-related health outcomes and promoting health equity in diverse populations.

Equity in Obesity Review

Obesity disproportionately affects racial and ethnic minoritized populations and those of lower socioeconomic status. Similarly, disparities exist in the development of its downstream consequences, such as type 2 diabetes and hypertension. The causes of these disparities are multifactorial and are influenced by structural factors such as segregation and healthcare access, and individual-level factors such as weight stigma. Interventions to decrease disparities in obesity should consider macro-level, community, and individual-level factors that might reduce disparities and improve equity in obesity care. Clinicians must also recognize the chronic nature of obesity, and how bias and stigma may impact patient care.

Why does type 2 diabetes mellitus impair weight reduction in patients with obesity? A review

Background

A common adiposopathic complication of obesity is type 2 diabetes mellitus. Healthful weight reduction in patients with obesity can improve glucose metabolism and potentially promote remission of type 2 diabetes mellitus. However, weight-reduction in patients with increased adiposity is impaired among patients with type 2 diabetes mellitus compared to patients without diabetes mellitus.

Methods

Data for this review were derived from PubMed and applicable websites.

Results

Among patients with increased body fat, the mechanisms underlying impaired weight reduction for those with type 2 diabetes mellitus are multifactorial, and include energy conservation (i.e., improved glucose control and reduced glucosuria), hyperinsulinemia (commonly found in many patients with type 2 diabetes mellitus), potential use of obesogenic anti-diabetes medications, and contributions from multiple body systems. Other factors include increased age, sex, genetic/epigenetic predisposition, and obesogenic environments.

Conclusions

Even though type 2 diabetes mellitus impairs weight reduction among patients with increased adiposity, clinically meaningful weight reduction improves glucose metabolism and can sometimes promote diabetes remission. An illustrative approach to mitigate impaired weight reduction due to type 2 diabetes mellitus is choosing anti-diabetes medications that increase insulin sensitivity and promote weight loss and deprioritize use of anti-diabetes medications that increase insulin exposure and promote weight gain.

Uncovering heterogeneous cardiometabolic risk profiles in US adults: the role of social and behavioral determinants of health

INTRODUCTION

Social and behavioral determinants of health (SBDH) have been linked to diabetes risk, but their role in explaining variations in cardiometabolic risk across race/ethnicity in US adults is unclear. This study aimed to classify adults into distinct cardiometabolic risk subgroups using SBDH and clinically measured metabolic risk factors, while comparing their associations with undiagnosed diabetes and pre-diabetes by race/ethnicity.

RESEARCH DESIGN AND METHODS

We analyzed data from 38,476 US adults without prior diabetes diagnosis from the National Health and Nutrition Examination Survey (NHANES) 1999-2018. The k-prototypes clustering algorithm was used to identify subgroups based on 16 SBDH and 13 metabolic risk factors. Each participant was classified as having no diabetes, pre-diabetes or undiagnosed diabetes using contemporaneous laboratory data. Logistic regression was used to assess associations between subgroups and diabetes status, focusing on differences by race/ethnicity.

RESULTS

Three subgroups were identified: cluster 1, primarily middle-aged adults with high rates of smoking, alcohol use, short sleep duration, and low diet quality; cluster 2, mostly young non-white adults with low income, low health insurance coverage, and limited healthcare access; and cluster 3, mostly older males who were the least physically active, but with high insurance coverage and healthcare access. Compared with cluster 2, adjusted ORs (95% CI) for undiagnosed diabetes were 14.9 (10.9, 20.2) in cluster 3 and 3.7 (2.8, 4.8) in cluster 1. Clusters 1 and 3 (vs cluster 2) had high odds of pre-diabetes, with ORs of 1.8 (1.6, 1.9) and 2.1 (1.8, 2.4), respectively. Race/ethnicity was found to modify the relationship between identified subgroups and pre-diabetes risk.

CONCLUSIONS

Self-reported SBDH combined with metabolic factors can be used to classify adults into subgroups with distinct cardiometabolic risk profiles. This approach may help identify individuals who would benefit from screening for diabetes and pre-diabetes and potentially suggest effective prevention strategies.

Who needs what (features) when? Personalizing engagement with data-driven self-management to improve health equity

OBJECTIVES

To examine the feasibility of promoting engagement with data-driven self-management of health among individuals from minoritized medically underserved communities by tailoring the design of self-management interventions to individuals' type of motivation and regulation in accordance with the Self-Determination Theory.

METHODS

Fifty-three individuals with type 2 diabetes from an impoverished minority community were randomly assigned to four different versions of an mHealth app for data-driven self-management with the focus on nutrition, Platano; each version was tailored to a specific type of motivation and regulation within the SDT self-determination continuum. These versions included financial rewards (external regulation), feedback from expert registered dietitians (RDF, introjected regulation), self-assessment of attainment of one's nutritional goals (SA, identified regulation), and personalized meal-time nutrition decision support with post-meal blood glucose forecasts (FORC, integrated regulation). We used qualitative interviews to examine interaction between participants' experiences with the app and their motivation type (internal-external).

RESULTS

As hypothesized, we found a clear interaction between the type of motivation and Platano features that users responded to and benefited from. For example, those with more internal motivation reported more positive experience with SA and FORC than those with more external motivation. However, we also found that Platano features that aimed to specifically address the needs of individuals with external regulation did not create the desired experience. We attribute this to a mismatch in emphasis on informational versus emotional support, particularly evident in RDF. In addition, we found that for participants recruited from an economically disadvantaged community, internal factors, such as motivation and regulation, interacted with external factors, most notably with limited health literacy and limited access to resources.

CONCLUSIONS

The study suggests feasibility of using SDT to tailor design of mHealth interventions for promoting data-driven self-management to individuals' motivation and regulation. However, further research is needed to better align design solutions with different levels of self-determination continuum, to incorporate stronger emphasis on emotional support for individuals with external regulation, and to address unique needs and challenges of underserved communities, with particular attention to limited health literacy and access to resources.

Inclusion of Middle Eastern and North African populations in diabetes clinical research

The need for diverse representation in clinical trials has recently been reinforced by the Food and Drug Administration's (FDA) guidance for industry entitled, "Diversity Plans to Improve Enrollment of Participants from Underrepresented Racial and Ethnic Populations in Clinical Trials." By ensuring inclusion of underrepresented racial and ethnic minority populations in clinical trials, results can be more generalizable and the safety and efficacy can be accurately assessed within the diverse U.S. population. Limitations exist in the interpretation and implementation of clinical trial results reported using the current racial and ethnic categories, as these standards do not reflect the true diversity of the U.S. population. This is particularly true for the Middle Eastern and North African (MENA) population, which is usually overlooked given the lack of an established category. Although the international MENA region demonstrates the highest prevalence of diabetes in the world at 12.2%, the actual prevalence among MENA individuals living in the U.S. may be "hidden" within the White category. Therefore, data on the MENA population should be disaggregated from data within the White category to not only unmask health disparities, but also to ensure adequate representation in clinical trials. This paper discusses the importance of appropriate representation and inclusion of the MENA population in diabetes clinical trials, which is a critical public health issue domestically and globally.

Promise and unrealized potential: 10 years of the American Medical Association classifying obesity as a disease

Introduction

In June 2013, the American Medical Association (AMA), one of the most influential healthcare organizations in the United States, voted to recognize obesity as a disease. Many who supported this change believed that recognition by AMA of obesity as a disease would serve as a tipping point that would increase access to care, accelerate training and research on the prevention and treatment of obesity, and reduce weight stigma. On the 10-year anniversary of this vote, this perspective piece outlines key advances made, as well as unrealized potential, in improving the obesity public health landscape since the AMA's classification of obesity as a disease.

Methods

We draw on the empirical literature, as well as our experiences as clinical psychologists, a physician, and public health researchers specializing in obesity, to provide an overview of major advances and continued challenges in improving access to obesity treatment, accelerating prevention and training, and reducing weight stigma. We also outline important next steps to advance these goals.

Results

While several notable advancements have occurred, significant work remains to create equitable access to evidence-based treatments, bring research and training on obesity on par with its prevalence, and reduce the pervasiveness and harm of weight stigma.

Conclusion

The past decade has witnessed some advances with respect to access to care and attention, yet there is unrealized potential that awaits attention. Truly conceptualizing and treating obesity as a chronic disease requires a major paradigm shift.

Fit24, a digital health intervention to reduce type 2 diabetes risk among Hispanic youth: Protocol for a feasibility pilot study

BACKGROUND

Poor physical activity (PA) and sleep behaviors in Hispanic adolescents contributes to increased risk for type 2 diabetes. Commonly owned digital devices and services like smartphones and text-messaging are highly used among adolescents and are promising intervention tools for reaching this age group. Personal activity trackers assess activity and sleep, making them ideal tools for addressing these behaviors. We propose to examine the feasibility of a 12-week intervention that uses theoretically grounded text messages and a Fitbit device to improve PA and sleep among Hispanic adolescents with obesity, as compared to a wait-list control group with a Fitbit device only.

METHODS

Participants (N = 48; 14-16 years) will be randomized (1:1) to the intervention or wait-list control group. Youth in the intervention will receive a Fitbit Charge 5 and daily text messages. Youth in the wait-list control group will receive a Fitbit Charge 5 and information on PA and sleep guidelines.

RESULTS

Feasibility will be examined by collecting process evaluation data on the following criteria: (1) recruit 48 Hispanic adolescents 14-16 years; (2) retain 85% of participants for post-assessments; (3) Fitbit wear ≥4 days/week and respond to 80% of text messages when prompted; (4) ≤10% technical issues; and (5) obtain 80% satisfaction from participants.

DISCUSSION

This study will advance our knowledge on the feasibility of digital prevention strategies to promote PA and sleep behaviors to reduce T2D risk among Hispanic youth. If feasible, this approach has the potential to be a scalable, cost-effective diabetes prevention strategy among high-risk youth.

TRIAL REGISTRATION

NCT04953442, registered on July 8, 2021.

Integrated Interventions to Bridge Medical and Social Care for People Living with Diabetes

Social drivers of health impact health outcomes for patients with diabetes, and are areas of interest to health systems, researchers, and policymakers. To improve population health and health outcomes, organizations are integrating medical and social care, collaborating with community partners, and seeking sustainable financing with payors. We summarize promising examples of integrated medical and social care from the Merck Foundation Bridging the Gap: Reducing Disparities in Diabetes Care initiative. The initiative funded eight organizations to implement and evaluate integrated medical and social care models, aiming to build a value case for services that are traditionally not eligible for reimbursement (e.g., community health workers, food prescriptions, patient navigation). This article summarizes promising examples and future opportunities for integrated medical and social care across three themes: (1) primary care transformation (e.g., social risk stratification) and workforce capacity (e.g., lay health worker interventions), (2) addressing individual social needs and structural changes, and (3) payment reform. Integrated medical and social care that advances health equity requires a significant paradigm shift in healthcare financing and delivery.

Perspective: Acknowledging a Hierarchy of Social Needs in Diabetes Clinical Care and Prevention

The evidence of suboptimal social determinants of health (SDoH) on poor health outcomes has resulted in widespread calls for research to identify ways to measure and address social needs to improve health outcomes and reduce disparities. While assessing SDoH has become increasingly important in diabetes care and prevention research, little guidance has been offered on how to address suboptimal determinants in diabetes-related clinical care, prevention efforts, medical education and research. Not surprisingly, many patients experience multiple social needs - some that are more urgent (housing) than others (transportation/resources), therefore the order in which these needs are addressed needs to be considered in the context of diabetes care/outcomes. Here we discuss how conceptualizing diabetes related health through the lens of Maslow's hierarchy of needs has potential to help prioritize individual social needs that should be addressed to improve outcomes in the context of population-level determinants in the communities where people live.

Continuous glucose monitors and virtual care in high-risk, racial and ethnic minority populations: Toward promoting health equity

Continuous glucose monitors (CGMs) have become an important tool to aid self-management of blood glucose for many patients with diabetes in the U.S., and the benefits of CGM use are well-documented. However, disparities in CGM use exist, with lower use in certain marginalized racial and ethnic groups. CGM may be an important and underutilized tool to help reduce inequities. Evidence supporting the use of CGMs as a part of virtual care is discussed, with an emphasis on designing virtual diabetes care programs to promote health equity. Recommendations for clinical practice and research are presented. In clinical practice, CGM should be an option for all people with diabetes who qualify based on clinical practice guidelines, regardless of race, ethnicity, or other individual characteristics. Future research should characterize the use of, benefit from, and preferences for CGM among individuals from racial and ethnic groups to guide interventions at the health system, clinic, provider, and patient levels to promote equitable, evidence-based, and guideline-directed CGM use in marginalized racial and ethnic groups with diabetes.

From Genes to Geography, from Cells to Community, from Biomolecules to Behaviors: The Importance of Social Determinants of Health

Much scientific work over the past few decades has linked health outcomes and disease risk to genomics, to derive a better understanding of disease mechanisms at the genetic and molecular level. However, genomics alone does not quite capture the full picture of one’s overall health. Modern computational biomedical research is moving in the direction of including social/environmental factors that ultimately affect quality of life and health outcomes at both the population and individual level. The future of studying disease now lies at the hands of the social determinants of health (SDOH) to answer pressing clinical questions and address healthcare disparities across population groups through its integration into electronic health records (EHRs). In this perspective article, we argue that the SDOH are the future of disease risk and health outcomes studies due to their vast coverage of a patient’s overall health. SDOH data availability in EHRs has improved tremendously over the years with EHR toolkits, diagnosis codes, wearable devices, and census tract information to study disease risk. We discuss the availability of SDOH data, challenges in SDOH implementation, its future in real-world evidence studies, and the next steps to report study outcomes in an equitable and actionable way.

A quasi-experimental study provides evidence that registered dietitian nutritionist care is aligned with the Academy of Nutrition and Dietetics evidence-based nutrition practice guidelines for type 1 and 2 diabetes

Background

One previous study examined implementation of evidence-based nutrition practice guidelines (EBNPG).

Objectives

To describe alignment of registered dietitian nutritionists' (RDNs) documented nutrition care with the Academy of Nutrition and Dietetics' EBNPG for Type 1 and Type 2 diabetes and examine impact of a midpoint training on care alignment with the guideline.

Methods

In this 2-year, quasi-experimental study, 19 RDNs providing outpatient medical nutrition therapy to adults with diabetes (n = 562) documented 787 initial and follow-up encounters. At study midpoint, RDNs received a guideline content training. A validated, automated tool was used to match standardized nutrition care process terminology (NCPT) in the documentation to NCPT expected to represent guideline implementation. A congruence score ranging from 0 (recommendation not identified) to 4 (recommendation fully implemented) was generated based on matching. Multilevel linear regression was used to examine pre-to-post training changes in congruence scores.

Results

Most patients (~75%) had only one documented RDN encounter. At least one guideline recommendation was fully implemented in 67% of encounters. The recommendations “individualize macronutrient composition” and “education on glucose monitoring” (partially or fully implemented in 85 and 79% of encounters, respectively) were most frequently implemented. The mean encounter congruence scores were not different from pre-to-post guideline training (n = 19 RDNs, 519 encounters pre-training; n = 14 RDNs, 204 encounters post-training; β = −0.06, SE = 0.04; 95% CI: −0.14, 0.03).

Conclusions

Most RDN encounters had documented evidence that at least one recommendation from the EBNPG was implemented. The most frequently implemented recommendations were related to improving glycemic control. A midpoint guideline training had no impact on alignment of care with the guideline.

Acculturation, Physical Activity, and Metabolic Syndrome in Asian American Adults

INTRODUCTION

Asian Americans (AsAms) have a high prevalence of metabolic syndrome (MetS) and are one of the least physically active racial groups in America. The purpose of this study was to examine the relationship between MetS and moderate-to-vigorous physical activity (MVPA) among AsAm adults and whether acculturation modifies this relationship.

METHOD

Data were from 2,259 AsAms participating in the 2011-2016 National Health and Nutrition Examination Survey. Physical activity (PA) was self-reported as minutes of weekly MVPA. Acculturation included nativity, length of residency in America, and language preference. The International Diabetes Federation criteria was used to determine the presence of MetS.

RESULTS

About 64.4% of AsAms did not meet the PA recommendation (MVPA ≥150 min/week). The prevalence of MetS was 39.2%. The odds of having MetS were greater among AsAms who did not meet MVPA recommendations compared with those who did meet MVPA recommendations (odds ratio [OR] = 1.5, 95% confidence interval [CI] = [1.11, 2.07]). When stratified by acculturation, this association remained statistically significant in the groups who immigrated to America more than 15 years ago and who spoke English only.

CONCLUSION

MVPA reduces MetS risk in AsAm adults, especially among more acculturated AsAms. Culturally adapted programs are warranted to promote PA and adequate knowledge of disease prevention in this population.

Bridge to Health/Puente a la Salud: a pilot randomized trial to address diabetes self-management and social needs among high-risk patients

Social needs contribute to persistent diabetes disparities; thus, it is imperative to address social needs to optimize diabetes management. The purpose of this study was to determine determine the feasibility and acceptability of health system-based social care versus social care + behavioral intervention to address social needs and improve diabetes self-management among patients with type 2 diabetes. Black/African American, Hispanic/Latino, and low-income White patients with recent hemoglobin A1C (A1C) ≥ 8%, and ≥1 social need were recruited from an integrated health system. Patients were randomized to one-of-two 6-month interventions: (a) navigation to resources (NAV) facilitated by a Patient Navigator; or (b) NAV + evidence-based nine-session diabetes self-management support (DSMS) program facilitated by a community health worker (CHW). A1C was extracted from the electronic health record. We successfully recruited 110 eligible patients (54 NAV; 56 NAV + DSMS). During the trial, 78% NAV and 80% NAV + DSMS participants successfully connected to a navigator; 84% NAV + DSMS connected to a CHW. At 6-month follow-up, 33% of NAV and 34% of NAV + DSMS participants had an A1C < 8%. Mean reduction in A1C was clinically significant in NAV (-0.65%) and NAV + DSMS (-0.72%). By follow-up, 89% of NAV and 87% of NAV + DSMS were successfully connected to resources to address at least one need. Findings suggest that it is feasible to implement a health system-based social care intervention, separately or in combination, with a behavioral intervention to improve diabetes management among a high-risk, socially complex patient population. A larger, pragmatic trial is needed to test the comparative effectiveness of each approach on diabetes-related outcomes.

Reach, acceptability, and perceived success of a telehealth diabetes prevention program among racially and ethnically diverse patients with gestational diabetes: the GEM cluster-randomized trial

Patients with gestational diabetes mellitus and from racial/ethnic minority groups face disproportionate risk for type 2 diabetes. Lifestyle interventions, if accessible and acceptable to diverse patients, could advance diabetes prevention and mitigate racial/ethnic disparities. Here we describe overall and race/ethnicity-specific reach, acceptability, and perceived success from an effective telehealth diabetes prevention lifestyle program for patients with gestational diabetes mellitus, implemented in the Gestational Diabetes Effects on Moms (GEM) cluster-randomized controlled trial. GEM tested a program of 13 telephone sessions and behavior change techniques (BCTs, e.g., goal setting) in a healthcare system. We evaluated participation (completing ≥1 session), acceptability of BCTs, and perceived success reaching program goals. Among 1,087 patients (75.2% from minority groups), 50.3% participated. Participation rates were 61.7% among Black, 56.4% among Hispanic, 55.6% among multiracial/other, 53.0% among White, and 43.7% among Asian/Pacific Islander patients. Evaluation survey respondents (n = 433/547; 79.2%) largely rated BCTs as very helpful (range 40.9%-58.4%) or moderately helpful (27.3%-34.9%). Respondents from minority groups largely rated goal setting for weight management as very or moderately helpful, with fewer minority respondents rating it as only a little/not at all helpful than White respondents (p = .02). Black and White respondents reported more limited success reaching a healthy weight than Asian/Pacific Islander, Hispanic, and multiracial/other women (p = .005). A telehealth diabetes prevention lifestyle program demonstrated reach and acceptability across racial/ethnic groups. While perceived success can be improved among Black and White participants, such programs could promote access to preventive care and help mitigate disparities in diabetes risk.

Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID-19 outcomes

OBJECTIVE

This study aimed to determine the ability of retrospective cardiometabolic disease staging (CMDS) and social determinants of health (SDoH) to predict COVID-19 outcomes.

METHODS

Individual and neighborhood SDoH and CMDS clinical parameters (BMI, glucose, blood pressure, high-density lipoprotein, triglycerides), collected up to 3 years prior to a positive COVID-19 test, were extracted from the electronic medical record. Bayesian logistic regression was used to model CMDS and SDoH to predict subsequent hospitalization, intensive care unit (ICU) admission, and mortality, and whether adding SDoH to the CMDS model improved prediction was investigated. Models were cross validated, and areas under the curve (AUC) were compared.

RESULTS

A total of 2,873 patients were identified (mean age: 58 years [SD 13.2], 59% were female, 45% were Black). CMDS, insurance status, male sex, and higher glucose values were associated with increased odds of all outcomes; area-level social vulnerability was associated with increased odds of hospitalization (odds ratio: 1.84, 95% CI: 1.38-2.45) and ICU admission (odds ratio 1.98, 95% CI: 1.45-2.85). The AUCs improved when SDoH were added to CMDS (p < 0.001): hospitalization (AUC 0.78 vs. 0.82), ICU admission (AUC 0.77 vs. 0.81), and mortality (AUC 0.77 vs. 0.83).

CONCLUSIONS

Retrospective clinical markers of cardiometabolic disease and SDoH were independently predictive of COVID-19 outcomes in the population.

Race Versus Social Determinants of Health in COVID-19 Hospitalization Prediction

INTRODUCTION

Including race as a biological construct in risk prediction models may guide clinical decisions in ways that cause harm and widen racial disparities. This study reports on using race versus social determinants of health (SDoH) in predicting the associations between cardiometabolic disease severity (assessed using cardiometabolic disease staging) and COVID-19 hospitalization.

METHODS

Electronic medical record data on patients with a positive COVID-19 polymerase chain reaction test in 2020 and a previous encounter in the electronic medical record where cardiometabolic disease staging clinical data (BMI, blood glucose, blood pressure, high-density lipoprotein cholesterol, and triglycerides) were available from 2017 to 2020, were analyzed in 2021. Associations between cardiometabolic disease staging and COVID-19 hospitalization adding race and SDoH (individual and neighborhood level [e.g., Social Vulnerability Index]) in different models were examined. Area under the curve was used to assess predictive performance.

RESULTS

A total of 2,745 patients were included (mean age of 58 years, 59% female, 47% Black). In the cardiometabolic disease staging model, area under the curve was 0.767 vs 0.777 when race was included. Adding SDoH to the cardiometabolic model improved the area under the curve to 0.809 (p<0.001), whereas the addition of SDoH and race increased the area under the curve to 0.811. In race-stratified models, the area under the curve for non-Hispanic Blacks was 0.781, whereas the model for non-Hispanic Whites performed better with an area under the curve of 0.821.

CONCLUSIONS

Cardiometabolic disease staging was predictive of hospitalization after a positive COVID-19 test. Adding race did not markedly increase the predictive ability; however, adding SDoH to the model improved the area under the curve to ≥0.80. Future research should include SDoH with biological variables in prediction modeling to capture social experience of race.

Hawk tea prevents high-fat diet-induced obesity in mice by activating the AMPK/ACC/SREBP1c signaling pathways and regulating the gut microbiota

Scope: Hawk tea, a non-Camellia tea, is an ancient tea drink from southwest China and has been proven to exhibit significant hypoglycaemic and lipid-lowering effects. The aim of this study was to evaluate whether Hawk tea extract (HTE) can improve obesity induced by a high-fat diet (HFD) in a mouse model and to determine whether its anti-obesity effects are related to improvements in lipid metabolism and the gut microbiota. Methods and results: We tested the ability of HTE to prevent obesity and regulate gut microbiota in C57BL/6J mice fed with a HFD. We found that HTE significantly reduced body weight, fat deposition, serum triglyceride (TG), total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels, and significantly increased serum levels of high-density lipoprotein cholesterol (HDL-C) induced by HFD. HTE also increased the levels of AMPK and ACC phosphorylation, up-regulated the expression of CPT-1, and downregulated the expression of SREBP1c and FAS. In addition, the administration of HTE significantly altered the composition of the gut microbiota, reduced the ratio of Firmicutes to Bacteroidetes, increased the relative abundance of Akkermansia muciniphila, Bacteroides-vulgatus, and Faecalibaculum_rodentium, and decreased the relative abundance of Desulfovibrionaceae and Lachnospiraceae. Conclusions: Collectively, our data demonstrate that HTE can prevent HFD-induced obesity by regulating the AMPK/ACC/SREBP1c signaling pathways and the gut microbiota.

Associations Between Obesity, Physical Inactivity, Healthcare Capacity, and the Built Environment: Geographic Information System Analysis

Background

Obesity is one of the major critical health conditions affecting many people across the world. One of the major causes of obesity is identified to be sedentary lifestyles and physical inactivity, which may be associated with environmental factors.

Objective

The study analyzes variations in obesity and physical inactivity in the State of South Carolina, US, and their association with healthcare capacity and the built environment.

Methods

Data were obtained from different secondary sources and surveys, 2012, and then linked on the county-level using ArcGIS. Global Moran's I was used to examine the spatial distribution at the state level, and Anselin's local Moran's I was used to detect any significant clusters at the county level. Ordinary least squares regression models were calculated for obesity and physical inactivity separately.

Results

More than 70% of SC counties had high levels of obesity and physical inactivity. Spatial analysis showed statistical clusters of high obesity, high physical inactivity, and low access to exercise opportunities in rural areas compared to urban areas. Conversely, clusters of high density of health-care facilities appeared in urban areas. Through the regression models, the density of primary care physicians (p = 0.025) and access to exercise opportunities (p = 0.075) were negatively associated with obesity, while the low perception of own health (p = 0.001) and obesity rate (0.011) were positively associated with physical inactivity.

Conclusion

GIS was useful to illustrate and identify significant geographic variations and high clusters of obesity and physical inactivity in rural areas, compared with high clusters of access to exercise opportunities and health-care facilities in urban areas. The international health community is encouraged to utilize spatial information systems to examine variations and recommend evidence-based recommendations to redistribute equitable public health efforts. The development of strategies and initiatives toward reducing variation in health and sustainable development is key to promote the population wellbeing.

Social Determinants of Health, Race, and Diabetes Population Health Improvement: Black/African Americans as a Population Exemplar

PURPOSE OF REVIEW

To summarize evidence of impact of social determinants of health (SDOH) on diabetes risk, morbidity, and mortality and to illustrate this impact in a population context.

RECENT FINDINGS

Key findings from the American Diabetes Association's scientific review of five SDOH domains (socioeconomic status, neighborhood and physical environment, food environment, health care, social context) are highlighted. Population-based data on Black/African American adults illustrate persisting diabetes disparities and inequities in the SDOH conditions in which this population is born, grows, lives, and ages, with historical contributors. SDOH recommendations from US national committees largely address a health sector response, including health professional education, SDOH measurement, and patient referral to services for social needs. Fewer recommendations address solutions for systemic racism and socioeconomic discrimination as root causes. SDOH are systemic, population-based, cyclical, and intergenerational, requiring extension beyond health care solutions to multi-sector and multi-policy approaches to achieve future population health improvement.

Maximizing diabetes education efforts in vulnerable populations - newer delivery concepts

PURPOSE OF REVIEW

Patients from ethnic/minority backgrounds or low socioeconomic status face numerous barriers to achieving ideal diabetes care goals. The purpose of this review is to describe the burden of diabetes in vulnerable populations; discuss the etiologic factors leading to health disparities in diabetes; and present challenges and solutions to improving diabetes care through novel diabetes self-management education and support interventions.

RECENT FINDINGS

Recent interventions to alleviate health disparities utilize a combination of community health workers, peer leaders and technology-based approaches to provide diabetes self-management education and support and overcome barriers to diabetes control such as low literacy, difficulty with transportation, and cultural beliefs. These interventions achieve clinically meaningful improvements in blood glucose control as measured by haemoglobin A1C and are effective in addressing psychosocial outcomes such as diabetes distress. Research is underway to address food insecurity through food delivery and use behavioural economics principles to provide financial incentives to diabetes control.

SUMMARY

Combining human interaction through peer or community health worker led diabetes educational efforts and support with technology-based interventions shows promise in improving diabetes outcomes for vulnerable populations.

Exercise/Physical Activity in Individuals with Type 2 Diabetes: A Consensus Statement from the American College of Sports Medicine

ABSTRACT

This consensus statement is an update of the 2010 American College of Sports Medicine position stand on exercise and type 2 diabetes. Since then, a substantial amount of research on select topics in exercise in individuals of various ages with type 2 diabetes has been published while diabetes prevalence has continued to expand worldwide. This consensus statement provides a brief summary of the current evidence and extends and updates the prior recommendations. The document has been expanded to include physical activity, a broader, more comprehensive definition of human movement than planned exercise, and reducing sedentary time. Various types of physical activity enhance health and glycemic management in people with type 2 diabetes, including flexibility and balance exercise, and the importance of each recommended type or mode are discussed. In general, the 2018 Physical Activity Guidelines for Americans apply to all individuals with type 2 diabetes, with a few exceptions and modifications. People with type 2 diabetes should engage in physical activity regularly and be encouraged to reduce sedentary time and break up sitting time with frequent activity breaks. Any activities undertaken with acute and chronic health complications related to diabetes may require accommodations to ensure safe and effective participation. Other topics addressed are exercise timing to maximize its glucose-lowering effects and barriers to and inequities in physical activity adoption and maintenance.

A Lifestyle Intervention Program to Improve Knowledge and Health Behaviors in Women

OBJECTIVE

To improve health knowledge, facilitate healthy behaviors, and provide social support for a high-risk population of women by implementing a culturally tailored, gender-specific lifestyle intervention program.

DESIGN

Evidence-based practice project.

SETTING/LOCAL PROBLEM

A Chicago neighborhood in Cook County, Illinois, where there are increased rates of diabetes, heart disease, cancer, stroke, hypertension, obesity, preterm birth, and violent crime.

PARTICIPANTS

Female residents of the community, ages 19 to 45 years; 12 participants registered for the program, and 11 participants completed the entire 2-month program.

INTERVENTIONS/MEASUREMENTS

The BE WISE Lifestyle Intervention, a culturally tailored, gender-specific behavior change curriculum, was used for this project. An interprofessional team was used to enhance participant experience, and neighborhood collaborations were established to create sustainability of efforts. Pre- and postintervention measures of diet, physical activity, social support, and knowledge acquisition were used to evaluate outcomes.

RESULTS

Improvement was noted for knowledge in most of the content areas, as well as for dietary and physical activity behaviors. All participants rated the program highly and responded positively to the social support experienced in the group.

CONCLUSION

Culturally tailored lifestyle interventions can be effective in improving knowledge and encouraging behavior change, especially when delivered in group settings to enhance social support. Community-level collaborations enhance interventions by providing local support and resources for participants. Because behavior change takes time, follow-up at the 6-month and 1-year intervals is recommended to determine longer-term outcomes.

Choices of (in)action in obesity: Implications for research on treatment and prevention

The obesity epidemic has crossed social-demographic barriers and is a matter of significant concern. Why do individuals fail to restrain from eating high-calorie foods and fail to follow treatment routines that reduce the risk of health complications? These questions have been addressed through behavioral and brain imaging studies on prefrontal cortex inhibitory mechanisms. Failure to inhibit undesirable behaviors has become a hallmark of obesity. In many life situations, obesity risk is increased by inaction (e.g., not taking blood pressure medication, not following a healthy diet). Risk by inaction has been defined as passive risk-taking, and it is correlated with traits such as procrastination, future time perspective, and cognitive avoidance. To the present, passive tendencies, specifically in the context of risk-taking behaviors, have not been addressed in the obesity literature. We introduce a framework in which active and passive risk-taking behaviors are integrated within the scope of bidirectional models of obesity that describe the brain as both the cause and the consequence of obesity vulnerability. The present perspective aims to foster new research on treatment and prevention, and also on the neurobiology of passive behaviors in obesity and other metabolic conditions.

Geographic Variation in Obesity at the State Level in the All of Us Research Program

INTRODUCTION

National obesity prevention strategies may benefit from precision health approaches involving diverse participants in population health studies. We used cohort data from the National Institutes of Health All of Us Research Program (All of Us) Researcher Workbench to estimate population-level obesity prevalence.

METHODS

To estimate state-level obesity prevalence we used data from physical measurements made during All of Us enrollment visits and data from participant electronic health records (EHRs) where available. Prevalence estimates were calculated and mapped by state for 2 categories of body mass index (BMI) (kg/m2): obesity (BMI >30) and severe obesity (BMI >35). We calculated and mapped prevalence by state, excluding states with fewer than 100 All of Us participants.

RESULTS

Data on height and weight were available for 244,504 All of Us participants from 33 states, and corresponding EHR data were available for 88,840 of these participants. The median and IQR of BMI taken from physical measurements data was 28.4 (24.4- 33.7) and 28.5 (24.5-33.6) from EHR data, where available. Overall obesity prevalence based on physical measurements data was 41.5% (95% CI, 41.3%-41.7%); prevalence of severe obesity was 20.7% (95% CI, 20.6-20.9), with large geographic variations observed across states. Prevalence estimates from states with greater numbers of All of Us participants were more similar to national population-based estimates than states with fewer participants.

CONCLUSION

All of Us participants had a high prevalence of obesity, with state-level geographic variation mirroring national trends. The diversity among All of Us participants may support future investigations on obesity prevention and treatment in diverse populations.

Diabetes Prevention and Care Capacity at Urban Indian Health Organizations

American Indian and Alaska Native (AI/AN) people suffer a disproportionate burden of diabetes and cardiovascular disease. Urban Indian Health Organizations (UIHOs) are an important source of diabetes services for urban AI/AN people. Two evidence-based interventions-diabetes prevention (DP) and healthy heart (HH)-have been implemented and evaluated primarily in rural, reservation settings. This work examines the capacity, challenges and strengths of UIHOs in implementing diabetes programs. Methods: We applied an original survey, supplemented with publicly-available data, to assess eight organizational capacity domains, strengths and challenges of UIHOs with respect to diabetes prevention and care. We summarized and compared (Fisher's and Kruskal-Wallis exact tests) items in each organizational capacity domain for DP and HH implementers vs. non-implementers and conducted a thematic analysis of strengths and challenges. Results: Of the 33 UIHOs providing services in 2017, individuals from 30 sites (91% of UIHOs) replied to the survey. Eight UIHOs (27%) had participated in either DP (n = 6) or HH (n = 2). Implementers reported having more staff than non-implementers (117.0 vs. 53.5; p = 0.02). Implementers had larger budgets, ~$10 million of total revenue compared to $2.5 million for non-implementers (p = 0.01). UIHO strengths included: physical infrastructure, dedicated leadership and staff, and community relationships. Areas to strengthen included: staff training and retention, ensuring sufficient and consistent funding, and data infrastructure. Conclusions: Strengthening UIHOs across organizational capacity domains will be important for implementing evidence-based diabetes interventions, increasing their uptake, and sustaining these interventions for AI/AN people living in urban areas of the U.S.

Insufficient Vitamin C Levels among Adults in the United States: Results from the NHANES Surveys, 2003-2006

Vitamin C, well-established in immune function and a key factor in epigenetic inflammatory modifications, is only obtained through consistent dietary intake. Identifying individuals at risk for Vitamin C insufficiency may guide prevention and treatment, however, national surveillance has not been evaluated in the United States since 2006. A descriptive, cross-sectional secondary analysis was performed utilizing data from the 2003–2006 National Health and Nutrition Examination Surveys (NHANES) assessing non-institutionalized adults. Five categories of plasma Vitamin C were delineated: deficiency (<11 μmol/L), hypovitaminosis (11–23 μmol/L), inadequate (23–49 μmol/L), adequate (50–69 μmol/L), and saturating (≥70 μmol/L). Results indicated 41.8% of the population possessed insufficient levels (deficiency, hypovitaminosis, and inadequate) of Vitamin C. Males, adults aged 20–59, Black and Mexican Americans, smokers, individuals with increased BMI, middle and high poverty to income ratio and food insecurity were significantly associated with insufficient Vitamin C plasma levels. Plasma Vitamin C levels reveal a large proportion of the population still at risk for inflammatory driven disease with little to no symptoms of Vitamin C hypovitaminosis. Recognition and regulation of the health impact of Vitamin C support the goal of Nutrition and Healthy Eating as part of the Healthy People 2030.

Opportunities to Address Obesity Disparities Among High-Risk Latino Children and Adolescents

PURPOSE OF REVIEW

This review highlights obesity-related disparities among Latino children and adolescents, discusses the social determinants of health (SDoH) that drive disparities, and presents case studies of strategies for reducing disparities and promoting health equity.

RECENT FINDINGS

Recommended strategies for reducing obesity-related disparities include the use of culturally grounded programming, multi-sector collaborations, and technology. We present two exemplar studies that demonstrate that integrating cultural values and enhancing the overall cultural fit of prevention programs can increase engagement among high-risk Latino families. We also examine the use of multi-sector collaborations to build community capacity and address key SDoH that impact health behaviors and outcomes. Our last example study demonstrates the utility of technology for engaging youth and extending the reach of prevention strategies in vulnerable communities. To address growing obesity-related disparities, there is an urgent need to develop and test these strategies among high-risk, vulnerable populations like Latino children and adolescents.

Bridge to Health/ Puente a la Salud: Rationale and design of a pilot feasibility randomized trial to address diabetes self-management and unmet basic needs among racial/ethnic minority and low-income patients

Introduction

Racial/ethnic and socioeconomic disparities in diabetes prevalence and management persist. Unmet basic needs such as food insecurity and unstable housing interfere with optimal diabetes self-management. Bridge to Health/Puente a la Salud is a randomized pilot trial designed to examine the feasibility of testing the effectiveness of addressing unmet basic needs via navigation services versus navigation plus diabetes self-management support (DSMS) on improving diabetes-related outcomes among racial/ethnic minority and low-income patients with uncontrolled diabetes.

Material and methods

We recruited and randomized 110 African American, Hispanic, and Medicaid patients (any race/ethnicity) with diabetes and recent hemoglobin A1C ≥ 8% to one of two 6-month interventions: 1) Navigation only; or 2) Navigation + DSMS. In both arms, practice-embedded patient navigators help participants navigate social services and community-based resources to address unmet basic needs. In Navigation + DSMS, participants are also assigned to a community health worker (CHW) embedded in a local community-based organization who provides additional navigation support and delivers DSMS. A1C and unmet basic needs data are collected via routine lab and survey, respectively, at baseline and 6-month follow-up. Qualitative interviews with participants, health system leaders, CHWs, and patient navigators are conducted to explore intervention acceptability and determinants of implementation in a health care setting.

Discussion

Findings from this pilot feasibility study will enhance understanding about acceptability, preliminary clinical effectiveness, and facilitators and barriers to implementation of the Navigation only and Navigation + DSMS interventions and inform refinements of the overall study design for the larger, randomized clinical trial.

A Pharmacist and Health Coach-Delivered Mobile Health Intervention for Type 2 Diabetes: Protocol for a Randomized Controlled Crossover Study

BACKGROUND

Aggressive management of blood glucose, blood pressure, and cholesterol through medication and lifestyle adherence is necessary to minimize the adverse health outcomes of type 2 diabetes. However, numerous psychosocial and environmental barriers to adherence prevent low-income, urban, and ethnic minority populations from achieving their management goals, resulting in diabetes complications. Health coaches working with clinical pharmacists represent a promising strategy for addressing common diabetes management barriers. Mobile health (mHealth) tools may further enhance their ability to support vulnerable minority populations in diabetes management.

OBJECTIVE

The aim of this study is to evaluate the impact of an mHealth clinical pharmacist and health coach-delivered intervention on hemoglobin A1c (HbA1c, primary outcome), blood pressure, and low-density lipoprotein (secondary outcomes) in African-Americans and Latinos with poorly controlled type 2 diabetes.

METHODS

A 2-year, randomized controlled crossover study will evaluate the effectiveness of an mHealth diabetes intervention delivered by a health coach and clinical pharmacist team compared with usual care. All patients will receive 1 year of team intervention, including lifestyle and medication support delivered in the home with videoconferencing and text messages. All patients will also receive 1 year of usual care without team intervention and no home visits. The order of the conditions received will be randomized. Our recruitment goal is 220 urban African-American or Latino adults with uncontrolled type 2 diabetes (HbA1c ≥8%) receiving care from a largely minority-serving, urban academic medical center. The intervention includes the following: health coaches supporting patients through home visits, phone calls, and text messaging and clinical pharmacists supporting patients through videoconferences facilitated by health coaches. Data collection includes physiologic (HbA1c, blood pressure, weight, and lipid profile) and survey measures (medication adherence, diabetes-related behaviors, and quality of life). Data collection during the second year of study will determine the maintenance of any physiological improvement among participants receiving the intervention during the first year.

RESULTS

Participant enrollment began in March 2017. We have recruited 221 patients. Intervention delivery and data collection will continue until November 2021. The results are expected to be published by May 2022.

CONCLUSIONS

This is among the first trials to incorporate health coaches, clinical pharmacists, and mHealth technologies to increase access to diabetes support among urban African-Americans and Latinos to achieve therapeutic goals.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/17170.

COVID-19 and metabolic diseases: a heightened awareness of health inequities and a renewed focus for research priorities

Chronic metabolic disorders such as diabetes and obesity are major public health issues in the United States. However, significant disparities in their prevalence and incidence place a greater burden on US racial and ethnic minority groups, contributing to worse COVID-19 outcomes in many. Improving treatment and prevention of diabetes and obesity is critical to the NIDDK. In this Perspective, we will review the burden of metabolic diseases in the United States, the observed disparities for metabolic diseases in relation to COVID-19, and research opportunities to address underlying causes of metabolic diseases, their associated health disparities, and COVID-19.

50 Years of Behavioral Science in Diabetes: A 2020 Vision of the Future

This article is adapted from a speech Dr. de Groot delivered in June 2020 as President, Health Care & Education, of the American Diabetes Association at the Association's 80th Scientific Sessions, which was held online as a result of coronavirus disease 2019. Dr. de Groot is an Associate Professor of Medicine in the Division of Endocrinology, Diabetes and Metabolism at Indiana University (IU) School of Medicine. She serves as the Acting Director of the IU Diabetes Translational Research Center. Dr. de Groot is the 2020 recipient of the Rachmiel Levine Medal for Leadership from the American Diabetes Association.In the year 2020, we marked the 50th anniversary of the field of behavioral science in diabetes in the modern era. Over this relatively short period of time, this field has charted the psychosocial landscape of prediabetes and diabetes by establishing the prevalence and impact of emotional and behavioral aspects of diabetes. Interventions to address these conditions have been developed that span the T2 to T4 translational research spectrum ranging from the intrapsychic to population-based interventions. Policies ranging from standards of care to Medicare benefits have been implemented. A review of research in the area of diabetes and depression is provided as an example of innovations in this field. Behavior is the foundation of all interventions we make in diabetes and prediabetes. As a mature science, it is critical to stemming the tide of diabetes and its outcomes. To make additional strides, we must rebalance our focus and augment funding for behavioral interventions for individuals, communities, and health care systems in conjunction with other forms of treatment.

To tackle diabetes, science and health systems must take into account social context

An increasing amount of publications are recognizing that a person’s risk of diabetes and diabetes outcomes are influenced largely by social determinants of health. This renewed understanding of disease should influence health provision and diabetes research, but will it?

A model of culturally-informed integrated diabetes education and eye screening in indigenous primary care services and specialist diabetes clinics: Study protocol

AIMS

To improve diabetes management in Indigenous Australians using an integrated nurse-led model of diabetes education and eye screening in indigenous primary care and specialist diabetes clinics.

DESIGN

A pre-post study.

METHODS

This study will be implemented in indigenous primary care and specialist diabetes clinics in Victoria, Australia. Participants recruited to the study will be existing adult patient with diagnosed diabetes attending study sites. A nurse-credentialled diabetes educator and certified retinal imager will deliver three study components: (a) retinal photography as a diabetic retinopathy screening and patient engagement tool; (b) lifestyle and behaviour surveys, administered at baseline and at the final visit, in 12 months. Findings from the surveys and participants' retinal images will be used to guide; and (c) personalized diabetes education. The primary outcomes are participant adherence to diabetic eye screening recommendations and health service diabetic retinopathy screening coverage. Secondary outcomes are baseline DR prevalence and changes in clinical and lifestyle risk factor levels, diabetes knowledge and satisfaction with diabetes care.

DISCUSSION

Compared with non-indigenous Australians, Indigenous Australians have a high prevalence of diabetic retinopathy and blindness, low adherence to eye screening recommendations and suboptimal health literacy. Nurse-credentialled diabetes educators can be trained to incorporate retinal imaging and eye screening into their clinical practice to give image-based diabetes education to facilitate diabetic retinopathy management.

IMPACT

Credentialled nurse diabetes educators who integrate eye screening and diabetes education can facilitate timelier diabetic retinopathy screening, referral pathways and treatment of sight-threatening retinopathy. We believe that this model of integrated diabetes education and eye screening will also improve adherence to eye screening recommendations, population screening coverage, health literacy, risk factor levels and diabetes self-care.

CLINICAL TRIAL REGISTRATION

ANZCTRN1261800120435.

50 Years of Behavioral Science in Diabetes: A 2020 Vision of the Future

Editor's Note: This article is adapted from a speech Dr. de Groot delivered in June 2020 as President, Health Care & Education, of the American Diabetes Association. She delivered her address at the Association's 80th Scientific Sessions, which was held online as a result of the coronavirus disease 2019.