The Science of Diabetes Self-Management and Care 2025, Vol. 51(6) 615–630 © The Author(s) 2025 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/26350106251383897 journals.sagepub.com/home/tde
Abstract
Purpose: The purpose of the study was to examine the relationship between receipt of diet/nutrition, exercise, weight reduction, and diabetes education and various demographic and clinical factors among adults with type 2 diabetes mellitus (T2DM) using 2012-2019 National Ambulatory Medical Care Survey (NAMCS) data.
Methods: This retrospective, cross-sectional study included adults ages ≥18 diagnosed with T2DM in the NAMCS from 2012 to 2019. Plots of the annual percentage of visits (and associated 95% CIs) at which each type of education was received were constructed. Chi-square tests and multivariable logistic regression models were employed to examine the association between receipt of each education types and various factors collected in the NAMCS.
Results: A total of 11 525 patient visits were analyzed. Education was provided at 16.7% of visits for exercise, 24.3% for diet/nutrition, 7.5% for weight reduction, and 16.6% for diabetes. Adjusting for available covariates, higher body mass index increased the odds of receiving education on exercise. Diet/nutrition education was more common in visits with physicians and allied health providers and less common with surgical or medical specialists compared to primary care, as was exercise education. Tobacco users were less likely to receive weight reduction education. Limited racial or ethnic disparities were observed, but rural visits were less likely than urban visits to receive exercise and weight reduction education.
Conclusion: Although racial and ethnic disparities in diabetes education were not observed when adjusting for available covariates, geographic, specialty-based, and tobacco-use-related disparities persist, particularly affecting rural areas, specialist care, and smokers. These disparities highlight the necessity of targeted interventions to enhance access to education and improve health outcomes.
Diabetes mellitus is a chronic, multisystemic, metabolic disorder that significantly impacts individuals, health care systems, and societies globally.1 The disease is characterized by insulin resistance and hyperglycemia due to impaired glucose metabolism, including excessive gluconeogenesis and glycogenolysis.2 Diagnostic criteria for diabetes include fasting plasma glucose levels of ≥126 mg/dL, a 2-hour plasma glucose level of ≥200 mg/dL during an oral glucose tolerance test, or an A1C level of ≥6.5%.2,3
In the United States, diabetes remains a major public health concern. Each year, approximately 1.2 million Americans are newly diagnosed with diabetes.4 As of 2021, the disease ranked as the eighth leading cause of death, affecting 38.1 million adults, of whom, 29.7 million were diagnosed and 8.7 million remained undiagnosed.4 According to this National Diabetes Statistics Report, among the different types of diabetes, type 2 diabetes mellitus (T2DM) is the most prevalent, accounting for approximately 90% to 95% of all diabetes cases. Unlike type 1 diabetes, which is primarily an autoimmune condition leading to the destruction of insulin-producing cells, T2DM is often associated with obesity,5 physical inactivity,6 and genetic predispositions.7 T2DM disproportionately affects ethnic minority populations.8 For instance, African American individuals have a higher likelihood of being diagnosed with T2DM compared to non-Hispanic White individuals.2 Minority populations also tend to experience lower medication adherence and poorer self-management behaviors, often due to socioeconomic constraints.6 Research studies have also found that only a third of patients receive comprehensive diabetes education and that an underutilization of educational interventions is evident.9 Various factors contribute to these realities, including access to health care, insurance coverage, geographic location, race, socioeconomic status, and psychosocial factors.10 Financial barriers, in particular, difficulty paying bills, can affect minority groups, resulting in poorer health outcomes due to worsening glycemic control and inadequate diabetes management.8 Consequently, socioeconomic factors also contribute to increased disease complications and self-management.11
Education and adherence to treatment are crucial for managing T2DM and reducing its complications. The updated standards of diabetes care, as provided by the American Diabetes Association (ADA) in 2025, emphasize key priorities that reflect the importance of health equity and access, expanded care models, and patient-centered education.12 Furthermore, diabetes self-management education and support (DSMES), a program developed by several health care organizations such as the ADA, the Association of Diabetes Care & Education Specialists (ADCES), and the Academy of Nutrition and Dietetics, to mention a few, provides a standardized and evidencebased approach to diabetes education.13 Additionally, technology has played a pivotal role in advancing diabetes self-care, which facilitated the development and implementation of the ADCES7 Self-Care Behaviors® model in 2021. This framework offers an expanded scope and guidance for dynamic health care teams and supports care for obesity, diabetes-related complications, and cardiometabolic diseases.14 Therefore, it is important to understand how findings on educational disparities reported in 2017, when considered alongside the latest 2025 ADA diabetes care guidelines and the evolving role of technology, have influenced the provision of diabetes education overtime.
Branoff et al15 analyzed data from the National Ambulatory Medical Care Survey (NAMCS) to identify disparities in health education among patients with T2DM between the years 2008 and 2011. The study revealed important gaps in the provision of diet/nutrition, exercise, and weight loss education, particularly among minority and low-income populations,15 despite the existence of national standards for DSMES during the timeline of that study.16 African American and Hispanic patients were 31% and 29% less likely, respectively, to receive comprehensive diabetes education compared to their non-Hispanic White counterparts. Moreover, patients with lower socioeconomic status, especially those on Medicaid, were 36% less likely to receive diet/nutrition education, and those without private insurance were 47% less likely to receive this essential education.15
The education disparities found in the 2017 Branoff et al15 article were attributed to various factors, including differences in health care access, provider biases, and systemic inequities within the health care system. Those findings were surprising given the approach to diabetes care and education published in 2007, which focused on empowerment-based diabetes self-management education and ongoing support beyond the initial education to ensure behavior change and positive health outcomes.16 The Branoff et al15 findings emphasized the need to assess the potential effectiveness of best practices in diabetes education and the possible change in health education disparities. Reassessing previous findings using an 8-year time frame can help determine whether education disparities have improved given the unified guidance from ADA, ADCES, and the Academy of Nutrition and Dietetics. Consequently, the objectives of this study were to examine disparities in the provision of patient education among adults diagnosed with T2DM over the years 2012 to 2019, which included 7 years of NAMCS data, and update the findings of Branoff et al,15 which were based on NAMCS data from 2008 to 2011. We examined how demographic and clinical factors, such as race, ethnicity, body mass index (BMI), age, gender, region, provider type, specialty, tobacco use, and metropolitan status area (MSA), were associated with the receipt of exercise, diet/nutrition, weight loss, and diabetes education in adult patients with T2DM.
This study was a retrospective, cross-sectional, observational study that used the most recently available NAMCS data sets from the years 2012-2016 and 2018-2019 (no NAMCS data were available for the year 2017). The NAMCS is an annual survey conducted by the Centers of Disease Control and Prevention (CDC). The annual NAMCS data are comprised of a national probability sample of visits made to the offices of nonfederally employed physicians classified by the American Medical Association or the American Osteopathic Association as providing primarily office-based medical care.17,18 This database includes data going back to 1993, and hundreds of publications are based on these annual data sets.19
The complexity of the methodology involved in the NAMCS surveys and the National Hospital Ambulatory Medical Care Survey led 2 National Center for Health Statistics (NCHS; the organization that oversees the conduct of these surveys) statisticians to publish an article explaining the methodology used and included sample text designed to be included as the methods sections of publications that use these data.20 It is this McCaig and Burt20 article and a previous publication using NAMCS data by some of the authors of the current article on which the following brief summary of the NAMCS survey methods is based. For interested readers, further details can be found in the McCaig and Burt20 article and/or on the NAMCS website.17
NAMCS data are collected from a group consisting of physicians and nonphysician clinicians, including nurse practitioners and physician assistants, from the United States (anesthesiologists, pathologists, and radiologists are excluded). The total physician sample is divided into 52 random subsamples approximately equal in size, with each subsample randomly assigned to 1 of the 52 weeks in a year. Each physician systematically selects a random sample of visits during an assigned reporting week, and then each physician, physician support staff, or the US Census Bureau’s field representative performs data collection. A random sample of these logged visits from the reporting week is then selected for inclusion in the database. The data collected include patient symptoms, diagnoses, medications, procedures, planned treatment, demographic, socioeconomic, dietary, and other healthrelated information.
The NAMCS is approved by the Ethics Review Board of the NCHS, with waivers of the requirements to obtain informed consent from patients and patients’ authorization of the release of medical record data by health care providers. Data processing, including all medical and drug coding, are performed by SRA International, Inc (Durham, NC) and subjected to quality-control procedures.17,18
NAMCS data sets from the years 2012-2016 and 2018-2019 were included in the study. Data from patients ≥18 years of age with a diagnosis of diabetes were included. Diabetes diagnosis was determined through the use of the following ICD-9/ICD-10 diagnosis codes (NAMCS variables DIAG1-DIAG5): 25000, 25002, 25010, 25012, 25020, 25022, 25030, 25032, 25040, 25042, 25050, 25052, 25060, 25062, 25070, 25072, 25080, 25082, 25090, 25092, E11. For eligible visits, information was included on patient age, gender, race, ethnicity, provider type, physician specialty, type of insurance, MSA, BMI category, and tobacco use.
Four diabetes-related education endpoints were used in this study: diet and nutrition education, exercise education, weight reduction education, and diabetes-specific education, all reported as received or not at each visit. Note that the diabetes-specific education question was not included in the survey until 2014. Thus, only the years 2014-2016 and 2018-2019 are included in the diabetes education-related analyses.
The collected NAMCS data were analyzed using the sampled visit weight, which represented the product of the corresponding sampling fractions at each stage in the sample design. The sampling weights were adjusted by the NCHS for survey nonresponse as appropriate within the database, yielding a nonbiased national estimate of visit occurrences, percentages, and characteristics. Consistent with the multistage, cluster-sampling methods used in NAMCS, all analyses were weighted and clustered to extrapolate results to generate average annual US national estimates. That is, the analysis of the survey, as designed, allows for the generation of national average annual ambulatory care visit totals for the years 2012-2016 and 2018-2019 by extrapolation of the survey sample (N = 11 525).19-21
Demographic and patient visit characteristics information was summarized using appropriate summary statistics. Plots of the annual percentage of visits (and associated 95% CIs) at which each type of education was received were constructed to assess any changes in this outcome over time. Chi-square tests to assess the association between all available covariates of interest and each of the 4 types of education received (diet and nutrition education, exercise education, weight reduction education, and diabetes-specific education) were conducted. (Unadjusted) Odds ratios (ORs) with corresponding 95% Wald CIs were computed. Furthermore, 4 multivariable logistic regression models were constructed to evaluate the predictive value of each independent variable of interest, adjusting for covariates available in the data sets, on each education type was received or not . (Adjusted) ORs with corresponding 95% Wald CIs for each level of each discrete variable included in the model, in comparison to each variable’s reference group, were generated and reported. The variables included in the models were grouped for analysis as shown in Table 1. The NCHS recommends that any variable with a survey estimate based on <30 records, with a >30% missing data, or a relative standard error of >30% not be reported for any analyses due to potential unreliability. As a result, the ORs and CIs for any variables that met 1 or more of these criteria were omitted from their respective table(s). The unimputed race and ethnicity variables in the NAMCS data set were missing data for nearly one third of visits. As such, the imputed versions of these variables were implemented in all analyses. From the data set documentation:
Race and ethnicity were imputed using a model-based, single, sequential regression imputation method. The model for imputing race and ethnicity used the following variables: Census race and ethnicity population estimates for ZIP code, duration of visit, patient age, patient sex, whether the visit occurred in an MSA, physician specialty recode, whether the visit included hypertension as a current diagnosis/chronic condition, diagnosis group, major reason for visit, and an indicator for patient ZIP code or provider ZIP (the latter was used for the Census variables if patient ZIP was not available).22
Any race and/or ethnicity estimates not included in the tables were due to the imputed versions of these variables not meeting the described criteria for reliability.
Because this was a retrospective, hypothesis-generating type of study, no adjustments for multiple comparisons were made. In addition, aligning with current thinking regarding best practices against significance testing from thought leaders in statistics, statistical significance was not reported for any results.23 Furthermore, a focus on the provided CIs is urged to ensure the readers’ awareness of both interval width (narrower being more informative) and location (further from zero indicating increasing importance). All analyses were generated using SAS version 9.4.24
Sampling errors were determined using appropriate SAS SURVEY procedures, which account for the clustered nature of the sample. Furthermore, the appropriate SAS procedure options (NOMCAR and DOMAIN) to address missing data and use of domains to determine accurate variance estimates were implemented in the analyses as recommended by the NCHS. The data for analyses was de-identified and cleaned by the CDC prior to release. Due to the data sources used being publicly available and de-identified, an exemption from Cambell University Institutional Review Board was received.
This study included an unweighted (raw) total of 11,525 visits from patients diagnosed with T2DM, based on the available NAMCS data from 2012 to 2019. The design of the NAMCS survey allows the extrapolation of this survey visit total to an estimated 60,758,180 ambulatory care visits annually in the United States among those that meet the study inclusion/exclusion criteria. As shown in Table 1, the study sample was evenly split between visits from females and males. Most patient visits included in the study were in the ≥65 age group (53.8%). The mean age was 64.9 (SE 0.33) years. The majority of visits were by White (76.2%) non-Hispanic or Latino (82.7%) patients not currently using tobacco (85.0%) and were seen by a physician in addition to an allied health provider (AHP; 53.0%). Most visits were with a primary care specialty (67.0%) and occurred in an urban area (90.7%). The primary insurance type was Medicare for just over half the visits (51.5%). In the adult T2DM population in the United States, the receipt of exercise education was reported at 16.7% of visits, diet/nutrition education at 24.3% of visits, weight reduction education at 7.5% of visits, and diabetes education at 16.6% of visits.
Figures 1 to 4 show very similar trends in the annual percentage of visits at which each education type of provided: an increase in the estimated percentage (and variability) through 2016, followed by a drop in 2018 (the NAMCS survey was not conducted in 2017) and a rise again in 2019.
The (unadjusted) ORs and associated 95% CIs resulting from the individual chi-square tests for exercise education indicated that visits by those 65 and older had 39% lower odds (OR: 0.61; 95% CI, 0.42-0.88) of receiving exercise education compared to those 18 to 44. Visits by obese individuals (BMI ≥30) had 93% higher odds (OR: 1.93; 95% CI, 1.37-2.72) of receiving exercise education compared to those who are under/normal weight (BMI <25). Visits by those with Medicare had 27% lower odds (OR: 0.73; 95% CI, 0.59-0.91) of receiving exercise education compared to those with private insurance, and visits to surgical and medical care specialists had 63% lower odds (OR: 0.37; 95% CI, 0.24-0.56) of receiving exercise education compared to visits to primary care providers (Table 2).
The multivariable logistic regression model for exercise education in Table 2 indicated that after adjusting for covariates, visits by obese patients (BMI ≥30) had more than double the odds (OR: 2.04; 95% CI, 1.27-3.26) of receiving exercise education compared to visits by those under/normal weight (BMI <25). Additionally, visits to surgical and medical care specialists had 41% lower odds (OR: 0.59; 95% CI, 0.35-0.98) of receiving exercise education compared to visits to primary care providers. The data did not suggest any other important differences between the levels of any of the other factors in the model in terms of the receipt of exercise education.
The (unadjusted) ORs and associated 95% CIs resulting from the individual chi-square tests for diet/nutrition education indicated that visits involving both a physician and an AHP had 65% higher odds (OR: 1.65; 95% CI, 1.21-2.25) of providing diet/nutrition education compared to those with a physician alone. Visits by both obese individuals (BMI ≥30) and overweight individuals (BMI 25 - <30) had 56% (OR: 1.56; 95% CI, 1.12-2.17) and 39% higher odds (OR: 1.39; 95% CI, 1.02-1.91), respectively, of receiving diet/nutrition education compared to those who are under/normal weight (BMI <25). Visits to surgical and medical care specialists had 60% lower odds (OR: 0.40; 95% CI, 0.28-0.57) of receiving diet/nutrition education compared to visits to primary care providers, and those with Medicare had 23% lower odds (OR: 0.77; 95% CI, 0.63-0.95) of receiving diet/nutrition education compared to those with private insurance (Table 3).
The multivariable logistic regression model for diet/nutrition education in Table 3 showed that after adjusting for covariates, visits involving both a physician and an AHP had 48% higher odds (OR: 1.48; 95% CI, 1.02-2.15) of providing diet/nutrition education compared to those with a physician alone. Visits to surgical and medical care specialists had 43% lower odds (OR: 0.57; 95% CI, 0.37-0.88) of receiving diet/nutrition education compared to those with primary care providers. The data did not suggest any other important differences between the levels of any of the other factors in the model in terms of the receipt of diet/nutrition education.
The (unadjusted) ORs and associated 95% CIs resulting from the individual chi-square tests for weight reduction education indicated that visits by those 65 and older had 47% lower odds (OR: 0.53; 95% CI, 0.34-0.84) of receiving weight reduction education compared to those 18 to 44. Visits by those in the “other” race group were found to have 54% lower odds (OR: 0.46; 95% CI, 0.23-0.92) of receiving weight reduction education compared to those in the White race group. Visits to surgical and medical care specialists had 57% lower odds (OR: 0.43; 95% CI, 0.26-0.73) of receiving weight reduction education compared to visits to primary care providers (Table 4).
The results for the multivariable logistic regression model for weight reduction education presented in Table 4 show that after adjusting for available covariates, visits in rural areas had 52% lower odds (OR: 0.48; 95% CI, 0.26-0.88) of receiving weight reduction education compared to visits in urban areas. Visits by current tobacco users had 43% lower odds (OR: 0.57; 95% CI, 0.34-0.95) of receiving weight reduction education compared to visits by non-tobacco users. The data did not suggest any other important differences between the levels of any of the other factors in the model in terms of the receipt of weight reduction education.
For the most recently added education variable, diabetes education, the (unadjusted) ORs and associated 95% CIs resulting from the individual chi-square tests for diabetes education indicated that visits to surgical and medical care specialists had 61% lower odds (OR: 0.39; 95% CI: 0.24-0.66) of receiving diabetes education compared to visits to primary care providers. After adjusting for covariates, the data did not suggest any important differences between the levels of any of the factors in the model in terms of the receipt of diabetes education (Table 5).
This study investigated the relationship between the receipt of each of 4 types of health education—diabetes, exercise, diet/nutrition, and weight loss—and disparities among T2DM patients in the United States. The study results did not reveal any disparities among racial or ethnic groups, suggesting that health care providers are equally supportive of all types of education across diverse demographic groups. Furthermore, there were no significant differences in the likelihood of receiving diabetes education among patients diagnosed with T2DM. These are positive outcomes—particularly because diabetes education disparities were found in the 2017 Branoff et al15 article. Therefore, efforts to deliver diabetes, exercise, diet/nutrition, and weight loss education are reaching diverse populations impartially. DSMES initiatives on diabetes education and the emphasis on health equity and personcentered care may have positively influenced the provision of diabetes education.25,26 However, the results of this study did indicate that various demographic factors, such as urbanicity, BMI, tobacco use, and provider type, remain associated with the odds of receiving these types of education.
Based on this study’s results, after adjusting for covariates of interest, patients with obesity (BMI ≥30) had higher odds of receiving exercise education compared to those with normal or underweight BMI (0-25). Despite only 16.7% of this study’s population receiving exercise education (and only 7.5% receiving weigh reduction education), this finding aligns with previous research suggesting that health care providers are more inclined to provide exercise-related advice to patients with higher BMI due to the strong correlation between obesity and diabetes complications.1 Receiving exercise education is supported by the DSMES curriculum, and it is 1 of the 7 key selfcare behaviors (“being active”).25,27 The LIFE is LIGHT study underscores the value of exercise education for individuals with both obesity and T2DM, having demonstrated that lifestyle modifications—including dietary changes, regular physical activity, and behavioral counseling—can significantly reduce key prognostic markers, such as A1C, BMI, and waist-to-hip ratio, while also promoting meaningful weight loss and improved quality of life.5 However, receiving exercise education should also be available to all individuals regardless of BMI and should be provided as a preventive measure given the positive effect of physical activity on T2DM management and overall health.28,29 Hence, it is not surprising that supporting positive behaviors and engaging in physical activity was one of the updated behavioral strategies that was a part of the multicomponent interventions to promote optimal health proposed by ADA in 2025.12
In the current study, after adjusting for covariates of interest, patients who consulted a surgical or medical specialist had lower odds of receiving exercise education compared to those who saw primary care providers. This education gap is a missed opportunity for multidisciplinary educational programs to reduce risks and lower the chances of diabetes complications given the strong association between T2DM and being overweight or obese.30 This observation echoes Branoff et al,15 highlighting persistent disparities and emphasizing the necessity of integrated education across all specialties to ensure consistent messaging about the advantages of exercise regardless of the type of health care provider visited.
The provision of diet/nutrition education was also associated with the type of health care provider involved. Visits that included both a physician and an AHP had higher odds of delivering such education compared to visits with a physician alone. This positive association supports the findings of Griauzde et al,31 who concluded that coordinated care among primary care providers and dietitians—alongside personalized counseling and the use of continuous glucose monitoring—yields significantly greater reductions in A1C levels for individuals with poorly controlled T2DM.
Although a multidisciplinary approach can enhance the delivery of comprehensive education programs—crucial for both patient engagement and the long-term management of T2DM—several systemic and patient-level challenges persist. These include broader issues in the health care system and specific barriers to effective education and disease management. From the patient’s perspective, areas for improvement include poor communication among health care providers, insufficient psychological support, inadequate provider training, and a lack of continuity in educational programs.32 To address these challenges, diabetes care should emphasize respectful and inclusive communication, adopt a strengths-based approach, and foster collaboration among patients, families, and the care team.13,33
The 2022 update to the National Standards for DSMES emphasized the importance of ongoing diabetes selfmanagement and broadened its relevance to diverse care settings, from small practices to large health systems.2,25 This marked a shift from the 2015 standards, which focused more narrowly on traditional clinical environments. The education gap identified in this study—particularly regarding diet and nutrition education during specialty visits—highlights opportunities to improve outcomes for individuals with or at risk for T2DM. Certified AHPs, diabetes educators, and clinicians can deliver this education effectively with proper training and adherence to principles of person-centered communication.33 These efforts align with the 2022 DSMES updates and diabetes care standards, which promote inclusive communication and ongoing monitoring.25
Another key finding across education types was weight reduction education. After adjusting for covariates of interest, the provision of weight loss education was most strongly associated with BMI, with obese patients having the highest odds of receiving this type of education. Rigorous lifestyle interventions, particularly those focused on weight management, have been demonstrated to improve glycemic control.34,35 For instance, the Diabetes Remission Clinical Trial (DiRECT) showcased that a 12-month intensive weight management program conducted in primary care settings achieved remarkable results, leading to diabetes remission and the discontinuation of antidiabetic medications in nearly half of the participants.34 Consequently, this finding is not surprising given the well-known benefit of weight loss in improving cardiovascular risk factors in overweight and obese individuals with T2DM.34 Furthermore, the impact of weight loss interventions, particularly education/coaching and behavioral interventions, have been shown to reduce biomarkers tied to gut health, as demonstrated by the SPIRIT randomized trial.35 Our findings on weight reduction education align with Branoff et al15 and suggest a continued emphasis on weight management in reducing diabetes-related complications and enhancing overall health outcomes. However, the lower odds of receiving weight loss education in rural areas identified in this study (OR: 0.50; 95% CI, 0.27-0.93) highlight the need for targeted interventions to enhance access to weight management resources in these regions, particularly given the growing burden of diabetes, obesity, and cardiovascular disease.30
Smoking can have negative effects on people with diabetes.36 In the current study, after adjusting for covariates of interest, visits made by current tobacco users had lower odds of receiving weight loss education compared to non-tobacco users. This finding is surprising given the psychosocial support offered in the DSMES services for reducing risks and the emotional response to diabetes and diabetes stress.25 Furthermore, smoking and smoking cessation programs can impact weight management and insulin sensitivity, which, in turn, also increases the vascular complications of people with diabetes.37,38 Therefore, integrating weight reduction education into smoking cessation programs could offer a dual benefit and support overall health improvement.29
Our findings reveal an encouraging absence of disparities among racial and ethnic groups in the receipt of exercise, diet/nutrition, weight loss, and diabetes education among patients with T2DM. Using the most recently available data from the NAMCS (from 2012 to 2019), the lack of identified disparities in diabetes education among patients diagnosed with T2DM could indicate that the provision of diabetes education continues to improve as the awareness of implementation of the DSMES initiatives and ongoing updates to diabetes care standards occurred. Such research findings are important to document in the most recent nationally representative data, particularly because according to the new ADA guidelines, social determinants of health (SDOH) are an essential aspect of diabetes care. Some of the evidence gathered from this study contrasts with the results of Branoff et al,15 given that they reported significant disparities in diabetes education, particularly among minority groups and those without private insurance. Utilizing the same data set as Branoff et al15 but with the most recent 7 years’ worth of data within an 8-year time frame, our study suggests improvements in the equitability of diabetes education delivery.
Persistent disparities in receipt of exercise, diet/nutrition, and weight reduction education based on body composition, tobacco use, and type of provider remain. The disparities associated with body composition particularly are not surprising and, in fact, are likely dictated specifically by the overweight or obese status of an individual. Therefore, the fact that only 7.5%, 16.6%, 16.7%, and 24.3% of this study’s population received weight reduction, diabetes, exercise, and/or diet/nutrition education, respectively, remains concerning. In conjunction with the disparities identified by provider type, opportunities exist for AHPs and a broader range of care delivery models to improve educational awareness. Expanding access to DSMES through alternative methods of education delivery and in collaboration with other health care education specialists, such as occupational therapy practitioners, can provide education and reinforce self-management skills to improve the health outcomes of individuals with T2DM. For example, by targeting modifiable risk factors for diabetes prevention and management, we can hypothesize that providing preventive services and exercise education to individuals at high risk of obesity should be a key component of DSMES initiatives aimed at monitoring or reducing risk—especially in underserved rural populations, where the potential for improvement is perhaps greatest.
The new ADA standards of care, published in 2025, clarified the timing for the provision of DSMES, which is recommended at the time of diagnosis, annually, during the transition of care, and when diabetes management complications exist.12 Perhaps this new update will be key in addressing these disparities and ensuring that DSMES interventions are provided to all individuals, regardless of the timeline of the diagnosis, the health care model, or provider type, and will continue to be a priority for effective diabetes prevention and management, predominantly because the new education and support recommendations to facilitate positive health behaviors and well-being include a focus on SDOH and the provision of culturally appropriate education addressing barriers at the payer, health system, setting, and team levels.12
The provision of exercise education and diet/nutrition education per the 2025 ADA standards of care in diabetes would assist in the care coordination and expansion of care models to improve the health outcomes of people with T2DM. Mainly because other studies have found that educational/counseling programs are mostly managed at the primary health care level, it remains a challenge to implement educational programs such that other diabetes medical care is provided by specialists.39 Therefore, the updated ADA guidelines promoting positive health behaviors and the ADCES7 Self-Care Behaviors® model may help reduce disparities in access to education, which may continue to be influenced by factors such as urbanicity, BMI, tobacco use, and provider type; particularly because the ADA updates aim to ensure that DSMES initiatives are more widespread and inclusive and responsive to these demographic differences. Future research and studies are encouraged to assess the impact of the diabetes care standards updated in 2022 and the 2025 ADA standards of care in diabetes.
The current study has strengths and limitations worth acknowledging. The NAMCS database is composed of a large, nationally representative sample that reflects the broader US population. Furthermore, because the data are reported directly by physicians, this reduces bias typically associated with patient-reported surveys. Additionally, this study is a follow-up to the original research by Branoff et al,15 published in 2017, and contains 7 years of the most recently available data. Some limitations of this study are inherent to its cross-sectional design. Observations captured do not follow patients over time, and the researcher cannot determine the quality of the education provided given the yes/no nature of the responses. Furthermore, only associations can be established because the study design does not allow for the inference of causality between any of the educations types investigated and the covariates included in the models.
This research was presented in part at the North American Primary Care Research Group Practice-Based Research Network conference held in Reston, Virginia, June 2-3, 2025.
All authors contributed to the study conception and design. Data management and analysis were performed by MJ. The first draft of the manuscript was written by JZ and SAM. All authors reviewed, commented on, and edited all previous versions of the manuscript, and all authors read and approved the final manuscript.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study conception, design, and analysis of this study was not supported by any funding. A fellowship award of $4500 was applied for and won Campbell University’s Ester H. Howard Student Research Fellows Program to support the writing, study analysis, and formation of the manuscript.
This is an observational study using de-identified, publicly available data sets and was therefore considered not human subject research by the Cambell University Institutional Review Board.
Michael R. Jiroutek https://orcid.org/0000-0002-0136-1305
Susan Avila Misciagno https://orcid.org/0000-0001-5374-2022
The data sets analyzed during the current study are available in the Centers for Disease Control and Prevention repository (https://www.cdc.gov/nchs/nhis/documentation/index.html).
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Galstyan GR, Valeeva FV, Motkova SI, et al. Lifestyle modification program, LIFE is LIGHT, in patients with type 2 diabetes mellitus and obesity: results from a 48-week, multicenter, non-randomized, parallel-group, open-label study. Obes Sci Pract. 2021;7(4):368-378. doi:10.1002/osp4.502
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Franzago M, Pilenzi L, Di Rado S, Vitacolonna E, Stuppia L. The epigenetic aging, obesity, and lifestyle. Front Cell Dev Biol. 2022;10:985274. doi:10.3389/fcell.2022.985274
Walker RJ, Garacci E, Palatnik A, Ozieh MN, Egede LE. The longitudinal influence of social determinants of health on glycemic control in elderly adults with diabetes. Diabetes Care. 2020;43(4):759-766. doi:10.2337/dc19-1586
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From Department of Pharmaceutical & Clinical Sciences, College of Pharmacy & Health Sciences, Campbell University, Buies Creek, North Carolina (Mr Zanek, Dr Jiroutek, Dr Misciagno).
Corresponding Author: Susan Avila Misciagno, Department of Pharmaceutical & Clinical Sciences, College of Pharmacy and Health Sciences, Campbell University, 450 Leslie Campbell Ave, Buies Creek, NC 27546, USA. Email: smisciagno@campbell.edu
