research papers on type 2 diabetes mellitus

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Clinical Research on Type 2 Diabetes: A Promising and Multifaceted Landscape

Type 2 diabetes constitutes an imposing epidemiological, economic, and scientific global challenge. The chronic complications of type 2 diabetes are a major cause of mortality and disability worldwide [ 1 , 2 ]. Clinical research is the main way to gain knowledge about long-term diabetic complications and reduce the burden of diabetes. This allows for designing effective programs for screening and follow-up and fine-targeted therapeutic interventions. However, new research methodologies are needed to obtain more accurate and useful insights into the biological and clinical processes involved in diabetic complication development.

During the last few years, new approaches for clinical research have incorporated digital tools to analyze the complex physiopathological background of type 2 diabetes. In this Special Issue, entitled “ Clinical Research on Type 2 Diabetes and Its Complications ” and published in the Journal of Clinical Medicine ( https://www.mdpi.com/journal/jcm/special_issues/Type_2_Diabetes_Complications ), some valuable digital methodologies were used in different studies focusing on the type 2 diabetes syndrome. Novel machine learning techniques for predicting long-term complications are one of these approaches, as the studies of Huang, Rashid, and Shin et al. depict [ 3 , 4 , 5 ]. The data presented by these authors suggest that machine learning may be more accurate in predicting diabetic microvascular complications than traditional methods. Additionally, digital tools such as artificial intelligence and machine learning can be implemented through an automated and rapid process.

Among the frequent causes of frustration for people with diabetes and the health care providers involved in their management is the delayed detection of diabetic complications. The outlook of clinical research appears promising in the near future owing to the development and implementation of advanced methods for the detection of early alterations in the micro- and macrovascular complications associated with diabetes. Two papers in this Special Issue cover the use of specific biomarkers tracing the progress of diabetic cardiovascular complications [ 6 , 7 ]. In another contribution, Lee et al. revisit the long-term glycemic variability and its relationship with end-stage kidney disease [ 8 ].

Besides the genetic approach, the application of digital techniques, including machine learning and artificial intelligence, and novel biomarkers could be crucial for individualized type 2 diabetes management, which is the backbone of precision medicine.

Two review papers address the complications that are non-traditionally linked to type 2 diabetes, although currently under exhaustive research: bone health and non-alcoholic fatty liver disease [ 9 , 10 ]. The multifaceted nature of type 2 diabetes is clearly visualized owing to the holistic angle used by these approaches.

The efficacy and safety of new type 2 diabetes pharmacological treatment are covered by three original papers [ 11 , 12 , 13 ]. The Yu-Chuan Kang et al. study includes a large population sample and an extended follow-up to evaluate the association between dipeptidyl peptidase-4 inhibitors and diabetic retinopathy [ 13 ]. This could be the first signal for a new safety risk of a pharmacological class of drugs used by millions worldwide.

The COVID-19 pandemic was first reported in China in December 2019 and continues to be a devastating condition for global health and economy. The COVID-19 disease has immediate implications for common chronic metabolic disorders such as type 2 diabetes. Both direct infection and the associated distress due to preventive measures in the general population have worsened the control of type 2 diabetes. Some factors indicate that COVID-19 or other coronavirus-caused diseases can be seasonal or persistent in the future. Type 2 diabetes has a strong negative effect on the prognosis of patients with COVID-19. Three papers in this Special Issue review the implications of this disease in relation to diabetes [ 14 , 15 , 16 ].

Finally, the aim of researchers in this field should be to make all these remarkable advances accessible to those populations experiencing more difficulties due to sociodemographic factors such as cultural deprivation, sex discrimination, or limited income [ 17 , 18 , 19 ].

Acknowledgments

The authors acknowledge the continuous editorial assistance of Nicole Quinn, Always English S.L.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, writing—original draft preparation, writing—review and editing were equally done by F.G.-P. and C.A. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Rulla Alsaedi , Kimberly McKeirnan; Literature Review of Type 2 Diabetes Management and Health Literacy. Diabetes Spectr 1 November 2021; 34 (4): 399–406. https://doi.org/10.2337/ds21-0014

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The purpose of this literature review was to identify educational approaches addressing low health literacy for people with type 2 diabetes. Low health literacy can lead to poor management of diabetes, low engagement with health care providers, increased hospitalization rates, and higher health care costs. These challenges can be even more profound among minority populations and non-English speakers in the United States.

A literature search and standard data extraction were performed using PubMed, Medline, and EMBASE databases. A total of 1,914 articles were identified, of which 1,858 were excluded based on the inclusion criteria, and 46 were excluded because of a lack of relevance to both diabetes management and health literacy. The remaining 10 articles were reviewed in detail.

Patients, including ethnic minorities and non-English speakers, who are engaged in diabetes education and health literacy improvement initiatives and ongoing follow-up showed significant improvement in A1C, medication adherence, medication knowledge, and treatment satisfaction. Clinicians considering implementing new interventions to address diabetes care for patients with low health literacy can use culturally tailored approaches, consider ways to create materials for different learning styles and in different languages, engage community health workers and pharmacists to help with patient education, use patient-centered medication labels, and engage instructors who share cultural and linguistic similarities with patients to provide educational sessions.

This literature review identified a variety of interventions that had a positive impact on provider-patient communication, medication adherence, and glycemic control by promoting diabetes self-management through educational efforts to address low health literacy.

Diabetes is the seventh leading cause of death in the United States, and 30.3 million Americans, or 9.4% of the U.S. population, are living with diabetes ( 1 , 2 ). For successful management of a complicated condition such as diabetes, health literacy may play an important role. Low health literacy is a well-documented barrier to diabetes management and can lead to poor management of medical conditions, low engagement with health care providers (HCPs), increased hospitalizations, and, consequently, higher health care costs ( 3 – 5 ).

The Healthy People 2010 report ( 6 ) defined health literacy as the “degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.” Diabetes health literacy also encompasses a wide range of skills, including basic knowledge of the disease state, self-efficacy, glycemic control, and self-care behaviors, which are all important components of diabetes management ( 3 – 5 , 7 ). According to the Institute of Medicine’s Committee on Health Literacy, patients with poor health literacy are twice as likely to have poor glycemic control and were found to be twice as likely to be hospitalized as those with adequate health literacy ( 8 ). Associations between health literacy and health outcomes have been reported in many studies, the first of which was conducted in 1995 in two public hospitals and found that many patients had inadequate health literacy and could not perform the basic reading tasks necessary to understand their treatments and diagnoses ( 9 ).

Evaluation of health literacy is vital to the management and understanding of diabetes. Several tools for assessing health literacy have been evaluated, and the choice of which to use depends on the length of the patient encounter and the desired depth of the assessment. One widely used literacy assessment tool, the Test of Functional Health Literacy in Adults (TOFHLA), consists of 36 comprehension questions and four numeric calculations ( 10 ). Additional tools that assess patients’ reading ability include the Rapid Estimate of Adult Literacy in Medicine (REALM) and the Literacy Assessment for Diabetes. Tests that assess diabetes numeracy skills include the Diabetes Numeracy Test, the Newest Vital Sign (NVS), and the Single-Item Literacy Screener (SILS) ( 11 ).

Rates of both diabetes and low health literacy are higher in populations from low socioeconomic backgrounds ( 5 , 7 , 12 ). People living in disadvantaged communities face many barriers when seeking health care, including inconsistent housing, lack of transportation, financial difficulties, differing cultural beliefs about health care, and mistrust of the medical professions ( 13 , 14 ). People with high rates of medical mistrust tend to be less engaged in their care and to have poor communication with HCPs, which is another factor HCPs need to address when working with their patients with diabetes ( 15 ).

The cost of medical care for people with diabetes was $327 billion in 2017, a 26% increase since 2012 ( 1 , 16 ). Many of these medical expenditures are related to hospitalization and inpatient care, which accounts for 30% of total medical costs for people with diabetes ( 16 ).

People with diabetes also may neglect self-management tasks for various reasons, including low health literacy, lack of diabetes knowledge, and mistrust between patients and HCPs ( 7 , 15 ).

These challenges can be even more pronounced in vulnerable populations because of language barriers and patient-provider mistrust ( 17 – 19 ). Rates of diabetes are higher among racial and ethnic minority groups; 15.1% of American Indians and Alaskan Natives, 12.7% of Non-Hispanic Blacks, 12.1% of Hispanics, and 8% of Asian Americans have diagnosed diabetes, compared with 7.4% of non-Hispanic Whites ( 1 ). Additionally, patient-provider relationship deficits can be attributed to challenges with communication, including HCPs’ lack of attention to speaking slowly and clearly and checking for patients’ understanding when providing education or gathering information from people who speak English as a second language ( 15 ). White et al. ( 15 ) demonstrated that patients with higher provider mistrust felt that their provider’s communication style was less interpersonal and did not feel welcome as part of the decision-making process.

To the authors’ knowledge, there is no current literature review evaluating interventions focused on health literacy and diabetes management. There is a pressing need for such a comprehensive review to provide a framework for future intervention design. The objective of this literature review was to gather and summarize studies of health literacy–based diabetes management interventions and their effects on overall diabetes management. Medication adherence and glycemic control were considered secondary outcomes.

Search Strategy

A literature review was conducted using the PubMed, Medline, and EMBASE databases. Search criteria included articles published between 2015 and 2020 to identify the most recent studies on this topic. The search included the phrases “diabetes” and “health literacy” to specifically focus on health literacy and diabetes management interventions and was limited to original research conducted in humans and published in English within the defined 5-year period. Search results were exported to Microsoft Excel for evaluation.

Study Selection

Initial screening of the articles’ abstracts was conducted using the selection criteria to determine which articles to include or exclude ( Figure 1 ). The initial search results were reviewed for the following inclusion criteria: original research (clinical trials, cohort studies, and cross-sectional studies) conducted in human subjects with type 2 diabetes in the United States, and published in English between 2015 and 2020. Articles were considered to be relevant if diabetes was included as a medical condition in the study and an intervention was made to assess or improve health literacy. Studies involving type 1 diabetes or gestational diabetes and articles that were viewpoints, population surveys, commentaries, case reports, reviews, or reports of interventions conducted outside of the United States were excluded from further review. The criteria requiring articles to be from the past 5 years and from the United States were used because of the unique and quickly evolving nature of the U.S. health care system. Articles published more than 5 years ago or from other health care systems may have contributed information that was not applicable to or no longer relevant for HCPs in the United States. Articles were screened and reviewed independently by both authors. Disagreements were resolved through discussion to create the final list of articles for inclusion.

PRISMA diagram of the article selection process.

Data Extraction

A standard data extraction was performed for each included article to obtain information including author names, year of publication, journal, study design, type of intervention, primary outcome, tools used to assess health literacy or type 2 diabetes knowledge, and effects of intervention on overall diabetes management, glycemic control, and medication adherence.

A total of 1,914 articles were collected from a search of the PubMed, MEDLINE, and EMBASE databases, of which 1,858 were excluded based on the inclusion and exclusion criteria. Of the 56 articles that met criteria for abstract review, 46 were excluded because of a lack of relevance to both diabetes management and health literacy. The remaining 10 studies identified various diabetes management interventions, including diabetes education tools such as electronic medication instructions and text message–based interventions, technology-based education videos, enhanced prescription labels, learner-based education materials, and culturally tailored interventions ( 15 , 20 – 28 ). Figure 1 shows the PRISMA diagram of the article selection process, and Table 1 summarizes the findings of the article reviews ( 15 , 20 – 28 ).

Findings of the Article Reviews (15,20–28)

SAHLSA, Short Assessment of Health Literacy for Spanish Adults.

Medical mistrust and poor communication are challenging variables in diabetes education. White et al. ( 15 ) examined the association between communication quality and medical mistrust in patients with type 2 diabetes. HCPs at five health department clinics received training in effective health communication and use of the PRIDE (Partnership to Improve Diabetes Education) toolkit in both English and Spanish, whereas control sites were only exposed to National Diabetes Education Program materials without training in effective communication. The study evaluated participant communication using several tools, including the Communication Assessment Tool (CAT), Interpersonal Processes of Care (IPC-18), and the Short Test of Functional Health Literacy in Adults (s-TOFHLA). The authors found that higher levels of mistrust were associated with lower CAT and IPC-18 scores.

Patients with type 2 diabetes are also likely to benefit from personalized education delivery tools such as patient-centered labeling (PCL) of prescription drugs, learning style–based education materials, and tailored text messages ( 24 , 25 , 27 ). Wolf et al. ( 27 ) investigated the use of PCL in patients with type 2 diabetes and found that patients with low health literacy who take medication two or more times per day have higher rates of proper medication use when using PCL (85.9 vs. 77.4%, P = 0.03). The objective of the PCL intervention was to make medication instructions and other information on the labels easier to read to improve medication use and adherence rates. The labels incorporated best-practice strategies introduced by the Institute of Medicine for the Universal Medication Schedule. These strategies prioritize medication information, use of larger font sizes, and increased white space. Of note, the benefits of PCL were largely seen with English speakers. Spanish speakers did not have substantial improvement in medication use or adherence, which could be attributed to language barriers ( 27 ).

Nelson et al. ( 25 ) analyzed patients’ engagement with an automated text message approach to supporting diabetes self-care activities in a 12-month randomized controlled trial (RCT) called REACH (Rapid Education/Encouragement and Communications for Health) ( 25 ). Messages were tailored based on patients’ medication adherence, the Information-Motivation-Behavioral Skills model of health behavior change, and self-care behaviors such as diet, exercise, and self-monitoring of blood glucose. Patients in this trial were native English speakers, so further research to evaluate the impact of the text message intervention in patients with limited English language skills is still needed. However, participants in the intervention group reported higher engagement with the text messages over the 12-month period ( 25 ).

Patients who receive educational materials based on their learning style also show significant improvement in their diabetes knowledge and health literacy. Koonce et al. ( 24 ) developed and evaluated educational materials based on patients’ learning style to improve health literacy in both English and Spanish languages. The materials were made available in multiple formats to target four different learning styles, including materials for visual learners, read/write learners, auditory learners, and kinesthetic learners. Spanish-language versions were also available. Researchers were primarily interested in measuring patients’ health literacy and knowledge of diabetes. The intervention group received materials in their preferred learning style and language, whereas the control group received standard of care education materials. The intervention group showed significant improvement in diabetes knowledge and health literacy, as indicated by Diabetes Knowledge Test (DKT) scores. More participants in the intervention group reported looking up information about their condition during week 2 of the intervention and showed an overall improvement in understanding symptoms of nerve damage and types of food used to treat hypoglycemic events. However, the study had limited enrollment of Spanish speakers, making the applicability of the results to Spanish-speaking patients highly variable.

Additionally, findings by Hofer et al. ( 22 ) suggest that patients with high A1C levels may benefit from interventions led by community health workers (CHWs) to bridge gaps in health literacy and equip patients with the tools to make health decisions. In this study, Hispanic and African American patients with low health literacy and diabetes not controlled by oral therapy benefited from education sessions led by CHWs. The CHWs led culturally tailored support groups to compare the effects of educational materials provided in an electronic format (via iDecide) and printed format on medication adherence and self-efficacy. The study found increased adherence with both formats, and women, specifically, had a significant increase in medication adherence and self-efficacy. One of the important aspects of this study was that the CHWs shared cultural and linguistic characteristics with the patients and HCPs, leading to increased trust and satisfaction with the information presented ( 22 ).

Kim et al. ( 23 ) found that Korean-American participants benefited greatly from group education sessions that provided integrated counseling led by a team of nurses and CHW educators. The intervention also had a health literacy component that focused on enhancing skills such as reading food package labels, understanding medical terminology, and accessing health care services. This intervention led to a significant reduction of 1–1.3% in A1C levels in the intervention group. The intervention established the value of collaboration between CHW educators and nurses to improve health information delivery and disease management.

A collaboration between CHW educators and pharmacists was also shown to reinforce diabetes knowledge and improve health literacy. Sharp et al. ( 26 ) conducted a cross-over study in four primary care ambulatory clinics that provided care for low-income patients. The study found that patients with low health literacy had more visits with pharmacists and CHWs than those with high health literacy. The CHWs provided individualized support to reinforce diabetes self-management education and referrals to resources such as food, shelter, and translation services. The translation services in this study were especially important for building trust with non-English speakers and helping patients understand their therapy. Similar to other studies, the CHWs shared cultural and linguistic characteristics with their populations, which helped to overcome communication-related and cultural barriers ( 23 , 26 ).

The use of electronic tools or educational videos yielded inconclusive results with regard to medication adherence. Graumlich et al. ( 20 ) implemented a new medication planning tool called Medtable within an electronic medical record system in several outpatient clinics serving patients with type 2 diabetes. The tool was designed to organize medication review and patient education. Providers can use this tool to search for medication instructions and actionable language that are appropriate for each patient’s health literacy level. The authors found no changes in medication knowledge or adherence, but the intervention group reported higher satisfaction. On the other hand, Yeung et al. ( 28 ) showed that pharmacist-led online education videos accessed using QR codes affixed to the patients’ medication bottles and health literacy flashcards increased patients’ medication adherence in an academic medical hospital.

Goessl et al. ( 21 ) found that patients with low health literacy had significantly higher retention of information when receiving evidence-based diabetes education through a DVD recording than through an in-person group class. This 18-month RCT randomized participants to either the DVD or in-person group education and assessed their information retention through a teach-back strategy. The curriculum consisted of diabetes prevention topics such as physical exercise, food portions, and food choices. Participants in the DVD group had significantly higher retention of information than those in the control (in-person) group. The authors suggested this may have been because participants in the DVD group have multiple opportunities to review the education material.

Management of type 2 diabetes remains a challenge for HCPs and patients, in part because of the challenges discussed in this review, including communication barriers between patients and HCPs and knowledge deficits about medications and disease states ( 29 ). HCPs can have a positive impact on the health outcomes of their patients with diabetes by improving patients’ disease state and medication knowledge.

One of the common themes identified in this literature review was the prevalence of culturally tailored diabetes education interventions. This is an important strategy that could improve diabetes outcomes and provide an alternative approach to diabetes self-management education when working with patients from culturally diverse backgrounds. HCPs might benefit from using culturally tailored educational approaches to improve communication with patients and overcome the medical mistrust many patients feel. Although such mistrust was not directly correlated with diabetes management, it was noted that patients who feel mistrustful tend to have poor communication with HCPs ( 20 ). Additionally, Latino/Hispanic patients who have language barriers tend to have poor glycemic control ( 19 ). Having CHWs work with HCPs might mitigate some patient-provider communication barriers. As noted earlier, CHWs who share cultural and linguistic characteristics with their patient populations have ongoing interactions and more frequent one-on-one encounters ( 12 ).

Medication adherence and glycemic control are important components of diabetes self-management, and we noted that the integration of CHWs into the diabetes health care team and the use of simplified medication label interventions were both successful in improving medication adherence ( 23 , 24 ). The use of culturally tailored education sessions and the integration of pharmacists and CHWs into the management of diabetes appear to be successful in reducing A1C levels ( 12 , 26 ). Electronic education tools and educational videos alone did not have an impact on medication knowledge or information retention in patients with low health literacy, but a combination of education tools and individualized sessions has the potential to improve diabetes medication knowledge and overall self-management ( 20 , 22 , 30 ).

There were several limitations to our literature review. We restricted our search criteria to articles published in English and studies conducted within the United States to ensure that the results would be relevant to U.S. HCPs. However, these limitations may have excluded important work on this topic. Additional research expanding this search beyond the United States and including articles published in other languages may demonstrate different outcomes. Additionally, this literature review did not focus on A1C as the primary outcome, although A1C is an important indicator of diabetes self-management. A1C was chosen as the method of evaluating the impact of health literacy interventions in patients with diabetes, but other considerations such as medication adherence, impact on comorbid conditions, and quality of life are also important factors.

The results of this work show that implementing health literacy interventions to help patients manage type 2 diabetes can have beneficial results. However, such interventions can have significant time and monetary costs. The potential financial and time costs of diabetes education interventions were not evaluated in this review and should be taken into account when designing interventions. The American Diabetes Association estimated the cost of medical care for people with diabetes to be $327 billion in 2017, with the majority of the expenditure related to hospitalizations and nursing home facilities ( 16 ). Another substantial cost of diabetes that can be difficult to measure is treatment for comorbid conditions and complications such as cardiovascular and renal diseases.

Interventions designed to address low health literacy and provide education about type 2 diabetes could be a valuable asset in preventing complications and reducing medical expenditures. Results of this work show that clinicians who are considering implementing new interventions may benefit from the following strategies: using culturally tailored approaches, creating materials for different learning styles and in patients’ languages, engaging CHWs and pharmacists to help with patient education, using PCLs for medications, and engaging education session instructors who share patients’ cultural and linguistic characteristics.

Diabetes self-management is crucial to improving health outcomes and reducing medical costs. This literature review identified interventions that had a positive impact on provider-patient communication, medication adherence, and glycemic control by promoting diabetes self-management through educational efforts to address low health literacy. Clinicians seeking to implement diabetes care and education interventions for patients with low health literacy may want to consider drawing on the strategies described in this article. Providing culturally sensitive education that is tailored to patients’ individual learning styles, spoken language, and individual needs can improve patient outcomes and build patients’ trust.

Duality of Interest

No potential conflicts of interest relevant to this article were reported.

Author Contributions

Both authors conceptualized the literature review, developed the methodology, analyzed the data, and wrote, reviewed, and edited the manuscript. R.A. collected the data. K.M. supervised the review. K.M. is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation

Portions of this research were presented at the Washington State University College of Pharmacy and Pharmaceutical Sciences Honors Research Day in April 2019.

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• Published: 20 December 2021

Machine learning and deep learning predictive models for type 2 diabetes: a systematic review

• Luis Fregoso-Aparicio   ORCID: orcid.org/0000-0003-4986-5745 1 ,
• Julieta Noguez   ORCID: orcid.org/0000-0002-6000-3452 2 ,
• Luis Montesinos   ORCID: orcid.org/0000-0003-3976-4190 2 &
• José A. García-García   ORCID: orcid.org/0000-0001-6876-4558 3  

Diabetology & Metabolic Syndrome volume  13 , Article number:  148 ( 2021 ) Cite this article

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Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model’s efficiency. Models trained on tidy datasets achieved almost perfect models.

Introduction

Diabetes mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both [ 1 ]. In particular, type 2 diabetes is associated with insulin resistance (insulin action defect), i.e., where cells respond poorly to insulin, affecting their glucose intake [ 2 ]. The diagnostic criteria established by the American Diabetes Association are: (1) a level of glycated hemoglobin (HbA1c) greater or equal to 6.5%; (2) basal fasting blood glucose level greater than 126 mg/dL, and; (3) blood glucose level greater or equal to 200 mg/dL 2 h after an oral glucose tolerance test with 75 g of glucose [ 1 ].

Diabetes mellitus is a global public health issue. In 2019, the International Diabetes Federation estimated the number of people living with diabetes worldwide at 463 million and the expected growth at 51% by the year 2045. Moreover, it is estimated that there is one undiagnosed person for each diagnosed person with a diabetes diagnosis [ 2 ].

The early diagnosis and treatment of type 2 diabetes are among the most relevant actions to prevent further development and complications like diabetic retinopathy [ 3 ]. According to the ADDITION-Europe Simulation Model Study, an early diagnosis reduces the absolute and relative risk of suffering cardiovascular events and mortality [ 4 ]. A sensitivity analysis on USA data proved a 25% relative reduction in diabetes-related complication rates for a 2-year earlier diagnosis.

Consequently, many researchers have endeavored to develop predictive models of type 2 diabetes. The first models were based on classic statistical learning techniques, e.g., linear regression. Recently, a wide variety of machine learning techniques has been added to the toolbox. Those techniques allow predicting new cases based on patterns identified in training data from previous cases. For example, Kälsch et al. [ 5 ] identified associations between liver injury markers and diabetes and used random forests to predict diabetes based on serum variables. Moreover, different techniques are sometimes combined, creating ensemble models to surpass the single model’s predictive performance.

The number of studies developed in the field creates two main challenges for researchers and developers aiming to build type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding machine learning techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used to train the models, which reduces their interpretability, a feature utterly relevant to the doctor.

This review aims to inform the selection of machine learning techniques and features to create novel type 2 diabetes predictive models. The paper is organized as follows. “ Background ” section provides a brief background on the techniques used to create predictive models. “ Methods ” section presents the methods used to design and conduct the review. “ Results ” section summarizes the results, followed by their discussion in “ Discussion ” section, where a summary of findings, the opportunity areas, and the limitations of this review are presented. Finally, “ Conclusions ” section presents the conclusions and future work.

Machine learning and deep learning

Over the last years, humanity has achieved technological breakthroughs in computer science, material science, biotechnology, genomics, and proteomics [ 6 ]. These disruptive technologies are shifting the paradigm of medical practice. In particular, artificial intelligence and big data are reshaping disease and patient management, shifting to personalized diagnosis and treatment. This shift enables public health to become predictive and preventive [ 6 ].

Machine learning is a subset of artificial intelligence that aims to create computer systems that discover patterns in training data to perform classification and prediction tasks on new data [ 7 ]. Machine learning puts together tools from statistics, data mining, and optimization to generate models.

Representational learning, a subarea of machine learning, focuses on automatically finding an accurate representation of the knowledge extracted from the data [ 7 ]. When this representation comprises many layers (i.e., a multi-level representation), we are dealing with deep learning.

In deep learning models, every layer represents a level of learned knowledge. The nearest to the input layer represents low-level details of the data, while the closest to the output layer represents a higher level of discrimination with more abstract concepts.

The studies included in this review used 18 different types of models:

Deep Neural Network (DNN): DNNs are loosely inspired by the biological nervous system. Artificial neurons are simple functions depicted as nodes compartmentalized in layers, and synapses are the links between them [ 8 ]. DNN is a data-driven, self-adaptive learning technique that produces non-linear models capable of real-world modeling problems.

Support Vector Machines (SVM): SVM is a non-parametric algorithm capable of solving regression and classification problems using linear and non-linear functions. These functions assign vectors of input features to an n-dimensional space called a feature space [ 9 ].

k-Nearest Neighbors (KNN): KNN is a supervised, non-parametric algorithm based on the “things that look alike” idea. KNN can be applied to regression and classification tasks. The algorithm computes the closeness or similarity of new observations in the feature space to k training observations to produce their corresponding output value or class [ 9 ].

Decision Tree (DT): DTs use a tree structure built by selecting thresholds for the input features [ 8 ]. This classifier aims to create a set of decision rules to predict the target class or value.

Random Forest (RF): RFs merge several decision trees, such as bagging, to get the final result by a voting strategy [ 9 ].

Gradient Boosting Tree (GBT) and Gradient Boost Machine (GBM): GBTs and GBMs join sequential tree models in an additive way to predict the results [ 9 ].

J48 Decision Tree (J48): J48 develops a mapping tree to include attribute nodes linked by two or more sub-trees, leaves, or other decision nodes [ 10 ].

Logistic and Stepwise Regression (LR): LR is a linear regression technique suitable for tasks where the dependent variable is binary [ 8 ]. The logistic model is used to estimate the probability of the response based on one or more predictors.

Linear and Quadratic Discriminant Analysis (LDA): LDA segments an n-dimensional space into two or more dimensional spaces separated by a hyper-plane [ 8 ]. The aim of it is to find the principal function for every class. This function is displayed on the vectors that maximize the between-group variance and minimizes the within-group variance.

Cox Hazard Regression (CHR): CHR or proportional hazards regression analyzes the effect of the features to occur a specific event [ 11 ]. The method is partially non-parametric since it only assumes that the effects of the predictor variables on the event are constant over time and additive on a scale.

Least-Square Regression: (LSR) method is used to estimate the parameter of a linear regression model [ 12 ]. LSR estimators minimize the sum of the squared errors (a difference between observed values and predicted values).

Multiple Instance Learning boosting (MIL): The boosting algorithm sequentially trains several weak classifiers and additively combines them by weighting each of them to make a strong classifier [ 13 ]. In MIL, the classifier is logistic regression.

Bayesian Network (BN): BNs are graphs made up of nodes and directed line segments that prohibit cycles [ 14 ]. Each node represents a random variable and its probability distribution in each state. Each directed line segment represents the joint probability between nodes calculated using Bayes’ theorem.

Latent Growth Mixture (LGM): LGM groups patients into an optimal number of growth trajectory clusters. Maximum likelihood is the approach to estimating missing data [ 15 ].

Penalized Likelihood Methods: Penalizing is an approach to avoid problems in the stability of the estimated parameters when the probability is relatively flat, which makes it difficult to determine the maximum likelihood estimate using simple methods. Penalizing is also known as shrinkage [ 16 ]. Least absolute shrinkage and selection operator (LASSO), smoothed clipped absolute deviation (SCAD), and minimax concave penalized likelihood (MCP) are methods using this approach.

Alternating Cluster and Classification (ACC): ACC assumes that the data have multiple hidden clusters in the positive class, while the negative class is drawn from a single distribution. For different clusters of the positive class, the discriminatory dimensions must be different and sparse relative to the negative class [ 17 ]. Clusters are like “local opponents” to the complete negative set, and therefore the “local limit” (classifier) has a smaller dimensional subspace than the feature vector.

Some studies used a combination of multiple machine learning techniques and are subsequently labeled as machine learning-based method (MLB).

Systematic literature review methodologies

This review follows two methodologies for conducting systematic literature reviews: the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 18 ] and the Guidelines for performing Systematic Literature Reviews in Software Engineering [ 19 ]. Although these methodologies hold many similarities, there is a substantial difference between them. While the former was tailored for medical literature, the latter was adapted for reviews in computer science. Hence, since this review focuses on computer methods applied to medicine, both strategies were combined and implemented. The PRISMA statement is the standard for conducting reviews in the medical sciences and was the principal strategy for this review. It contains 27 items for evaluating included studies, out of which 23 are used in this review. The second methodology is an adaptation by Keele and Durham Universities to conduct systematic literature reviews in software engineering. The authors provide a list of guidelines to conduct the review. Two elements were adopted from this methodology. First, the protocol’s organization in three stages (planning, conducting, and reporting). Secondly, the quality assessment strategy to select studies based on the information retrieved by the search.

Related works

Previous reviews have explored machine learning techniques in diabetes, yet with a substantially different focus. Sambyal et al. conducted a review on microvascular complications in diabetes (retinopathy, neuropathy, nephropathy) [ 20 ]. This review included 31 studies classified into three groups according to the methods used: statistical techniques, machine learning, and deep learning. The authors concluded that machine learning and deep learning models are more suited for big data scenarios. Also, they observed that the combination of models (ensemble models) produced improved performance.

Islam et al. conducted a review with meta-analysis on deep learning models to detect diabetic retinopathy (DR) in retinal fundus images [ 21 ]. This review included 23 studies, out of which 20 were also included for meta-analysis. For each study, the authors identified the model, the dataset, and the performance metrics and concluded that automated tools could perform DR screening.

Chaki et al. reviewed machine learning models in diabetes detection [ 22 ]. The review included 107 studies and classified them according to the model or classifier, the dataset, the features selection with four possible kinds of features, and their performance. The authors found that text, shape, and texture features produced better outcomes. Also, they found that DNNs and SVMs delivered better classification outcomes, followed by RFs.

Finally, Silva et al. [ 23 ] reviewed 27 studies, including 40 predictive models for diabetes. They extracted the technique used, the temporality of prediction, the risk of bias, and validation metrics. The objective was to prove whether machine learning exhibited discrimination ability to predict and diagnose type 2 diabetes. Although this ability was confirmed, the authors did not report which machine learning model produced the best results.

This review aims to find areas of opportunity and recommendations in the prediction of diabetes based on machine learning models. It also explores the optimal performance metrics, the datasets used to build the models, and the complementary techniques used to improve the model’s performance.

Objective of the review

This systematic review aims to identify and report the areas of opportunity for improving the prediction of diabetes type 2 using machine learning techniques.

Research questions

Research Question 1 (RQ1): What kind of features make up the database to create the model?

Research Question 2 (RQ2): What machine learning technique is optimal to create a predictive model for type 2 diabetes?

Research Question 3 (RQ3): What are the optimal validation metrics to compare the models’ performance?

Information sources

Two search engines were selected to search:

PubMed, given the relationship between a medical problem such as diabetes and a possible computer science solution.

Web of Science, given its extraordinary ability to select articles with high affinity with the search string.

These search engines were also considered because they search in many specialized databases (IEEE Xplore, Science Direct, Springer Link, PubMed Central, Plos One, among others) and allow searching using keywords combined with boolean operators. Likewise, the database should contain articles with different approaches to predictive models and not specialized in clinical aspects. Finally, the number of articles to be included in the systematic review should be sufficient to identify areas of opportunity for improving models’ development to predict diabetes.

Search strategy

Three main keywords were selected from the research questions. These keywords were combined in strings as required by each database in their advanced search tool. In other words, these strings were adapted to meet the criteria of each database Table  1 .

Eligibility criteria

Retrieved records from the initial search were screened to check their compliance with eligibility criteria.

Firstly, papers published from 2017 to 2021 only were considered. Then, two rounds of screening were conducted. The first round focused mainly on the scope of the reported study. Articles were excluded if the study used genetic data to train the models, as this was not a type of data of interest in this review. Also, articles were excluded if the full text was not available. Finally, review articles were also excluded.

In the second round of screening, articles were excluded when machine learning techniques were not used to predict type 2 diabetes but other types of diabetes, treatments, or diseases associated with diabetes (complications and related diseases associated with metabolic syndrome). Also, studies using unsupervised learning were excluded as they cannot be validated using the same performance metrics as supervised learning models, preventing comparison.

Quality assessment

After retrieving the selected articles, three parameters were selected, each one generated by each research question. The eligibility criteria are three possible subgroups according to the extent to which the article satisfied it.

The dataset contains sociodemographic and lifestyle data, clinical diagnosis, and laboratory test results as attributes for the model.

Dataset contains only one kind of attributes.

Dataset contains similar kinds of attributes.

Dataset uses EHRs with multiple kinds of attributes.

The article presents a model with a machine learning technique to predict type 2 diabetes.

Machine Learning methods are not used at all.

The prediction method in the model is used as part of the prepossessing for the data to do data mining.

Model used a machine learning technique to predict type 2 diabetes.

The authors use supervised learning with validation metrics to contrast their results with previous work.

The authors used unsupervised methods.

The authors used a supervised method with one validation metric or several methods with supervised and unsupervised learning.

The authors used supervised learning with more than one metric to validate the model (accuracy, specificity, sensitivity, area under the ROC, F1-score).

Data extraction

After assessing the papers for quality, the intersection of the subgroups QA2.3 and QA1.1 or QA1.2 or QA1.3 and QA3.2 or QA3.3 were processed as follows.

First, the selected articles were grouped in two possible ways according to the data type (glucose forecasting or electronic health records). The first group contains models that screen the control levels of blood glucose, while the second group contains models that predict diabetes based on electronic health records.

The second classification was more detailed, applying for each group the below criteria.

The data extraction criteria are:

Machine learning model (specify which machine learning method use)

Validation parameter (accuracy, sensitivity, specificity, F1-score, AUC (ROC))

Complementary techniques (complementary statistics and machine learning techniques used for the models)

Data sampling (cross-validation, training-test set, complete data)

Description of the population (age, balanced or imbalance, population cohort size).

Risk of bias analyses

Risk of bias in individual studies.

The risk of bias in individual studies (i.e., within-study bias) was assessed based on the characteristics of the sample included in the study and the dataset used to train and test the models. One of the most common risks of bias is when the data is imbalanced. When the dataset has significantly more observations for one label, the probability of selecting that label increases, leading to misclassification.

The second parameter that causes a risk of bias is the age of participants. In most cases, diabetes onset would be in older people making possible bound between 40 to 80 years. In other cases, the onset occurs at early age generating another dataset with a range from 21 to 80.

A third parameter strongly related to age is the early age onset. Complications increase and appear early when a patient lives more time with the disease, making it harder to develop a model only for diabetes without correlation of their complications.

Finally, as the fourth risk of bias, according to Forbes [ 24 ] data scientists spend 80% of their time on data preparation, and 60% of it is in data cleaning and organization. A well-structured dataset is relevant to generate a good performance of the model. That can be check in the results from the data items extraction the datasets like PIMA dataset that is already clean and organized well generate a model with the recall of 1 [ 25 ] also the same dataset reach an accuracy of 0.97 [ 26 ] in another model. Dirty data can not achieve values as good as clean data.

Risk of bias across studies

The items considered to assess the risk of bias across the studies (i.e., between-study bias) were the reported validation parameters and the dataset and complementary techniques used.

Validation metrics were chosen as they are used to compare the performance of the model. The studies must be compared using the same metrics to avoid bias from the validation methods.

The complementary techniques are essential since they can be combined with the primary approach to creating a better performance model. It causes a bias because it is impossible to discern if the combination of the complementary and the machine learning techniques produces good performance or if the machine learning technique per se is superior to others.

Search results and reduction

The initial search generated 1327 records, 925 from PubMed and 402 from Web of Science. Only 130 records were excluded when filtering by publication year (2017–2021). Therefore, further searches were conducted using fine-tuned search strings and options for both databases to narrow down the results. The new search was carried out using the original keywords but restricting the word ‘diabetes’ to be in the title, which generated 517 records from both databases. Fifty-one duplicates were discarded. Therefore, 336 records were selected for further screening.

Further selection was conducted by applying the exclusion criteria to the 336 records above. Thirty-seven records were excluded since the study reported used non-omittable genetic attributes as model inputs, something out of this review’s scope. Thirty-eight records were excluded as they were review papers. All in all, 261 articles that fulfill the criteria were included in the quality assessment.

Figure  1 shows the flow diagram summarizing this process.

Flow diagram indicating the results of the systematic review with inclusions and exclusions

The 261 articles above were assessed for quality and classified into their corresponding subgroup for each quality question (Fig.  2 ).

Percentage of each subgroup in the quality assessment. The criteria does not apply for two result for the Quality Assessment Questions 1 and 3

The first question classified the studies by the type of database used for building the models. The third subgroup represents the most desirable scenario. It includes studies where models were trained using features from Electronic Health Records or a mix of datasets including lifestyle, socio-demographic, and health diagnosis features. There were 22, 85, and 154 articles in subgroups one to three, respectively.

The second question classified the studies by the type of model used. Again, the third subgroup represents the most suitable subgroup as it contains studies where a machine learning model was used to predict diabetes onset. There were 46 studies in subgroup one, 66 in subgroup two, and 147 in subgroup three. Two studies were omitted from these subgroups: one used cancer-related model; another used a model of no interest to this review.

The third question clustered the studies based on their validation metrics. There were 25 studies in subgroup one (semi-supervised learning), 68 in subgroup two (only one validation metric), and 166 in subgroup three ( \(>1\) validation parameters). The criteria are not applied to two studies as they used special error metrics, making it impossible to compare their models with the rest.

Data extraction excluded 101 articles from the quantitative synthesis for two reasons. twelve studies used unsupervised learning. Nineteen studies focused on diabetes treatments, 33 in other types of diabetes (eighteen type 1 and fifteen Gestational), and 37 associated diseases.

Furthermore, 70 articles were left out of this review as they focus on the prediction of diabetes complications (59) or tried to forecast levels of glucose (11), not onset. Therefore, 90 articles were chosen for the next steps.

Table  2 summarize the results of the data extraction. These tables are divided into two main groups, each of them corresponding to a type of data.

For the risk of bias in the studies: unbalanced data means that the number of observations per class is not equally distributed. Some studies applied complementary techniques (e.g., SMOTE) to prevent the bias produced by unbalance in data. These techniques undersample the predominant class or oversample the minority class to produce a balanced dataset.

Other studies used different strategies to deal with other risks for bias. For instance, they might exclude specific age groups or cases presenting a second disease that could interfere with the model’s development to deal with the heterogeneity in some cohorts’ age.

For the risk of bias across the studies: the comparison between models was performed on those reporting the most frequently used validation metrics, i.e., accuracy and AUC (ROC). The accuracy is estimated to homogenize the criteria of comparison when other metrics from the confusion matrix were calculated, or the population’s knowledge is known. The confusion matrix is a two-by-two matrix containing four counts: true positives, true negatives, false positives, and false negatives. Different validation metrics such as precision, recall, accuracy, and F1-score are computed from this matrix.

Two kinds of complementary techniques were found. Firstly, techniques for balancing the data, including oversampling and undersampling methods. Secondly, feature selection techniques such as logistic regression, principal component analysis, and statistical testing. A comparison still can be performed between them with the bias caused by the improvement of the model.

This section discusses the findings for each of the research questions driving this review.

RQ1: What kind of features makes up the database to create the model?

Our findings suggest no agreement on the specific features to create a predictive model for type 2 diabetes. The number of features also differs between studies: while some used a few features, others used more than 70 features. The number and choice of features largely depended on the machine learning technique and the model’s complexity.

However, our findings suggest that some data types produce better models, such as lifestyle, socioeconomic and diagnostic data. These data are available in most but not all Electronic Health Records. Also, retinal fundus images were used in many of the top models, as they are related to eye vessel damage derivated from diabetes. Unfortunately, this type of image is no available in primary care data.

RQ2: What machine learning technique is optimal to create a predictive model for type 2 diabetes?

Figure  3 shows a scatter plot of studies that reported accuracy and AUC (ROC) values (x and y axes, respectively. The color of the dots represents thirteen of the eighteen types of model listed in the background. Dot labels represent the reference number of the study. A total of 30 studies is included in the plot. The studies closer to the top-right corner are the best ones, as they obtained high values for both validation metrics.

Scatterplot of AUC (ROC) vs. Accuracy for included studies. Numbers correspond to the number of reference and color dot the type of model, desired model has values of x-axis equal 1 and y-axis also equal 1

Figures  4 and 5 show the average accuracy and AUC (ROC) by model. Not all models from the background appear in both graphs since not all studies reported both metrics. Notably, most values represent a single study or the average of two studies. The exception is the average values for SVMs, RFs, GBTs, and DNNs, calculated with the results reported by four studies or more. These were the most popular machine learning techniques in the included studies.

Average accuracy by model. For papers with more than one model the best score is the model selected to the graph. A better model has a higher value

Average AUC (ROC) by model. For papers with more than one model the best score is the model selected to the graph. A better model has a higher value

RQ3: Which are the optimal validation metrics to compare the models’ improvement?

Considerable heterogeneity was found in this regard, making it harder to compare the performance between the models. Most studies reported some metrics computed from the confusion matrix. However, studies focused on statistical learning models reported hazard ratios and the c-statistic.

This heterogeneity remains an area of opportunity for further studies. To deal with it, we propose reporting at least three metrics from the confusion matrix (i.e., accuracy, sensitivity, and specificity), which would allow computing the rest. Additionally, the AUC (ROC) should be reported as it is a robust performance metric. Ideally, other metrics such as the F1-score, precision, or the MCC score should be reported. Reporting more metrics would enable benchmarking studies and models.

Summary of the findings

Concerning the datasets, this review could not identify an exact list of features given the heterogeneity mentioned above. However, there are some findings to report. First, the model’s performance is significantly affected by the dataset: the accuracy decreased significantly when the dataset became big and complex. Clean and well-structured datasets with a few numbers of samples and features make a better model. However, a low number of attributes may not reflect the real complexity of the multi-factorial diseases.

The top-performing models were the decision tree and random forest, with an similar accuracy of 0.99 and equal AUC (ROC) of one. On average, the best models for the accuracy metric were Swarm Optimization and Random Forest with a value of one in both cases. For AUC (ROC) decision tree with an AUC (ROC) of 0.98, respectively.

The most frequently-used methods were Deep Neural Networks, tree-type (Gradient Boosting and Random Forest), and support vector machines. Deep Neural Networks have the advantage of dealing well with big data, a solid reason to use them frequently [ 27 , 28 ]. Studies using these models used datasets containing more than 70,000 observations. Also, these models deal well with dirty data.

Some studies used complementary techniques to improve their model’s performance. First, resampling techniques were applied to otherwise unbalanced datasets. Second, feature selection techniques were used to identify the most relevant features for prediction. Among the latter, there is principal component analysis and logistic regression.

The model that has a good performance but can be improved is the Deep Neural Network. As shown in Figure  4 , their average accuracy is not top, yet some individual models achieved 0.9. Hence, they represent a technique worth further exploration in type 2 diabetes. They also have the advantage that can deal with large datasets. As shown in Table  2 many of the datasets used for DNN models were around 70,000 or more samples. Also, DNN models do not require complementary techniques for feature selection.

Finally, model performance comparison was challenging due to the heterogeneity in the metrics reported.

Conclusions

This systematic review analyzed 90 studies to find the main opportunity areas in diabetes prediction using machine learning techniques.

The review finds that the structure of the dataset is relevant to the accuracy of the models, regardless of the selected features that are heterogeneous between studies. Concerning the models, the optimal performance is for tree-type models. However, even tough they have the best accuracy, they require complementary techniques to balance data and reduce dimensionality by selecting the optimal features. Therefore, K nearest neighborhoods, and Support vector machines are frequently preferred for prediction. On the other hand, Deep Neural Networks have the advantage of dealing well with big data. However, they must be applied to datasets with more than 70,000 observations. At least three metrics and the AUC (ROC) should be reported in the results to allow estimation of the others to reduce heterogeneity in the performance comparison. Therefore, the areas of opportunity are listed below.

Areas of opportunity

First, a well-structured, balanced dataset containing different types of features like lifestyle, socioeconomically, and diagnostic data can be created to obtain a good model. Otherwise, complementary techniques can be helpful to clean and balance the data.

The machine learning model will depend on the characteristics of the dataset. When the dataset contains a few observations, machine learning techniques present a better performance; when observations are more than 70,000, Deep Learning has a good performance.

To reduce the heterogeneity in the validation parameters, the best way to do it is to calculate a minimum of three parameters from the confusion matrix and the AUC (ROC). Ideally, it should report five or more parameters (accuracy, sensitivity, specificity, precision, and F1-score) to become easier to compare. If one misses, it can be estimated from the other ones.

Limitations of the study

The study’s limitations are observed in the heterogeneity between the models that difficult to compare them. This heterogeneity is present in many aspects; the main is the populations and the number of samples used in each model. Another significant limitation is when the model predicts diabetes complications, not diabetes.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its references.

Abbreviations

Deep Neural Network

Random forest

Support Vector Machine

k-Nearest Neighbors

Decision tree

Gradient Boosting Tree

Gradient Boost Machine

J48 decision tree

Logistic regression and stepwise regression

Linear and quadratric discriminant analysis

Multiple Instance Learning boosting

Bayesian Network

Latent growth mixture

Cox Hazard Regression

Least-Square Regression

Least absolute shrinkage and selection operator

Smoothed clipped absolute deviation

Minimax concave penalized likelihood

Alternating Cluster and Classification

Machine learning-based method

Synthetic minority oversampling technique

Area under curve (receiver operating characteristic)

Diabetic retinopathy

Gaussian mixture

Naive Bayes

Average weighted objective distance

Swarm Optimization

Newton’s Divide Difference Method

Root-mean-square error

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Acknowledgements

We would like to thank Vicerrectoría de Investigación y Posgrado, the Research Group of Product Innovation, and the Cyber Learning and Data Science Laboratory, and the School of Engineering and Science of Tecnologico de Monterrey.

This study was funded by Vicerrectoría de Investigación y Posgrado and the Research Group of Product Innovation of Tecnologico de Monterrey, by a scholarship provided by Tecnologico de Monterrey to graduate student A01339273 Luis Fregoso-Aparicio, and a national scholarship granted by the Consejo Nacional de Ciencia y Tecnologia (CONACYT) to study graduate programs in institutions enrolled in the Padron Nacional de Posgrados de Calidad (PNPC) to CVU 962778 - Luis Fregoso-Aparicio.

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Individual contributions are the following; conceptualization, methodology, and investigation: LF-A and JN; validation: LM and JAGG; writing—original draft preparation and visualization: LF-A; writing—review and editing: LM and JN; supervision: JAG-G; project administration: JN; and funding acquisition: LF and JN. All authors read and approved the final manuscript.

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Fregoso-Aparicio, L., Noguez, J., Montesinos, L. et al. Machine learning and deep learning predictive models for type 2 diabetes: a systematic review. Diabetol Metab Syndr 13 , 148 (2021). https://doi.org/10.1186/s13098-021-00767-9

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Trace elements are essential for the biological, chemical and molecular activities of cell. These agents play a key role in biochemical reactions by acting as cofactors for enzymes. The use of trace elements including copper, zinc, selenium, and magnesium is an important procedure in the management of type 2 ...

Keywords : Trace elements, Minerals, Saliva, Type II Diabetes, Ferroptosis, Apoptosis, Autophagy, Cell Death

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Metformin in elderly type 2 diabetes mellitus: dose-dependent dementia risk reduction

Mingyang Sun and Wan-Ming Chen contributed equally to this work.

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Mingyang Sun, Wan-Ming Chen, Szu-Yuan Wu, Jiaqiang Zhang, Metformin in elderly type 2 diabetes mellitus: dose-dependent dementia risk reduction, Brain , Volume 147, Issue 4, April 2024, Pages 1474–1482, https://doi.org/10.1093/brain/awad366

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This study aimed to investigate the controversial association between metformin use and diabetes-associated dementia in elderly patients with type 2 diabetes mellitus (T2DM) and evaluate the potential protective effects of metformin, as well as its intensity of use and dose-dependency, against dementia in this population.

The study used a time-dependent Cox hazards model to evaluate the effect of metformin use on the incidence of dementia. The case group included elderly patients with T2DM (≥60 years old) who received metformin, while the control group consisted of elderly patients with T2DM who did not receive metformin during the follow-up period.

Our analysis revealed a significant reduction in the risk of dementia among elderly individuals using metformin, with an adjusted hazard ratio of 0.34 (95% confidence interval: 0.33 to 0.36). Notably, metformin users with a daily intensity of 1 defined daily dose (DDD) or higher had a lower risk of dementia, with an adjusted hazard ratio (95% confidence interval) of 0.46 (0.22 to 0.6), compared to those with a daily intensity of <1 DDD. Additionally, the analysis of cumulative DDDs of metformin showed a dose-response relationship, with progressively lower adjusted hazard ratio across quartiles (0.15, 0.21, 0.28, and 0.53 for quartiles 4, 3, 2 and 1, respectively), compared to never metformin users ( P for trend < 0.0001).

Metformin use in elderly patients with T2DM is significantly associated with a substantial reduction in the risk of dementia. Notably, the protective effect of metformin demonstrates a dose-dependent relationship, with higher daily and cumulative dosages of metformin showing a greater risk reduction.

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Quality of measurement properties of medication adherence instruments in cardiovascular diseases and type 2 diabetes mellitus: a systematic review and meta-analysis

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Medication adherence has a major impact on reducing mortality and healthcare costs related to the treatment of cardiovascular diseases and diabetes mellitus. Selecting the best patient-reported outcome measure (PROM) among the many available for this kind of patient is extremely important. This study aims to critically assess, compare and synthesize the quality of the measurement properties of patient-reported outcome measures to assess medication adherence among patients with cardiovascular diseases and/or type 2 diabetes mellitus.

This review followed the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines and was reported according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). The searches were performed in Web of Science, SCOPUS, PubMed, CINAHL, EMBASE, LILACS, PsycINFO, and ProQuest (gray literature).

A total of 110 records encompassing 27 different PROMs were included in the review. The included records were published between 1986 and 2023, most of which reported studies conducted in the United States and were published in English. None of the PROMs were classified in the category “a”, thus being recommended for use due to the quality of its measurement properties. The PROMs that should not be recommended for use (category “c”) are the MTA, GMAS, DMAS-7, MALMAS, ARMS-D, and 5-item questionnaire. The remaining PROMs, e.g., MMAS-8, SMAQ, MEDS, MNPS, ARMS-12, MGT, MTA-OA, MTA-Insulin, LMAS-14, MARS-5, A-14, ARMS-10, IADMAS, MAQ, MMAS-5, ProMAS, ARMS‐7, 3-item questionnaire, AS, 12-item questionnaire, and Mascard were considered as having the potential to be recommended for use (category “b”).

None of the included PROMs met the criteria for being classified as trusted and recommended for use for patients with cardiovascular diseases and/or type 2 diabetes mellitus. However, 21 PROMs have the potential to be recommended for use, but further studies are needed to ensure their quality based on the COSMIN guideline for systematic reviews of PROMs.

Systematic review registration

PROSPERO CRD42019129109

Peer Review reports

Medication adherence has a major impact on reducing mortality and healthcare costs related to the treatment of noncommunicable diseases (NCDs), especially cardiovascular diseases (CVDs) and diabetes mellitus [ 1 , 2 , 3 ].

Data from 2019 by the World Health Organization (WHO) show that 7 of the top 10 causes of death in the world are noncommunicable diseases (NCDs) [ 4 ]. Ischemic heart disease is the leading cause of death and the top 10 causes of death also include stroke, hypertensive heart disease, and diabetes mellitus [ 5 ]. The United Nations General Assembly established the reduction of premature mortality from NCDs by one-third as a target for 2030 [ 6 ].

Since many patients do not adhere to treatment as prescribed [ 7 , 8 ] it is paramount to properly measure medication adherence and to take actions that increase patient’s adherence. Medication adherence involves a complex set of behaviors that are influenced by a number of psychosocial determinants such as motivation, self-efficacy, beliefs, and perceived barriers, which makes its measurement particularly challenging [ 9 ].

One of the most practical and low-cost ways to assess medication adherence is through the use of measures of patient-reported outcomes (PROs), i.e., any aspect of a patient's health status that is directly assessed by the patient, without interpretation of their response by anyone other than themselves [ 10 ]. Patient-Reported Outcome Measures (PROMs) range from simple single-item measures of omitted medication doses to multi-item instruments that aggregate reasons for non-adherence.

The task of selecting the best PROM among the many available for measuring medication adherence in patients with CVDs or type 2 diabetes mellitus (T2DM) [ 11 , 12 ] requires taking into consideration its conceptual structure and measurement properties.

The COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) initiative has recently published a guideline for conducting systematic reviews of studies evaluating the measurement properties of PROMs [ 13 ]. This guideline proposes the criteria to assess the methodological quality of studies on measurement properties and the quality of the self-reported measurement itself.

There are systematic reviews evaluating the quality of the measurement properties of medication adherence PROMs in patients with diabetes mellitus using the COSMIN checklist [ 14 , 15 , 16 ]. However, in these systematic reviews, primary studies using PROMS to measure factors related to medication non-adherence, such as beliefs, self-efficacy, satisfaction, among others, were included. To our knowledge, no systematic review has been conducted according to the COSMIN guidelines to evaluate the quality of the measurement properties of PROMs that exclusively measure medication adherence in patients with CVDs and/or T2DM.

Therefore, this systematic review aims to critically assess, compare, and synthesize the quality of the measurement properties of PROMs for medication adherence among patients with CVDs and/or T2DM.

Protocol development

This systematic review was reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) [ 17 ] (checklist available in Additional file 1 ) and the COSMIN guidelines for systematic reviews on PROMs [ 13 ]. The protocol of this review was registered in the International Prospective Register of Systematic Reviews (PROSPERO CRD42019129109) and published elsewhere [ 18 ].

Eligibility criteria

Inclusion criteria:

Studies that aimed to develop or culturally adapt a PROM to measure medication adherence among patients aged 18 or older with a CVD and/or T2DM, regardless of the language and date of publication;

Studies reporting the assessment of one or more properties of the PROMs.

Exclusion criteria:

Studies in which a PROM was used to measure an outcome (e.g., randomized clinical trials);

Studies in which a PROM was used to validate another measure;

Studies that evaluated the measurement properties of PROMs that aimed to evaluate factors related to medication nonadherence (self-efficacy, beliefs, intention, etc.);

Study that did not provide minimally sufficient data on the results of the investigated measurement properties, even after contacting the authors.

Sources and search strategy

The electronic literature searches were performed in July 2020 in the following databases without time limits: Web of Science, Scopus, PubMed (including Medline), CINAHL, EMBASE, LILACS, PsycINFO, and ProQuest (gray literature). Manual searches were performed in the reference lists of the articles in order to complement the main literature. An update of the searches was performed in May 2023 considering the period from 2020 to 2023. The search strategy was based on the second version of the search filter for measurement properties proposed by the COSMIN initiative [ 19 ] and also included keywords and MeSH terms related to CVDs, T2DM, PROMs, medication adherence, and measurement properties. The search strategy used in each database was created with the support of an experienced librarian and can be found in Additional file 2 . The online software Rayyan QCRI was used for reference management which included the exclusion of duplicates and the evaluation of titles and abstracts [ 20 ].

Study selection

The study selection was reported according to the PRISMA flow diagram model [ 17 ]. The evaluation of titles and abstracts after the exclusion of duplicates was done independently by three pairs of reviewers (HCO, DH, SDLC, RCLB, MLSN, and CRSA) following a practice set of 50 titles and abstracts to improve inter-reviewer agreement. Inter-reviewer agreement ranged from 96 to 98%, with an overall agreement rate of 94%. Two reviewers independently appraised full-texts for inclusion (HCO and DH). Disagreements were discussed until a consensus was reached. Lastly, the list of references of the included studies was examined to identify other studies that had not been previously identified.

Data extraction

Data were independently extracted by two reviewers (HCO and RCMR) using an adapted version of the extraction form available in the COSMIN manual for systematic reviews of PROMs [ 21 ] which included additional fields for other relevant information. The form contains information about the study design, sample size, participants’ demographic and clinical characteristics (gender, age, disease, disease duration, and number of medications in use), response rate, presence of conflicts of interest, funding, setting, country, and language, PROMs’ number of items and domains, mode of administration, recall period, response options, range of scores, original language, available translations, number of studies evaluating the PROM, measurement properties (PROM development, content validity, structural validity, internal consistency, cross-cultural validity/measurement invariance, reliability, measurement error, criterion validity, hypothesis testing for construct validity and responsiveness), interpretability and feasibility and information to assess the studies’ methodological quality.

Methodological quality of the studies: assessment of risk of bias

The methodological quality of the studies was assessed independently by two reviewers (HCO and RCMR) using the COSMIN Risk of Bias checklist for systematic reviews of PROMs [ 22 , 23 ]. This checklist comprises items that assess the methodological quality of studies that evaluate the measurement properties of PROMs. Disagreements were discussed until a consensus was obtained and a third reviewer (NMCA) was consulted when the reviewers were among the authors of the evaluated paper. According to the COSMIN Risk of Bias checklist, items can be rated as 'very good', 'adequate', 'doubtful', 'inadequate', or 'not applicable' (NA), and each measurement property receives an overall rating based on the worst scored item [ 21 , 23 ].

Quality of the measurement properties

For each PROM, the quality of each measurement property reported by the included studies was assessed.

These results were assessed independently by two reviewers (HCO and RCMR) based on the quality criteria for good measurement properties proposed by COSMIN [ 21 , 23 ]. Disagreements were discussed until a consensus was obtained. A third reviewer (NMCA) was consulted when the reviewers were among the authors of the evaluated paper. The quality of the measurement properties of each assessed PROM was classified as sufficient ( +), insufficient (-), inconsistent ( ±), or indeterminate (?), according to the proposed criteria [ 13 , 21 , 22 , 23 ] (Table 1 ).

When evaluating the exploratory factor analysis (EFA) in structural validity, we established a different criterion from what was previously defined by the COSMIN team [ 24 ]. The COSMIN criteria consider a sufficient result in an EFA when the first factor accounts for at least 20% of the variability and the ratio of the variance explained by the first to the second factor is greater than four [ 24 ]. Since most PROMs included in our systematic review are one-dimensional, we considered a sufficient result when the total variance explained was at least 60% and when factor loadings were equal to or greater than 0.30 [ 25 ]. In addition to the structural features of PROMS, our decision also considered the recommendation of the COSMIN manual that new criteria may be proposed by reviewers if those established by the COSMIN team do not fully meet the evaluation of one or more properties of the measure [ 21 ].

When assessing the criterion validity, it was considered that the statistical results obtained in the assessment of the relationship between the PROM and the direct objective measures would be treated as a criterion validity result. Additionally, the statistical results obtained when evaluating the relationship between the PROMs and the direct objective measures of glycosylated hemoglobin and glycaemia (used to assess metabolic control in DM) and the measures of systolic and diastolic blood pressure (used for the control of blood pressure in hypertension) were treated as a result of criterion validity, regardless of how the authors named such validity in the primary studies.

Also in the criterion validity evaluation, when dichotomous variables are evaluated, it is recommended to apply sensitivity and specificity measures, according to the risk of bias checklist, to evaluate these results. However, the guideline does not establish the reference values for the sensitivity and specificity measures for the attribution of the quality of the results. For this reason, the sensitivity and specificity results observed in the primary studies were not considered for assigning the quality ratings of the criterion validity results.

Data synthesis

Meta-analysis was performed to pool the results of internal consistency of the PROMs, estimated by Cronbach's alpha coefficient [ 26 ]. The analysis was performed considering a random effects model and a significance level of 5%. At first, the Cronbach's alphas values of each study were transformed to Fisher's ɀ values according to the following equation [ 26 ]:

The next step was to calculate the average of ɀ weighting according to the sample size of the studies (n j ) [ 26 ]:

The final step was the conversion of the average weighted ɀ to the estimated value of the pooled Cronbach's alpha coefficient [ 26 ]:

These calculations were performed in the software SPSS 23 using an SPSS Meta-Analysis Macro [ 27 ]. The heterogeneity was evaluated using the Chi-squared test and the I 2 coefficient.

Quality of evidence: adapted Grading of Recommendations Assessment, Development, and Evaluation (GRADE) by COSMIN

The overall quality of evidence of all studies was assessed using an adapted version of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) proposed by the COSMIN initiative [ 13 ]. The quality of the evidence was classified as high, moderate, low, or very low based on the following factors: risk of bias, inconsistency (of the results of the studies), imprecision (related to the sample size of the studies), and indirect results (the evidence comes from the studies that were performed in a population or context other than the ones defined in the review) [ 21 ].

The risk of bias could be classified as serious, very serious, or extremely serious, resulting in the downgrade of 1 to 3 levels, respectively (Table 2 ) [ 21 ].

Regarding the inconsistency, when the reviewers could not find an explanation for inconsistent results observed across the studies, these results are considered inconsistent. Consequently, the quality of evidence is not applicable. Concerning the imprecision, when the total sample size of the summarized studies is lower than 100 (serious), one level must be downgraded and when it is lower than 50 (very serious) two levels must be downgraded. For indirectness, the reviewers can downgrade the level of evidence by one or two levels.

Thus, according to the GRADE approach, it was initially assumed that the summarized results were of high quality and, subsequently, downgraded by one or two levels per factor, considering the following aspects: risk of bias, inconsistency, imprecision, or indirect results. When the evidence was based on only one inadequate study (extremely serious risk of bias) quality of evidence was downgraded by three levels [ 21 ].

The results were assessed independently by two reviewers (HCO and RCMR). Disagreements were discussed until a consensus was obtained and a third reviewer (NMCA) was consulted when the reviewers were among the authors of the evaluated paper.

Recommendations for selecting a PROM

The review's final stage was the establishment of recommendations to select the most appropriate PROM. PROMs were classified into three categories:

PROMs that presented sufficient content validity and at least low quality of evidence for sufficient internal consistency;

PROMs that are not classified in categories (a) or (c);

PROMs that presented high-quality evidence for an insufficient measurement property.

A PROM that falls under category (a) means it is reliable and can be recommended. A PROM that falls under category (b) means it has the potential to be recommended, though further studies are needed to ensure its quality. A PROM classified under category (c) should not be recommended.

Study selection and data extraction

The results of the selection and data extraction of the studies are presented in the PRISMA flow diagram (Fig.  1 ). The searches done in July 2020 resulted in a total of 41,886 records published between 1973 and June of 2020 were considered potentially eligible and retrieved from eight databases. A total of 14,826 duplicates were removed. The titles and abstracts of 27,060 records were peer-reviewed by three pairs of peer reviewers, who evaluated 9,020 records each. A total of 336 records were identified for full-text assessment and 84 records were included. Eight additional relevant records were added after manually searching the lists of references from the included studies.

PRISMA flow diagram. Note: ARMS = Adherence to Refills and Medication Scale; AS = Adherence Scale; DMAS-7 = 7-item Diabetes Medication Adherence Scale; GMAS = General Medication Adherence Scale; IADMAS = Iraqi Anti-Diabetic Medication Adherence Scale; LMAS-14 = Fourteen-item Lebanese Medication Adherence Scale; MALMAS = Malaysian Medication Adherence Scale; MAQ = Medication Adherence Questionnaire; MARS-5 = 5-item Medication Adherence Report Scale; Mascard = Medication Adherence Scale in Cardiovascular disorders; MEDS = Medication Adherence Estimation and Differentiation Scale; MGT = Morisky-Green test; MMAS-5 = 5-item adapted Morisky Medication Adherence Scale; MMAS-8 = 8-item Morisky Medication Adherence Scale; MNPS = Medication Non-persistence Scale; MTA = Measurement of Treatment Adherence; MTA-Insulin = Measurement of Treatment Adherence—Insulin; MTA-OA = Measurement of Treatment Adherence—Oral Antidiabetics; PROM = Patient-reported outcome measures; ProMAS = Probabilistic Medication Adherence Scale; SMAQ = Simplified Medication Adherence Questionnaire

The update done in May 2023 resulted in 11,538 records published between 2020 and 2023 where 4,370 duplicates were removed. Out of 52 records assessed for full-text, 18 records were included resulting in a total of 110 records and 27 PROMs included in the systematic review (Fig.  1 ).

Study and PROMs characteristics

The included studies were published between 1986 and 2023, and most of them were conducted in the United States in the English language ( n  = 19). The sample size of included studies ranged from 30 to 6,261 participants. The percentage of females ranged from 17.0% to 79.3% and the mean age ranged from 43.1 to 81.9 years. About half of the studies were conducted in hospital settings ( n  = 54) and observed an average disease duration of 9.8 years ( n  = 26) and an average of 4.5 medications in use ( n  = 29).

Most of the 27 PROMs included in the review are one-dimensional and composed of items with a Likert-type scale response. A total of 39 studies [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ] conducted with patients who only had TD2M, another 47 studies [ 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 ] with patients who only had CVD, and the remaining 24 studies with patients having both T2DM and/or CVD [ 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 ]. The most prevalent original language of the 27 PROMs included in this review was English ( n  = 15). In addition to the original versions, translated versions of the PROMs were also included in the review. Of 27 PROMs included, 10 had been translated into at least another language. Regarding the application characteristics, for the majority of PROMs ( n  = 20) it was not clear what the recall period was (Table 3 ). It was not possible to describe the time to complete the PROMs because the majority of the studies did not present this information.

A total of 38 studies reported a response rate for PROMs that ranged from 21.1% to 100.0%. Out of the 110 records, only 63.6% of the studies presented information about conflict of interests, and 68.2% informed if the research had any source of funding (Additional file 3 ).

The two reviewers (HCO and RCMR) are authors of one of the included studies [ 120 ]. The analysis of this article was done by a third reviewer (NMCA).

Evidence synthesis

The summary of findings of measurement properties of the PROMs is presented in Table 4 . The summary of the assessment of risk of bias can be found in the Additional files 4 and 5 . The results based on each measurement property of the PROMs are presented below.

Content validity

The content validity resulted in overall ratings per PROM for relevance, comprehensiveness and comprehensibility, and overall content validity of the PROM. The indeterminate ratings for development or content validity studies were ignored in the overall rating assignment ( n  = 30). All the PROM development studies were classified as having inadequate methodological quality except the studies that developed the PROMs ProMAS [ 130 ]. and Mascard [ 113 ]. which were classified as having doubtful methodological quality. Very few studies assessed the target population's comprehensibility of the developed items through cognitive interviews or debriefing [ 38 , 123 ]. According to the COSMIN, the criteria for recommending the use of a PROM is based on a sufficient content validity and at least low quality of evidence for internal consistency. The content validity encompasses the evaluation of aspects such as relevance, comprehensiveness, and comprehensibility of the PROM. The comprehensibility was the most often evaluated aspect in the records, but even studies that evaluated it had done so incompletely. Most of the studies assessed comprehensibility of the items [ 28 , 41 , 47 , 57 , 67 , 68 , 73 , 76 , 86 , 88 , 91 , 93 , 100 , 104 , 108 , 113 , 132 , 134 , 136 ], but only in a few of the studies participants were asked about the comprehensibility of response options or recall periods [ 28 , 44 , 91 , 105 , 106 ]. Relevance and comprehensiveness of PROMS were rarely evaluated among patients [ 32 , 52 , 67 , 105 ] and expertise committee [ 32 , 52 , 61 , 66 , 105 , 113 , 114 , 128 ]. In some aspects the PROMs, overall rating was based only on the rating of the reviewers. The evaluation of the risk of bias of the development and content validity studies resulted in studies being rated as doubtful or inadequate because some of the criteria evaluated in the COSMIN checklist were not clearly described in the records.

The PROM MGT showed moderate-quality evidence for sufficient content validity and the PROMs MMAS-8, MTA-OA, MTA – Insulin, MARS-5, ARMS-12, MTA, ARMS-7, MEDS, IADMAS, GMAS, ProMAS, A-14, 12-item questionnaire, and Mascard showed showed inconsistent content validity. The remaining PROMs included in the review did not have their content validity evaluated in the selected papers.

Structural validity

The EFA was the most commonly applied statistical method in the evaluation of structural validity of the PROMs (ARMS-7, ARMS-10, ARMS-12, ARMS-D, DMAS-7, GMAS, LMAS-14, MARS-5, MGT, MMAS-8, SMAQ, and Mascard), [ 29 , 31 , 35 , 37 , 39 , 40 , 44 , 46 , 47 , 50 , 51 , 52 , 55 , 57 , 61 , 62 , 67 , 68 , 71 , 75 , 79 , 81 , 86 , 87 , 88 , 93 , 100 , 106 , 111 , 113 , 117 , 119 , 121 , 123 , 125 , 127 , 128 , 129 , 132 , 133 ] followed by the confirmatory factor analysis (ARMS-7, ARMS-12, GMAS, MGT, MEDS, MMAS-8, MNPS, MTA, and MARS-5), [ 46 , 52 , 55 , 57 , 65 , 72 , 73 , 80 , 87 , 88 , 108 , 114 , 115 , 119 , 128 , 129 , 132 , 133 , 134 ] and the item response theory (MARS-5, GMAS, MMAS-8, and ProMAS) [ 28 , 35 , 92 , 130 , 135 ].

Regarding the assessment of the methodological quality of EFA, some studies were classified as having doubtful quality, since they did not report the rotation method used in the analysis [ 29 , 39 , 81 , 86 , 117 ].

In the evaluation of the EFA, some studies were classified as indeterminate because they did not report the percentage of variance explained [ 29 , 35 , 39 , 40 , 63 , 81 , 93 , 117 , 133 ] or the factor loadings [ 35 , 81 , 121 ]. One study did not report the results of the indices used to evaluate the confirmatory factor analysis [ 88 ] and another study [ 35 ] did not present the results of the indices of the item response theory analysis.

The PROMs MEDS, MNPS, GMAS, ProMAS, and ARMS-7 showed high-quality evidence for sufficient structural validity. The PROMs DMAS-7, MARS-5, ARMS-D, and ARMS-10 showed moderate-quality evidence for sufficient structural validity. Moderate and high-quality evidence for insufficient structural validity was observed for the Mascard and MTA, respectively.

However, the structural validity of the MMAS-8, MGT, LMAS-14, and the ARMS-12 were classified as inconsistent. The MMAS-8 presented results with one or two-factor solutions and also sufficient, insufficient, and indeterminate ratings. Similarly, the ARMS-12 presented sufficient and insufficient results in two or three-factor solutions, while the MGT presented only one-dimensional solution, but with sufficient, insufficient, and indeterminate ratings. LMAS-14 presented three or four -factor solutions. An overall rating indeterminate was attributed to SMAQ, since the included studies for this PROM were classified as indeterminate [ 29 , 63 ]. The remaining PROMs included in the systematic review did not have their structural validity evaluated in the selected records.

Internal consistency

Regarding the analysis of the internal consistency property, the original factor structure of the PROM was considered in order to evaluate if Cronbach's alpha should be calculated for the total scale and or subscales or domains. One included study [ 91 ] of the PROM MMAS-8 was classified as of doubtful methodological quality, since the authors excluded four items from the PROM because of the low Cronbach's alpha coefficient obtained, without considering other reliability or validity estimates. In two studies for the PROM A-14 [ 74 , 131 ] it was not clear the number of the subscales of the PROM and in another three studies [ 29 , 65 , 75 ] that used the PROMs ARMS-10, SMAQ, and GMAS, the Cronbach’s alpha was not calculated for each of the subscales of the PROMs.

Four PROMs (MEDS, MNPS, ARMS-D, and ProMAS) showed high-quality evidence for sufficient internal consistency. However, very low-quality evidence for sufficient internal consistency for the ARMS-10 it was observed, while the PROMs DMAS-7 and ARMS-7 showed moderate quality evidence for insufficient internal consistency. Also GMAS showed low quality evidence for insufficient internal consistency. The internal consistency of the 15 PROMs (MMAS-8, SMAQ, ARMS-12, MGT, MTA-OA, MTA-Insulin, LMAS-14, MTA, A-14, MALMAS, IADMAS, MAQ, AS, 12-item questionnaire, and Mascard) were classified as indeterminate. The PROM MARS-5 had its internal consistency classified as inconsistent. The remaining PROMs included in the review did not have their internal consistency evaluated in the selected papers.

Reliability

All included studies that evaluated the reliability of the PROMs (ARMS-7, ARMS-10, ARMS-12, GMAS, IADMAS, MALMAS, MAQ, MARS-5, MGT, and MMAS-8) were classified as of doubtful or inadequate methodological quality [ 33 , 34 , 38 , 41 , 45 , 46 , 47 , 50 , 52 , 54 , 55 , 61 , 62 , 69 , 75 , 76 , 77 , 79 , 82 , 86 , 87 , 88 , 93 , 94 , 97 , 104 , 117 , 119 , 123 , 127 , 128 , 129 , 132 , 136 ] and did not provide enough data to address items 4 (“Did the professional(s) administer the measurement without knowledge of scores or values of other repeated measurement(s) in the same patients?”) and 5 (“Did the professional(s) assign scores or determine values without knowledge of the scores or values of other repeated measurement(s) in the same patients?”) of the risk of bias checklist [ 138 ]. The other included studies [ 46 , 47 , 52 , 60 , 86 , 87 , 88 , 93 , 117 , 127 ] were classified as inadequate, since the evaluation of the item 3 of the risk of bias checklist (“Were the measurement conditions similar for the repeated measurements – except for the condition being evaluated as a source of variation?”) was considered inadequate.

Considering that the statistical analyses recommended to estimate reliability were intraclass correlation coefficient (ICC) and kappa, the results of some studies [ 33 , 34 , 38 , 41 , 45 , 52 , 54 , 69 , 76 , 77 , 79 , 86 , 117 , 119 , 123 , 128 , 136 ] were classified as indeterminate because Spearman's or Person's correlation coefficients were used to estimate the reliability of PROMs.

Regarding the best evidence of the reliability, the PROMs ARMS-10, ARMS-12, and MMAS-8 showed low-quality evidence for sufficient reliability, while the PROMs MAQ and ARMS-7 presented very low-quality evidence for sufficient reliability. The PROM GMAS showed moderate-quality evidence for insufficient reliability. The reliability of the PROMs MGT, MARS-5, MALMAS, and IADMAS were classified as indeterminate.

A meta-analysis to the reliability results was not performed, because the included studies did not show good methodological quality, according to the COSMIN guideline.

Criterion validity

The analyses applied in the evaluation of the criterion validity were area under the curve, sensitivity, specificity, and some hypothesis tests. Some of the included studies that did not report sensitivity and specificity analyses of the PROMs ARMS-12 [ 44 , 106 , 123 ], GMAS [ 128 , 129 ], MARS-5 [ 117 ], MMAS-8 [ 43 , 121 ], and SMAQ [ 29 ], and one study regarding the PROM LMAS-14 [ 71 ] that did not provide the area under the curve analysis were classified as of inadequate methodological quality.

The PROMs DMAS-7, MTA, MALMAS, ARMS-D, and 5-item questionnaire showed high-quality evidence for insufficient criterion validity. MMAS-8 and MGT showed low-quality evidence for insufficient criterion validity. IADMAS and SMAQ presented moderate and very low-quality evidence for insufficient criterion validity, respectively. ARMS-12 and MARS-5 presented inconsistent results and MEDS, MNPS, LMAS-14, GMAS, 3-item questionnaire, and Mascard had its criterion validity classified as indeterminate. The criterion validity was not evaluated in the included papers regarding the remaining PROMs included in the review.

Hypotheses testing

There were four included studies in which the methodological quality regarding the convergent validity of the PROMs was considered inadequate. Two of them that used the MMAS-8 [ 50 , 103 ] were rated as inadequate because the comparator instrument had insufficient measurement properties. In the other two studies [ 32 , 71 ] that used the PROMs LMAS-14, MTA-OA, and MTA-Insulin, the statistical tests applied were not optimal or appropriate. One study that used MALMAS was classified as having indeterminate quality, because the PROMs being correlated were not applied to the same participants [ 33 ].

Concerning the known‐groups validity, there was one included study for the PROM GMAS [ 128 ] that did not provide a description of the important characteristics of the groups being compared.

The analysis applied by the included studies were mainly correlation coefficients, regression models, and comparison and association tests.

The PROMs MEDS, DMAS-7, ARMS-12, A-14, ARMS-D, ProMAS, 5-item questionnaire, and AS showed high-quality evidence for sufficient construct validity. IADMAS and GMAS showed moderate-quality evidence for sufficient construct validity. LMAS-14 and MALMAS showed low-quality evidence for sufficient construct validity. MTA-OA and MTA-Insulin presented very low-quality evidence for sufficient construct validity. The PROMs MMAS-8, MGT, MTA, and MARS-5 showed inconsistent construct validity. The remaining PROMs included in the review did not have their construct validity evaluated in the selected papers.

Responsiveness

Responsiveness was evaluated only for an adapted version of MMAS-8 composed by 5 items, nominated in this systematic review as MMAS-5, in a single study [ 98 ]. The study reported very good methodological quality and high-quality evidence for sufficient responsiveness.

Meta-analysis

The meta-analysis was performed to pool the results of Cronbach’s Alpha of the included studies. Considering a Cronbach's Alpha equal to or higher than 0.7 to be satisfactory [ 13 ], the PROM MARS-5 showed high-quality evidence for sufficient internal consistency. GMAS showed high-quality evidence for insufficient internal consistency. The PROMs MMAS-8, ARMS-12, MTA, and MGT were classified as indeterminate because their structural validity was classified as inconsistent. The MALMAS was classified as indeterminate because it did not have its structural validity evaluated in the included studies. Values of I 2 equal or higher than 50% and 75% indicates the presence of moderate and high heterogeneity, respectively [ 139 ]. Moderate or high heterogeneity was observed in the PROMs MMAS-8, ARMS-12, MGT, MARS-5, and GMAS (Table 5 ).

The graphical representation of the pooled Alpha results for each of the included PROM in the meta-analyzes are shown in the Fig.  2 .

Pooled Cronbach's alpha estimates of the PROMs included in the meta-analyses. Note: ARMS = Adherence to Refills and Medication Scale; GMAS = General Medication Adherence Scale; MALMAS = Malaysian Medication Adherence Scale; MARS-5 = 5-item Medication Adherence Report Scale; MGT = Morisky-Green test; MMAS-8 = 8-item Morisky Medication Adherence Scale; MTA = Measurement of Treatment Adherence; PROM = Patient-reported outcome measures

Interpretability and Feasibility

It was not possible to identify the information needed to evaluate the interpretability and feasibility in most of the included records. Considering that the evaluation of these aspects would be incomplete because of the lack of information, the reviewers decided not to evaluate these aspects.

According to the results of our systematic review, none of the evaluated PROMs reached the criteria of category “a”, i.e., the results obtained across the studies can be trusted and the PROM can be recommended for use.

The PROMs MTA, GMAS, DMAS-7, MALMAS, ARMS-D, and 5-item questionnaire were categorized as not recommended for use (category “c”), because they presented high-quality evidence for at least one insufficient measurement property.

The remaining PROMs, i.e., MMAS-8, SMAQ, MEDS, MNPS, ARMS-12, MGT, MTA-OA, MTA-Insulin, LMAS-14, MARS-5, A-14, ARMS-10, IADMAS, MAQ, MMAS-5, ProMAS, ARMS‐7, 3-item questionnaire, AS, 12-item questionnaire, and Mascard were considered as having the potential to be recommended for use (category “b”) because they did not reach the criteria of the categories “a” or “c”.

The objective of this systematic review was to critically assess, compare, and synthesize the quality of the psychometric properties of PROMs for the assessment of medication adherence among patients with CVDs and/or T2DM.

To our knowledge, this is the first systematic review to assess the quality of the measurement properties of instruments that exclusively measure medication adherence using the COSMIN guideline. The results obtained allowed the identification of which instruments presented the best measurement properties in the population considered in this review. According to the COSMIN guidelines used in this systematic review, of the 27 PROMs extracted from the 110 studies included, none of the PROMs were recommended for use, 21 PROMs were considered to have the potential to be recommended and 6 PROMs were not recommended for use, as they did not meet the minimum criteria, i.e., demonstrated sufficient content validity and at least low quality of evidence for internal consistency. The summarized results of the meta-analysis and quality of evidence of internal consistency showed that, based on the results of three studies, only the MARS-5 has a high-quality evidence for a sufficient internal consistency.

As mentioned before, no systematic review was found in the literature evaluating the quality of the measurement properties of medication adherence PROMs, according to COSMIN guidelines specifically in patients with CVDs and/or T2DM. A recent study [ 140 ] analyzed systematic reviews in order to assess scope, validity, and reporting of PROMS of medication adherence in patients with T2DM. However, as previously noted, it included systematic reviews that do not specifically assess medication adherence and included studies that assessed factors related to medication adherence (self-efficacy, for an example), and reviews that used the PROM to evaluate interventions to promote medication adherence and did not apply a robust tool such as the COSMIN initiative guidelines for evaluating studies assessing the measurement properties of PROMs.

The main result of this review of reviews [ 140 ] was identifying that many PROMs have been translated into other languages without first presenting minimally adequate measurement properties in previous validation studies. It also pointed out that in some studies, the PROM was applied to a population without having been translated into the language of the country. The authors suggested that translated and adapted versions of PROMs that might in some way affect their items and/or subscales should be categorized separately from PROMs in their original format. In our review, studies of adapted versions of the PROMs were included, but they were not categorized separately. This may be a topic for future investigation of the PROMs evaluated in this review.

The findings of this systematic review about summarizing the data on the content validity of medication adherence PROMs showed that there were deficits and high heterogeneity of data in the included studies that investigated this property of the measure. The checklist for evaluation of methodological quality and the criteria for evaluation of the results related to the content validity of the PROMs proposed by the COSMIN initiative [ 141 ] are detailed in a large set of items, which were not covered by most of the studies included in the review. The reporting and data on the content validity of the included PROMs were extremely brief, with little information about the procedures performed, which hindered an adequate evaluation of this measurement property.

One difficulty observed in the evaluation of content validity was the absence of detailed information about the evaluation of relevance and comprehensiveness by the Expert Committee, as well as the comprehensibility by the target population in primary studies. Most of the included studies did not inform which one was investigated, and when they informed it, there was great heterogeneity in the way they were evaluated, implying inconsistent content validity. Our findings are congruent with a previous systematic review and meta-analysis that used the COSMIN guidelines to evaluate the evidence on measurement properties of the Hip disability and Osteoarthritis Outcome Score—Physical function Shortform (HOOS-P) and the Knee Injury and Osteoarthritis Outcome Score Physical function Shortform (KOOS-PS) [ 142 ], in which aspects such as the appropriateness of the response options and recall period and the relevance of the construct and context of the use were not evaluated in the primary studies, as in our review.

The internal consistency is an important measurement property that was included as an essential criterion to determine the recommendation for use of the PROM. However, the internal consistency of 15 PROMs in the included studies was rated as indeterminate. These results can be explained by the absence of at least low evidence for sufficient structural validity for these PROMS or because the structural validity of these measures was not performed in the included studies. The results of the meta-analysis and quality of evidence of internal consistency of the seven PROMS included in this analysis showed that only the MARS-5 has high-quality evidence for sufficient internal consistency, GMAS showed high-quality evidence for insufficient internal consistency and the other five PROMs showed indeterminate results. The meta-analysis resulted in many indeterminate results because of the structural validity results of the PROMs ARMS-12, MALMAS, MAT, MGT, and MMAS-8. The PROM ARMS-12 presented a pooled alpha that would result in a sufficient overall rating, but the structural validity limited this evaluation.

Regarding the evaluation of the structural validity, the PROMs MMAS-8, ARMS-12, MGT, and LMAS-14 were rated as inconsistent. This rating was attributed because different factor solutions were observed, i.e., different numbers of factors across the included records. Furthermore, four PROMs (MGT, MARS-5, MALMAS, and IADMAS) had their reliability rated as inconsistent because the results of the included records applied different coefficients (e.g. Pearson correlation coefficient) from the ones considered in the criteria stablished by the COSMIN initiative, i.e., ICC or Kappa.

As previously described, in the evaluation of the criterion validity, the review team considered that the obtained statistical results in the assessment of the relation between the PROMs and objective measures should be treated as a result of criterion validity, despite how the authors considered it in the primary studies. The reviewers considered that this change in the evaluation of the results would be beneficial as it would produce a standardization in the assessments. The findings showed that most of the evaluated PROMs presented an insufficient criterion validity and a sufficient overall rating in the hypothesis testing evaluation.

Another measurement property that had its evaluation compromised was the reliability. As previously mentioned, some items of the checklist for evaluation of the methodological quality were not described in the included studies, which resulted in studies being rated as having doubtful or inadequate methodological quality.

The evaluation of the measurement properties of the PROM’s included in this review indicated that none of the included PROMs could be considered trusted and recommended for use according to the criteria proposed by the COSMIN initiative. These results can be explained by the complexity of the medication adherence construct itself, which has made it challenging for researchers to obtain a PROM with good measurement properties [ 9 ]. The second aspect refers to the number of included studies in which each selected PROM was used. The evaluation of the measurement properties of a given PROM in several studies included in this review contributed to some measurement properties being rated as inconsistent because of the observed heterogeneity in the results of that PROM. The MMAS-8, for example, was the PROM for which the measurement properties were evaluated in 37 studies included in this review. Therefore with this large number of studies, heterogeneous results were likely for MMAS-8, which contributed to this PROM not being classified in the "a" category of recommendations. The other aspect that had a huge impact on the results was the evaluation of content validity, since this was one of the major gaps in the measurement properties of the medication adherence instruments, due to the lack of details of the data and analysis in primary studies, as previously mentioned. In addition to these issues identified when assessing PROM development and content validity, the fact that the methodological quality score of the measure properties considered the worst score of the COSMIN checklist may have contributed to downgrading the overall rating of the properties of the measure evaluated, as highlighted in previous studies [ 15 , 143 ]. According to the COSMIN guideline, it is recommended to consider the worst score assigned to one of the assessment items of all COSMIN boxes, since methodological aspects considered poor in primary studies cannot be compensated by aspects considered to be good. In the guideline was highlighted that only significant flaws in study design or statistical analysis should be classified with the worst score [ 141 ]. Although, for some standars of the boxes, the worst possible response option was defined by COSMIN as "doubtful" or "adequate" rather than "inadequate" by the guideline, in order to reduce the impact of these assessments on the risk of bias, our results showed the influence of this criteria on the risk of bias assessment. Another point was the absence of a criteria for the evaluation of sensitivity and specificity results, when testing the criterion validity of PROMs. Furthermore, most of the studies included in the review were developed before the release of the guideline proposed by the COSMIN initiative, which may justify the fact that many of the assessment criteria proposed by the initiative were not performed or presented in the expected way in the primary studies evaluated. Thus, since the COSMIN guideline has not yet been widely used, future studies are recommended to refine its suitability, acceptability and quality.

The findings of our systematic review have implications for clinical practice, since it contributes to improve the evidence-based selection of PROMs in research and practice. Considering the perception of the patient about their adherence to medication treatment contributes to promoting a person-centered model of care, whose results are known to be promising in the management of chronic diseases. Therefore, the knowledge about which PROMs are evidence-based, recommended, or potentially recommended for use in clinical practice is crucial for positively impacting human health. The use of PROMs with high quality of evidence contributes to improving implementation science, because they have the best properties to measure the behavior, to evaluating the effect of interventions to optimize medication adherence, and to positive changes in chronic disease management and clinical practice.

Strengths and limitations

One of the strengths of this systematic review was the careful application of the methodology proposed by the COSMIN initiative for the conduction of the systematic reviews in the evaluation of measurement properties of PROMs [ 21 ]. Another strength is the number of databases included in the literature searches which contributed to a more complete result. The review team should also be highlighted because it was composed of professionals from different areas, including nurses, dietitians, statistician, and researchers with expertise in research methodology and the use of PROMs. The different expertises of the team allowed for contributions related to the evaluation of the methodological rigor and statistical analyses of the studies, as well as those related to the evaluation of the pertinence and clinical relevance of different PROMS in measuring medication adherence.

A limitation of our study was the lack of detailed data in many studies to evaluate some of the measurement properties, especially content validity. This characteristic observed in the studies hampered the evaluation of many PROMs which resulted in many of them being poorly evaluated.

Another limitation was the high heterogeneity observed in the meta-analyses performed. Even if models with random effects were applied, the presence of high heterogeneity may bring limitations to the estimates obtained.

Conclusions

The conclusion of this systematic review none of the evaluated PROMs could be considered trusted and recommended for use for patients with cardiovascular diseases and/or type 2 diabetes mellitus. However, another 21 PROMs have the potential to be recommended for use but need further studies to ensure their quality, according to the COSMIN guidelines for systematic reviews of PROMs. Furthermore, the findings showed that it is key to improve the reporting of results in PROM validation studies, especially with regard to content validity.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Adherence to Refills and Medication Scale

Adherence Scale

Area under the curve

Confirmatory factor analysis

Cumulative index to nursing and allied health literature

Consensus-based standards for the selection of health measurement instruments

• Cardiovascular diseases

7-item Diabetes Medication Adherence Scale

Exploratory factor analysis

Excerpta medica database

General Medication Adherence Scale

Grading of recommendations assessment, development, and evaluation

Hip disability and osteoarthritis outcome score - physical function shortform

Iraqi Anti-Diabetic Medication Adherence Scale

Intraclass correlation coefficient

Item response theory

Knee injury and osteoarthritis outcome score physical function shortform

Literatura latino-americana e do Caribe em ciências da saúde

Fourteen-item Lebanese Medication Adherence Scale

Malaysian Medication Adherence Scale

Medication Adherence Scale in Cardiovascular disorders

Medication Adherence Questionnaire

5-item Medication Adherence Report Scale

Medication Adherence Estimation and Differentiation Scale

Morisky-Green test

5-item adapted Morisky Medication Adherence Scale

8-item Morisky Medication Adherence Scale

Medication Non-persistence Scale

Measurement of Treatment Adherence

Measurement of Treatment Adherence - Insulin

MTA-Oral Antidiabetics

Noncommunicable diseases

Negative predictive value

Probabilistic Medication Adherence Scale

Positive predictive value

Preferred reporting items for systematic review and meta-analyses

• Patient-reported outcome measures

International prospective register of systematic reviews

Simplified Medication Adherence Questionnaire

Type 2 diabetes mellitus

World Health Organization

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Acknowledgements

We would like to thank the Fundo de Apoio ao Ensino, Pesquisa e Extensão—FAEPEX, University of Campinas – Unicamp, Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq, and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001—PrInt—Programa Institucional de Internacionalização for the funding support to this research.

The authors acknowledge the following financial support: Fundo de Apoio ao Ensino, Pesquisa e Extensão—FAEPEX, University of Campinas – Unicamp, São Paulo, Brazil (grant: 3056/18; No. 519.292), Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq (grant No. 312367/2017–1), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001—PrInt—Programa Institucional de Internacionalização (Process number: 8881.311294/201800).

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The authors HCO, NMCA and RCMR conceived and developed this research. PABR contributed to the methodological design, analysis, and review of the manuscript. HCO, DHN, SDLC, RCLB, CRSA and MLSN evaluated the titles and abstracts. Full-text assessment was done by HCO and DH. Data extraction, risk of bias and results assessment were done by HCO and RCMR. All authors contributed to the improvement, revised and approved the final manuscript.

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Quality of studies on the PROM development and content validity.

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Oliveira, H.C., Hayashi, D., Carvalho, S.D.L. et al. Quality of measurement properties of medication adherence instruments in cardiovascular diseases and type 2 diabetes mellitus: a systematic review and meta-analysis. Syst Rev 12 , 222 (2023). https://doi.org/10.1186/s13643-023-02340-z

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DOI : https://doi.org/10.1186/s13643-023-02340-z

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Kenyan adults with type 2 diabetes mellitus (t2dm): increase diabetic knowledge and self-efficacy and decrease hemoglobina1c levels post-educational program, sabina jeruto bet, jochebed bosede ade-oshifogun.

Introduction: Literature supports the relationship between increased diabetic knowledge and improved health outcomes among individuals with Type II diabetes mellitus (T2DM). In Kenya, knowledge gaps within the at-risk population still exist about the symptoms, complications, and management strategies of T2DM, making it challenging to achieve the required personal and community health levels. The project’s objective was to determine whether a structured educational intervention for patients in Eldoret, Kenya, would increase diabetic knowledge and self-efficacy and reduce HbA1c levels.

Method: We utilized an experimental study with a convenience sample of 143 participants systematically grouped into control and experimental. The experimental group only received a structured educational intervention based on the health belief model. Pre- and post-intervention data for diabetic knowledge, self-efficacy, and HbA1c were analyzed using the independent T and ANOVA tests.

Results: We observed significant between-group differences for diabetic knowledge (t (116) = 7.22, p<0.001), self-efficacy t (96) =5.323, p<0.001; and HbA1c level t (121) =-2.87, p = .003. We also observed significant within-group differences for diabetic knowledge, t (12.6), p<0.001); self-efficacy t (5.32), p<.001); and HbA1c, t (4.4), p<0.001, in the experimental group only.

Conclusions: This study reveals the effect of a structured education intervention in increasing diabetic knowledge and self-efficacy while reducing HbA1c levels in T2DM patients in Eldoret, Kenya.

Keywords: Education program; type 2 diabetes; Kenya.

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What Is Diabetes Mellitus?

Diabetes mellitus—more commonly known as diabetes —is a chronic disease that occurs when you have higher than normal levels of blood glucose (or, blood sugar). Glucose is the body’s main source of energy. Too much glucose can lead to symptoms such as fatigue , feeling thirsty, and blurry vision.

Research estimates that 11% of the U.S. population has some form of diabetes. Fortunately, there are several treatments that can help you manage diabetes, such as lifestyle changes and medications. Learning more about diabetes and understanding how to manage your condition can help prevent long-term complications and improve your quality of life.

Types of Diabetes

There are several different types of diabetes. While the symptoms, diagnostic process, and treatment options have some overlap, each type of diabetes is specific. These types include:

• Type 1 diabetes (T1D): About 5% to 10% of people with diabetes have this form. Historically, people sometimes called type 1 diabetes “juvenile diabetes” or “insulin-dependent diabetes.” This is because type 1 diabetes often starts during childhood and early adolescence—though anyone can develop T1D at any age. 
• Type 2 diabetes (T2D): About 90% to 95% of people with diabetes have what is called type 2 diabetes. Oftentimes, this type develops in adulthood, but some children can also have T2D.
• Type 3c diabetes: Roughly 4% to 5% of people with diabetes this type. Researchers believe that some sort of physical damage to the pancreas can lead to type 3c diabetes. It's worth noting that this type is sometimes misdiagnosed as type 2 diabetes.
• Gestational diabetes : Due to physical and hormonal changes during pregnancy , some people develop diabetes when they're expecting. This type of diabetes usually goes away after the baby is born. However, people who have had gestational diabetes have a higher risk of eventually developing type 2 diabetes in the future.

Generally, there is overlap between the symptoms of the different types of diabetes. However, each type can present some unique symptoms that you should know. Here's how to recognize the differences.

Type 1 Diabetes Symptoms

Symptoms of T1D often develop quickly, in a matter of just a few weeks. It's also common to experience more severe symptoms if you have this type. Some hallmark characteristics of T1D include:

• Feeling extremely hungry
• Being very thirsty
• Needing to use the bathroom more often
• Unintentional weight loss
• Blurry vision

People with type 1 diabetes may also eventually notice symptoms of a diabetes complication called diabetic ketoacidosis, or DKA. This complication can cause vomiting , stomach pain, rapid breathing, and fruity-smelling breath. 

Type 2 Diabetes Symptoms

With T2D, it can be common to not experience symptoms at first. When symptoms do gradually begin, they may be a result of elevated blood sugar levels or from damage to the organs that diabetes can cause. If you suspect you may have type 2 diabetes, here are some symptoms to keep in mind:

• Extreme fatigue
• Repeated infections or sores and cuts that heal slowly
• Feeling thirsty
• Urinating frequently
• Tingling , numbness, or pain in the hands and feet
• Dark patches on the skin
• Irritability or other mood changes

Type 3c Diabetes Symptoms

Most people with type 3c diabetes also experience some symptoms of type 1 and type 2 diabetes. But, those with type 3c diabetes can also have symptoms that appear as a result of damage to the pancreas. These include:

• Fatty stools (poops)
• Stomach pain

Gestational Diabetes Symptoms

Gestational diabetes usually doesn’t cause any symptoms. If you do develop this condition while pregnant, you may however notice symptoms such as being thirstier than normal or needing to urinate more frequently. 

Your pancreas makes a hormone called insulin . This hormone plays an important role in signaling cells to allow glucose from your blood to enter the cells in your muscles, fat, and liver . Your muscles, fat, and liver can then use glucose as energy for your body. You get glucose from the food you eat. After eating a meal, your blood sugar naturally rises. When this happens, your pancreas creates insulin and releases it into your blood to lower your blood sugar and keep it in a normal range.

When something interferes with your pancreas' ability to produce insulin, your blood sugar levels can stay elevated, increasing your risk of having too much glucose in your blood. Excess blood sugar can lead to the onset of diabetes symptoms. All types of diabetes occur as a result of a problem with your pancreas. What exactly leads to issues with your pancreas and your body's ability to produce insulin depends on the type of diabetes you have.

What Causes Type 1 Diabetes?

Scientists believe that type 1 diabetes occurs because of an abnormal autoimmune response, which causes your immune system to attack your pancreas by mistake. This immune system response specifically destroys healthy cells in the pancreas that are responsible for making insulin. Once these cells become damaged, your pancreas can't process insulin normally which can increase your risk of having high blood sugar levels and developing diabetes symptoms. 

What Causes Type 2 Diabetes?

With type 2 diabetes , the cells in your body stop responding normally to insulin. As a result, they become what's called " insulin resistant ." Your pancreas may initially make more insulin to try to help keep your blood sugar levels low. But eventually, your pancreas isn't able to make the insulin your body needs and the amount of glucose in your blood rises.

It's not completely clear what can cause insulin resistance. However, researchers believe that having a family history of diabetes, carrying higher amounts of adipose tissue (or, body fat around your waist), and living a sedentary lifestyle can increase your likelihood of becoming insulin resistant. When this happens, your body can't use glucose normally—which leads to elevated levels of blood sugar and boosts your risk of developing T2D symptoms.

What Causes Type 3c Diabetes?

Type 3c diabetes occurs as a result of broader damage to your pancreas. Your pancreas can become damaged for several reasons including chronic pancreatitis , cystic fibrosis, and pancreatic cancer. These conditions can lower your pancreas' ability to function normally and cause problems with producing insulin.

If you suspect you have symptoms of diabetes or have a family history of the condition, it's good practice to see your healthcare provider for proper testing. Your provider will ask you about your personal and family medical history, learn about your symptoms and lifestyle habits, and perform a physical exam to assess whether they should order additional tests.

The two most common diagnostic tests for diabetes include:

• Fasting glucose test: Measures the glucose in your blood to see if it is elevated after at least 8 hours without food. Over 126 milligrams of glucose per deciliter of blood (mg/dL) may indicate that you have diabetes. It's worth noting that 100 to 125 mg/dL is a sign of having prediabetes.
• Hemoglobin A1C test: Estimates how elevated your blood glucose has been over the last three months. A normal A1C level is under 5.7%. Receiving a result of 5.7% to 6.4% means that you have prediabetes—which occurs when you have higher than average blood sugar, but not high enough where you have diabetes. An A1C level of 6.5% or more can result in a diabetes diagnosis.

Your healthcare provider may also use the following tests:

• Random plasma glucose test: While not as helpful or reliable of a test for a diabetes diagnosis, your healthcare provider may use this test if you haven't fasted.
• Glucose challenge test: A test that providers more commonly use to check for gestational diabetes. This test involves measuring the amount of glucose in your blood after you drink something very sweet.

If your healthcare provider suspects you have T1D, they may order additional testing, such as a blood test that checks if your body is producing antibodies against the cells in your pancreas. These antibodies can help diagnose T1D, but are not found in your body if you have T2D.

If you receive a diagnosis for a type of diabetes , your healthcare provider can help you understand your treatment options and support you as you learn to manage your condition. There is no cure for diabetes, but the goal of treatment is to keep your blood sugar within a normal range.

Making small, healthy changes to your diet and increasing your movement or physical activity can have positive effects on lowering your insulin resistance. In fact, some people with T2D specifically can manage their condition with these lifestyle changes without the need to take medications.

Your healthcare provider will often discuss a comprehensive treatment plan which includes a combination of both lifestyle changes and medical treatments, such as non-insulin medications and insulin treatments.

Non-Insulin Medications

If lifestyle changes aren't giving you the results you're aiming for, medication can help you manage your diabetes. Non-insulin medications are a common treatment for people with T2D. There are currently several medications on the market to help you keep your blood sugar levels in a normal range. The most common drug healthcare providers prescribe first is called Glucophage ( metformin )—mostly because of its low cost, minimal side effects, and ability to promote weight loss.

If treatment with Glucophage and lifestyle changes aren’t enough, The American Academy of Family Physicians recommends adding one of the following classes of oral medications to your treatment plan if you have T2D. Examples of these medications include:

• Diabeta (glyburide)
• Avandia (rosiglitazone)
• Invokana (canagliflozin)
• Januvia (sitagliptin)

However, other options are available, including some injectable medications such as Ozempic or Wegovy (semaglutide). If you are interested in trying injectable treatments, ask your healthcare provider if this is a good option for you.

Insulin Treatments

Insulin treatment is the standard treatment option for people with T1D. You can receive insulin treatments via a needle and syringe, an insulin pen, or a pump. It's worth noting that insulin treatments are not available in the form of a pill. Eventually, many people with type 2 diabetes may also need some type of insulin to manage their condition, especially if their diabetes progresses. 

Different types of insulin are available. Your healthcare provider can help you figure out what type of insulin treatments you need and how often you should take them. If you are on insulin treatment, your healthcare provider will recommend doing regular check-ins to ensure that your blood sugar levels are staying close to your target goal.

How to Prevent Diabetes

There is no known way to prevent type 1 diabetes—mostly because researchers don't know what triggers type 1 diabetes. However, healthcare providers recommend incorporating dietary changes and increasing your physical activity to prevent type 2 diabetes. This is especially important if you have a family history of the condition, carry excess adipose tissue around the waist, or have prediabetes .

Any type of physical exercise that you can do regularly can help you reduce your risk. This may include options such as strength training, aerobic activity (e.g., swimming ), walking , sports, or even completing household chores. The most important thing is to find ways to move your body more frequently and in ways that you enjoy.

Dietary changes are also important to prevent the onset of T2D symptoms. Some recommendations from the American Diabetes Association include:

• Eating more whole grains and high-fiber foods
• Reducing your intake of foods high in carbohydrates and increasing your protein intake
• Avoiding sugar-sweetened beverages and foods high in sugar

It's important to note that weight isn’t the only indicator of your overall health. However, for some people, losing a bit of weight (if needed) can help their body become less resistant to insulin. One study found that for every kilogram (about 2.2 lbs) that people lost, they reduced their risk of diabetes by about 16%.

Complications

Diabetes can lead to several different complications. That's why it's essential to manage your condition and work with your healthcare provider to find the treatment options that are right for you. Some of these complications include:

• Diabetic ketoacidosis (DKA): Diabetic ketoacidosis is a potentially life-threatening complication that is more common in type 1 diabetes than in type 2 diabetes. The condition can happen when your body doesn't have enough insulin and has to use fat for energy instead. This causes ketones (or, chemicals that your liver produces when it breaks down fat) to develop and build up in your body. The production of ketones may happen if you become sick or miss an insulin shot. Unfortunately, if your condition isn't well-managed, DKA can sometimes lead to diabetic coma or death. 
• Hypoglycemia: Living with diabetes can also cause you to develop hypoglycemia —a condition that occurs when you have very low blood sugar. It may sound counterintuitive to diabetes, but this condition may occur if you took too much insulin or drank an excessive amount of alcohol. If this condition becomes severe, you might develop serious symptoms such as a seizure.
• Organ damage: In the long-term, elevated blood sugar from any type of diabetes damages multiple organ systems, especially if you aren't managing your condition well or following your treatment plan. As a result, diabetes can cause damage to your kidneys, eyes, nerves, blood vessels, and heart—while also increasing your risk of having a heart attack or stroke .

Living With Diabetes

Getting a diabetes diagnosis can feel discouraging and difficult. This condition often requires a lot of management and lifestyle changes. You’ll want to be proactive about your choices to prevent short-term and long-term complications. It can seem overwhelming when you are first diagnosed—and that's OK. Your healthcare team can help you understand diabetes and support you in managing your condition.

Living a full life with diabetes is possible. The following tips may help as you navigate your diagnosis:

• Follow your healthcare provider’s instructions on medications and blood sugar monitoring
• Check your feet regularly for signs of damage or infection
• Get regular eye exams to check for eye complications 
• Move your body for 150 minutes or more per week, if possible
• Make small, but important changes to your diet, such as eating fewer foods high in sugar and more foods high in protein
• Work with your healthcare provider to manage any underlying health conditions (e.g., high blood pressure) that may be increasing your risk of diabetes-related complications
• Talk to your loved ones, join a support group, or meet with a mental health professional to learn how to navigate your condition, receive support, and take care of your emotional well-being

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