Dentistry Section DOI : 10.7860/JCDR/2018/36584.11993
Year : 2018 | Month : Sep | Volume : 12 | Issue : 09 Page : LC01 - LC05

Evolving a Structural Model in Type 2 Diabetes Mellitus: Influence of Knowledge, Attitudes, and Self-Management Practices on Glycaemic Control

Matpady Prabhath Kalkura1, Shashikiran Umakanth2, Arun Gundmi Maiya3, Shreemathi Sureshramana Mayya4, Krishnanda Prabhu Renjala Vasudeva5, Mamatha Shivananda Pai6, Pallavi Prakash Saraswat7, Balkudru Kiran Aithal8

1 Project Manager, World Diabetes Foundation 15: 941, Department of Physiotherapy, School of Allied Health Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India.
2 Professor and Head, Department of Medicine, Dr. TMA Pai Hospital, Melaka Manipal Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India.
3 Professor and Associate Dean, Department of Physiotherapy, School of Allied Health Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India.
4 Associate Professor, Department of Statistics, Manipal Academy of Higher Education, Manipal, Karnataka, India.
5 Professor and Head, Department of Biochemistry, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India.
6 Professor and Head, Department of Child Health Nursing, Manipal College of Nursing, Manipal Academy of Higher Education, Manipal, Karnataka, India.
7 Research Assistant, Department of Medicine, Melaka Manipal Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India.
8 Senior Scientist 2, Aurigene Discovery Technologies, Aurigene Discovery Technologies, Bengaluru, Karnataka, India.

NAME, ADDRESS, E-MAIL ID OF THE CORRESPONDING AUTHOR: Dr. Shashikiran Umakanth, Professor and Head, Department of Medicine, Dr. TMA Pai Hospital, Opposite Taluk Office, Court Road, Udupi-576101, Karnataka, India.


Regularity in diabetes self-management practices among people with Type 2 Diabetes Mellitus (T2DM) is a predictor of glycaemic control. Knowledge and attitude have linear relationships with diabetes self-management, and hence exert a positive influence on glycaemic control.


The study aimed to develop and examine a structural model describing the inter-relationship between diabetes knowledge, attitude, self-management practices, and glycaemic control {demonstrated by glycated haemoglobin (HbA1c) levels}.

Materials and Methods

A cross-divtional study was conducted among people with T2DM in Udupi taluk, Karnataka, India. A total of 432 participants, selected by simple random sampling and fulfilling the inclusion criteria, were included in the study. Descriptive, correlative and comparative analysis of data was done using Statistical Package for the Social Sciences (SPSS) version 16.0. The Structural Equation Modelling (SEM) technique was used for the model; and path analysis was performed using Analysis of Moment Structures (AMOS) version 25.0 software.


Diabetes knowledge was a significant predictor of attitude, which in turn were a significant predictor of diabetes self-management and HbA1c. Importantly, diabetes self-management was a significant predictor for HbA1c. The final model reflected good fit (χ2 (5) =5.849, p=0.321, RMSEA= 0.019, CFI= 1, NFI=0.997).


The present study strongly indicates that attitude and self-management practices can have a direct impact on HbA1c levels of people with T2DM. Knowledge has an indirect impact on self-management and HbA1c through attitude. Developing a self-management intervention model (which can be tailored to suit the needs of the individual with T2DM) encompassing knowledge, positive attitude and diabetes self-management practices, might result in improved glycaemic control among people with T2DM, and it will be more sustainable than other interventions since it would work at the community level.


T2DM is a chronic metabolic condition with hyperglycaemia due to insulin resistance [1]. T2DM is a concern world over as it is increasing at a startling rate, particularly in developing countries. The International Diabetes Federation (IDF) reported that the total number of people with diabetes in India was 72.9 million in 2017 and this would rise to 119.6 million by the year 2045 [1]. Increased prevalence of T2DM in India is primarily attributed to lifestyle changes and other factors associated with societal transitions [2].

T2DM is a complex, chronic illness requiring continuous medical care. Patient self-management programs are critical in preventing acute complications and reducing the risk of long-term complications [3]. Self-management of T2DM often involves lifestyle modification (including glucose management, dietary management, physical activities, stress management, drug adherence), periodic health review while managing their health expenditure on a routine basis for a lifetime [3-6].

Based on existing literature, three parameters emerge as key factors influencing the HbA1c viz., knowledge about diabetes, patients’ attitude towards management of diabetes and the practice of self-management [7,8]. Furthermore, these parameters are significantly associated with age, gender, educational status, duration of diabetes and history of previous hospitalization [7-10].

T2DM, being chronic lifestyle disorder, requires a high level of motivation for self-management [3]. While people have access to exhaustive information on diabetes and its self-management through easy sources like internet, family, friends and quacks, this also poses a real challenge for healthcare teams. This is so, especially because most of the information available freely is not reliable. It is imperative to facilitate uptake of the right information and behaviour through planned and well-structured educational modules on diabetes self-management [11,12]. The investigation of the comprehensive relationship between knowledge, attitude, and self-management practices with the HbA1c level among people with diabetes, as undertaken in this study, is expected to set precedent for the development of an innovative intervention model which will help negate these challenges.

In an exhaustive literature search, we could not find any community-based studies evaluating the comprehensive relationship of knowledge, attitude, and self-management practices with HbA1c levels among people with T2DM. Most studies have examined bivariate relationships between knowledge, attitude, self-management practice, and HbA1c [7-9,13-17]. However, examining all the relationships comprehensively in one model will be beneficial in deepening the understanding of factors associated with the glycaemic control. Such a model predicts the paths between the key variables, which also examine the paths suggested by previous research on bivariate relationships. By using a structural equation modelling approach, we can achieve simultaneous examination of all these paths.

In the present community-based study, we aimed to address the gap in research on the inter-relationship between the key variables of knowledge, attitude, self-management practices, and HbA1c levels. To begin with, we designed a hypothetical model [Table/Fig-1] and conducted a cross-sectional study to gather baseline data. Based on the findings (presented in this paper) and literature review, we drafted an initial model. After the structural modelling, path analysis, and other modifications, the final structural model was developed. Currently, we are testing the effectiveness of the final model in people with T2DM, along with considering the influence of certain demographic variables.

Hypothetical Structural Model of age, duration of diabetes, gender, education, hospitalization, knowledge, attitude, self-management, and HbA1c in people with T2DM.

Materials and Methods

After obtaining an institutional ethical committee approval, a cross-sectional study was conducted amongst people with T2DM in Udupi taluk, Karnataka, India. This study protocol is registered with the Clinical Trials Registry of India (bearing the registration number: CTRI/2017/02/007945). For recruitment of participants, a comprehensive registry of 12,478 people with diabetes was developed. A sample size of 600 was determined for the study. Using simple random sampling technique, we selected and contacted 600 people from the diabetes registry. Out of these, 482 responded to the researcher. A final sample size of the study was 432, as these patients fulfilled the inclusion criteria (people with T2DM, age between 30 to 65 years, people who are able to understand Kannada language). Other participants were excluded from the study as per the exclusion criteria (people not on medication, people with Type1DM, those who are critically ill, inability and refusal to sign the informed consent or comply with protocol, serious psychiatric illness, self-reported alcoholic or illicit drug use). In addition, 11 participants were excluded as their blood samples coagulated during transport from community to the central biochemistry laboratory.


The data collection instruments included a demographic proforma, three questionnaires (measuring diabetes knowledge, attitude to living with diabetes, and diabetes self-management) and HbA1c test. Many knowledge, attitude and self-management practice questionnaires have been developed worldwide for assessing knowledge about T2DM and its management [18-25]. We modified existing tools for contextualisation and validated them for our use. The questionnaires used are all open-access, the authors were contacted before use, and they have been duly cited.

The demographic proforma included age, gender, duration of diabetes (time elapsed since diagnosis), previous hospitalisation, education, and income.

Diabetes Knowledge Questionnaire (DKQ): Developed by Eigenmann CA et al., [22], this represents questions on ideal range of blood glucose level and HbA1c, nature of diabetes, dietary management, physical activities, complications, management of diabetes when person is ill, frequency of medical check-up, and National Program for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke.

Diabetes Integration Scale – 19 (ATT-19): This encompassed six key factors linked to the management of diabetes including diabetes stress, responsiveness to treatment, trust on treatment, personal effectiveness, perception about health and social acceptance [26].

Diabetes Self-Management Questionnaire (DSMQ): This allowed the summation of four “subscale” score as well as estimation of four subscale scores. Contents of the subscales are labelled “Glucose Management,” “Dietary Control” “Physical Activity,” and “Health-Care Use” and “overall” rating [27].

HbA1c Testing: Primarily used for monitoring diabetes control, this test is also used to diagnose the T2DM [3] and hence was used. The National Glyco-haemoglobin Standardization Program (NGSP) approved HbA1c test method was followed to assess the glycaemic control of the participants inthis study. The blood sample collected in the field was tested, subsequently, in the biochemistry laboratory of a medical college that is NABH (National Accreditation Board for Hospitals and Healthcare Providers) accredited.

Validation and Reliability Testing of Questionnaires

Vigorous standard procedures were followed to culturally contextualise and adapt the questionnaires for this study. A professional translator was employed for the translation of these questionnaires into Kannada, the local language. A team of experts validated the translated questionnaire; the experts included a physician, a community medicine specialist, a private medical practitioner, a government medical officer, a Taluk Health Officer, a physiotherapist, a statistician, a dietician, a medical-surgical nursing specialist, a mental-health nursing specialist and a language expert. Once scrutinised, comparison of Kannada questionnaires with the original was made, and they were finalised. A professional translated the finalised tools back into English, and these back-translated versions were compared with the original English tools, as well. Two senior medical practitioners assessed the English version of the questionnaires, for good face and content validity.

For comprehensive evaluation of the reliability of the tool and its cross-cultural validation, the instruments were pilot tested. These questionnaires were administered to 30 people with T2DM at the Diabetic Foot Clinic of Kasturba Hospital, Manipal, Karnataka, India. After test and re-test, the items in these tools were found to be internally consistent, and strength of the agreement was substantial.

Cronbach’s alpha for testing the internal consistency: DKQ (0.774: acceptable), ATT 19 (0.931: excellent) and DSMQ (0.909: excellent).

Intra-Class Correlation Coefficient for testing for the test and retest: DKQ (0.751: acceptable), ATT 19 (0.89: good) and DSMQ (0.92: excellent).

Data Collection

The researcher personally contacted the participants and scheduled an interview based on participants’ availability and convenience. The interviews were conducted at patients’ homes, wherein first, their informed consent was obtained, and then the interview was recorded, along with blood sample collection. Demographic details were recorded according to proforma, and then the DKQ, ATT 19 and DSMQ were administered. Also, 2 mL of participant’s blood was collected in an EDTA container, for HbA1c evaluation. The researcher spent around 35 minutes per participant for data collection. The participants did not receive any monetary incentives or reimbursements for participating in the study.

Statistical Analysis

Using SPSS version 16.0, the data set was prepared and analysed. Descriptive statistics were used to analyse the characteristics of the participants and compare the relationship between gender, education and previous hospitalisation. Correlations were tested to explore the strength of relationships between various variables (age, duration of diabetes since diagnosis) and knowledge, attitude, and self-management practices. Independent t-tests were used to analyse the strength of the relationship between gender and education with knowledge, attitude, and practices of self-management.

Structural Modelling

Parsimonious model is preferred in path analysis [28,29]. Therefore, the significant correlation of variables was taken into consideration when the initial path model was developed. AMOS version 25.0 software was used for path analysis in SEM. Several fit indices were considered to determine the goodness-of-fit of the path model. The statistics included chi-square (with the desired value of p >0.05), the Root Mean Square Error of Approximation (RMSEA) (with desired value of >0.05), the Comparative Fit Index (CFI) and Normed Fit Index (NFI) it ranges from 0 to 1, with desired values of greater than 0.95 indicate good fit [29]. After obtaining the final path model, the significance of the indirect effect of diabetes knowledge, attitude and self-management practices on HbA1c were examined through AMOS. In order to determine the significance level (p-value) of the indirect effect, we performed bootstrapping in the modelling analysis [29].


Characteristics of Participants

The descriptive analysis of all the study participants is presented in [Table/Fig-2]. In addition, the mean of HbA1c of study participants was 8.57% (SD=1.98%). Mean percent score of DKQ was 57.88 (SD=15.71), mean score of ATT-19 was 53.88 (SD=24.06) and mean score of DSMQ was 7.10 (SD=7.10).

Participants’ characteristics (n = 432).

Participant characteristicsMean (SD)Number of Participants (Frequency %)
Age (years)55.03 (8.13)
Gender • Females • Males199 (46.1 %)233 (53.9 %)
Duration of diabetes since diagnosis (in years)7.19 (5.66)
Education • Secondary or below • PUC or above183 (42.4%)249 (57.6%)
Previous Hospitalisation • Yes • No73 (16.9%)359 (83.1%)
Income (In Rupees per month) • less than 10,000 • 10000-20000 • 20000-30000 • 30000-40000 • 40000-50000 • above 50000307 (71.1%)82 (19.0%)24 (5.6%)9 (2.1%)5 (1.2%)5 (1.2%)

The relationship between variables

A correlation matrix was created, which tested correlations between age and duration of diabetes with the knowledge, attitude, practices, and HbA1c levels [Table/Fig-3]. The correlation between attitude and self-management practices was very strongly positive. Additionally, there was a very strong negative correlation of HbA1c levels with attitude and self-management practices, individually, at 99% confidence interval. This implies that improved attitude and greater adherence to self management practices translate to lower HbA1c level.

Correlation of study variables (n = 432).

1. Age10.386*0.0180.0880.128**-0.086
2. Duration of diabetes since diagnosis10.022-0.091-0.0690.080
3. Knowledge of Diabetes10.278**0.248**-0.244**
4. Attitude10.921**-0.891**
5. Diabetes Self-Management Practice1-0.899**
6. HbA1c1



Structural Model

The hypothetical model based on the empirical findings is illustrated in [Table/Fig-1]. Based on the literature review, and results from the correlation and comparative analysis in this study, an initial hypothetical model was developed [Table/Fig-4]. This initially hypothesized model did not result in a good fit to data (χ2 (15) =132.455, p <0.001, RMSEA= 0.130, CFI= 0.939, NFI= 0.932). An evaluation of each of the 15 path relationships in the initially hypothesized model, showed that some paths were not significant, and three variables (duration of diabetes, previous hospitalisation and education level) contributed to a poor fit of the model.

Initialstructural model of age, duration of diabetes, education level, hospitalization, gender, knowledge, attitudes, self-management practice and HbA1c. Path loadings are standardised path coefficient.

*p-value < 0.05.

Based on these initial assessments, some modifications were made. Path relationships that were not significant were removed, variables that contributed to poor fit of the model were omitted. Non-significant pathways linking knowledge and self-management practice and HbA1c were removed. The model was retested, and any variable that did not contribute as a significant predictor was also removed from the initial model. The fit indices of the final model [Table/Fig-5] resulted in good fit (χ2 (5) =5.849, p=0.321, RMSEA= 0.019, CFI= 1, NFI=0.997). The regression coefficients between the variables improved.

Final Structural model of age, gender, knowledge, attitude, self-management practice, and HbA1c. Path loadings are standardised path coefficient. *p-value < 0.05.

In the final model, better knowledge enhanced positive attitude there by improving HbA1c level. While there was no direct effect of knowledge on HbA1c, positive attitude contributed indirectly to better HbA1c levels through enhanced self-management practices. Age and gender were, also found to have a direct influence on diabetes knowledge.


T2DM, being a chronic lifestyle condition with no day-to-day symptoms, presents some unique challenges in its management. People with T2DM need to stay motivated to maintain a healthy lifestyle and medication compliance. While there is an information explosion, along with ease of access due to the advent of the Internet over the last 20 years, it also offers some unique challenges. Social media affects their knowledge about diseases, influences their attitude and practices towards their management. In the middle of this “noise,” it is a challenge for clinicians to channel critically useful health information to patients. Therefore, patients’ existing knowledge and its effects on attitude, self-management plays a pivotal for any interventions.

The present study provides valuable insights into this with the use of path analysis (an extension of multiple regressions) for testing the hypothesis in data related to diabetes knowledge, CS, the practice of self-management, and HbA1c levels among people with T2DM. Path analysis is different from other traditional regression analysis. Path analysis is the family of Structured Equation. This model allowed the probe of a network, those are relationships between levels of diabetes knowledge, attitude, self-management, HbA1c and other variables such as age, gender, duration of diabetes since diagnosis, hospitalisation and education level and among people with T2DM in one single model.

We identified a significant relationship between knowledge on diabetes and attitude on diabetes management, attitude on diabetes management and practice of diabetes self-management, attitude and HbA1c, diabetes self-management and HbA1c.

In the present study, diabetes knowledge has a positive linear relationship with attitude, which is in line with other existing reports [7,30]. This finding will benefit health care providers who are involved in imparting education on diabetes self-management for people with diabetes [3,31]. We also found that diabetes self-management education helped in improving HbA1c levels at immediate follow-up. However, the same was not evident one to three months post conclusion of intervention [32], indicating the importance of regular support in glycaemic control among people with T2DM.

There was no significant association found between the knowledge on diabetes and diabetes self-management practice, as well as knowledge and HbA1c in the present study. Contrary to our findings, knowledge was established as a direct predictor for diabetes self-management and glycaemic control [7,8,28,33]. Though there was no significant relationship amongst knowledge and diabetes self-management, between knowledge and HbA1c in this study, we found that lack of knowledge and understanding of the plan of care adversely affects diabetes self-management, that could sway glycaemic control [34].

The present study also demonstrated that positive attitude among participants led to better diabetes self-management practices as well as resulting in effective glycaemic control. Earlier reports suggest that positive attitude is a predictor of diabetes self-management and glycaemic control [7,28]. Thus, it was essential to create a positive attitude about the six key factors linked to management of diabetes (including stress, response to treatment, trust on treatment, personal effectiveness, perception about health and social acceptance) [26], which is reported to help improve compliance in relation to dietary control [28], more regular physical activity, health care use, overall diabetes self-management practice and glycaemic control.

Further, we also noted an inverse correlation between diabetes self-management practice and HbA1c levels, indicating that regularity in glucose management, dietary control, and physical activity improves the overall self-management of people with T2DM. Earlier reports from a qualitative study carried out in Mexico also concluded that regular concurrence to naturally occurring lifestyle and self-care practices resulted in effective glycaemic control [10]. Another recent study found people with T2DM with better glycaemic control had better knowledge scores, attitude scores and practice scores [7].


The participants for this study were recruited from the People with Diabetes registry (developed under World Diabetes Foundation 15:941 project), which is not an extensive registry. Except HbA1c levels, all other parameters were self-reported by participants during the interview. Thus, conclusions need to be drawn carefully from this study, and they will be restricted to a population defined by these characteristics.


This study concludes that attitude and diabetes self-management practices are reliable predictors of HbA1c values. Diabetes knowledge plays a pivotal role in developing positive attitude in diabetes management, which consequently improves glycaemic control. Attitude and diabetes self-management practice are an amalgamation of different factors such as diabetes stress, responsiveness to treatment, trust on treatment, personal effectiveness, perception about health, social acceptance and glucose management, dietary control, physical activity, health care use, and overall self-care management. Hence, Diabetes Self-Diabetes Self-Management Education (DSME) should be individually tailored and person-centred which aims to achieve a broader humanistic and societal perspective on the needs of people with diabetes. Consequently, DSME is likely to be more effective and evolve into a sustainable model in the community. This, in turn, might foster a positive attitude and help ease the practice of self-management in diabetes thereby leading to better glycaemic control.


[1]International Diabetes Federation. IDF Diabetes Atlas, 8th edn. Brussels, Belgium: International Diabetes Federation, 2017. Available from:  [Google Scholar]

[2]Ramachandran A, Snehalatha C, Baskar AD, Mary S, Kumar CK, Selvam S, Temporal changes in prevalence of diabetes and impaired glucose tolerance associated with lifestyle transition occurring in the rural population in India Diabetologia [Internet] 2004 47(5):860-65.10.1007/s00125-004-1387-615114469  [Google Scholar]  [CrossRef]  [PubMed]

[3]Standards of Medical Care in Diabetes-2018 Diabetes Care 2018 41(Suppl. 1):S1-S51.10.2337/dc18-Sint01  [Google Scholar]  [CrossRef]

[4]Montague MC, Nichols SA, Dutta AP, Self-management in African American women with diabetes Diabetes Educ [Internet] 2005 31(5):700-11.10.1177/014572170528041416203854  [Google Scholar]  [CrossRef]  [PubMed]

[5]Shen H, Effectiveness of a peer-led self-management program for older people with type 2 diabetes in China 2008   [Google Scholar]

[6]Norris SL, Engelgau MM, Venkat Narayan KM, Effectiveness of self-management training in type 2 diabetes Diabetes Care 2002 25:1159-71.10.2337/diacare.25.7.115912087014  [Google Scholar]  [CrossRef]  [PubMed]

[7]Solanki JD, Sheth NS, Shah CJ, Mehta HB, Knowledge, attitude, and practice of urban Gujarati type 2 diabetics: Prevalence and impact on disease control J Edu Health Promot 2017 6:3510.4103/jehp.jehp_101_1528584835  [Google Scholar]  [CrossRef]  [PubMed]

[8]Basu S, Khobragade M, Raut DK, Garg S, Knowledge of diabetes among diabetic patients in government hospitals of Delhi Int J Non-Commun Dis 2017 2(1):08-10.10.4103/jncd.jncd_44_16  [Google Scholar]  [CrossRef]

[9]Prasad KN, A community based study on perceived knowledge of diabetes on cause, control, prevention and complications among diabetic patients in Bengaluru city International Journal of Community Medicine and Public Health 2017 4(9):3416-23.10.18203/2394-6040.ijcmph20173855  [Google Scholar]  [CrossRef]

[10]de Alba Garcia JG, Rocha ALS, Lopez I, Baer RD, Dressler W, Weller SC, “Diabetes is my companion”: Lifestyle and self-management among good and poor control Mexican diabetic patients Soc Sci Med 2007 64(11):2223-35.10.1016/j.socscimed.2007.02.00117383785  [Google Scholar]  [CrossRef]  [PubMed]

[11]Shaw RJ, Johnson CM, Health information seeking and social media use on the internet among people with diabetes Online J Public Health Inform [Internet] 2011 3(1)10.5210/ojphi.v3i1.356123569602  [Google Scholar]  [CrossRef]  [PubMed]

[12]Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, Hoving C, A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication J Med Internet Res 2013 15(4):e8510.2196/jmir.193323615206  [Google Scholar]  [CrossRef]  [PubMed]

[13]Murata GH, Shah JH, Hoffman RM, Wendel CS, Adam KD, Solvas PA, Intensified blood glucose monitoring improves glycaemic control in stable, insulin-treated veterans with type 2 diabetes: The diabetes outcomes in veterans study (DOVES) Diabetes Care 2003 26(6):1759-63.10.2337/diacare.26.6.175912766106  [Google Scholar]  [CrossRef]  [PubMed]

[14]Kamel NM, Badawy YA, El-Zeiny NA, Merdan IA, Sociodemographic determinants of management behaviour of diabetic patients. Part II. Diabetics’ knowledge of the disease and their management behaviour East Mediterr Heal J 1999 5(5):974-83.  [Google Scholar]

[15]Rafique G, Azam SI, White F, Diabetes knowledge, beliefs and practices among people with diabetes attending a university hospital in Karachi, Pakistan East Mediterr Heal J 2006 12(5):590-98.  [Google Scholar]

[16]Friis AM, Johnson MH, Cutfield RG, Consedine NS, Does kindness matter? Self-compassion buffers the negative impact of diabetes-distress on HbA1c Diabet Med 2015 32(12):1634-40.10.1111/dme.1277425845983  [Google Scholar]  [CrossRef]  [PubMed]

[17]Tan MY, Magarey J, Self-care practices of Malaysian adults with diabetes and sub-optimal glycaemic control Patient Educ Couns 2008 72(2):252-67.10.1016/j.pec.2008.03.01718467068  [Google Scholar]  [CrossRef]  [PubMed]

[18]Dunn SM, Bryson JM, Hoskins PL, Alford JB, Handelsman DJ, Turtle JR, Development of the Diabetes Knowledge (DKN) scales: Forms DKNA, DKNB, and DKNC Diabetes Care 1984 7(1):36-41.10.2337/diacare.7.1.366705664  [Google Scholar]  [CrossRef]  [PubMed]

[19]Quandt SA, ip EH, Kirk JK, Saldana S, Chen SH, Nguyen H, Assessment of a Short Diabetes Knowledge Instrument for Older and Minority Adults Diabetes Educ 2014 40(1):68-76.10.1177/014572171350882424163359  [Google Scholar]  [CrossRef]  [PubMed]

[20]Fitzgerald JT, Funnell MM, Hess GE, Barr PA, Anderson RM, Hiss RG, The reliability and validity of a brief diabetes knowledge test Diabetes Care 1998 21(5):706-10.10.2337/diacare.21.5.7069589228  [Google Scholar]  [CrossRef]  [PubMed]

[21]Collins GS, Mughal S, Barnett AH, Fitzgerald J, Lloyd CE, Modification and validation of the Revised Diabetes Knowledge Scale Diabet Med 2011 28(3):306-10.  [Google Scholar]

[22]Eigenmann CA, Skinner T, Colagiuri R, Development and validation of a diabetes knowledge questionnaire Pract Diabetes Int 2011 28(4):166-70d.10.1002/pdi.1586  [Google Scholar]  [CrossRef]

[23]Rothman RL, Malone R, Bryant B, Wolfe C, Padgett P, DeWalt DA, The spoken knowledge in low literacy in diabetes scale: A diabetes knowledge scale for vulnerable patients Diabetes Educ 2005 31(2):215-24.10.1177/014572170527500215797850  [Google Scholar]  [CrossRef]  [PubMed]

[24]Garcia AA, Villagomez ET, Brown SA, Kouzekanani K, Hanis CL, The Starr County Diabetes Education Study: Development of the Spanish-language diabetes knowledge questionnaire Diabetes Care 2001 24(1):16-21.10.2337/diacare.24.1.1611194219  [Google Scholar]  [CrossRef]  [PubMed]

[25]Rao ARC, Sreelakshmi PR, Dinesh PD, Alex R, A Malayalam questionnaire for the assessment of knowledge regarding diabetes IMA Kerala Medical Journal 2016 9(1):07-11.  [Google Scholar]

[26]Welch G, Beeney LJ, Dunn SM, Smith RBW, The development of the diabetes integration scale: a psychometric study of the ATT39 Multivariant Experimental Clinical Research 1996 11(2):75-88.  [Google Scholar]

[27]Schmitt A, Gahr A, Hermanns N, Kulzer B, Huber J, Haak T, The Diabetes Self-Management Questionnaire (DSMQ): development and evaluation of an instrument to assess diabetes self-care activities associated with glycaemic control Health Qual Life Outcomes 2013 11(1):138-10.10.1186/1477-7525-11-13823937988  [Google Scholar]  [CrossRef]  [PubMed]

[28]Kueh YC, Morris T, Borkoles E, Shee H, Modelling of diabetes knowledge, attitudes, self-management, and quality of life: A cross-sectional study with an Australian sample Health Qual Life Outcomes [Internet] 2015 13(1):01-11.10.1186/s12955-015-0303-826286395  [Google Scholar]  [CrossRef]  [PubMed]

[29]Byrne BM, Structural Equation Modeling with AMOS [Internet]. Vol. 22, Structural Equation Modeling 2010 2009  [Google Scholar]

[30]Greenhalgh T, Collard A, Campbell-Richards D, Vijayaraghavan S, Malik F, Morris J, Storylines of self-management: Narratives of people with diabetes from a multiethnic inner city population J Heal Serv Res Policy 2011 16(1):37-43.10.1258/jhsrp.2010.00916020819914  [Google Scholar]  [CrossRef]  [PubMed]

[31]Peyrot M, Burns KK, Davies M, Forbes A, Hermanns N, Holt R, Diabetes attitudes Wishes and Needs 2 (DAWN2): A multinational, multi-stakeholder study of psychosocial issues in diabetes and person-centred diabetes care Diabetes Res Clin Pract [Internet] 2013 99(2):174-84.10.1016/j.diabres.2012.11.01623273515  [Google Scholar]  [CrossRef]  [PubMed]

[32]Norris SL, Lau J, Smith SJ, Schmid CH, Engelgau MM, Self-management education for adults with type 2 diabetes Diabetes Care 2002 25(7):1159-71.10.2337/diacare.25.7.115912087014  [Google Scholar]  [CrossRef]  [PubMed]

[33]Bains SS, Egede LE, Associations between health literacy, diabetes knowledge, self-care behaviors, and glycaemic control in a low income population with type 2 diabetes Diabetes Technol Ther [Internet] 2011 13(3):335-41.10.1089/dia.2010.016021299402  [Google Scholar]  [CrossRef]  [PubMed]

[34]Nagelkerk J, Reick K, Meengs L, Perceived barriers and effective strategies to diabetes self-management J Adv Nurs 2006 54(2):151-58.10.1111/j.1365-2648.2006.03799.x16553701  [Google Scholar]  [CrossRef]  [PubMed]