Association between diabetes-related distress and glycemic control in primary care patients with Type 2 diabetes during the coronavirus disease 2019 (COVID-19) pandemic in Egypt : Journal of Family and Community Medicine

Secondary Logo

Journal Logo

Original Article

Association between diabetes-related distress and glycemic control in primary care patients with Type 2 diabetes during the coronavirus disease 2019 (COVID-19) pandemic in Egypt

Elotla, Sally F.1; Fouad, Ahmed M.1,; Mohamed, Samar F.2; Joudeh, Anwar I.3,4; Mostafa, Mona5; Hayek, Samer El6; Shah, Jaffer7; Ahmed, Hazem A. S.2

Author Information
Journal of Family and Community Medicine 30(1):p 42-50, Jan–Mar 2023. | DOI: 10.4103/jfcm.jfcm_238_22
  • Open



The prevalence of Type 2 diabetes mellitus (T2DM) continues to rise at an alarming rate, presenting a global public health concern.[1] In 2021, the International Diabetes Federation (IDF) reported that worldwide, about 537 million people have DM. This represents 9.8% of adults aged between 20 and 79 years, compared to 8.5% (366 million) in 2011. The IDF expected a further increase to 11.2% (784 million) in 2045. In 2021, Egypt ranked tenth on the list of countries with the highest prevalence rates of DM, with a 20.9% age-adjusted prevalence and 8.4% diabetes-related deaths in people under 60 years.[2]

Successful management of T2DM entails good adherence to the medical regimen, an appropriate diet and lifestyle changes, and regular blood glucose monitoring.[3] The central goal of diabetes management is to achieve an acceptable control of blood glucose, avoid diabetes-related complications, and maintain an adequate quality of life.[4] Suboptimal glycemic control in diabetic patients shows a wide variation, with rates ranging between 40% and 78.8%.[5] A recent Egyptian study reported a 77% prevalence of suboptimal glycemic control in T2DM patients treated in urban primary healthcare settings.[6]

Living with DM is a stressful experience since affected patients experience many worries and concerns related to medical management and diabetes-related health risks.[7] Diabetes-related distress was described by Polonsky et al.,[8] as the significant negative psychological response to the diagnosis of diabetes, the risk for diabetic complications, self-management needs, and the lack of support from interpersonal relationships, including healthcare providers.[9] Increased diabetes-related distress has been linked with reduced self-management, limited adherence to medications, suboptimal glycemic control, more frequent complications, and poor quality of life.[10-13]

Several studies have reported a substantial relationship between distress and glycemic control in patients with T2DM.[3,14-17] However, the impact of the coronavirus disease 2019 (COVID-19) pandemic on this relationship is not much investigated, particularly in less developed countries. There was a great deal of concern about diabetes during the pandemic because of the increased risk of SARS-Cov-2 infection and its adverse outcomes.[18] In addition, the pandemic has been associated with increased psychological distress, changes in lifestyle (e.g. increased high-caloric foods consumption, physical inactivity, and screen time), and limitations in access to healthcare.[19] These COVID-19-related factors have negatively influenced glycemic control. In their study during the pandemic, Tao et al., demonstrated that 25.5% of patients with T2DM achieved an optimal glycemic control.[20]

Therefore, our aim was to assess the relationship between the diabetes-related distress and glycemic control in Egypt during the COVID-19 pandemic, and identify the predictors of glycemic control as measured by the glycosylated hemoglobin (HbA1c) in patients with T2DM attending the primary healthcare clinics (PHC) during the pandemic.

Materials and Methods

Using a cross-sectional design, we carried out this study at the rural PHC in Ismailia, Egypt, from September 2020 to June 2021. This study relied on our earlier work on mental health and Type 2 diabetes in Egypt during the COVID-19 pandemic.[21] During this period in Egypt, about 182,000 confirmed cases of COVID-19 with about 10,500 deaths, were reported to the WHO.[22] G*Power software (version, Franz Faul, Kiel University, Kiel, Germany, 2020) was used to calculate the sample size given a 0.05 α-error, an 0.80 power, and a 0.029 effect size (i.e. the estimated regression coefficient of the relationship between HbA1c and problem areas in diabetes scale (PAID) score and after controlling for the type and duration of DM, age, the Short-Form Health Survey-Mental Component Summary-12 and Patient Health Questionnaire-9 scores).[23] The sample size was calculated as 365 but was increased (by about 15%) to a total of 430 patients to maximize the sample size obtained from the available data. Ethical Approval was obtained from the Research Ethics Committee vide Letter No. 4277 dated 10/09/2020, and written informed consent was taken from all participants.

Patients were enrolled if they were ≥18 years old, diagnosed as T2DM for at least 1 year and gave their consent to participate in the study. Exclusion criteria involved a diagnosis of gestational diabetes or a severe mental illness or cognitive impairment (conditions that might prevent them from completing the interview).

All enrolled patients were interviewed. Collected data included demographic characteristics, lifestyle-related factors, and diabetes-related and clinical characteristics. Diabetes-related complications included diabetic retinopathy, neuropathy, nephropathy, cardiovascular, cerebrovascular, or peripheral vascular diseases. The questionnaire also included the PAID scale for assessment of diabetes-related distress. The PAID scale comprises 20 items measured on a 5-point Likert scale from 0 to 4, where 0 refers to “not a problem” and 4 refers to “serious problem.”[8] The sum of all items multiplied by 1.25 gives the total score. A rise in PAID score indicates higher diabetes distress, and severe diabetes-related distress is considered if the total score is ≥40.[24] The Arabic translation of the PAID was validated and showed a 0.96 Cronbach’s alpha and a 0.97 intraclass correlation.[25] Four sub-domains were previously identified for the of the PAID: (1) Lack of support, (2) Emotional problems, (3) Treatment problems, and (4) Food problems.[26]

The most recent measurements of HbA1c (within a period of 12 weeks prior to the interview). Optimal glycemic control was considered if HbA1c levels were <7% in adults, or <7.5% in the elderly over 65 years.[27] Body mass index (BMI) was estimated as weight (kg)/square root of height (meter). Normal, overweight, or obese patients were identified if BMI was 18.5–24.9, 25–29.9, or ≥30.0, respectively.[28] The World Health Organization (WHO) definition of regular physical activity was used to identify regular activity in study participants.[29]

All statistical procedures were carried out using the SPSS® software version 25.0 (IBM Corporation, NY, Armonk, USA). Two-sided P values were considered statistically significant if <0.05. Means and standard deviations or medians and interquartile ranges were used to summarize continuous variables. The Kolmogorov–Smirnov test was used to test for data normality. Correlations between age, BMI, diabetes duration, HbA1c, and PAID variables were estimated with Spearman’s rank correlation (rho). Statistical significance of the differences across the categorical variables was assessed by Mann–Whitney or Kruskal–Wallis tests, given that all the continuous variables in the study were not normally distributed. Quantile regression model (0.50 quantile) was used to perform the multivariate analysis to identify significant predictors associated with HbA1c level. The variables in the model were identified on the basis of significant bivariate associations. GraphPad® software version 8.0.0 was used to create Figure 1 (San Diego, California USA).

Figure 1:
Distribution of the HbA1c by the diabetes-related distress status (n = 430). HbA1c: Glycated hemoglobin


The mean age of participants was 48.1 years (±11.6). Most were females (60.7%), married (76.3%), educated (77.2%), and not working or homemakers (58.4%). Family income was inadequate in 25.8% of the patients. About one-third of the patients (30.7%) were smokers, while 28.4% were physically inactive and 32.1% obese. About two-thirds of the patients (65.6%) had had diabetes for at least 5 years and 68.4% were on oral hypoglycemic medications. Oral hypoglycemic medications are described in detail in Table 1. One-fourth of the patients (25.3%) had experienced at least one diabetes-related complication. The most frequent complications were peripheral neuropathy (53.0%), diabetic retinopathy (37.7%), peripheral vascular disease (30.0%), and diabetic nephropathy (23.0%). Only one-third of patients (32.3%) had chronic comorbid diseases (24.9% had hypertension, 12.6% had dyslipidemia, and 18.2% had other chronic diseases), 15.3% had two or more chronic diseases. Only 16 patients (3.7%) had a history of polymerase chain reaction-confirmed COVID-19 infection [Table 2].

Table 1:
Distribution of study participants according to the anti-diabetic medications (n=430)
Table 2:
Associations of glycated hemoglobin and diabetes-related distress with the sociodemographic and disease characteristics (n=430)

Bivariate analysis showed that increased median HbA1c level and PAID score were significantly associated with older age, female gender, married or divorced or widow status, illiteracy or low education, nonwork, insufficient income, increased BMI, smoking, physical inactivity, diabetes diagnosis of long duration, insulin-based regimen, diabetes-related complications, and chronic comorbidities [Table 2].

Most of the participants had a suboptimal glycemic control (92.3%), while 13.3% had severe diabetes-related distress (PAID score ≥40). Figure 1 shows that high diabetes-related distress was significantly associated with elevated HbA1c (P < 0.001). Table 3 shows that HbA1c level correlated positively and significantly with the total PAID score (rho = 0.271, P < 0.001). Likewise, HbA1c showed significant positive correlation with the PAID subdomains (emotional problems: rho = 0.286, P < 0.001; treatment problems: rho = 0.200, P < 0.001; food problems: rho = 0.180, P < 0.001, and lack of support: rho = 0.153, P = 0.001).

Table 3:
Correlations between glycated hemoglobin and problem areas in diabetes scores (n=430)

A multivariate quantile regression analysis was used to evaluate the relationship between diabetes-related distress and the level of HbA1c (at 0.50 quantile), adjusted for other study variables [Table 4]. Variables that showed significant regression coefficients were obesity, multi-morbidity, and experiencing severe diabetes-related distress, given that all other variables were kept constant. Obesity was significantly associated with increased median HbA1c (coefficient = 0.25, P < 0.001). Patients with two or more comorbidities (i.e. multi-morbidity) had significantly increased median HbA1c compared to patients with single or no chronic diseases (coefficient = 0.41, P < 0.001). Likewise, high diabetes-related distress was significantly associated with an increased median HbA1c compared to patients who had no severe diabetes-related distress (coefficient = 0.20, P = 0.018).

Table 4:
Multivariate quantile regression model: Factors related to glycated hemoglobin (at 0.50 quantile) (n=430)


This observational study assessed glycemic control in adult patients with T2DM who attended primary care clinics in Egypt during the COVID-19 pandemic. We found a positive association between HbA1c levels with obesity, multiple comorbidities, and severe diabetes-related distress. Results also highlighted several social determinants for diabetes control and diabetes-related distress in the participants.

The current study showed that about 9 out of 10 participants had a suboptimal glycemic control, a high rate compared to an earlier study in Egypt in which 77% of the study sample had suboptimal glycemic control.[6] However, the latter study was carried out in an urban area in Egypt with a well-established local health system. Moreover, our study was done during the COVID-19 pandemic, which might have interfered with the usual healthcare for patients with chronic illnesses like DM, and affected their physical activity, eating habits and mental well-being.[20]

Previous studies suggested that social determinants such as socioeconomic and psychosocial factors, affect patients’ health outcomes, particularly in those with DM.[30,31] According to the WHO conceptual framework, pathways between social determinants and health outcomes are divided into material circumstances, such as living and working conditions, behavioral and biological factors including genetics and lifestyle, psychosocial factors, such as coping styles and diabetes-related distress, as well as local health systems factors.[32]

The study results also indicate that several socioeconomic factors were associated with increased HbA1c levels and diabetes-related distress. Along the same lines, the study by Walker et al., showed that increased educational level was associated with lower HbA1c values in American patients with T2DM attending primary care.[31] Silva-Tinoco et al., also demonstrated that higher level of education of Mexicans with T2DM attending primacy care was linked to better glycemic control.[33] The relationship between educational level and glycemic control is thought to be mediated by an improvement in the knowledge of diabetes and the development of better self-care.[33,34]

Regarding the relation of demographic and clinical characteristics with diabetes control, this study showed that several variables were linked to higher HbA1c levels, including older age, female gender, obesity, physical inactivity, long duration of diagnosis of diabetes, having several diabetes-related complications, and/or associated comorbidities. Comparably, a Chinese study by Lin et al., found that older age, long duration of diabetes, higher BMI, as well as multiple diabetes complications had significant association with suboptimal glycemic control.[35] In Malaysia, Mahmood et al., also found that living with both obesity and diabetes for more than 5 years were predictors of poor glycemic control. However, in that study male gender and younger age were associated with poor glycemic control, which might reflect different lifestyle factors in the Malaysian population.[36] The relationship between obesity and high HbA1c levels could be related to the insulin resistance observed in obesity. In addition, obese people usually consume excessive carbohydrates and tend to be physically inactive.[6]

Although using insulin-based treatment is the definitive option for patients with long-standing T2DM, the use of insulin-containing regimens was showed to be linked to a worse control of glycemic levels. These results parallel the findings of a longitudinal study conducted in primary care patients with T2DM in Singapore, in which insulin therapy was related to an elevation of HbA1c level (≥1%) from 1 year to another.[37] This can be explained by the fact that insulin therapy is usually prescribed for patients who have been diagnosed with T2DM for a long time or have comorbidities that limit the use of oral hypoglycemic agents. In addition, healthcare workers might delay the initiation of insulin therapy or may prescribe sub-therapeutic doses to avoid hypoglycemia. Furthermore, patients may be reluctant to use injectable medications or are fearful of their perceived side effects.[38,39] Therefore, healthcare policymakers should facilitate structured educational programs on diabetes management and the proper use of insulin that target both patients and healthcare providers.

Previous studies supported the beneficial impact of glycemic control on microvasculature in diabetic patients.[40] According to the study results, having two or more diabetes-related complications had a statistically significant association with higher HbA1c levels. Similarly, Fasil et al., demonstrated a higher prevalence of diabetes complications if T2DM is uncontrolled. The authors found that an increased rate of diabetes complications was linked with a diagnosis of more than 7 years, obesity, high-risk waist circumference, and a level of serum triglycerides of <150 mg/dl.[41] It is plausible that the link between glycemic control and diabetes complications is bidirectional, with one affecting the other. As uncontrolled diabetes is linked to a higher rate of complications, this subgroup of patients probably faces more difficulties in controlling glycemic levels as a result of polypharmacy and/or end-organ damage.

The results showed that symptoms of diabetes-related distress had a weak positive correlation with HbA1c levels, which is similar to the findings of other studies.[10,23,42-44] Another relevant finding of this study is that diabetes-related distress score was a significant determinant of higher HbA1c levels after controlling other significant variables. This is also replicated in an earlier study of patients with T2DM receiving insulin therapy.[44] Diabetes-related distress can adversely influence HbA1c levels by contributing to deficient self-care activities, concomitant depression, and dysregulating stress hormones.[45,46] Reducing diabetes-related distress may allow diabetic patients to become more responsive to interventions that target glycemic control or self-care.[47] Along the same lines, Fonda et al., found that lower diabetes-related distress was associated with better HbA1c levels and vice versa.[48] Nevertheless, it should be emphasized that the described relationships are only associative and do not imply causality. Moreover, owing to the complex interaction between distress and glycemic control, a reversed causality could not be ruled out, considering the cross-sectional design of the study. Interestingly, a prospective study in a specialized psychosocial care clinic in diabetes showed that there was greater probability of patients with high grades of diabetes-related distress of engaging with the psychosocial interventions provided and achieving mastery of their diabetes through self-care behaviors in addition to improving their diabetes-related distress.[49] Therefore, the integration of diabetes self-management education programs in Egypt’s PHC settings can be effective in improving HbA1c level, BMI, comorbidities (e.g. lipid profile and blood pressure),[50] and symptoms of distress.[51]

The study findings revealed that all subdomains of the PAID scale were weakly but significantly correlated with glycemic control, particularly with the domain of emotional problems. In fact, the emotional impact of diabetes on patients was the first domain to be recognized as an important construct of diabetes distress and was later expanded to include negative emotional reactions toward different aspects of daily living as opposed to diabetic patients’ coping capability.[52] The significant, but weak correlation of other subdomains reflects the different contributions of each aspect toward diabetes distress and glycemic control, which might vary over time in the same patient and between individual patients. This finding also highlights the importance of addressing the impact of treatment on diabetic patients by providing effective, safe, and tolerable medications, and utilizing community and social services for a targeted holistic approach of diabetes distress. Earlier research also supported the importance of including the emotional problem domain of the PAID scale as an integral part in shorter forms of the scale, such as the five-item PAID, and one-item PAID.[53] Based on these findings, primary care physicians should be encouraged to implement the use of PAID scale in their routine care for diabetic patients. The Arabic translation of the PAID-5 scale is currently available and can be used for Arabic-speaking diabetic patients treated in PHC settings.[54]

Bivariate analysis showed that several demographic and clinical factors were significantly related to both glycemic control and the severity of diabetes distress. However, the multivariate analysis did not confirm this association raising the possibility of confounding factors. The interaction between social determinants of health and clinical outcomes such as glycemic control and diabetes distress, are to be likely complex, with one factor interacting with the other. However, seeing that the study was a cross-sectional design, it would be difficult to establish a temporal association among the study variables. Nevertheless, the study findings imply that being obese, having multiple morbidities and suffering from diabetes-related distress were predictors of poor glycemic control.

The authors acknowledge several limitations to the study. First, causality cannot be determined because of the cross-sectional study design. Second, baseline information on glycemic control were not available, so we could not assess the effect of the COVID-19 pandemic on the studied outcomes. Third, part of the collected data was based on self-reports, which makes social-desirability bias possible. However, this was partially accounted for by using an Arabic-validated scale to evaluate for diabetes-related distress and by collecting objective laboratory data. Despite these limitations, the study findings provide evidence-based guidance for the planning of future interventional programs to improve glycemic control in primary care patients with T2DM.


Diabetes-related distress, multiple comorbidities, and obesity were significantly associated with worse glycemic control in Egyptian primary care patients with T2DM during the COVID-19 pandemic. Family physicians should actively screen for and manage patients with diabetes distress in a patient-centered approach. Utilizing a multidisciplinary team to combat obesity and manage comorbidities and diabetes distress could be helpful for patients with poor glycemic control.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.


The authors acknowledge all primary care patients with T2DM who had participated in this study, as well as the administrative authorities of the primary care units where the study was conducted.


1. Khan MA, Hashim MJ, King JK, Govender RD, Mustafa H, Al Kaabi J. Epidemiology of Type 2 diabetes –Global burden of disease and forecasted trends. J Epidemiol Glob Health 2020;10:107–11.
2. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas:Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 2022;183:109119.
3. Fayed A, AlRadini F, Alzuhairi RM, Aljuhani AE, Alrashid HR, Alwazae MM, et al. Relation between diabetes related distress and glycemic control:The mediating effect of adherence to treatment. Prim Care Diabetes 2022;16:293–300.
4. Papatheodorou K, Banach M, Bekiari E, Rizzo M, Edmonds M. Complications of diabetes 2017. J Diabetes Res 2018;2018:3086167. DOI:10.1155/2018/3086167.
5. Alzaheb RA, Altemani AH. The prevalence and determinants of poor glycemic control among adults with type 2 diabetes mellitus in Saudi Arabia. Diabetes Metab Syndr Obes 2018;11:15–21.
6. Saudi RA, Abbas RA, Nour-Eldein H, Ahmed HA. Illness perception, medication adherence and glycemic control among primary health-care patients with type 2 diabetes mellitus at Port Said City, Egypt. Diabetol Int 2022;13:522–30.
7. Hilliard ME, Yi-Frazier JP, Hessler D, Butler AM, Anderson BJ, Jaser S. Stress and A1c among people with diabetes across the lifespan. Curr Diab Rep 2016;16:67.
8. Polonsky WH, Anderson BJ, Lohrer PA, Welch G, Jacobson AM, Aponte JE, et al. Assessment of diabetes-related distress. Diabetes Care 1995;18:754–60.
9. Gonzalez JS, Fisher L, Polonsky WH. Depression in diabetes:Have we been missing something important?. Diabetes Care 2011;34:236–9.
10. Asuzu CC, Walker RJ, Williams JS, Egede LE. Pathways for the relationship between diabetes distress, depression, fatalism and glycemic control in adults with type 2 diabetes. J Diabetes Complications 2017;31:169–74.
11. Gonzalez JS, Shreck E, Psaros C, Safren SA. Distress and type 2 diabetes-treatment adherence:A mediating role for perceived control. Health Psychol 2015;34:505–13.
12. Young CF, Cheng J, McCarter G. Associations between diabetes-related distress and cardiovascular complication risks in patients with type 2 diabetes and lower socioeconomic status:A pilot study. Diabetes Spectr 2019;32:257–63.
13. Carper MM, Traeger L, Gonzalez JS, Wexler DJ, Psaros C, Safren SA. The differential associations of depression and diabetes distress with quality of life domains in type 2 diabetes. J Behav Med 2014;37:501–10.
14. Cummings DM, Lutes L, Littlewood K, DiNatale E, Hambidge B, Schulman K, et al. Regimen-Related distress, medication adherence, and glycemic control in rural African American women with type 2 diabetes mellitus. Ann Pharmacother 2014;48:970–7.
15. Fisher L, Mullan JT, Arean P, Glasgow RE, Hessler D, Masharani U. Diabetes distress but not clinical depression or depressive symptoms is associated with glycemic control in both cross-sectional and longitudinal analyses. Diabetes Care 2010;33:23–8.
16. Chew BH, Vos R, Mohd-Sidik S, Rutten GE. Diabetes-Related distress, depression and distress-depression among adults with type 2 diabetes mellitus in Malaysia. PLoS One 2016;11:e0152095.
17. Aikens JE. Prospective associations between emotional distress and poor outcomes in type 2 diabetes. Diabetes Care 2012;35:2472–8.
18. Bonora E, Fedeli U, Schievano E, Trombetta M, Saia M, Scroccaro G, et al. SARS-CoV-2 and COVID-19 in diabetes mellitus. Population-based study on ascertained infections, hospital admissions and mortality in an Italian region with ~5 million inhabitants and ~250,000 diabetic people. Nutr Metab Cardiovasc Dis 2021;31:2612–8.
19. Eberle C, Stichling S. Impact of COVID-19 lockdown on glycemic control in patients with type 1 and type 2 diabetes mellitus:A systematic review. Diabetol Metab Syndr 2021;13:95.
20. Tao J, Gao L, Liu Q, Dong K, Huang J, Peng X, et al. Factors contributing to glycemic control in diabetes mellitus patients complying with home quarantine during the coronavirus disease 2019 (COVID-19) epidemic. Diabetes Res Clin Pract 2020;170:108514.
21. Sayed Ahmed HA, Fouad AM, Elotla SF, Joudeh AI, Mostafa M, Shah A, et al. Prevalence and associated factors of diabetes distress, depression and anxiety among primary care patients with type 2 diabetes during the COVID-19 pandemic in Egypt:A cross-sectional study. Front Psychiatry 2022;13:937973.
22. WHO COVID-19 Dashboard. Geneva: World Health Organization; 2020. Available from: 22. Last accessed on 2020 May 15.
23. Reddy J, Wilhelm K, Campbell L. Putting PAID to diabetes-related distress:The potential utility of the problem areas in diabetes (PAID) scale in patients with diabetes. Psychosomatics 2013;54:44–51.
24. Hermanns N, Kulzer B, Krichbaum M, Kubiak T, Haak T. How to screen for depression and emotional problems in patients with diabetes:Comparison of screening characteristics of depression questionnaires, measurement of diabetes-specific emotional problems and standard clinical assessment. Diabetologia 2006;49:469–77.
25. Sayed Ahmed HA, Mohamed SF, Elotla SF, Mostafa M, Shah J, Fouad AM. Corrigendum:Psychometric properties of the Arabic version of the problem areas in diabetes scale in primary care. Front Public Health 2022;10:994563.
26. Beléndez M, Hernández-Mijares A, Marco J, Domínguez JR, Pomares FJ. Validation of the spanish version of the problem areas in diabetes (PAID-SP) scale. Diabetes Res Clin Pract 2014;106:e93–5.
27. American Diabetes Association. Standards of Medical Care in Diabetes-2020 Abridged for Primary Care Providers. Clin Diabetes 2020;38:10–38.
28. Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice:The sixth joint task force of the European society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of 10 societies and by invited experts) developed with the special contribution of the European association for cardiovascular prevention and rehabilitation (EACPR). Eur J Prev Cardiol 2016;37:2315–81.
29. Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med 2020;54:1451–62.
30. Marmot M, Allen JJ. Social determinants of health equity. Am J Public Health 2014;104 Suppl 4:S517–9.
31. Walker RJ, Gebregziabher M, Martin-Harris B, Egede LE. Independent effects of socioeconomic and psychological social determinants of health on self-care and outcomes in Type 2 diabetes. Gen Hosp Psychiatry 2014;36:662–8.
32. World Health Organization. Conceptual Framework for Action on the Social Determinants of Health. Geneva: World Health Organization; 2010. Available from: Last accessed 2020 Sep 19.
33. Silva-Tinoco R, Cuatecontzi-Xochitiotzi T, De la Torre-Saldaña V, León-García E, Serna-Alvarado J, Guzmán-Olvera E, et al. Role of social and other determinants of health in the effect of a multicomponent integrated care strategy on type 2 diabetes mellitus. Int J Equity Health 2020;19:75.
34. Silva-Tinoco R, Cuatecontzi-Xochitiotzi T, De la Torre-Saldaña V, León-García E, Serna-Alvarado J, Orea-Tejeda A, et al. Influence of social determinants, diabetes knowledge, health behaviors, and glycemic control in type 2 diabetes:An analysis from real-world evidence. BMC Endocr Disord 2020;20:130.
35. Lin K, Park C, Li M, Wang X, Li X, Li W, et al. Effects of depression, diabetes distress, diabetes self-efficacy, and diabetes self-management on glycemic control among Chinese population with type 2 diabetes mellitus. Diabetes Res Clin Pract 2017;131:179–86.
36. Mahmood MI, Daud F, Ismail A. Glycaemic control and associated factors among patients with diabetes at public health clinics in Johor, Malaysia. Public Health 2016;135:56–65.
37. Tan NC, Barbier S, Lim WY, Chia KS. 5-Year longitudinal study of determinants of glycemic control for multi-ethnic Asian patients with type 2 diabetes mellitus managed in primary care. Diabetes Res Clin Pract 2015;110:218–23.
38. Tan AM, Muthusamy L, Ng CC, Phoon KY, Ow JH, Tan NC. Initiation of insulin for type 2 diabetes mellitus patients:What are the issues?A qualitative study. Singapore Med J 2011;52:801–9.
39. Heng BH, Sun Y, Cheah JT, Jong M. The Singapore National healthcare group diabetes registry –Descriptive epidemiology of type 2 diabetes mellitus. Ann Acad Med Singap 2010;39:348–52.
40. Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med 2008;359:1577–89.
41. Fasil A, Biadgo B, Abebe M. Glycemic control and diabetes complications among diabetes mellitus patients attending at university of Gondar hospital, Northwest Ethiopia. Diabetes Metab Syndr Obes 2019;12:75–83.
42. Welch GW, Jacobson AM, Polonsky WH. The problem areas in diabetes scale. An evaluation of its clinical utility. Diabetes Care 1997;20:760–6.
43. Tol A, Baghbanian A, Sharifirad G, Shojaeizadeh D, Eslami A, Alhani F, et al. Assessment of diabetic distress and disease related factors in patients with type 2 diabetes in Isfahan:A way to tailor an effective intervention planning in Isfahan-Iran. J Diabetes Metab Disord 2012;11:20.
44. Nichols GA, Hillier TA, Javor K, Brown JB. Predictors of glycemic control in insulin-using adults with type 2 diabetes. Diabetes Care 2000;23:273–7.
45. Snoek FJ, Bremmer MA, Hermanns N. Constructs of depression and distress in diabetes:Time for an appraisal. Lancet Diabetes Endocrinol 2015;3:450–60.
46. Skinner TC, Joensen L, Parkin T. Twenty-five years of diabetes distress research. Diabet Med 2020;37:393–400.
47. Fisher L, Polonsky WH, Hessler D. Addressing diabetes distress in clinical care:A practical guide. Diabet Med 2019;36:803–12.
48. Fonda SJ, McMahon GT, Gomes HE, Hickson S, Conlin PR. Changes in diabetes distress related to participation in an internet-based diabetes care management program and glycemic control. J Diabetes Sci Technol 2009;3:117–24.
49. Sturt J, McCarthy K, Dennick K, Narasimha M, Sankar S, Kumar S. What characterises diabetes distress and its resolution?A documentary analysis. Int Diabetes Nurs 2015;12:56–62.
50. Mikhael EM, Hassali MA, Hussain SA. Effectiveness of diabetes self-management educational programs for type 2 diabetes mellitus patients in middle East countries:A Systematic review. Diabetes Metab Syndr Obes 2020;13:117–38.
51. Gutierrez AP, Fortmann AL, Savin K, Clark TL, Gallo LC. Effectiveness of diabetes self-management education programs for US Latinos at improving emotional distress:A systematic review. Diabetes Educ 2019;45:13–33.
52. Dennick K, Sturt J, Speight J. What is diabetes distress and how can we measure it?A narrative review and conceptual model. J Diabetes Complications 2017;31:898–911.
53. McGuire BE, Morrison TG, Hermanns N, Skovlund S, Eldrup E, Gagliardino J, et al. Short-form measures of diabetes-related emotional distress:The problem areas in diabetes scale (PAID)-5 and PAID-1. Diabetologia 2010;53:66–9.
54. Sayed Ahmed HA, Mohamed SF, Mostafa M, Elotla SF, Shah A, Shah J, et al. Psychometric evaluation of the Arabic version of the 5-item problem areas in diabetes (AR-PAID-5) scale. BMC Prim Care 2022;23:148.

Coronavirus disease 2019; Egypt; glycated hemoglobin; problem areas in diabetes scale; Type-2 diabetes mellitus

Copyright: © 2022 Journal of Family and Community Medicine