Exploring the facilitators and barriers to effective diabetes self-management among type 2 diabetic patients at a teaching hospital in Ghana

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In Sub-Saharan Africa, diabetes prevalence is growing alongside lingering health system and economic challenges. This study explored the support system and hindrances to controlling diabetes and consequently examined how socioeconomic factors influenced adults with T2DM at Sunyani Teaching Hospital in Ghana. Methods: A cross-sectional study was carried out on 320 adults receiving care at a tertiary hospital with the diagnosis of T2DM. The data collection was conducted using a structured questionnaire that comprised the perceived facilitators and barriers to the self-management rating scale on a 5-point Likert scale. An index for facilitators and barriers was then computed. Multiple linear regressions with socio-economic determinants such as educational attainment, income and insurance coverage as study variables were conducted on self-care assessment scores while controlling for demographic variables. Results: The composite facilitator score was modest (mean = 2.82/5), while the composite barrier score indicated moderate constraints (mean = 3.16/5). Transportation difficulty and affordability of medications and monitoring supplies were the most strongly perceived barriers. Education (B = 0.334, p < 0.001), income (B = 0.332, p < 0.001), and insurance status (B = 0.388, p < 0.001) were significant positive predictors of facilitator scores. Conversely, education (B = −0.367, p < 0.001), income (B = −0.336, p < 0.001), and insurance status (B = −0.373, p < 0.001) were significant negative predictors of barrier scores. Age, sex, marital status, and diabetes duration were not independently associated with self-management scores. Conclusions: Socioeconomic conditions significantly influence diabetes self-management among patients at Sunyani Teaching Hospital. Factors such as formal education, income, and insurance coverage significantly determine both enabling and constraining characteristics, while demographic characteristics per se do not appear to predict self-management capacity. Therefore, helping to build structured self-management interventions and improving protection mechanisms with financial resources, as well as resolving transportation and cost, would be crucial to enhancing diabetes outcomes in resource-limited settings. Type 2 diabetes self-management socioeconomic determinants facilitators barriers health insurance chronic disease management Figures Figure 1 Figure 2 Figure 3 Introduction Type 2 diabetes mellitus (T2DM) encompasses one of the fastest-growing diseases that are non-communicable around the world and is also a major cause of premature morbidity and mortality. Diabetes is defined by chronic hyperglycaemia due to one of two possible causes: insufficient insulin release or lack of proper insulin function [1]. Without control, the disease will lead to both microvascular and macrovascular complications such as stroke, end-stage renal disease, blindness, lower extremity amputation, and cardiovascular disease [2]. Approximately 11.3% of deaths globally are attributed to diabetes, with approximately half occurring among those below 60 years of age [3]. The burden is shifting to low- and middle-income countries (LMICs), which are home to nearly 75% of the diabetic population [4]. An epidemiological transition in sub-Saharan Africa is rapidly driven by a myriad of factors, including urbanisation, sedentation, shifts in nutritional patterns with age, and other co-factors. The prevalence of type 2 diabetes is thought to lie between 6% and 6.5% [5], [6], [7], [8], with projections suggesting this to rise to levels beyond what we may even predict in 2040. T2DM also creates substantial socioeconomic burdens through reduced workforce efficiency, rising healthcare costs, and eventual disability from other problems like amputation. T2DM is becoming an enormous question block on the way to Ghana’s Sustainable Development Goal 3, specifically targeting substantially reducing premature deaths from non-communicable diseases. Effective T2DM management goes beyond pharmacotherapy and requires a continuous commitment to self-management behaviours. Diabetes self-management is the process in which patients can fit medical recommendations with real-time daily living situations within their own social environment [9]. According to the [10], essential self-management domains include healthy eating, physical activity, blood glucose monitoring, medication adherence, risk reduction, problem solving, and healthy coping. Engagement in these behaviours has been associated with improved glycaemic control, reduced complications, and enhanced quality of life [11], [12]. Despite clear clinical guidelines, optimal self-management remains difficult to achieve, particularly in resource-constrained settings. Indeed, evidence from LMICs suggests that patients confront a multi-sectoral array of obstacles consisting of structural, socioeconomic, psychological, and health-system-related layers [13], [14], [15]. Financial hardship, limited insurance coverage, transportation challenges, inadequate access to healthy food, and weak continuity of care frequently undermine adherence. At the same time, facilitators such as family support, patient education, affordable medications, and structured follow-up systems can strengthen self-management capacity. However, existing research reveals three important gaps. First, there is a pronounced geographical imbalance in the literature. Although evidence from high-income countries and certain parts of Asia is solid, this clearly doesn't apply to sub-Saharan Africa [15]. Ghana-specific data that focused on facilitators and barriers of hospitals in the tertiary context remains scanty. Second, many studies examine either psychosocial facilitators or structural barriers in isolation. Few studies simultaneously evaluate how socioeconomic characteristics (education, income, insurance status) are statistically associated with perceived facilitators and barriers of self-management. Understanding these relationships is critical for identifying structurally vulnerable subgroups. Third, most available evidence is qualitative or descriptive. There is limited quantitative modelling of socioeconomic gradients in self-management within Ghanaian tertiary care settings, where patient volume, clinic organisation, and structural constraints may uniquely shape behaviour. The number of patients attending the diabetes clinic at Sunyani Teaching Hospital sharply increased from 1,032 in 2022 to 1,150 in 2024 (Internal Medicine Unit, Sunyani Teaching Hospital, 2024). The presence of medications alone has proven insufficient to effectively address suboptimal glycaemic outcomes. Thus, gaining insight into the factors and mechanisms that strengthen self-management of patients would be essential for designing contextually appropriate interventions. Study aim and objectives This study aimed to assess the facilitators and barriers to effective type 2 diabetes self-management and to examine the influence of socioeconomic factors on perceived self-management conditions among patients attending Sunyani Teaching Hospital in Ghana. This study specifically aimed to: Identify facilitators of effective diabetes self-management among adults with T2DM attending Sunyani Teaching Hospital. Determine barriers to diabetes self-management in this population. Examine the association between socioeconomic characteristics and composite facilitator and barrier scores. Methods Study design and setting This analytical cross-sectional study was conducted between March and April 2025 at Sunyani Teaching Hospital, a tertiary referral facility located in the Bono Region of Ghana. The hospital serves as a major referral centre for the Bono, Bono East, and Ahafo regions and provides specialist outpatient services through a dedicated diabetic clinic under the Internal Medicine Unit. The facility had a bed capacity larger than 400 at the time of the study and managed a large case load of patients with type 2 diabetes mellitus (T2DM). This design permitted a context whereby the perspective from either case could be utilised to analyse perceived facilitators, barriers, and socioeconomic correlates of diabetes between an individual at a marked time and in a naturalistic clinical setting. Study population and eligibility The study population consisted of adult type 2 diabetes mellitus (T2DM) patients attending an outpatient diabetes clinic during the period of the study. The inclusion criteria were that the study enrollees were 18 years or older, had a definite diagnosis of T2DM lasting 6 months or more, and were under treatment at the clinic. A 6-month limit was prescribed so that the subjects could have had an adequate exposure to practices of diabetes self-management to report their experience meaningfully. Patients with type 1 diabetes mellitus or gestational diabetes were not included; neither were individuals who were mentally unstable at the moment, suffering from severe psychiatric illnesses or cognitive impairment interfering with informed consent or questionnaire completion. Sample size and sampling procedure The estimated population of the diabetic clinic was 1151, and the margin of error was 5%, so the initial sample size was estimated through the formula by [16]. Based on this, the determined maximum sample size came out as 297. After allowing for the contingency of 10% in the sample due to non-response, 327 participants were included. There were a total of 320 participants who filled out and returned their questionnaires, which gave a 97.9% response rate. A convenience sampling approach was utilised to recruit the participants. Meeting the inclusion criteria, willing participants were solicited from those with appointments in the daily clinic sessions during the data collection period. This non-probability sampling technique was infeasible to extrapolate for generalisation but was practically operational in an outpatient setting, coupled with extra feasibility for the timely inclusion of subjects in the study. Data collection instrument and procedure A structured questionnaire (see Supplementary File 1_Exploring the facilitators and barriers) was used to collect data on diabetes self-management, culled from established literature that was adapted to the local clinical context. This instrument also collected information about the general characteristics of the patient population in terms of socioeconomic and clinical variables, such as age, sex, marital status, educational attainment, occupation, monthly income, duration of diabetes, and health insurance status. The study further measured barriers and enabling factors about self-care in diabetes through fourteen items scored Likert-style to build potential responses, which were graded on a five-point scale, ranging from one to five, with one indicating little agreement and five indicating agreement.. Facilitator items assessed domains such as adequacy of knowledge, receipt of education from healthcare professionals, medication affordability, family support, appointment accessibility, access to home glucometers, and availability of support groups. The barriers evaluated were financial constraints, diet-related difficulties, sheer access to food, time availability for exercise, memory lapses, transportation barriers, and expenses associated with blood glucose monitoring tools. Also included in the questionnaire were open-ended questions about the respondents' lived experiences, perceived barriers, and feedback on potential improvements in diabetes care. The instrument was pretested among 50 T2DM patients at Sunyani Municipal Hospital to assess clarity, comprehension, and average completion time. Minor wording adjustments were made to improve clarity, and pretest responses were excluded from the final analysis. Data were collected through face-to-face administration by three trained research assistants during routine clinic sessions. For participants with limited literacy, questions were read aloud in English or Twi while maintaining neutral delivery to minimise interviewer bias. Each questionnaire required approximately 20–30 minutes to complete, and completed forms were reviewed daily for completeness and consistency. Variable measurement Two composite outcome variables were developed for overall perceptions of facilitators and barriers. A facilitator score was calculated by using the average of 7 facilitation items, while the impediment score was calculated using an average of 7 barriers. Each composite score ranged from 1 to 5, with higher facilitator scores indicating stronger perceived enabling conditions and higher barrier scores indicating greater perceived obstacles to effective self-management. Socioeconomic variables, including education, monthly income, occupation, and health insurance status, were treated as primary exposure variables in regression modelling. Age, sex, marital status, and duration of diabetes were included as covariates. Statistical analysis Data were analysed using IBM SPSS version 27. Descriptive statistics were used for summarising participant characteristics, whereby categorical variables were shown as frequencies and percentages, with continuous variables summarised by means and standard deviations. Composite facilitator and barrier scores were computed before inferential analysis. The multiple linear regression models were employed to evaluate the associations of socioeconomic conditions with the respective composite outcomes. The values of the corresponding beta coefficients (B), standard errors, t-values, and p-values related to the variables were reported. The statistical significance level was set at p < 0.05. Before modelling, assumptions of linear regression, including normality, homoscedasticity, and absence of multicollinearity, were assessed. Bias and study limitations This study has certain limitations. The cross-sectional design does not allow for causality inferences and further limits the interpretation to associations between socioeconomic factors and perceived self-management conditions. The study location, being a tertiary clinical facility where convenience sampling was available, should be taken as a restriction in generalising to wider healthcare settings. Self-reported data may introduce recall or social desirability bias. Additionally, the study did not include objective clinical indicators such as HbA1c, limiting direct assessment of the relationship between perceived barriers and glycaemic outcomes. Results Participant characteristics A total of 320 adults with type 2 diabetes mellitus were included in the analysis. As shown in Table 1, the sample was predominantly older, with 37.5% aged ≥60 years and 26.6% aged 45–59 years. Females constituted 52.8% of the sample. More than half were married (54.1%). Educational attainment was generally low to moderate, with only 12.2% having a tertiary education. Over one-third (34.7%) reported earning less than GHS 500 monthly. Most participants (83.1%) had active health insurance coverage (see Table 1). Table 1: Socio-demographic characteristics of participants Variable Category n (%) Age <30 years 25 (7.8) 30–44 years 90 (28.1) 45–59 years 85 (26.6) ≥60 years 120 (37.5) Sex Male 151 (47.2) Female 169 (52.8) Marital status Single 53 (16.6) Married 173 (54.1) Divorced/Separated 39 (12.2) Widowed 55 (17.2) Education No formal education 70 (21.9) Primary 86 (26.9) JHS 64 (20.0) SHS 61 (19.1) Tertiary 39 (12.2) Monthly income (GHS) <500 111 (34.7) 500–999 73 (22.8) 1000–1499 67 (20.9) 1500–1999 56 (17.5) ≥2000 13 (4.1) Duration of diabetes 5 years 115 (35.9) Health insurance No 54 (16.9) Yes 266 (83.1) Facilitators of diabetes self-management The distribution of responses for each facilitator item is presented in Table 2. The composite facilitator score was 2.82 (SD = 0.74), indicating modest perceived enabling conditions overall. Table 2: Distribution of responses to facilitator items Facilitator Item Mean (SD) Adequate knowledge of diabetes management 2.89 (0.82) Sufficient education from healthcare professionals 2.84 (0.74) Access to affordable medications 2.89 (0.78) Family support in treatment 2.77 (0.78) Accessibility of follow-up appointments 2.71 (0.78) Access to home glucometer 2.78 (0.76) Support group availability 2.86 (0.81) Composite Facilitator Score 2.82 (0.74) Barriers to diabetes self-management The distribution of barrier responses is shown in Table 3. The composite barrier score was 3.16 (SD = 0.77), indicating moderate perceived obstacles. Table 3: Distribution of responses to barrier items Barrier Item Mean (SD) Cannot afford prescribed medications 3.20 (0.82) Difficulty maintaining healthy diet 3.17 (0.82) Limited access to healthy food 3.11 (0.83) Lack of time for exercise 3.14 (0.85) Forgetting to take medication 3.05 (0.84) Transportation challenges 3.26 (0.77) Monitoring supplies too expensive 3.19 (0.82) Composite Barrier Score 3.16 (0.77) Self-reported facilitators and challenges Participants’ qualitative responses were summarised quantitatively and are presented in Figures 1–3. Regular clinic visits (19.7%) were most frequently reported, followed by nurse-provided education (17.5%), family support (15.9%), community support (15.9%), health insurance coverage (15.6%), and access to home glucose monitoring equipment (15.3%). Busy work schedules (18.8%) were the most commonly reported challenge, followed by limited access to healthy food (18.1%), transportation difficulties (16.9%), medication cost (16.6%), dietary restrictions (15.0%), and forgetfulness (14.7%). Participants prioritised reducing clinic waiting time (17.8%), increasing diabetes education (17.2%), improving affordability of monitoring supplies (16.9%), expanding insurance coverage (16.6%), strengthening community outreach programmes (15.9%), and reducing medication costs (15.6%). Socioeconomic predictors of facilitator scores Multiple linear regression analysis was conducted to examine the association between socioeconomic characteristics and composite facilitator scores. The results are presented in Table 4. Education was a strong positive predictor of facilitator scores (B = 0.334, SE = 0.018, t = 18.74, p < 0.001), indicating that higher educational attainment was associated with significantly higher perceived enabling conditions. Monthly income similarly demonstrated a significant positive association (B = 0.332, SE = 0.019, t = 17.69, p < 0.001). Health insurance status showed the largest positive coefficient among predictors (B = 0.388, SE = 0.062, t = 6.23, p < 0.001), suggesting that insured participants reported substantially stronger facilitating conditions compared to uninsured participants. In contrast, age (B = −0.001, SE = 0.001, t = −0.55, p = 0.581), sex (B = 0.029, SE = 0.047, t = 0.61, p = 0.544), marital status (B = 0.017, SE = 0.025, t = 0.68, p = 0.494), and duration of diabetes (B = −0.016, SE = 0.033, t = −0.50, p = 0.620) were not statistically significant predictors. Table 4: Multiple linear regression model predicting facilitator scores Predictor B SE t p-value Age -0.001 0.001 -0.55 0.581 Sex 0.029 0.047 0.61 0.544 Marital status 0.017 0.025 0.68 0.494 Education 0.334 0.018 18.74 <0.001 Income 0.332 0.019 17.69 <0.001 Diabetes duration -0.016 0.033 -0.50 0.620 Insurance 0.388 0.062 6.23 <0.001 Note: B = unstandardized regression coefficient; SE = standard error. Statistically significant predictors are indicated at p < 0.001. Socioeconomic predictors of barrier scores A second multiple linear regression model was fitted to examine predictors of composite barrier scores. The results are presented in Table 5. Education was a statistically significant negative predictor of barrier scores (B = −0.367, SE = 0.018, t = −20.09, p < 0.001), indicating that higher education levels were associated with lower perceived barriers. Monthly income similarly showed a significant inverse association (B = −0.336, SE = 0.019, t = −17.45, p < 0.001). Health insurance coverage was also significantly associated with reduced perceived barriers (B = −0.373, SE = 0.064, t = −5.84, p < 0.001). Age (B = −0.001, SE = 0.002, t = −0.37, p = 0.714), sex (B = −0.013, SE = 0.048, t = −0.28, p = 0.782), marital status (B = −0.004, SE = 0.026, t = −0.16, p = 0.872), and duration of diabetes (B = −0.018, SE = 0.034, t = −0.52, p = 0.601) were not statistically significant predictors. Table 5: Multiple linear regression model predicting barrier scores Predictor B SE t p-value Age -0.001 0.002 -0.37 0.714 Sex -0.013 0.048 -0.28 0.782 Marital status -0.004 0.026 -0.16 0.872 Education -0.367 0.018 -20.09 <0.001 Income -0.336 0.019 -17.45 <0.001 Diabetes duration -0.018 0.034 -0.52 0.601 Insurance -0.373 0.064 -5.84 <0.001 Note: B = unstandardized regression coefficient; SE = standard error. Statistically significant predictors are indicated at p < 0.001. Discussions This study sought to evaluate the facilitation, obstacles and socioeconomic factors that self-management might enjoy among adults with type 2 diabetes receiving treatment at the Sunyani Teaching Hospital. The results present an appreciable socioeconomic gradient; the impact of education, income, and health insurance status on their ability to be the key determinants of the facilitator and barrier variables, while sociodemographic factors such as age, gender, and marital and disease duration possessed no independent associations with these scales. These results are strongly in line with numerous others advocating the theory of the social determinants of health, where the management of a chronic disease is believed to be shaped more by certain structural and economic concerns rather than based solely on demography. A modest composite facilitator score implies that patients do not really believe that the support provided for the practice of self-management is very good. This is in common with much more evidence that in low- and middle-income countries, ongoing support for self-management remains unreliable. Sub-Saharan Africa has seen a rapid growth in diabetes, and these countries do not have adequate resources and systems to ensure long-term care provision for self-management.[ 4 ] reported on a rise in the global prevalence of diabetes, with priority populations residing in low- to middle-income countries, thus serving as a reminder about the overall structural problems regarding the control of diabetes. A recent systematic review and meta-analysis by[ 17 ] demonstrated that diabetes self-management education and support (DSMES) interventions significantly improve glycaemic outcomes among people with type 2 diabetes in the WHO African Region.[ 5 ] similarly documented increasing diabetes prevalence and the accompanying yawning gaps in infrastructure around chronic care in Ghana. Within these contexts, self-management becomes dependent more on the strength of the individual than on the integrity of system-level support. The relatively weak endorsement of perceived diabetes knowledge as a facilitator contrasts with evidence that objective diabetes knowledge improves self-care practices. [ 18 ] determined that Ethiopian patients with adequate knowledge were more likely to adhere to recommended diets while [ 19 ] showed that understanding the therapeutic goals improved medication and lifestyle compliance. It may also be attributable to the fact that the study is focusing more on perceived knowledge than on actual knowledge, where, put simply, perceived knowledge does not necessarily translate into practical capability. This difference is evident more broadly in the discourses concerning personal health behaviour-educational interventions should move from knowledge and be channelled into skills. [ 20 ] has further shown how structured diabetes self-management education improved self-care behaviours among adults with type 2 diabetes, suggesting that education must be translated into action skills rather than being just about knowledge. Further, according to Suglo & Evans, culturally specific ways of understanding and feeling about diabetes shaped self-management practices across African contexts, underscoring, thus, the need for education to be culturally appropriate and practically supportive [ 15 ]. Family support was also modestly perceived, despite evidence showing that family involvement enhances self-care. [ 21 ], [ 22 ] reported that family partnerships motivated self-care behaviours, but also acknowledged that family support can be inconsistent or even counterproductive if not well informed. This apparent ambivalence may reinforce the neutral reactions seen within this setting, suggesting that family interactions may not work as consistently as facilitators without being enveloped in structured guidance. In the study, structural barriers had a stronger impact on access to diabetes self-management care. Transportation challenges in the form of most acutely felt barriers indicate the hospital as the main tertiary referral centre for the Bono region. Of note are similar but less statistically significant findings from other rural settings and settings with lower resources, where long travel distances coupled with raised costs and increased burdens immensely put patients off the care and follow-up path. Also highlighted amid the financial barriers, particularly the cost of medications and monitoring supplies, is perhaps a pattern of similar contexts in which resilience evaporates alongside the acuteness of economic hardship for individual self-care efforts. Woodward et al. positioned socioeconomic deprivation as a consistent proponent of major barriers to diabetes self-management, especially in an environment where the cost of care, medications, and nutritious food keeps significantly rising [ 23 ]. Diet-related barriers further illustrate structural constraints. [ 18 ] showed that household food security predicts dietary adherence, underscoring how income volatility and food availability shape daily management choices. The importance of income as a determinant of diabetes outcomes is further supported by [ 24 ], who demonstrated that income significantly influences self-management behaviours and health outcomes in pediatric type 1 diabetes, suggesting that economic resources are foundational determinants of chronic disease management across age groups and diabetes types. Forgetfulness and limited time for exercise, while sometimes framed as behavioural issues, are better understood within the context of competing daily demands, as described by [ 25 ] and [ 26 ], who associated workload pressures with challenges in maintaining medication schedules and planned physical activity. The regression analysis provides the clearest evidence that socio-economic determinants play a key role in a person's health literacy and knowledge of the system. Education appears to be the most important predictor for the facilitator or the barrier outcomes. This is consistent with studies showing that knowledge improves health literacy and system navigation. [ 27 ] and [ 28 ] similarly identified education as a key factor in participation in self-management programmes. Income also showed bidirectional associations, confirming its crucial role in enabling access to necessary resources, a pattern echoed in the Rwandan study by Munezero et al., which found that even patients with adequate self-care practices reported significant financial barriers to consistent diabetes management [ 29 ]. Having health insurance helped increase enablers while decreasing barriers, which speaks to the financial protection offered by the National Health Insurance Scheme. Nevertheless, some barriers may exist among insured patients, thereby leading to possible insufficiency and lack of cost coverage by insurance in diabetes care. Emphatically, that quality of health insurance affects vulnerable groups and utilisation patterns is supported by the [ 30 ] finding of increased access and access demand in the context of health insurance coverage; minimal variations occurred in the independent demographic variables after adjustment for differential impacts of socioeconomic exclusion, suggesting that these are being mediated by SES vulnerabilities rather than by the demographic characteristics themselves as implied previously in various other studies on differing health topics. Strengths and limitations Some of the major strengths of this study are that it attempts to contribute quantitative evidence related to the facilitators and barriers to diabetes self-management in a third-level institution in Ghana. In fact, the empirical data leave gaps while discussing the socioeconomic factors affecting diabetes self-management. The research used descriptive and multivariable analyses to explain the uniformity of socioeconomic disparities in perceived facilitators and barriers. The use of composite indices allowed simultaneous modelling of enabling and constraining conditions, strengthening the internal coherence of the findings. However, several limitations should be considered. The limitations are that a cross-sectional design excludes certain causal implications and emphasises associations rather than causal relations. Sampling convenience restricts the generalizability of this study to similar populations from other tertiary clinics. Self-report measures have the potential for recall and social desirability bias. Also, the absence of clinical indicators such as glycaemic control usually precludes direct linkage of perceived barriers to the health outcomes. Implications for practice and policy From a clinical standpoint and as far as healthcare policy is concerned, the findings of this study have far-reaching implications-a case for a major reorientation of diabetes care, shifting from the focus on behaviour in education to a structurally supportive care system. Clinically speaking, diabetes practice should henceforth integrate standard brief questioning on socioeconomic risks in such a way that it can be recognised who are the most financially or logistically at risk in order to allow support of these individuals, rather than counselling the whole population. Diabetes self-management education should be delivered as a structured, longitudinal programme adapted to varying literacy levels and reinforced through follow-up rather than provided as isolated sessions. At the health-system level, reducing clinic waiting times, decentralising follow-up services, and expanding community-based or outreach models could mitigate transportation-related constraints. Policy reforms should focus on enhancing financial risk protection by extending insurance to cover items like essential monitoring supplies and by dealing with other forms of indirect costs that act against adherence. Conclusion The study aimed to identify factors inhibiting and enabling effective self-management in type II diabetes and to explore the relationship between socio-economic factors and perceived self-management conditions. The findings indicate a clear socio-economic gradient with education, income, and health insurance status predicting both higher enabler scores and lower barrier scores, while demographic variables did not significantly predict anything. The participants reported modest enabler conditions and moderate structural barriers, especially related to transportation and affordability of medications and monitoring supplies. These results suggest that, in this setting, diabetes self-management is shaped predominantly by structural socio-economic factors rather than demographic characteristics alone. Improvements in outcomes are possible only through interventions that move beyond awareness raising to promote financial insurance, service access, and ongoing, structured self-support across constrained health systems. Declarations Human Ethics and Consent to Participate Ethical approval was obtained from the Committee on Human Research, Publications and Ethics (CHRPE) of Kwame Nkrumah University of Science and Technology, and institutional permission was granted by Sunyani Teaching Hospital. All procedures performed in the study involving human participants were conducted in accordance with the ethical standards of the institutional research committee and the Declaration of Helsinki. Written informed consent (included in the questionnaire) was obtained from all participants before they participated in the study. Consent for Publication Not applicable. Availability of Data and Materials The datasets generated and/or analysed during the current study are not publicly available due to institutional data protection policies but are available from the corresponding author on reasonable request and with permission from Bono Regional Hospital. Conflict of Interest / Competing Interests The authors declare no competing interests concerning the work presented in this manuscript. Funding The authors declare that no funding was received for this manuscript's research, authorship, or publication. Author Contributions Y.G.F. conceived and designed the study, collected the data, and drafted the initial manuscript. Y.M.B. and P.P.A. contributed to the study design, data interpretation, and critical revision of the manuscript. M.B.U. contributed to study conceptualisation, supervised the research process, performed statistical analysis, and critically revised the manuscript for important intellectual content. P.G., W.K.A., M.M.A., and G.O.T. contributed to data interpretation, manuscript review, and approval of the final version. All authors read and approved the final manuscript. Acknowledgements Not applicable. Clinical Trial Number Clinical trial number: Not applicable References World Health Organisation, “Economic and financing considerations of self-care interventions for sexual and reproductive health and rights: United Nations University Center for policy research,” 2020. Accessed: Feb. 28, 2026. [Online]. Available: https://apps.who.int/iris/handle/10665/331195 A. 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Stewart Williams, “Sociodemographic and behavioral characteristics associated with self-reported diagnosed diabetes mellitus in adults aged 50+ years in Ghana and South Africa: results from the WHO-SAGE wave 1,” BMJ Open Diabetes Res. Care , vol. 6, no. 1, p. e000449, Feb. 2018, doi: 10.1136/bmjdrc-2017-000449. H. Y. Gudjinu, Sarfo, and B, “Risk factors for type 2 diabetes mellitus among out-patients in Ho, the Volta regional capital of Ghana: A case-control study,” BMC Research Notes, 10(1), 110 , 2017, doi: 10.1186/s13104-017-2648-z. J. N. Suglo and C. Evans, “Factors influencing self-management in relation to type 2 diabetes in Africa: A qualitative systematic review,” PLoS One , vol. 15, no. 10, p. e0240938, Oct. 2020, doi: 10.1371/journal.pone.0240938. T. Yamane, Statistics: An Introductory Analysis , 2nd ed. New York: Harper and Row, 1967. Y. S. Yimer, A. Addissie, E. G. Kidane, A. Reja, A. A. Abdela, and A. A. Ahmed, “Effectiveness of diabetes self-management education and support interventions on glycemic levels among people living with type 2 diabetes in the WHO African Region: a Systematic Review and meta-analysis,” Frontiers in Clinical Diabetes and Healthcare , vol. 6, Jun. 2025, doi: 10.3389/fcdhc.2025.1554524. D. Tirfessa, M. Abebe, J. Darega, and M. Aboma, “Dietary practice and associated factors among type 2 diabetic patients attending chronic follow-up in public hospitals, central Ethiopia, 2022,” BMC Health Serv. Res. , vol. 23, no. 1, p. 1273, Nov. 2023, doi: 10.1186/s12913-023-10293-1. M. Waheedi, A. Awad, H. T. Hatoum, and H. Enlund, “The relationship between patients’ knowledge of diabetes therapeutic goals and self-management behaviour, including adherence,” Int. J. Clin. Pharm. , vol. 39, no. 1, pp. 45–51, Feb. 2017, doi: 10.1007/s11096-016-0375-5. M. Gehlawat, G. Thumati, and S. Gundala, “Role of diabetes self-management education in improving self-care behavior among adult type 2 diabetics,” MRIMS Journal of Health Sciences , vol. 11, no. 1, pp. 9–16, Jan. 2023, doi: 10.4103/mjhs.mjhs_71_22. J. Vongmany, T. Luckett, L. Lam, and J. L. Phillips, “Family behaviours that have an impact on the self‐management activities of adults living with Type 2 diabetes: a systematic review and meta‐synthesis,” Diabetic Medicine , vol. 35, no. 2, pp. 184–194, Feb. 2018, doi: 10.1111/dme.13547. O. J. Jordan, A. Benitez, D. L. Burnet, M. T. Quinn, and A. A. Baig, “The role of family in diabetes management for Mexican American adults,” Hispanic Health Care International, 22(2), 109118 , 2024, doi: 10.1177/15404153231206086. A. Woodward et al. , “Barriers and facilitators of self‐management of diabetes amongst people experiencing socioeconomic deprivation: A systematic review and qualitative synthesis,” Health Expectations , vol. 27, no. 3, Jun. 2024, doi: 10.1111/hex.14070. K. Rechenberg, R. Whittemore, M. Grey, and S. Jaser, “Contribution of income to self-management and health outcomes in pediatric type 1 diabetes,” Pediatr. Diabetes , vol. 17, no. 2, pp. 120–126, Mar. 2016, doi: 10.1111/pedi.12240. H. Li, Y. Li, J. Wang, Y. Zhang, and S. Ben, “Enablers and Barriers to Medication Self-Management in Patients With Type 2 Diabetes: A Qualitative Study Using the COM-B Model,” Patient Prefer. Adherence , vol. Volume 19, pp. 485–501, Mar. 2025, doi: 10.2147/PPA.S503350. L. Laranjo, A. L. Neves, A. Costa, R. T. Ribeiro, L. Couto, and A. B. Sá, “Facilitators, barriers and expectations in the self-management of type 2 diabetes—a qualitative study from Portugal,” European Journal of General Practice , vol. 21, no. 2, pp. 103–110, Apr. 2015, doi: 10.3109/13814788.2014.1000855. J. Bobitt, L. Aguayo, L. Payne, T. Jansen, and A. Schwingel, “Geographic and Social Factors Associated With Chronic Disease Self-Management Program Participation: Going the ‘Extra-Mile’ for Disease Prevention,” Prev. Chronic Dis. , vol. 16, p. 180385, Mar. 2019, doi: 10.5888/pcd16.180385. E. Srulovici et al. , “Which patients with Type 2 diabetes will have greater compliance to participation in the Diabetes Conversation Map TM program? A retrospective cohort study,” Diabetes Res. Clin. Pract. , vol. 143, pp. 337–347, Sep. 2018, doi: 10.1016/j.diabres.2018.07.037. M. C. Munezero, V. Bagweneza, T. Kubahoniyesu, and A. Collins, “Knowledge, Practices, and Barriers to Diabetes Self-Management Among Patients with Type 2 Diabetes in a Rwandan Referral Hospital,” Patient Prefer. Adherence , vol. Volume 19, pp. 3409–3419, Nov. 2025, doi: 10.2147/PPA.S552026. A. Mishra, S. K. Pradhan, B. K. Sahoo, A. Das, A. K. Singh, and S. P. Parida, “Assessment of medication adherence and associated factors among patients with diabetes attending a non-communicable disease clinic in a community health centre in Eastern India,” Cureus, 15(8), e43779 , 2023, doi: 10.7759/cureus.43779. Additional Declarations No competing interests reported. Supplementary Files SupplementaryfileExploringthefacilitatorsandbarriers.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor invited by journal 06 Mar, 2026 Editor assigned by journal 05 Mar, 2026 Submission checks completed at journal 05 Mar, 2026 First submitted to journal 03 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Kumasi","correspondingAuthor":false,"prefix":"","firstName":"Gloria","middleName":"Ohene","lastName":"Takyi","suffix":""}],"badges":[],"createdAt":"2026-03-03 07:54:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9017488/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9017488/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106382368,"identity":"447987d2-db8b-4980-ad2e-6cb79364c7b0","added_by":"auto","created_at":"2026-04-08 05:26:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42193,"visible":true,"origin":"","legend":"\u003cp\u003eSelf-reported factors supporting diabetes management\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9017488/v1/4855b309cf899f998c334c7e.png"},{"id":106382355,"identity":"52f74f4a-87ef-41ab-b1dc-5468906bf39f","added_by":"auto","created_at":"2026-04-08 05:26:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53930,"visible":true,"origin":"","legend":"\u003cp\u003eSelf-reported challenges in diabetes self-management\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9017488/v1/0f272daa81efdf70d73c6338.png"},{"id":106382363,"identity":"1dde6dd7-1106-4e20-9a38-5483d5f0e016","added_by":"auto","created_at":"2026-04-08 05:26:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40076,"visible":true,"origin":"","legend":"\u003cp\u003eSuggested improvements to diabetes care\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9017488/v1/986fefd5023c4f9ac2f36f03.png"},{"id":106382401,"identity":"814c8e00-3998-42ba-ac1f-12636e7eb161","added_by":"auto","created_at":"2026-04-08 05:26:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":912698,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9017488/v1/1a18c491-64f2-4a22-8f7d-675f949e51b5.pdf"},{"id":106382364,"identity":"f62c32af-60f8-4860-a9b8-cde8bca1b31e","added_by":"auto","created_at":"2026-04-08 05:26:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":129523,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileExploringthefacilitatorsandbarriers.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9017488/v1/12ce489a0851f32f2cd9a716.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the facilitators and barriers to effective diabetes self-management among type 2 diabetic patients at a teaching hospital in Ghana","fulltext":[{"header":"Introduction","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) encompasses one of the fastest-growing diseases that are non-communicable around the world and is also a major cause of premature morbidity and mortality. Diabetes is defined by chronic hyperglycaemia due to one of two possible causes: insufficient insulin release or lack of proper insulin function [1]. Without control, the disease will lead to both microvascular and macrovascular complications such as stroke, end-stage renal disease, blindness, lower extremity amputation, and cardiovascular disease [2]. Approximately 11.3% of deaths globally are attributed to diabetes, with approximately half occurring among those below 60 years of age [3]. The burden is shifting to low- and middle-income countries (LMICs), which are home to nearly 75% of the diabetic population [4].\u003c/p\u003e\n\u003cp\u003eAn epidemiological transition in sub-Saharan Africa is rapidly driven by a myriad of factors, including urbanisation, sedentation, shifts in nutritional patterns with age, and other co-factors. The prevalence of type 2 diabetes is thought to lie between 6% and 6.5% [5], [6], [7], [8], with projections suggesting this to rise to levels beyond what we may even predict in 2040. T2DM also creates substantial socioeconomic burdens through reduced workforce efficiency, rising healthcare costs, and eventual disability from other problems like amputation. T2DM is becoming an enormous question block on the way to Ghana\u0026rsquo;s Sustainable Development Goal 3, specifically targeting substantially reducing premature deaths from non-communicable diseases.\u003c/p\u003e\n\u003cp\u003eEffective T2DM management goes beyond pharmacotherapy and requires a continuous commitment to self-management behaviours. Diabetes self-management is the process in which patients can fit medical recommendations with real-time daily living situations within their own social environment [9]. According to the [10], essential self-management domains include healthy eating, physical activity, blood glucose monitoring, medication adherence, risk reduction, problem solving, and healthy coping. Engagement in these behaviours has been associated with improved glycaemic control, reduced complications, and enhanced quality of life [11], [12].\u003c/p\u003e\n\u003cp\u003eDespite clear clinical guidelines, optimal self-management remains difficult to achieve, particularly in resource-constrained settings. Indeed, evidence from LMICs suggests that patients confront a multi-sectoral array of obstacles consisting of structural, socioeconomic, psychological, and health-system-related layers [13], [14], [15]. Financial hardship, limited insurance coverage, transportation challenges, inadequate access to healthy food, and weak continuity of care frequently undermine adherence. At the same time, facilitators such as family support, patient education, affordable medications, and structured follow-up systems can strengthen self-management capacity.\u003c/p\u003e\n\u003cp\u003eHowever, existing research reveals three important gaps. First, there is a pronounced geographical imbalance in the literature. Although evidence from high-income countries and certain parts of Asia is solid, this clearly doesn\u0026apos;t apply to sub-Saharan Africa [15]. Ghana-specific data that focused on facilitators and barriers of hospitals in the tertiary context remains scanty. Second, many studies examine either psychosocial facilitators or structural barriers in isolation. Few studies simultaneously evaluate how socioeconomic characteristics (education, income, insurance status) are statistically associated with perceived facilitators and barriers of self-management. Understanding these relationships is critical for identifying structurally vulnerable subgroups. Third, most available evidence is qualitative or descriptive. There is limited quantitative modelling of socioeconomic gradients in self-management within Ghanaian tertiary care settings, where patient volume, clinic organisation, and structural constraints may uniquely shape behaviour.\u003c/p\u003e\n\u003cp\u003eThe number of patients attending the diabetes clinic at Sunyani Teaching Hospital sharply increased from 1,032 in 2022 to 1,150 in 2024 (Internal Medicine Unit, Sunyani Teaching Hospital, 2024). The presence of medications alone has proven insufficient to effectively address suboptimal glycaemic outcomes. Thus, gaining insight into the factors and mechanisms that strengthen self-management of patients would be essential for designing contextually appropriate interventions.\u003c/p\u003e\n\u003ch2\u003eStudy aim and objectives\u003c/h2\u003e\n\u003cp\u003eThis study aimed to assess the facilitators and barriers to effective type 2 diabetes self-management and to examine the influence of socioeconomic factors on perceived self-management conditions among patients attending Sunyani Teaching Hospital in Ghana.\u003c/p\u003e\n\u003cp\u003eThis study specifically aimed to:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eIdentify facilitators of effective diabetes self-management among adults with T2DM attending Sunyani Teaching Hospital.\u003c/li\u003e\n \u003cli\u003eDetermine barriers to diabetes self-management in this population.\u003c/li\u003e\n \u003cli\u003eExamine the association between socioeconomic characteristics and composite facilitator and barrier scores.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy design and setting\u003c/h2\u003e\n\u003cp\u003eThis analytical cross-sectional study was conducted between March and April 2025 at Sunyani Teaching Hospital, a tertiary referral facility located in the Bono Region of Ghana. The hospital serves as a major referral centre for the Bono, Bono East, and Ahafo regions and provides specialist outpatient services through a dedicated diabetic clinic under the Internal Medicine Unit. The facility had a bed capacity larger than 400 at the time of the study and managed a large case load of patients with type 2 diabetes mellitus (T2DM). This design permitted a context whereby the perspective from either case could be utilised to analyse perceived facilitators, barriers, and socioeconomic correlates of diabetes between an individual at a marked time and in a naturalistic clinical setting.\u003c/p\u003e\n\n\u003ch2\u003eStudy population and eligibility\u003c/h2\u003e\n\u003cp\u003eThe study population consisted of adult type 2 diabetes mellitus (T2DM) patients attending an outpatient diabetes clinic during the period of the study. The inclusion criteria were that the study enrollees were 18 years or older, had a definite diagnosis of T2DM lasting 6 months or more, and were under treatment at the clinic. A 6-month limit was prescribed so that the subjects could have had an adequate exposure to practices of diabetes self-management to report their experience meaningfully. Patients with type 1 diabetes mellitus or gestational diabetes were not included; neither were individuals who were mentally unstable at the moment, suffering from severe psychiatric illnesses or cognitive impairment interfering with informed consent or questionnaire completion.\u003c/p\u003e\n\n\u003ch2\u003eSample size and sampling procedure\u003c/h2\u003e\n\u003cp\u003eThe estimated population of the diabetic clinic was 1151, and the margin of error was 5%, so the initial sample size was estimated through the formula by [16]. Based on this, the determined maximum sample size came out as 297. After allowing for the contingency of 10% in the sample due to non-response, 327 participants were included. There were a total of 320 participants who filled out and returned their questionnaires, which gave a 97.9% response rate. A convenience sampling approach was utilised to recruit the participants. Meeting the inclusion criteria, willing participants were solicited from those with appointments in the daily clinic sessions during the data collection period. This non-probability sampling technique was infeasible to extrapolate for generalisation but was practically operational in an outpatient setting, coupled with extra feasibility for the timely inclusion of subjects in the study.\u003c/p\u003e\n\n\u003ch2\u003eData collection instrument and procedure\u003c/h2\u003e\n\u003cp\u003eA structured questionnaire (see Supplementary File 1_Exploring the facilitators and barriers) was used to collect data on diabetes self-management, culled from established literature that was adapted to the local clinical context. This instrument also collected information about the general characteristics of the patient population in terms of socioeconomic and clinical variables, such as age, sex, marital status, educational attainment, occupation, monthly income, duration of diabetes, and health insurance status. The study further measured barriers and enabling factors about self-care in diabetes through fourteen items scored Likert-style to build potential responses, which were graded on a five-point scale, ranging from one to five, with one indicating little agreement and five indicating agreement.. Facilitator items assessed domains such as adequacy of knowledge, receipt of education from healthcare professionals, medication affordability, family support, appointment accessibility, access to home glucometers, and availability of support groups. The barriers evaluated were financial constraints, diet-related difficulties, sheer access to food, time availability for exercise, memory lapses, transportation barriers, and expenses associated with blood glucose monitoring tools. Also included in the questionnaire were open-ended questions about the respondents\u0026apos; lived experiences, perceived barriers, and feedback on potential improvements in diabetes care.\u003c/p\u003e\n\n\u003cp\u003eThe instrument was pretested among 50 T2DM patients at Sunyani Municipal Hospital to assess clarity, comprehension, and average completion time. Minor wording adjustments were made to improve clarity, and pretest responses were excluded from the final analysis. Data were collected through face-to-face administration by three trained research assistants during routine clinic sessions. For participants with limited literacy, questions were read aloud in English or Twi while maintaining neutral delivery to minimise interviewer bias. Each questionnaire required approximately 20\u0026ndash;30 minutes to complete, and completed forms were reviewed daily for completeness and consistency.\u003c/p\u003e\n\n\u003ch2\u003eVariable measurement\u003c/h2\u003e\n\u003cp\u003eTwo composite outcome variables were developed for overall perceptions of facilitators and barriers. A facilitator score was calculated by using the average of 7 facilitation items, while the impediment score was calculated using an average of 7 barriers. Each composite score ranged from 1 to 5, with higher facilitator scores indicating stronger perceived enabling conditions and higher barrier scores indicating greater perceived obstacles to effective self-management. Socioeconomic variables, including education, monthly income, occupation, and health insurance status, were treated as primary exposure variables in regression modelling. Age, sex, marital status, and duration of diabetes were included as covariates.\u003c/p\u003e\n\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eData were analysed using IBM SPSS version 27. Descriptive statistics were used for summarising participant characteristics, whereby categorical variables were shown as frequencies and percentages, with continuous variables summarised by means and standard deviations. Composite facilitator and barrier scores were computed before inferential analysis. The multiple linear regression models were employed to evaluate the associations of socioeconomic conditions with the respective composite outcomes. The values of the corresponding beta coefficients (B), standard errors, t-values, and p-values related to the variables were reported. The statistical significance level was set at p \u0026lt; 0.05. Before modelling, assumptions of linear regression, including normality, homoscedasticity, and absence of multicollinearity, were assessed.\u003c/p\u003e\n\n\u003ch2\u003eBias and study limitations\u003c/h2\u003e\n\u003cp\u003eThis study has certain limitations. The cross-sectional design does not allow for causality inferences and further limits the interpretation to associations between socioeconomic factors and perceived self-management conditions. The study location, being a tertiary clinical facility where convenience sampling was available, should be taken as a restriction in generalising to wider healthcare settings. Self-reported data may introduce recall or social desirability bias. Additionally, the study did not include objective clinical indicators such as HbA1c, limiting direct assessment of the relationship between perceived barriers and glycaemic outcomes.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eParticipant characteristics\u003c/h2\u003e\n\u003cp\u003eA total of 320 adults with type 2 diabetes mellitus were included in the analysis. As shown in Table 1, the sample was predominantly older, with 37.5% aged \u0026ge;60 years and 26.6% aged 45\u0026ndash;59 years. Females constituted 52.8% of the sample. More than half were married (54.1%). Educational attainment was generally low to moderate, with only 12.2% having a tertiary education. Over one-third (34.7%) reported earning less than GHS 500 monthly. Most participants (83.1%) had active health insurance coverage (see Table 1).\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 1: Socio-demographic characteristics of participants\u003c/p\u003e\n\u003ctable cellspacing=\"3\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u0026ndash;44 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90 (28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u0026ndash;59 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e120 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e151 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e169 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e173 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDivorced/Separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eJHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMonthly income (GHS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e111 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e500\u0026ndash;999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1000\u0026ndash;1499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1500\u0026ndash;1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDuration of diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u0026ndash;5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e146 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e115 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHealth insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e266 (83.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eFacilitators of diabetes self-management\u003c/h2\u003e\n\u003cp\u003eThe distribution of responses for each facilitator item is presented in Table 2. The composite facilitator score was 2.82 (SD = 0.74), indicating modest perceived enabling conditions overall.\u003c/p\u003e\n\u003cp\u003eTable 2: Distribution of responses to facilitator items\u003c/p\u003e\n\u003ctable cellspacing=\"3\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFacilitator Item\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdequate knowledge of diabetes management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.89 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSufficient education from healthcare professionals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.84 (0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAccess to affordable medications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.89 (0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFamily support in treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.77 (0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAccessibility of follow-up appointments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.71 (0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAccess to home glucometer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.78 (0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSupport group availability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.86 (0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eComposite\u0026nbsp;Facilitator Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.82 (0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eBarriers to diabetes self-management\u003c/h2\u003e\n\u003cp\u003eThe distribution of barrier responses is shown in Table 3. The composite barrier score was 3.16 (SD = 0.77), indicating moderate perceived obstacles.\u003c/p\u003e\n\u003cp\u003eTable 3: Distribution of responses to barrier items\u003c/p\u003e\n\u003ctable cellspacing=\"3\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBarrier Item\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCannot afford prescribed medications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.20 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDifficulty maintaining healthy diet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.17 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLimited access to healthy food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.11 (0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLack of time for exercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.14 (0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eForgetting to take medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.05 (0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTransportation challenges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.26 (0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMonitoring supplies too expensive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.19 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eComposite Barrier Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.16 (0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eSelf-reported facilitators and challenges\u003c/h2\u003e\n\u003cp\u003eParticipants\u0026rsquo; qualitative responses were summarised quantitatively and are presented in Figures 1\u0026ndash;3. Regular clinic visits (19.7%) were most frequently reported, followed by nurse-provided education (17.5%), family support (15.9%), community support (15.9%), health insurance coverage (15.6%), and access to home glucose monitoring equipment (15.3%).\u003c/p\u003e\n\u003cp\u003eBusy work schedules (18.8%) were the most commonly reported challenge, followed by limited access to healthy food (18.1%), transportation difficulties (16.9%), medication cost (16.6%), dietary restrictions (15.0%), and forgetfulness (14.7%).\u003c/p\u003e\n\u003cp\u003eParticipants prioritised reducing clinic waiting time (17.8%), increasing diabetes education (17.2%), improving affordability of monitoring supplies (16.9%), expanding insurance coverage (16.6%), strengthening community outreach programmes (15.9%), and reducing medication costs (15.6%).\u003c/p\u003e\n\u003ch2\u003eSocioeconomic predictors of facilitator scores\u003c/h2\u003e\n\u003cp\u003eMultiple linear regression analysis was conducted to examine the association between socioeconomic characteristics and composite facilitator scores. The results are presented in Table 4. Education was a strong positive predictor of facilitator scores (B = 0.334, SE = 0.018, t = 18.74, p \u0026lt; 0.001), indicating that higher educational attainment was associated with significantly higher perceived enabling conditions. Monthly income similarly demonstrated a significant positive association (B = 0.332, SE = 0.019, t = 17.69, p \u0026lt; 0.001). Health insurance status showed the largest positive coefficient among predictors (B = 0.388, SE = 0.062, t = 6.23, p \u0026lt; 0.001), suggesting that insured participants reported substantially stronger facilitating conditions compared to uninsured participants. In contrast, age (B = \u0026minus;0.001, SE = 0.001, t = \u0026minus;0.55, p = 0.581), sex (B = 0.029, SE = 0.047, t = 0.61, p = 0.544), marital status (B = 0.017, SE = 0.025, t = 0.68, p = 0.494), and duration of diabetes (B = \u0026minus;0.016, SE = 0.033, t = \u0026minus;0.50, p = 0.620) were not statistically significant predictors.\u003c/p\u003e\n\u003cp\u003eTable 4: Multiple linear regression model predicting facilitator scores\u003c/p\u003e\n\u003ctable cellspacing=\"3\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: B = unstandardized regression coefficient; SE = standard error. Statistically significant predictors are indicated at p \u0026lt; 0.001.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003eSocioeconomic predictors of barrier scores\u003c/h2\u003e\n\u003cp\u003eA second multiple linear regression model was fitted to examine predictors of composite barrier scores. The results are presented in Table 5. Education was a statistically significant negative predictor of barrier scores (B = \u0026minus;0.367, SE = 0.018, t = \u0026minus;20.09, p \u0026lt; 0.001), indicating that higher education levels were associated with lower perceived barriers. Monthly income similarly showed a significant inverse association (B = \u0026minus;0.336, SE = 0.019, t = \u0026minus;17.45, p \u0026lt; 0.001). Health insurance coverage was also significantly associated with reduced perceived barriers (B = \u0026minus;0.373, SE = 0.064, t = \u0026minus;5.84, p \u0026lt; 0.001). Age (B = \u0026minus;0.001, SE = 0.002, t = \u0026minus;0.37, p = 0.714), sex (B = \u0026minus;0.013, SE = 0.048, t = \u0026minus;0.28, p = 0.782), marital status (B = \u0026minus;0.004, SE = 0.026, t = \u0026minus;0.16, p = 0.872), and duration of diabetes (B = \u0026minus;0.018, SE = 0.034, t = \u0026minus;0.52, p = 0.601) were not statistically significant predictors.\u003c/p\u003e\n\u003cp\u003eTable 5: Multiple linear regression model predicting barrier scores\u003c/p\u003e\n\u003ctable cellspacing=\"3\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-20.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-17.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: B = unstandardized regression coefficient; SE = standard error. Statistically significant predictors are indicated at p \u0026lt; 0.001.\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eThis study sought to evaluate the facilitation, obstacles and socioeconomic factors that self-management might enjoy among adults with type 2 diabetes receiving treatment at the Sunyani Teaching Hospital. The results present an appreciable socioeconomic gradient; the impact of education, income, and health insurance status on their ability to be the key determinants of the facilitator and barrier variables, while sociodemographic factors such as age, gender, and marital and disease duration possessed no independent associations with these scales. These results are strongly in line with numerous others advocating the theory of the social determinants of health, where the management of a chronic disease is believed to be shaped more by certain structural and economic concerns rather than based solely on demography.\u003c/p\u003e \u003cp\u003eA modest composite facilitator score implies that patients do not really believe that the support provided for the practice of self-management is very good. This is in common with much more evidence that in low- and middle-income countries, ongoing support for self-management remains unreliable. Sub-Saharan Africa has seen a rapid growth in diabetes, and these countries do not have adequate resources and systems to ensure long-term care provision for self-management.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] reported on a rise in the global prevalence of diabetes, with priority populations residing in low- to middle-income countries, thus serving as a reminder about the overall structural problems regarding the control of diabetes. A recent systematic review and meta-analysis by[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] demonstrated that diabetes self-management education and support (DSMES) interventions significantly improve glycaemic outcomes among people with type 2 diabetes in the WHO African Region.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] similarly documented increasing diabetes prevalence and the accompanying yawning gaps in infrastructure around chronic care in Ghana. Within these contexts, self-management becomes dependent more on the strength of the individual than on the integrity of system-level support.\u003c/p\u003e \u003cp\u003eThe relatively weak endorsement of perceived diabetes knowledge as a facilitator contrasts with evidence that objective diabetes knowledge improves self-care practices. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] determined that Ethiopian patients with adequate knowledge were more likely to adhere to recommended diets while [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] showed that understanding the therapeutic goals improved medication and lifestyle compliance. It may also be attributable to the fact that the study is focusing more on perceived knowledge than on actual knowledge, where, put simply, perceived knowledge does not necessarily translate into practical capability. This difference is evident more broadly in the discourses concerning personal health behaviour-educational interventions should move from knowledge and be channelled into skills. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] has further shown how structured diabetes self-management education improved self-care behaviours among adults with type 2 diabetes, suggesting that education must be translated into action skills rather than being just about knowledge. Further, according to Suglo \u0026amp; Evans, culturally specific ways of understanding and feeling about diabetes shaped self-management practices across African contexts, underscoring, thus, the need for education to be culturally appropriate and practically supportive [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFamily support was also modestly perceived, despite evidence showing that family involvement enhances self-care. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] reported that family partnerships motivated self-care behaviours, but also acknowledged that family support can be inconsistent or even counterproductive if not well informed. This apparent ambivalence may reinforce the neutral reactions seen within this setting, suggesting that family interactions may not work as consistently as facilitators without being enveloped in structured guidance.\u003c/p\u003e \u003cp\u003eIn the study, structural barriers had a stronger impact on access to diabetes self-management care. Transportation challenges in the form of most acutely felt barriers indicate the hospital as the main tertiary referral centre for the Bono region. Of note are similar but less statistically significant findings from other rural settings and settings with lower resources, where long travel distances coupled with raised costs and increased burdens immensely put patients off the care and follow-up path. Also highlighted amid the financial barriers, particularly the cost of medications and monitoring supplies, is perhaps a pattern of similar contexts in which resilience evaporates alongside the acuteness of economic hardship for individual self-care efforts. Woodward et al. positioned socioeconomic deprivation as a consistent proponent of major barriers to diabetes self-management, especially in an environment where the cost of care, medications, and nutritious food keeps significantly rising [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiet-related barriers further illustrate structural constraints. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] showed that household food security predicts dietary adherence, underscoring how income volatility and food availability shape daily management choices. The importance of income as a determinant of diabetes outcomes is further supported by [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], who demonstrated that income significantly influences self-management behaviours and health outcomes in pediatric type 1 diabetes, suggesting that economic resources are foundational determinants of chronic disease management across age groups and diabetes types. Forgetfulness and limited time for exercise, while sometimes framed as behavioural issues, are better understood within the context of competing daily demands, as described by [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], who associated workload pressures with challenges in maintaining medication schedules and planned physical activity.\u003c/p\u003e \u003cp\u003eThe regression analysis provides the clearest evidence that socio-economic determinants play a key role in a person's health literacy and knowledge of the system. Education appears to be the most important predictor for the facilitator or the barrier outcomes. This is consistent with studies showing that knowledge improves health literacy and system navigation. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] similarly identified education as a key factor in participation in self-management programmes. Income also showed bidirectional associations, confirming its crucial role in enabling access to necessary resources, a pattern echoed in the Rwandan study by Munezero et al., which found that even patients with adequate self-care practices reported significant financial barriers to consistent diabetes management [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHaving health insurance helped increase enablers while decreasing barriers, which speaks to the financial protection offered by the National Health Insurance Scheme. Nevertheless, some barriers may exist among insured patients, thereby leading to possible insufficiency and lack of cost coverage by insurance in diabetes care. Emphatically, that quality of health insurance affects vulnerable groups and utilisation patterns is supported by the [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] finding of increased access and access demand in the context of health insurance coverage; minimal variations occurred in the independent demographic variables after adjustment for differential impacts of socioeconomic exclusion, suggesting that these are being mediated by SES vulnerabilities rather than by the demographic characteristics themselves as implied previously in various other studies on differing health topics.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eSome of the major strengths of this study are that it attempts to contribute quantitative evidence related to the facilitators and barriers to diabetes self-management in a third-level institution in Ghana. In fact, the empirical data leave gaps while discussing the socioeconomic factors affecting diabetes self-management. The research used descriptive and multivariable analyses to explain the uniformity of socioeconomic disparities in perceived facilitators and barriers. The use of composite indices allowed simultaneous modelling of enabling and constraining conditions, strengthening the internal coherence of the findings. However, several limitations should be considered. The limitations are that a cross-sectional design excludes certain causal implications and emphasises associations rather than causal relations. Sampling convenience restricts the generalizability of this study to similar populations from other tertiary clinics. Self-report measures have the potential for recall and social desirability bias. Also, the absence of clinical indicators such as glycaemic control usually precludes direct linkage of perceived barriers to the health outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eImplications for practice and policy\u003c/h2\u003e \u003cp\u003e From a clinical standpoint and as far as healthcare policy is concerned, the findings of this study have far-reaching implications-a case for a major reorientation of diabetes care, shifting from the focus on behaviour in education to a structurally supportive care system. Clinically speaking, diabetes practice should henceforth integrate standard brief questioning on socioeconomic risks in such a way that it can be recognised who are the most financially or logistically at risk in order to allow support of these individuals, rather than counselling the whole population. Diabetes self-management education should be delivered as a structured, longitudinal programme adapted to varying literacy levels and reinforced through follow-up rather than provided as isolated sessions. At the health-system level, reducing clinic waiting times, decentralising follow-up services, and expanding community-based or outreach models could mitigate transportation-related constraints. Policy reforms should focus on enhancing financial risk protection by extending insurance to cover items like essential monitoring supplies and by dealing with other forms of indirect costs that act against adherence.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study aimed to identify factors inhibiting and enabling effective self-management in type II diabetes and to explore the relationship between socio-economic factors and perceived self-management conditions. The findings indicate a clear socio-economic gradient with education, income, and health insurance status predicting both higher enabler scores and lower barrier scores, while demographic variables did not significantly predict anything. The participants reported modest enabler conditions and moderate structural barriers, especially related to transportation and affordability of medications and monitoring supplies. These results suggest that, in this setting, diabetes self-management is shaped predominantly by structural socio-economic factors rather than demographic characteristics alone. Improvements in outcomes are possible only through interventions that move beyond awareness raising to promote financial insurance, service access, and ongoing, structured self-support across constrained health systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eHuman Ethics and Consent to Participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Committee on Human Research, Publications and Ethics (CHRPE) of Kwame Nkrumah University of Science and Technology, and institutional permission was granted by Sunyani Teaching Hospital. All procedures performed in the study involving human participants were conducted in accordance with the ethical standards of the institutional research committee and the Declaration of Helsinki. Written informed consent (included in the questionnaire) was obtained from all participants before they participated in the study.\u003c/p\u003e\n\u003cp\u003eConsent for Publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of Data and Materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to institutional data protection policies but are available from the corresponding author on reasonable request and with permission from Bono Regional Hospital.\u003c/p\u003e\n\u003cp\u003eConflict of Interest / Competing Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests concerning the work presented in this manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funding was received for this manuscript\u0026apos;s research, authorship, or publication.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eY.G.F. conceived and designed the study, collected the data, and drafted the initial manuscript. Y.M.B. and P.P.A. contributed to the study design, data interpretation, and critical revision of the manuscript. M.B.U. contributed to study conceptualisation, supervised the research process, performed statistical analysis, and critically revised the manuscript for important intellectual content. P.G., W.K.A., M.M.A., and G.O.T. contributed to data interpretation, manuscript review, and approval of the final version. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eClinical Trial Number\u003c/p\u003e\n\u003cp\u003eClinical trial number: Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organisation, \u0026ldquo;Economic and financing considerations of self-care interventions for sexual and reproductive health and rights: United Nations University Center for policy research,\u0026rdquo; 2020. 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Parida, \u0026ldquo;Assessment of medication adherence and associated factors among patients with diabetes attending a non-communicable disease clinic in a community health centre in Eastern India,\u0026rdquo; \u003cem\u003eCureus, 15(8), e43779\u003c/em\u003e, 2023, doi: 10.7759/cureus.43779. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes, self-management, socioeconomic determinants, facilitators, barriers, health insurance, chronic disease management","lastPublishedDoi":"10.21203/rs.3.rs-9017488/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9017488/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Type 2 diabetes mellitus (T2DM) requires persistent self-care to prevent complications; however, good self-care is often blocked by structural and socio-economic conditions. In Sub-Saharan Africa, diabetes prevalence is growing alongside lingering health system and economic challenges. This study explored the support system and hindrances to controlling diabetes and consequently examined how socioeconomic factors influenced adults with T2DM at Sunyani Teaching Hospital in Ghana.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional study was carried out on 320 adults receiving care at a tertiary hospital with the diagnosis of T2DM. The data collection was conducted using a structured questionnaire that comprised the perceived facilitators and barriers to the self-management rating scale on a 5-point Likert scale. An index for facilitators and barriers was then computed. Multiple linear regressions with socio-economic determinants such as educational attainment, income and insurance coverage as study variables were conducted on self-care assessment scores while controlling for demographic variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The composite facilitator score was modest (mean = 2.82/5), while the composite barrier score indicated moderate constraints (mean = 3.16/5). Transportation difficulty and affordability of medications and monitoring supplies were the most strongly perceived barriers. Education (B = 0.334, p \u0026lt; 0.001), income (B = 0.332, p \u0026lt; 0.001), and insurance status (B = 0.388, p \u0026lt; 0.001) were significant positive predictors of facilitator scores. Conversely, education (B = −0.367, p \u0026lt; 0.001), income (B = −0.336, p \u0026lt; 0.001), and insurance status (B = −0.373, p \u0026lt; 0.001) were significant negative predictors of barrier scores. Age, sex, marital status, and diabetes duration were not independently associated with self-management scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Socioeconomic conditions significantly influence diabetes self-management among patients at Sunyani Teaching Hospital. Factors such as formal education, income, and insurance coverage significantly determine both enabling and constraining characteristics, while demographic characteristics per se do not appear to predict self-management capacity. Therefore, helping to build structured self-management interventions and improving protection mechanisms with financial resources, as well as resolving transportation and cost, would be crucial to enhancing diabetes outcomes in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Exploring the facilitators and barriers to effective diabetes self-management among type 2 diabetic patients at a teaching hospital in Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 05:25:51","doi":"10.21203/rs.3.rs-9017488/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-26T22:35:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200093764608170474865504838805356322268","date":"2026-04-10T17:50:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235795690091940670075031435857135665215","date":"2026-04-09T02:48:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148854623980044630971050902017214202488","date":"2026-04-06T19:46:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-01T19:23:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-06T14:43:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-05T17:24:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-05T17:19:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2026-03-03T07:39:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"48ae7086-9f2a-42be-909f-676484ab92c9","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T05:25:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 05:25:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9017488","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9017488","identity":"rs-9017488","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}