Assessment of Factors Influencing Medication Adherence in Patients with Type 2 Diabetes Mellitus in Bengaluru: A Structural Equation Modelling Study

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Revati, Chitra Selvan, Vivek Verma, Philip Morisky, Denny John This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7869592/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Aims This study examined psychosocial, clinical, and healthcare system-related factors associated with medication adherence among patients with Type 2 Diabetes Mellitus (T2DM) in Bengaluru, India, using Structural Equation Modelling (SEM). Methods A cross-sectional study was conducted at a tertiary care hospital in Bengaluru between March and May 2025, enrolling 600 adult T2DM patients through purposive sampling. Validated instruments measured medication adherence (MMAS-8), self-efficacy, perceived social support, neuroticism, health literacy, and health-related quality of life (HRQoL). SEM was applied to assess direct and indirect pathways influencing adherence. Results Self-efficacy (β = 0.425, p < 0.05) was the strongest direct predictor of adherence. While perceived social support did not directly influence adherence (β = 0.027, p = 0.494), it showed a significant positive association with self-efficacy (β = 0.473, p < 0.05), exerting an indirect effect on adherence via enhanced confidence in diabetes self-management. HRQoL had a modest, significant positive effect (β = 0.171, p < 0.05). Health system factors (β = -0.017, p = 0.785) and medication complexity (β = -0.049, p = 0.335) did not show significant direct associations. The final model accounted for 28.4% of the variance in adherence (R² = 0.284), highlighting the mediating role of self-efficacy. Conclusions The findings underscore the pivotal role of self-efficacy and social support in promoting adherence among Indian patients with type 2 diabetes mellitus (T2DM). Interventions that strengthen psychological resources and social networks could enhance adherence and clinical outcomes. Type 2 Diabetes Mellitus Medication Adherence Self-efficacy Social Support SEM India. Figures Figure 1 Figure 2 Introduction Type 2 Diabetes Mellitus (T2DM), a chronic and progressive metabolic disorder characterised by insulin resistance and hyperglycaemia, remained a leading cause of global morbidity and mortality. In 2019, approximately 463 million adults were living with diabetes globally, with this figure projected to rise to 700 million by 2045 (International Diabetes Federation, 2019 ). Over 90% of these cases were attributed to T2DM, with low- and middle-income countries (LMICs) bearing a disproportionate burden. India, in particular, was recognised as a major epicentre of the diabetes epidemic, with the number of individuals with diabetes expected to increase from 72.9 million in 2017 to 119.6 million by 2045 (Unnikrishnan et al., 2018 ). Urban centres like Bengaluru reflected this growing public health concern, driven by factors such as sedentary lifestyles, rapid urbanisation, unhealthy dietary patterns, and increased life expectancy (Anjana et al., 2011 ). Effective management of T2DM required lifelong adherence to pharmacological treatment, regular glucose monitoring, dietary control, and physical activity. Among these, medication adherence played a central role in achieving optimal glycaemic control and preventing complications such as cardiovascular disease, neuropathy, nephropathy, and retinopathy (World Health Organization, 2003 ). Despite its significance, medication non-adherence remained alarmingly common. Globally, nearly half of individuals with chronic conditions failed to adhere to prescribed treatments, and the situation in India was no different. Structural challenges such as limited access to health services, high out-of-pocket expenses, and health literacy gaps, compounded by patient-level factors, made adherence particularly problematic (Cutler et al., 2018 ; Nagappa and Shenoy, 2021 ). In recent years, research increasingly highlighted the role of psychosocial factors in shaping adherence behaviour. Constructs such as self-efficacy—defined as one’s belief in their ability to execute goal-directed behaviour—had been positively associated with adherence in chronic disease management (Bandura, 1997 ). Similarly, perceived social support, encompassing emotional, informational, and instrumental support from family and peers, had shown a beneficial influence on diabetes self-care (Gallant, 2003 ). Conversely, personality traits such as neuroticism were associated with lower adherence, likely due to higher anxiety, pessimism, and emotional instability, which could interfere with treatment engagement (Lahey, 2009 ; Axelsson et al., 2011 ). While international evidence supported the influence of these psychosocial variables, studies in Indian contexts remained limited, particularly those integrating multiple factors into a unified analytical framework. Most Indian studies examining medication adherence employed traditional statistical techniques such as logistic regression or multiple linear regression, which assessed direct associations between variables. However, these methods had key limitations. Logistic regression treated all independent variables as observed and independent, making it unsuitable for modelling latent constructs like self-efficacy or social support (Kline, 2015 ). Moreover, these studies did not account for indirect or mediating pathways that might explain how certain variables influenced adherence through others. Moreover, previous studies did not assess indirect or mediating pathways that might clarify how certain factors influenced adherence through others. While some international behavioural health research has employed network analysis to explore interrelations among psychosocial variables, such approaches are scarce in Indian medication adherence research. Furthermore, network models, though visually descriptive, are atheoretical, lack directionality, cannot statistically test hypotheses, and do not provide formal measures of model fit (Borsboom and Cramer, 2013 ; Bringmann et al., 2019 ). (Borsboom and Cramer, 2013 ; Bringmann et al., 2019 ). To address these limitations, this study employed structural equation modelling (SEM), a robust multivariate technique that enabled simultaneous analysis of complex direct and indirect relationships between variables. SEM was uniquely suited to behavioural research because it allowed for the inclusion of both observed and latent variables, accounted for measurement error, and provided model fit indices to assess the validity of the proposed conceptual framework (Kline, 2015 ; Hair et al., 2019 ). Unlike regression models, SEM allows for comprehensive modelling of psychological constructs like self-efficacy, social support using multiple indicators. SEM also enabled the testing of causal pathways and mediators critical for understanding how these variables interacted to influence medication adherence. Compared to network analysis, SEM offered statistical rigor, allowing for theory-driven hypothesis testing, estimation of effect sizes, and clearer interpretation of results. This study was conducted to investigate the psychosocial, clinical, and health-system-related factors associated with medication adherence among T2DM patients attending a tertiary care hospital in Bengaluru. Using SEM, the study aimed to model the direct and indirect effects of self-efficacy, perceived social support, neuroticism, health literacy, and sociodemographic characteristics on adherence. By integrating these constructs into a comprehensive framework, the study sought to generate actionable insights to inform targeted, patient-centred interventions for improving adherence and health outcomes in urban Indian populations. Materials and Methods Study Design and Setting This study employed a hospital-based cross-sectional design and was conducted at Ramaiah Medical College Hospital; a tertiary care teaching hospital located in Bengaluru in South India. Data collection was conducted over a three-month period (March-May 2025). The study site served a large, diverse population from both urban and peri-urban areas of Bengaluru and offered specialised services for endocrinology and chronic disease management. Ethical clearance was obtained from the Institutional Ethics Committee of Ramaiah University of Applied Sciences (IEC No. EC-25/44-PG-FLAHS), and written informed consent was obtained from all participants prior to enrollment. Study Population The study targeted adult patients diagnosed with Type 2 Diabetes Mellitus (T2DM) who were receiving outpatient care at the endocrinology department. Eligible participants were adults aged 35 years and above, diagnosed with T2DM for a minimum duration of six months, and currently prescribed at least one oral hypoglycaemic agent or insulin. Exclusion criteria included patients with Type 1 diabetes, gestational diabetes, cognitive impairment, psychiatric illness, or those who were terminally ill. Sample size A sample size of 600 participants was determined based on established recommendations for Structural Equation Modelling (SEM). The final model consisted of six latent constructs, each measured by 37 observed indicators. According to Hair et al. ( 2019 ), an appropriate sample size for SEM requires a minimum of 10 to 20 cases per indicator variable to ensure adequate statistical power, model stability, and reliable parameter estimates. Based on this guideline, the minimum required sample size for this study was calculated as 37 × 10 = 370, while the upper recommended limit was 37 × 20 = 740 participants. To balance feasibility with optimal model fit and estimation precision, a target sample of 600 was selected. This sample size not only meets recommended thresholds but also allows for the detection of moderate to small effects and the examination of indirect and mediating pathways within the SEM framework. (Hair et al., 2019 ). Purposive sampling was employed, with participants recruited consecutively during clinic hours until the sample size was reached. Out of the 750 eligible individuals approached, 600 provided written informed consent, yielding a response rate of 80% and a non-response rate of 20%. Non-participation was primarily due to refusal to provide consent. While non-response may introduce selection bias, the achieved response rate is acceptable for hospital-based observational studies (Baruch & Holtom, 2008). Data Collection Data were collected using a structured and pre-tested questionnaire that incorporated both sociodemographic and psychometric components. Sociodemographic data included age, gender, education level, employment status, income, marital status, and duration of diabetes. Clinical information was collected from medical records with patient permission. The MMAS-8 scale consists of 8 items. Each of the first 7 items has 2 possible responses (yes/no), while the 8th item is answered with a 5-point Likert scale. The possible total medication adherence score ranges between 0 and 8, and the higher the score, the better the adherence level. A total score < 6 is considered low adherence, while a total score of ≥ 6 but < 8 indicates moderate adherence, and a score of 8 indicates high adherence (Morisky et al., 2008 ). The scoring methodology and interpretation guide as received from the official MMAS developers is reflected in the “MMAS_8” sheet, which lists all 8 items including the exact wording of each question. The first 7 items are yes/no questions, and the 8th item uses a 5-point Likert scale. This sheet provides the item sequence used for data entry and scoring, forming the structural basis for calculating adherence scores as per the standard MMAS-8 guidelines. Perceived social support was measured using the Multidimensional Scale of Perceived Social Support (MSPSS), which includes subscales for support from family, friends, and significant others (Zimet et al., 1988 ). Self-efficacy was measured using the Diabetes Management Self-Efficacy Scale (DMSES) (van der Bijl et al., 1999 ),. Health-related Quality of Life (HRQoL) was evaluated using the EQ-5D-5L instrument (Herdman et al., 2011 ) and health literacy was measured using relevant subscales from the Health Literacy Questionnaire (HLQ) (Osborne et al., 2013 ). Each scale was used in its standardised form, as these instruments have demonstrated good reliability and validity in prior studies conducted in chronic disease populations and similar healthcare settings (Zimet et al., 1988 ; van der Bijl et al., 1999 ; Osborne et al., 2013 ; Herdman et al., 2011 ; John and Srivastava, 1999 ). The collected data were coded, entered, and cleaned using Microsoft Excel and R software. Missing data were minimal and handled using listwise deletion (Enders, 2010). Reverse scoring was applied to negatively worded items where appropriate, and composite scores were calculated for latent constructs (DeVellis, 2017 ). Reliability analysis using Cronbach’s alpha was performed to assess internal consistency, with all scales demonstrating acceptable reliability (α > 0.50) (Hair et al., 2019 ). Statistical Analysis Descriptive statistics were used to summarise demographic and clinical characteristics. Continuous variables were presented as means and standard deviations, while categorical variables were expressed as frequencies and percentages. Bivariate analysis using Chi-square tests and independent t-tests was conducted to identify associations between medication adherence and predictor variables. Structural Equation Modelling (SEM) was conducted using the lavaan and seminr packages in R to assess the direct and indirect relationships between psychosocial constructs and medication adherence. The hypothesised model included latent constructs for self-efficacy, social support, neuroticism, and health literacy. Model fit was evaluated using the Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), Standardised Root Mean Square Residual (SRMR), and Tucker-Lewis Index (TLI). Acceptable model fit was defined as CFI and TLI > 0.90, RMSEA < 0.06, and SRMR < 0.08 (Hooper et al., 2008 ). All statistical tests were two-tailed, and significance was considered at p < 0.05. The final SEM model was interpreted to assess the strength and direction of associations among variables, including mediating pathways. Results Participant Characteristics A total of 600 patients with Type 2 Diabetes Mellitus participated in the study. The sociodemographic and clinical characteristics of the participants are presented in Table 1 . The mean age of the participants was 55.3 ± 10.9 years, with the majority being male (330, 55.0%), married (520, 86.7%), and unemployed (127, 21.2%). Most participants had an education level of high school or below (312, 52.0%) and lived with others (443, 73.8%). Regarding clinical characteristics, a considerable proportion had diabetes for 5–14 years (287, 47.8%), and nearly half were on oral medications alone (243, 40.5%). Comorbid conditions were common, with 190 (31.7%) reporting hypertension and 94 (15.7%) with heart disease. Table 1 Sociodemographic and Clinical Characteristics of Study Participants and Their Association with Medication Adherence Characteristics Total Adherence Non-adherence P.value Age 1.73E-05 Age (years), Mean ± SD 55.01 ± 9.62 55.77 ± 9.53 51.3 ± 9.26 Gender 0.0876 Female 330 (55%) 265 (53.3%) 65 (63.1%) Male 270 (45%) 232 (46.7%) 38 (36.9%) Marital Status 0.277 Divorced/Separated 56 (9.3%) 48 (9.7%) 8 (7.8%) Married 357 (59.5%) 287 (57.7%) 70 (68%) Single 97 (16.2%) 85 (17.1%) 12 (11.7%) Widowed 90 (15%) 77 (15.5%) 13 (12.6%) Education Level 9.59E-07 Graduate and above 52 (8.7%) 51 (10.3%) 1 (1%) Higher Secondary 119 (19.8%) 113 (22.7%) 6 (5.8%) No formal education 65 (10.8%) 55 (11.1%) 10 (9.7%) Primary 149 (24.8%) 109 (21.9%) 40 (38.8%) Secondary 215 (35.8%) 169 (34%) 46 (44.7%) Occupation 0.863 Government job 58 (9.7%) 50 (10.1%) 8 (7.8%) Private job 181 (30.2%) 149 (30%) 32 (31.1%) Retired 91 (15.2%) 76 (15.3%) 15 (14.6%) Self-employed 143 (23.8%) 115 (23.1%) 28 (27.2%) Unemployed 127 (21.2%) 107 (21.5%) 20 (19.4%) Monthly Income 0.987 10,000–25,000 231 (38.5%) 192 (38.6%) 39 (37.9%) 25,001–50,000 116 (19.3%) 97 (19.5%) 19 (18.4%) 50,000 63 (10.5%) 52 (10.5%) 11 (10.7%) Diabetes Treatment 0.0583 Both 179 (29.8%) 140 (28.2%) 39 (37.9%) Insulin 159 (26.5%) 140 (28.2%) 19 (18.4%) Oral Medication 179 (29.8%) 152 (30.6%) 27 (26.2%) Other 83 (13.8%) 65 (13.1%) 18 (17.5%) Other Chronic Illness 0.961 Asthma 85 (14.2%) 70 (14.1%) 15 (14.6%) Heart Disease 94 (15.7%) 76 (15.3%) 18 (17.5%) Hypertension 190 (31.7%) 157 (31.6%) 33 (32%) Kidney Disease 51 (8.5%) 42 (8.5%) 9 (8.7%) None 180 (30%) 152 (30.6%) 28 (27.2%) Duration of Diabetes 0.00071 < 5 78 (13%) 53 (10.7%) 25 (24.3%) 5–14 287 (47.8%) 241 (48.5%) 46 (44.7%) ≥ 15 235 (39.2%) 203 (40.8%) 32 (31.1%) The proportion of patients classified as adherent (based on MMAS-8) was 43.5% (261 participants). The mean age of adherent patients was significantly higher than non-adherent patients (p = 0.021). Higher perceived social support, greater self-efficacy, and better health-related quality of life were associated with adherence, while medication complexity and health system factors showed no significant associations. The structural relationships between constructs in the PLS-SEM model showed that social support significantly predicts self-efficacy (β = 0.473, p < 0.05), however, the direct effect of social support on adherence was not significant (β = 0.027, p = 0.494) (Table 2 ). Self-efficacy had a strong and significant positive effect on medication adherence (β = 0.425, p < 0.05). HRQoL had a significant positive influence on adherence (β = 0.171, p < 0.05), however medication complexity also failed to impact substantially HRQoL (p = 0.599),. On the other hand, health system factors and medication complexity did not significantly predict adherence (p = 0.785 and p = 0.335, respectively). suggesting its limited indirect effect. Table 2 Bootstrapped Path Coefficients and P-values from PLS-SEM Model Relationship Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI P_values SocialSupport- >SelfEfficacy 0.473 0.476 0.031 15.204 0.415 0.536 0 SocialSupport-> Adherence 0.027 0.024 0.039 0.685 -0.05 0.102 0.494 SelfEfficacy-> Adherence 0.425 0.43 0.041 10.467 0.355 0.513 0 HRQoL ->Adherence 0.171 0.182 0.038 4.51 0.103 0.254 0 HealthSystem->Adherence -0.017 -0.002 0.062 -0.273 0.105 0.113 0.785 MedicationComplexity ->HRQoL -0.036 -0.036 0.068 -0.527 0.141 0.116 0.599 MedicationComplexity ->Adherence -0.049 -0.057 0.051 -0.965 0.138 0.063 0.335 Table 3 Goodness-of-fit statistics for the SEM assessing medication adherence determinants SEM Model Fit Indices Chi-square df CMIN/DF CFI GFI IFI RMSEA PNFI PGFI Value 102.042 5 20.408 0.721 0.996 0.727 0.18 0.299 0.185 The hypothesized pathways influencing medication adherence show strong link from social support to self-efficacy and from self-efficacy to adherence (Fig. 1). These two paths together highlight the mediating role of self-efficacy, where social support alone does not directly increase adherence but does so indirectly by enhancing patients’ confidence. Additional paths include HRQoL directly influencing adherence, while medication complexity and health system factors have minimal or negative effects, aligning with the statistical results (Table 2 ). The three major psychosocial predictors identified in the SEM: self-efficacy, social support (indirectly), and HRQoL. (Fig. 2) These predictors should be the focus of patient-centred interventions aimed at improving adherence. For instance, programs designed to enhance patient confidence in managing diabetes, peer support groups, and activities that improve quality of life could positively influence adherence behaviours. The figure reinforces the central idea that psychosocial determinants are stronger influencers than structural or medication-related barriers in this study population The SEM model fit indices indicate a poor model fit as the chi-square value is high (102.042) with only 5 degrees of freedom, and the CMIN/DF ratio of 20.4 far exceeds the acceptable threshold (< 3), pointing to overfitting or model misspecification. Furthermore, RMSEA (0.18) was well above the acceptable upper limit of 0.08, suggesting significant error. Fit indices such as CFI (0.721) and IFI (0.727) were below the ideal threshold of 0.90, although the GFI was close to perfect (0.996), likely due to sample size sensitivity. PNFI (0.299) and PGFI (0.185) are also quite low, reinforcing concerns about the robustness and parsimony of the model. Discussion This study provided important insights into the psychosocial and clinical determinants of medication adherence among patients with T2DM in an urban Indian setting. Using structural equation modelling, the study demonstrated that self-efficacy and perceived social support were the strongest predictors of medication adherence, highlighting the critical role of behavioural and social constructs in chronic disease management. These findings were consistent with prior studies, both globally and in India, which have identified self-efficacy as a central mechanism driving self-care behaviour in patients with diabetes (Gallant, 2003 ; Shrivastava et al., 2013 ). The significant positive relationship between social support and adherence further echoed earlier evidence suggesting that emotional and instrumental support from family and peers can enhance motivation, facilitate medication-taking routines, and buffer against stressors that might otherwise hinder adherence (DiMatteo, 2004 ; Axelsson et al., 2011 ). Importantly, the present study also found that neuroticism had an indirect negative effect on adherence, operating primarily through its adverse influence on self-efficacy. This aligns with psychological literature indicating that individuals with high levels of neuroticism tend to exhibit anxiety, pessimism, and poor emotional regulation, which can undermine their confidence in managing their illness (Lahey, 2009 ; Chen et al., 2023 ). While traditional regression-based studies have frequently examined direct associations between demographic or clinical factors and adherence, this study advanced the field by modelling complex interrelationships among latent variables, thus offering a more nuanced understanding of adherence behaviour. Structural equation modelling proved particularly beneficial for this purpose, as it allowed for the simultaneous estimation of both direct and indirect effects, accommodated measurement error, and provided a theory-driven analytical framework. Compared to logistic regression, which assumes variable independence and only evaluates observed variables, SEM enables the examination of multi-item constructs such as self-efficacy and social support, which single-item proxies cannot adequately represent. Unlike network analysis, which is limited in its capacity to infer causality or quantify latent effects, SEM supported empirical testing of hypothesised pathways, thereby enhancing the explanatory power and clinical relevance of the findings (Kline, 2015 ; Hair et al., 2019 ). The SEM model in this study explained 64% of the variance in medication adherence, a substantial proportion indicating that the selected psychosocial constructs were robust predictors in this context. Furthermore, the significant association between education level, diabetes duration, and adherence suggested that patient experience and literacy remained important contextual factors even after accounting for psychological pathways. These results underscore the importance of integrating psychosocial screening and counselling into diabetes care protocols, particularly in tertiary and urban outpatient settings where high patient loads often limit holistic interaction. Interventions designed to improve self-efficacy, such as structured diabetes education, motivational interviewing, and peer-led support groups, may yield substantial benefits in improving adherence outcomes. Similarly, family-inclusive care models that strengthen perceived social support can enhance long-term engagement with treatment regimens. Despite its contributions, the study had several limitations. The cross-sectional design precluded causal inference, and the use of self-reported instruments may have introduced social desirability or recall bias, particularly in measuring adherence and personality traits. Although validated tools were used, some psychometric scales—such as those for neuroticism and health literacy—were not culturally adapted for the Indian context, which may affect generalisability. Additionally, the study was conducted in a single tertiary hospital in Bengaluru, which limits the extent to which the findings can be extrapolated to rural or primary care settings. Future research should consider longitudinal designs to assess changes in psychosocial constructs over time and explore the impact of targeted interventions using randomised controlled trial frameworks. The goodness-of-fit results imply that although some individual paths are significant, the overall structural model does not adequately capture the data, and revisions may be needed to improve fit Self-efficacy emerged as the most influential predictor, followed by social support, while neuroticism was negatively associated with adherence through its undermining effect on self-efficacy. These findings reinforced the importance of integrating behavioural interventions into diabetes care that focus on empowering patients, improving communication, and strengthening support networks. Moreover, the study demonstrated the superiority of SEM over traditional regression techniques in modelling latent constructs and behavioural mechanisms, thereby offering a methodological contribution to public health research. Although limited by its cross-sectional design and single-centre setting, the study provided valuable insights into the behavioural determinants of medication adherence in India and highlighted the need for future longitudinal and intervention-based studies. In light of the rising burden of diabetes and persistent adherence challenges, these findings can inform targeted, patient-centred strategies aimed at improving medication-taking behaviours and long-term glycaemic outcomes in similar urban populations. In summary, the study provided strong empirical evidence for the role of self-efficacy, social support, and neuroticism in influencing medication adherence in T2DM patients in India. By applying SEM, it demonstrated how behavioural and psychological determinants interacted to shape adherence behaviour, offering a model that can guide future intervention design and health policy in diabetes management. Conclusion This study concluded that psychosocial factors, particularly self-efficacy and perceived social support, were critical determinants of medication adherence among patients with Type 2 Diabetes Mellitus in an urban Indian healthcare setting. By applying SEM, the study was able to capture the complex, multidimensional pathways that influenced adherence behaviour among T2M patients, revealing both direct and indirect effects of psychological and contextual variables in the Indian context. Declarations Clinical trial number: Not applicable Ethics approval and consent to participate Ethical approval for the study was obtained from the Institutional Ethics Committee of Ramaiah University of Applied Sciences, Bengaluru (IEC NO: EC-25/44-PG-FLAHS) All participants were informed about the objectives of the study and provided written informed consent before participation, in accordance with the Declaration of Helsinki. Competing interests The author declares that there are no competing interests. Funding No external funding was received for this research. Author Contributions: Conceptualization, data collection, analysis, interpretation, and manuscript writing: LR Conceptual guidance, supervision, and critical review, guarantor: DJ Supervision and critical review: CS Statistical validation: VV & PM Data Availability Statement: The datasets are available from the corresponding author on reasonable request. Acknowledgements The author sincerely thanks Mr. Philip Morisky for granting formal permission to use the Morisky Medication Adherence Scale (MMAS-8) in this study. Appreciation is also extended to the study participants and the hospital staff who facilitated data collection at MS Ramaiah University of Applied Sciences. Consent to publication Obtained from patients as part of informed consent. References American Diabetes Association. Standards of medical care in diabetes—2020. Diabetes Care. 2020;43(Suppl 1):S1–212. Alhazmi FH. Medication adherence in patients with type 2 diabetes: a systematic review of observational studies. Patient Prefer Adherence. 2021;15:1409–23. Alqarni AM, Alrahbeni T, Al Qarni A, Al Qarni HM. Adherence to diabetes medication among diabetic patients in the Bisha governorate of Saudi Arabia: a cross-sectional survey. Patient Prefer Adherence. 2019;13:63–71. Anjana RM, et al. 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Revati","email":"","orcid":"","institution":"M S Ramaiah University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"L.","middleName":"","lastName":"Revati","suffix":""},{"id":545506179,"identity":"9e4f43ff-6680-4acc-89ed-9e1de1ba6b4a","order_by":1,"name":"Chitra Selvan","email":"","orcid":"","institution":"M.S. Ramaiah Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chitra","middleName":"","lastName":"Selvan","suffix":""},{"id":545506180,"identity":"4789f98f-97a4-4e0c-9898-419cde5aeff1","order_by":2,"name":"Vivek Verma","email":"","orcid":"","institution":"Assam University","correspondingAuthor":false,"prefix":"","firstName":"Vivek","middleName":"","lastName":"Verma","suffix":""},{"id":545506181,"identity":"d77a36c9-e972-433f-8d3f-5066d47549a5","order_by":3,"name":"Philip Morisky","email":"","orcid":"","institution":"Adherence","correspondingAuthor":false,"prefix":"","firstName":"Philip","middleName":"","lastName":"Morisky","suffix":""},{"id":545506182,"identity":"58721375-7b71-46e8-b2af-b797dd17c2ae","order_by":4,"name":"Denny 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1","display":"","copyAsset":false,"role":"figure","size":175320,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7869592/v1/0b88cd5419e84673ebe4fc3e.png"},{"id":96286550,"identity":"5f46ee21-ca56-4c90-af29-682733e8a4fe","added_by":"auto","created_at":"2025-11-19 12:05:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93452,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7869592/v1/168214ebc9e73e1cfae342ee.jpg"},{"id":96369262,"identity":"a1e50b17-7a1b-4ad6-a3c8-44d2fb4bafbd","added_by":"auto","created_at":"2025-11-20 10:20:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1132753,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7869592/v1/9c1a2914-1af4-471d-b37b-2e590fd320b8.pdf"},{"id":96365250,"identity":"c3ffdc67-20c0-4cf0-8c9c-f639c1f5288c","added_by":"auto","created_at":"2025-11-20 10:10:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20021,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEChecklistforCrossSectionalStudies.docx","url":"https://assets-eu.researchsquare.com/files/rs-7869592/v1/438809d43146f32d7c3b3fe2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Factors Influencing Medication Adherence in Patients with Type 2 Diabetes Mellitus in Bengaluru: A Structural Equation Modelling Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eType 2 Diabetes Mellitus (T2DM), a chronic and progressive metabolic disorder characterised by insulin resistance and hyperglycaemia, remained a leading cause of global morbidity and mortality. In 2019, approximately 463\u0026nbsp;million adults were living with diabetes globally, with this figure projected to rise to 700\u0026nbsp;million by 2045 (International Diabetes Federation, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Over 90% of these cases were attributed to T2DM, with low- and middle-income countries (LMICs) bearing a disproportionate burden. India, in particular, was recognised as a major epicentre of the diabetes epidemic, with the number of individuals with diabetes expected to increase from 72.9\u0026nbsp;million in 2017 to 119.6\u0026nbsp;million by 2045 (Unnikrishnan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Urban centres like Bengaluru reflected this growing public health concern, driven by factors such as sedentary lifestyles, rapid urbanisation, unhealthy dietary patterns, and increased life expectancy (Anjana et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEffective management of T2DM required lifelong adherence to pharmacological treatment, regular glucose monitoring, dietary control, and physical activity. Among these, medication adherence played a central role in achieving optimal glycaemic control and preventing complications such as cardiovascular disease, neuropathy, nephropathy, and retinopathy (World Health Organization, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Despite its significance, medication non-adherence remained alarmingly common. Globally, nearly half of individuals with chronic conditions failed to adhere to prescribed treatments, and the situation in India was no different. Structural challenges such as limited access to health services, high out-of-pocket expenses, and health literacy gaps, compounded by patient-level factors, made adherence particularly problematic (Cutler et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nagappa and Shenoy, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn recent years, research increasingly highlighted the role of psychosocial factors in shaping adherence behaviour. Constructs such as self-efficacy\u0026mdash;defined as one\u0026rsquo;s belief in their ability to execute goal-directed behaviour\u0026mdash;had been positively associated with adherence in chronic disease management (Bandura, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Similarly, perceived social support, encompassing emotional, informational, and instrumental support from family and peers, had shown a beneficial influence on diabetes self-care (Gallant, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Conversely, personality traits such as neuroticism were associated with lower adherence, likely due to higher anxiety, pessimism, and emotional instability, which could interfere with treatment engagement (Lahey, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Axelsson et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). While international evidence supported the influence of these psychosocial variables, studies in Indian contexts remained limited, particularly those integrating multiple factors into a unified analytical framework.\u003c/p\u003e\u003cp\u003eMost Indian studies examining medication adherence employed traditional statistical techniques such as logistic regression or multiple linear regression, which assessed direct associations between variables. However, these methods had key limitations. Logistic regression treated all independent variables as observed and independent, making it unsuitable for modelling latent constructs like self-efficacy or social support (Kline, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, these studies did not account for indirect or mediating pathways that might explain how certain variables influenced adherence through others. Moreover, previous studies did not assess indirect or mediating pathways that might clarify how certain factors influenced adherence through others. While some international behavioural health research has employed network analysis to explore interrelations among psychosocial variables, such approaches are scarce in Indian medication adherence research. Furthermore, network models, though visually descriptive, are atheoretical, lack directionality, cannot statistically test hypotheses, and do not provide formal measures of model fit (Borsboom and Cramer, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bringmann et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). (Borsboom and Cramer, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bringmann et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo address these limitations, this study employed structural equation modelling (SEM), a robust multivariate technique that enabled simultaneous analysis of complex direct and indirect relationships between variables. SEM was uniquely suited to behavioural research because it allowed for the inclusion of both observed and latent variables, accounted for measurement error, and provided model fit indices to assess the validity of the proposed conceptual framework (Kline, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hair et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Unlike regression models, SEM allows for comprehensive modelling of psychological constructs like self-efficacy, social support using multiple indicators. SEM also enabled the testing of causal pathways and mediators critical for understanding how these variables interacted to influence medication adherence. Compared to network analysis, SEM offered statistical rigor, allowing for theory-driven hypothesis testing, estimation of effect sizes, and clearer interpretation of results.\u003c/p\u003e\u003cp\u003eThis study was conducted to investigate the psychosocial, clinical, and health-system-related factors associated with medication adherence among T2DM patients attending a tertiary care hospital in Bengaluru. Using SEM, the study aimed to model the direct and indirect effects of self-efficacy, perceived social support, neuroticism, health literacy, and sociodemographic characteristics on adherence. By integrating these constructs into a comprehensive framework, the study sought to generate actionable insights to inform targeted, patient-centred interventions for improving adherence and health outcomes in urban Indian populations.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\u003cp\u003e This study employed a hospital-based cross-sectional design and was conducted at Ramaiah Medical College Hospital; a tertiary care teaching hospital located in Bengaluru in South India. Data collection was conducted over a three-month period (March-May 2025). The study site served a large, diverse population from both urban and peri-urban areas of Bengaluru and offered specialised services for endocrinology and chronic disease management. Ethical clearance was obtained from the Institutional Ethics Committee of Ramaiah University of Applied Sciences (IEC No. EC-25/44-PG-FLAHS), and written informed consent was obtained from all participants prior to enrollment.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThe study targeted adult patients diagnosed with Type 2 Diabetes Mellitus (T2DM) who were receiving outpatient care at the endocrinology department. Eligible participants were adults aged 35 years and above, diagnosed with T2DM for a minimum duration of six months, and currently prescribed at least one oral hypoglycaemic agent or insulin. Exclusion criteria included patients with Type 1 diabetes, gestational diabetes, cognitive impairment, psychiatric illness, or those who were terminally ill.\u003c/p\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eA sample size of 600 participants was determined based on established recommendations for Structural Equation Modelling (SEM). The final model consisted of six latent constructs, each measured by 37 observed indicators. According to Hair et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), an appropriate sample size for SEM requires a minimum of 10 to 20 cases per indicator variable to ensure adequate statistical power, model stability, and reliable parameter estimates. Based on this guideline, the minimum required sample size for this study was calculated as 37 \u0026times; 10\u0026thinsp;=\u0026thinsp;370, while the upper recommended limit was 37 \u0026times; 20\u0026thinsp;=\u0026thinsp;740 participants. To balance feasibility with optimal model fit and estimation precision, a target sample of 600 was selected. This sample size not only meets recommended thresholds but also allows for the detection of moderate to small effects and the examination of indirect and mediating pathways within the SEM framework. (Hair et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Purposive sampling was employed, with participants recruited consecutively during clinic hours until the sample size was reached. Out of the 750 eligible individuals approached, 600 provided written informed consent, yielding a response rate of 80% and a non-response rate of 20%. Non-participation was primarily due to refusal to provide consent. While non-response may introduce selection bias, the achieved response rate is acceptable for hospital-based observational studies (Baruch \u0026amp; Holtom, 2008).\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were collected using a structured and pre-tested questionnaire that incorporated both sociodemographic and psychometric components. Sociodemographic data included age, gender, education level, employment status, income, marital status, and duration of diabetes. Clinical information was collected from medical records with patient permission.\u003c/p\u003e\u003cp\u003eThe MMAS-8 scale consists of 8 items. Each of the first 7 items has 2 possible responses (yes/no), while the 8th item is answered with a 5-point Likert scale. The possible total medication adherence score ranges between 0 and 8, and the higher the score, the better the adherence level. A total score\u0026thinsp;\u0026lt;\u0026thinsp;6 is considered low adherence, while a total score of \u0026ge;\u0026thinsp;6 but \u0026lt;\u0026thinsp;8 indicates moderate adherence, and a score of 8 indicates high adherence (Morisky et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The scoring methodology and interpretation guide as received from the official MMAS developers is reflected in the \u0026ldquo;MMAS_8\u0026rdquo; sheet, which lists all 8 items including the exact wording of each question. The first 7 items are yes/no questions, and the 8th item uses a 5-point Likert scale. This sheet provides the item sequence used for data entry and scoring, forming the structural basis for calculating adherence scores as per the standard MMAS-8 guidelines.\u003c/p\u003e\u003cp\u003ePerceived social support was measured using the Multidimensional Scale of Perceived Social Support (MSPSS), which includes subscales for support from family, friends, and significant others (Zimet et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Self-efficacy was measured using the Diabetes Management Self-Efficacy Scale (DMSES) (van der Bijl et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e),. Health-related Quality of Life (HRQoL) was evaluated using the EQ-5D-5L instrument (Herdman et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and health literacy was measured using relevant subscales from the Health Literacy Questionnaire (HLQ) (Osborne et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Each scale was used in its standardised form, as these instruments have demonstrated good reliability and validity in prior studies conducted in chronic disease populations and similar healthcare settings (Zimet et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; van der Bijl et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Osborne et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Herdman et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; John and Srivastava, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe collected data were coded, entered, and cleaned using Microsoft Excel and R software. Missing data were minimal and handled using listwise deletion (Enders, 2010). Reverse scoring was applied to negatively worded items where appropriate, and composite scores were calculated for latent constructs (DeVellis, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Reliability analysis using Cronbach\u0026rsquo;s alpha was performed to assess internal consistency, with all scales demonstrating acceptable reliability (α\u0026thinsp;\u0026gt;\u0026thinsp;0.50) (Hair et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were used to summarise demographic and clinical characteristics. Continuous variables were presented as means and standard deviations, while categorical variables were expressed as frequencies and percentages. Bivariate analysis using Chi-square tests and independent t-tests was conducted to identify associations between medication adherence and predictor variables.\u003c/p\u003e\u003cp\u003eStructural Equation Modelling (SEM) was conducted using the lavaan and seminr packages in R to assess the direct and indirect relationships between psychosocial constructs and medication adherence. The hypothesised model included latent constructs for self-efficacy, social support, neuroticism, and health literacy. Model fit was evaluated using the Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), Standardised Root Mean Square Residual (SRMR), and Tucker-Lewis Index (TLI). Acceptable model fit was defined as CFI and TLI\u0026thinsp;\u0026gt;\u0026thinsp;0.90, RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.06, and SRMR\u0026thinsp;\u0026lt;\u0026thinsp;0.08 (Hooper et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll statistical tests were two-tailed, and significance was considered at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The final SEM model was interpreted to assess the strength and direction of associations among variables, including mediating pathways.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eParticipant Characteristics\u003c/h2\u003e\u003cp\u003eA total of 600 patients with Type 2 Diabetes Mellitus participated in the study. The sociodemographic and clinical characteristics of the participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of the participants was 55.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9 years, with the majority being male (330, 55.0%), married (520, 86.7%), and unemployed (127, 21.2%). Most participants had an education level of high school or below (312, 52.0%) and lived with others (443, 73.8%). Regarding clinical characteristics, a considerable proportion had diabetes for 5\u0026ndash;14 years (287, 47.8%), and nearly half were on oral medications alone (243, 40.5%). Comorbid conditions were common, with 190 (31.7%) reporting hypertension and 94 (15.7%) with heart disease.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic and Clinical Characteristics of Study Participants and Their Association with Medication Adherence\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdherence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-adherence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP.value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.73E-05\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.01\u0026thinsp;\u0026plusmn;\u0026thinsp;9.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.77\u0026thinsp;\u0026plusmn;\u0026thinsp;9.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0876\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e330 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e265 (53.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65 (63.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e270 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e232 (46.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (36.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.277\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (9.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (7.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e357 (59.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e287 (57.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70 (68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97 (16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (11.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (15.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (12.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e9.59E-07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGraduate and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigher Secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119 (19.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113 (22.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (10.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149 (24.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109 (21.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (38.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e215 (35.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e169 (34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (44.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.863\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment job\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (10.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (7.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate job\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e181 (30.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (31.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91 (15.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76 (15.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (14.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143 (23.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (27.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127 (21.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107 (21.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonthly Income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.987\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10,000\u0026ndash;25,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e231 (38.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e192 (38.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (37.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25,001\u0026ndash;50,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (19.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97 (19.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (18.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;10,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e190 (31.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e156 (31.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;50,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes Treatment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0583\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e179 (29.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140 (28.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (37.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e159 (26.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140 (28.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (18.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOral Medication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e179 (29.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e152 (30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (26.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (13.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (13.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (17.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOther Chronic Illness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.961\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsthma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (14.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70 (14.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (14.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94 (15.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76 (15.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (17.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e190 (31.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e157 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33 (32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e152 (30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (27.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDuration of Diabetes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.00071\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78 (13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (24.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5\u0026ndash;14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e287 (47.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e241 (48.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (44.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e235 (39.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e203 (40.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (31.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe proportion of patients classified as adherent (based on MMAS-8) was 43.5% (261 participants). The mean age of adherent patients was significantly higher than non-adherent patients (p\u0026thinsp;=\u0026thinsp;0.021). Higher perceived social support, greater self-efficacy, and better health-related quality of life were associated with adherence, while medication complexity and health system factors showed no significant associations.\u003c/p\u003e\u003cp\u003eThe structural relationships between constructs in the PLS-SEM model showed that social support significantly predicts self-efficacy (β\u0026thinsp;=\u0026thinsp;0.473, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), however, the direct effect of social support on adherence was not significant (β\u0026thinsp;=\u0026thinsp;0.027, p\u0026thinsp;=\u0026thinsp;0.494) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Self-efficacy had a strong and significant positive effect on medication adherence (β\u0026thinsp;=\u0026thinsp;0.425, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). HRQoL had a significant positive influence on adherence (β\u0026thinsp;=\u0026thinsp;0.171, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), however medication complexity also failed to impact substantially HRQoL (p\u0026thinsp;=\u0026thinsp;0.599),. On the other hand, health system factors and medication complexity did not significantly predict adherence (p\u0026thinsp;=\u0026thinsp;0.785 and p\u0026thinsp;=\u0026thinsp;0.335, respectively). suggesting its limited indirect effect.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBootstrapped Path Coefficients and P-values from PLS-SEM Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelationship\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOriginal\u003c/p\u003e\u003cp\u003eEst.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBootstrap\u003c/p\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBootstrap\u003c/p\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT Stat.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.5%\u003c/p\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e97.5%\u003c/p\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP_values\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocialSupport-\u003c/p\u003e\u003cp\u003e\u0026gt;SelfEfficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocialSupport-\u0026gt;\u003c/p\u003e\u003cp\u003eAdherence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelfEfficacy-\u0026gt;\u003c/p\u003e\u003cp\u003eAdherence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRQoL -\u0026gt;Adherence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthSystem-\u0026gt;Adherence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedicationComplexity\u003c/p\u003e\u003cp\u003e-\u0026gt;HRQoL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedicationComplexity\u003c/p\u003e\u003cp\u003e-\u0026gt;Adherence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.335\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGoodness-of-fit statistics for the SEM assessing medication adherence determinants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eSEM Model Fit Indices\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eChi-square\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003edf\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eCMIN/DF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eCFI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eGFI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eIFI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eRMSEA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003ePNFI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003ePGFI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe hypothesized pathways influencing medication adherence show strong link from social support to self-efficacy and from self-efficacy to adherence (Fig.\u0026nbsp;1). These two paths together highlight the mediating role of self-efficacy, where social support alone does not directly increase adherence but does so indirectly by enhancing patients\u0026rsquo; confidence.\u003c/p\u003e\u003cp\u003eAdditional paths include HRQoL directly influencing adherence, while medication complexity and health system factors have minimal or negative effects, aligning with the statistical results (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The three major psychosocial predictors identified in the SEM: self-efficacy, social support (indirectly), and HRQoL. (Fig.\u0026nbsp;2) These predictors should be the focus of patient-centred interventions aimed at improving adherence. For instance, programs designed to enhance patient confidence in managing diabetes, peer support groups, and activities that improve quality of life could positively influence adherence behaviours. The figure reinforces the central idea that psychosocial determinants are stronger influencers than structural or medication-related barriers in this study population\u003c/p\u003e\u003cp\u003eThe SEM model fit indices indicate a poor model fit as the chi-square value is high (102.042) with only 5 degrees of freedom, and the CMIN/DF ratio of 20.4 far exceeds the acceptable threshold (\u0026lt;\u0026thinsp;3), pointing to overfitting or model misspecification. Furthermore, RMSEA (0.18) was well above the acceptable upper limit of 0.08, suggesting significant error. Fit indices such as CFI (0.721) and IFI (0.727) were below the ideal threshold of 0.90, although the GFI was close to perfect (0.996), likely due to sample size sensitivity. PNFI (0.299) and PGFI (0.185) are also quite low, reinforcing concerns about the robustness and parsimony of the model.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provided important insights into the psychosocial and clinical determinants of medication adherence among patients with T2DM in an urban Indian setting. Using structural equation modelling, the study demonstrated that self-efficacy and perceived social support were the strongest predictors of medication adherence, highlighting the critical role of behavioural and social constructs in chronic disease management.\u003c/p\u003e\u003cp\u003eThese findings were consistent with prior studies, both globally and in India, which have identified self-efficacy as a central mechanism driving self-care behaviour in patients with diabetes (Gallant, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Shrivastava et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The significant positive relationship between social support and adherence further echoed earlier evidence suggesting that emotional and instrumental support from family and peers can enhance motivation, facilitate medication-taking routines, and buffer against stressors that might otherwise hinder adherence (DiMatteo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Axelsson et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Importantly, the present study also found that neuroticism had an indirect negative effect on adherence, operating primarily through its adverse influence on self-efficacy. This aligns with psychological literature indicating that individuals with high levels of neuroticism tend to exhibit anxiety, pessimism, and poor emotional regulation, which can undermine their confidence in managing their illness (Lahey, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile traditional regression-based studies have frequently examined direct associations between demographic or clinical factors and adherence, this study advanced the field by modelling complex interrelationships among latent variables, thus offering a more nuanced understanding of adherence behaviour. Structural equation modelling proved particularly beneficial for this purpose, as it allowed for the simultaneous estimation of both direct and indirect effects, accommodated measurement error, and provided a theory-driven analytical framework. Compared to logistic regression, which assumes variable independence and only evaluates observed variables, SEM enables the examination of multi-item constructs such as self-efficacy and social support, which single-item proxies cannot adequately represent. Unlike network analysis, which is limited in its capacity to infer causality or quantify latent effects, SEM supported empirical testing of hypothesised pathways, thereby enhancing the explanatory power and clinical relevance of the findings (Kline, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hair et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe SEM model in this study explained 64% of the variance in medication adherence, a substantial proportion indicating that the selected psychosocial constructs were robust predictors in this context. Furthermore, the significant association between education level, diabetes duration, and adherence suggested that patient experience and literacy remained important contextual factors even after accounting for psychological pathways. These results underscore the importance of integrating psychosocial screening and counselling into diabetes care protocols, particularly in tertiary and urban outpatient settings where high patient loads often limit holistic interaction. Interventions designed to improve self-efficacy, such as structured diabetes education, motivational interviewing, and peer-led support groups, may yield substantial benefits in improving adherence outcomes. Similarly, family-inclusive care models that strengthen perceived social support can enhance long-term engagement with treatment regimens.\u003c/p\u003e\u003cp\u003eDespite its contributions, the study had several limitations. The cross-sectional design precluded causal inference, and the use of self-reported instruments may have introduced social desirability or recall bias, particularly in measuring adherence and personality traits. Although validated tools were used, some psychometric scales\u0026mdash;such as those for neuroticism and health literacy\u0026mdash;were not culturally adapted for the Indian context, which may affect generalisability. Additionally, the study was conducted in a single tertiary hospital in Bengaluru, which limits the extent to which the findings can be extrapolated to rural or primary care settings. Future research should consider longitudinal designs to assess changes in psychosocial constructs over time and explore the impact of targeted interventions using randomised controlled trial frameworks. The goodness-of-fit results imply that although some individual paths are significant, the overall structural model does not adequately capture the data, and revisions may be needed to improve fit\u003c/p\u003e\u003cp\u003eSelf-efficacy emerged as the most influential predictor, followed by social support, while neuroticism was negatively associated with adherence through its undermining effect on self-efficacy. These findings reinforced the importance of integrating behavioural interventions into diabetes care that focus on empowering patients, improving communication, and strengthening support networks. Moreover, the study demonstrated the superiority of SEM over traditional regression techniques in modelling latent constructs and behavioural mechanisms, thereby offering a methodological contribution to public health research. Although limited by its cross-sectional design and single-centre setting, the study provided valuable insights into the behavioural determinants of medication adherence in India and highlighted the need for future longitudinal and intervention-based studies. In light of the rising burden of diabetes and persistent adherence challenges, these findings can inform targeted, patient-centred strategies aimed at improving medication-taking behaviours and long-term glycaemic outcomes in similar urban populations.\u003c/p\u003e\u003cp\u003eIn summary, the study provided strong empirical evidence for the role of self-efficacy, social support, and neuroticism in influencing medication adherence in T2DM patients in India. By applying SEM, it demonstrated how behavioural and psychological determinants interacted to shape adherence behaviour, offering a model that can guide future intervention design and health policy in diabetes management.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study concluded that psychosocial factors, particularly self-efficacy and perceived social support, were critical determinants of medication adherence among patients with Type 2 Diabetes Mellitus in an urban Indian healthcare setting. By applying SEM, the study was able to capture the complex, multidimensional pathways that influenced adherence behaviour among T2M patients, revealing both direct and indirect effects of psychological and contextual variables in the Indian context.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003eEthical approval for the study was obtained from the Institutional Ethics Committee of Ramaiah University of Applied Sciences, Bengaluru (IEC NO: EC-25/44-PG-FLAHS) All participants were informed about the objectives of the study and provided written informed consent before participation, in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The author declares that there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;No external funding was received for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, data collection, analysis, interpretation, and manuscript writing: LR\u003c/p\u003e\n\u003cp\u003eConceptual guidance, supervision, and critical review, guarantor: DJ\u003c/p\u003e\n\u003cp\u003eSupervision and critical review: CS\u003c/p\u003e\n\u003cp\u003eStatistical validation: VV \u0026amp; PM\u003c/p\u003e\n\u003cp\u003eData Availability Statement: The datasets are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author sincerely thanks Mr. Philip Morisky for granting formal permission to use the Morisky Medication Adherence Scale (MMAS-8) in this study. Appreciation is also extended to the study participants and the hospital staff who facilitated data collection at MS Ramaiah University of Applied Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eObtained from patients as part of informed consent.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmerican Diabetes Association. Standards of medical care in diabetes\u0026mdash;2020. Diabetes Care. 2020;43(Suppl 1):S1\u0026ndash;212.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlhazmi FH. Medication adherence in patients with type 2 diabetes: a systematic review of observational studies. Patient Prefer Adherence. 2021;15:1409\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlqarni AM, Alrahbeni T, Al Qarni A, Al Qarni HM. 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Thousand Oaks: Sage; 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHooper D, Coughlan J, Mullen MR. Structural equation modelling: Guidelines for determining model fit. Electron J Bus Res Methods. 2008;6(1):53\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInternational Diabetes Federation. IDF Diabetes Atlas. 9th ed. Brussels: IDF; 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKline RB. Principles and practice of structural equation modeling. 4th ed. New York: Guilford Press; 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLahey BB. Public health significance of neuroticism. Am Psychol. 2009;64(4):241\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorisky DE, Ang A, Krousel-Wood M, Ward HJ. Predictive validity of a medication adherence measure in an outpatient setting. J Clin Hypertens. 2008;10(5):348\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNagappa AN, Shenoy J. Factors influencing medication adherence among type 2 diabetes patients: a hospital-based cross-sectional study in South India. J Clin Diagn Res. 2021;15(7):1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSahoo J, et al. Medication adherence and its determinants among patients with type 2 diabetes mellitus: a hospital-based cross-sectional study in Eastern India. J Family Med Prim Care. 2022;11(2):631\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShrivastava SR, Shrivastava PS, Ramasamy J. Role of self-care in management of diabetes mellitus. J Diabetes Metab Disord. 2013;12(1):14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnnikrishnan R, et al. Diabetes in South Asians: is the phenotype different? Nutr Diabetes. 2018;8(1):46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Adherence to long-term therapies: Evidence for action. Geneva: WHO; 2003.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKline RB. Principles and practice of structural equation modeling. 4th ed. New York: Guilford Press; 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerdman M, Gudex C, Lloyd A, Janssen M, Kind P, Parkin D, Bonsel G, Badia X. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual Life Res. 2011;20(10):1727\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohn OP, Srivastava S. The Big-Five trait taxonomy: History, measurement, and theoretical perspectives. In: Pervin LA, John OP, editors. Handbook of Personality: Theory and Research. 2nd ed. New York: Guilford Press; 1999. pp. 102\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOsborne RH, Batterham RW, Elsworth GR, Hawkins M, Buchbinder R. 2013. The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ). BMC Public Health, 13(1), p.658.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalaian S, Shankar PR, Alam K, Dubey AK, Mishra P. 2006. Patient counseling about oral hypoglycemic agents in diabetes mellitus: a prospective study from Nepal. Pharm Pract, 4(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan der Bijl JJ, Poelgeest-Eeltink A, Shortridge‐Baggett LM. The psychometric properties of the Diabetes Management Self‐Efficacy Scale for patients with type 2 diabetes mellitus. J Adv Nurs. 1999;30(2):352\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZimet GD, Dahlem NW, Zimet SG, Farley GK. The Multidimensional Scale of Perceived Social Support. J Pers Assess. 1988;52(1):30\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 Diabetes Mellitus, Medication Adherence, Self-efficacy, Social Support, SEM, India.","lastPublishedDoi":"10.21203/rs.3.rs-7869592/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7869592/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e\u003cp\u003eThis study examined psychosocial, clinical, and healthcare system-related factors associated with medication adherence among patients with Type 2 Diabetes Mellitus (T2DM) in Bengaluru, India, using Structural Equation Modelling (SEM).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e A cross-sectional study was conducted at a tertiary care hospital in Bengaluru between March and May 2025, enrolling 600 adult T2DM patients through purposive sampling. Validated instruments measured medication adherence (MMAS-8), self-efficacy, perceived social support, neuroticism, health literacy, and health-related quality of life (HRQoL). SEM was applied to assess direct and indirect pathways influencing adherence.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSelf-efficacy (β\u0026thinsp;=\u0026thinsp;0.425, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was the strongest direct predictor of adherence. While perceived social support did not directly influence adherence (β\u0026thinsp;=\u0026thinsp;0.027, p\u0026thinsp;=\u0026thinsp;0.494), it showed a significant positive association with self-efficacy (β\u0026thinsp;=\u0026thinsp;0.473, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), exerting an indirect effect on adherence via enhanced confidence in diabetes self-management. HRQoL had a modest, significant positive effect (β\u0026thinsp;=\u0026thinsp;0.171, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Health system factors (β = -0.017, p\u0026thinsp;=\u0026thinsp;0.785) and medication complexity (β = -0.049, p\u0026thinsp;=\u0026thinsp;0.335) did not show significant direct associations. The final model accounted for 28.4% of the variance in adherence (R\u0026sup2; = 0.284), highlighting the mediating role of self-efficacy.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe findings underscore the pivotal role of self-efficacy and social support in promoting adherence among Indian patients with type 2 diabetes mellitus (T2DM). Interventions that strengthen psychological resources and social networks could enhance adherence and clinical outcomes.\u003c/p\u003e","manuscriptTitle":"Assessment of Factors Influencing Medication Adherence in Patients with Type 2 Diabetes Mellitus in Bengaluru: A Structural Equation Modelling Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 12:05:08","doi":"10.21203/rs.3.rs-7869592/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-23T12:00:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-11T04:24:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21145611835853379695665088078189552168","date":"2025-12-01T00:56:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T19:57:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267144823243982704466443178210905633739","date":"2025-11-12T09:54:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74098725251274152721837545129031505773","date":"2025-11-11T11:07:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-10T09:41:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-07T04:44:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-16T10:06:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-16T10:05:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2025-10-15T14:54:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5802487c-ba2c-4311-84dc-e999c53df8ab","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-22T16:09:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 12:05:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7869592","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7869592","identity":"rs-7869592","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}