Application of the path analysis model to evaluate the role of distress, mental health literacy and burnout in predicting self-care behaviors among patients with type 2 diabetes

• Published in: Diabetology and metabolic syndrome Vol. 16; no. 1; pp. 137 - 10
• Main Authors: Jafari, Alireza, Moshki, Mahdi, Naddafi, Fatemehzahra, Taghinezhad, Fatemeh, Charoghchian Khorasani, Elham, Karimian, Negar, Farhadian, Zohre, Alizadeh, Hassan
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 23.06.2024; BioMed Central; BMC
• Subjects: Behavior; Burnout; Cardiovascular disease; Data collection; Data entry; Diabetes; Diabetes burnout; Diabetes distress; Diabetes mellitus (non-insulin dependent); Diabetes therapy; Diabetics; Exercise; Health education; Health facilities; Health literacy; Health services; Likert scale; Medical research; Medical screening; Medicine, Experimental; Mental disorders; Mental health; Meta-analysis; Population; Questionnaires; Sample size; Self-care behaviors; Surveys; Systematic review; Type 2 diabetes; Iran; Self-care behaviors; Health literacy; Diabetes distress; Diabetes burnout
• ISSN: 1758-5996; 1758-5996
• DOI: 10.1186/s13098-024-01375-z

Mental complications of diabetes are one of the main obstacles to the implementation of self -care behaviors that have been less studied. Therefore, this study was conducted to survey the effective factors in predicting burnout and self-care behaviors among patients with type 2 diabetes. In this Path analysis, 1280 patients with type 2 diabetes were selected from Mashhad (Iran) in 2023 to 2024. Four scales, the mental health literacy (MHL) scale, diabetes burnout scale, diabetes distress scale, and self-care behavior scale were used for data gathering. AMOS software checked the direct and indirect paths between the variables. In the path analysis, variables of MHL and diabetes distress predicted 25% variance of diabetes burnout (R  = 0.25), and diabetes distress (total effect = 0.491) had the greatest impact on predicting diabetes burnout. Variables of MHL, diabetes distress, and diabetes burnout predicted 12% variance of Self-care behaviors (R  = 0.12) and MHL (total effect = -0.256), age of onset of diabetes (total effect = 0.199), and diabetes burnout (total effect = - 0.167) had the greatest impact on prediction of self-care behaviors. MHL could reduce diabetes distress and burnout and eventually promote self-care behaviors among patients with type 2 diabetes. Therefore, screening and identifying psychological problems (such as distress and burnout) and designing interventions to increase MHL can ultimately increase the health of patients with diabetes.