Factors Associated with Hemodialysis Patient Adherence at Dr. Wahidin Sudirohusodo General Hospital - Annisa Dwi Saputri; Andi Selvi Yusnitasari; Nurzakiah Hasan (PT Yapindo Jaya Abadi, 2026)
Introduction: Non-adherence among patients with chronic kidney disease (CKD) undergoing hemodialysis has been reported to range from 10% to nearly 60% and may lead to acute and chronic complications, reduced quality of life, and increased mortality risk. Variations in previous findings indicate that factors influencing adherence require further investigation within specific local contexts. This study aimed to analyze factors associated with adherence among hemodialysis patients at Dr. Wahidin Sudirohusodo General Hospital, Makassar. Methods: This study employed a quantitative cross-sectional design and was conducted at the hemodialysis unit of Dr. Wahidin Sudirohusodo General Hospital, Makassar, involving 103 patients selected through purposive sampling. Data were collected through structured interviews using validated questionnaires and review of medical records. Data were analyzed using the Chi-Square test and Fisher’s Exact Test. Ethical approval was obtained from the Health Research Ethics Committee of the Faculty of Public Health, Hasanuddin University (Approval Number: 3500/UN4.14.1/TP.01.02/2024). Results: The analysis showed significant associations between age (p=0.032), family support (p=0.025), and self-efficacy (p=0.025) with hemodialysis adherence. No significant associations were found between sex (p=0.447), education level (p=0.916), duration of hemodialysis (p=0.738), and anxiety level (p=0.738) with adherence. Self-efficacy was identified as the most strongly associated factor with patient adherence. Conclusion: Age, family support, and self-efficacy were significantly associated with hemodialysis adherence. These findings highlight the importance of psychosocial approaches, particularly strengthening self-efficacy and family support, in improving adherence among hemodialysis patients. Future research should employ longitudinal designs to better explore causal relationships.