Evidence-Based Summary on Medication Adherence Management for Adult Tuberculosis Patients With Multi-Dimensional Strategies and Practice Framework

Objective This study aims to systematically retrieve, evaluate and synthesize the best evidence for medication adherence management in adult tuberculosis (TB) patients, providing an evidence-based foundation for optimizing clinical practice. Methods Evidence-based questions were formulated using the PIPOST model. Following the '6S' model, 19 Chinese and international databases (BMJ Best Practice, WHO, Cochrane Library, etc.), guidelines and professional association websites were searched from their inception to March 2025, using keywords such as tuberculosis, TB, medication adherence and drug adherence. Clinical guidelines, systematic reviews and manuals were included. Four researchers independently assessed the quality of the literature using AGREE II, AMSTAR and the Johns Hopkins Nursing Evidence-Based Practice Manual tools. Extracted evidence was classified, summarized and graded using the JBI Evidence-Based Healthcare Center's evidence hierarchy system (2014). Results Fourteen publications were included (five systematic reviews, six guidelines, three manuals), yielding 25 pieces of evidence across four dimensions: healthcare services, medication adherence supervision, adverse drug reaction management and patient social support. Key evidence included: Providing patient-centred care (Level 1 evidence); delivering health education and counselling throughout treatment (Level 1); implementing directly observed therapy (DOT) and digital health technologies (Level 1); prioritizing adverse drug reaction management (Level 4); strengthening social support systems (Level 1). Conclusion This study integrates a multidimensional intervention framework to systematize adherence management in adult TB patients. However, clinical implementation must consider regional resources, cultural differences and individual patient needs. Future research should focus on three priorities: (1) scaling digital technologies; (2) developing adverse reaction early-warning systems; and (3) exploring cost-effective interventions.