Effectiveness of electronic reminder systems in improving treatment adherence and completion rates in tuberculosis patients: a meta-analysis
Novian Mahayu Adiutama, Yudisa Diaz Lutfi Sandi, Wardah Fauziah, Nurrizi Rifqi Ferdian
Retos · 2025-11
Abstract
Introduction: Numerous studies have evaluated electronic reminder systems (ERSs) to enhance tuberculosis (TB) treatment outcomes and recently inconsistent result. Objective: This study aimed to evaluate the effectiveness of ERSs in improving treatment adherence and completion among TB patients. Methodology: A systematic review and meta-analysis design. The included studies were obtained from six databases (PubMed, MedLine Ultimate, WOS, ASC, CINAHL, and Garuda) up to 26 may 2025. Risk of Bias tool was employed to assess the article quality. The Odd Ratio (OR) was calculated and analyzed using random effect and 95% confidence interval (CI). Results: Eleven studies were eligible criterion in this review. Across six studies (N= 5,302), ERS showed a non-significant trend toward improved adherence (OR = 1.98, 95% CI [0.94, 4.17], p = 0.07), with high heterogeneity (I² = 95%). Subgroup analysis indicated a significant effect in LMICs (OR = 1.97, 95% CI [1.16, 3.35], p = 0.01), but not in UMICs (OR = 1.87, p = 0.18). For treatment completion, nine studies (N= 6,474) shown a significantly improved outcomes (OR = 1.66, 95% CI [1.13, 2.44], p= 0.01), though heterogeneity was substantial (I² = 81%). Both subgroup difference tests were not statistically significant across income levels. Conclusions: ERS is effective in LMICs for improving adherence, and appear to support treatment completion overall, though variability across studies suggests context matters. These findings reinforce that simple digital tools can make a meaningful difference, especially when thoughtfully deployed in the right settings. Registration: CRD420251072331 was registered in PROSPERO
MeSH terms
- Medicine
- Confidence interval
- Tuberculosis
- Context (archaeology)
- MEDLINE
- Subgroup analysis
- Meta-analysis
- Intensive care medicine
- Random effects model
- Directly Observed Therapy