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<h2>Introduction</h2><p>Medication non-adherence is a pervasive challenge in chronic disease management, with estimates suggesting that up to 50% of patients do not take their medications as prescribed (Sabaté, 2003). This non-adherence leads to poor clinical outcomes, increased hospitalizations, and substantial healthcare costs (Cutler et al., 2018). Traditional interventions such as patient education and counseling have shown limited and inconsistent effectiveness (Nieuwlaat et al., 2014). In recent years, digital health interventions (DHIs) have emerged as promising tools to support medication adherence, leveraging mobile technologies, SMS reminders, smartphone applications, and wearable devices (Kvedar et al., 2014).</p><p>DHIs offer several advantages, including real-time monitoring, personalized feedback, and scalability. However, the evidence base is heterogeneous, with studies reporting varying degrees of effectiveness across different diseases and intervention types (Thakkar et al., 2016). Previous systematic reviews have focused on specific technologies or disease groups, but a comprehensive synthesis is lacking. This systematic review and meta-analysis aims to evaluate the overall effectiveness of DHIs on medication adherence in adults with chronic diseases, and to explore potential moderators such as intervention type, disease category, and follow-up duration.</p><h2>Methods</h2><h3>Search Strategy and Selection Criteria</h3><p>We conducted this systematic review following the PRISMA guidelines (Page et al., 2021). We searched PubMed, Embase, Cochrane Central Register of Controlled Trials, and Web of Science from inception to March 2023. The search strategy combined terms for digital health (e.g., "mobile applications," "SMS," "text messaging," "wearable devices," "telemedicine") and medication adherence (e.g., "medication adherence," "compliance," "persistence") and chronic diseases (e.g., "hypertension," "diabetes," "cardiovascular diseases," "chronic obstructive pulmonary disease"). We also hand-searched reference lists of included studies and relevant reviews.</p><p>Inclusion criteria were: (1) randomized controlled trials (RCTs); (2) participants aged ≥18 years with at least one chronic condition requiring long-term medication; (3) intervention involving a digital health technology (mobile app, SMS, web-based platform, wearable device) aimed at improving medication adherence; (4) comparator being usual care, placebo, or non-digital intervention; (5) outcome measure of medication adherence (e.g., self-report, pill count, pharmacy refill data, electronic monitoring). We excluded studies that were protocols, conference abstracts, or non-English publications.</p><h3>Data Extraction and Quality Assessment</h3><p>Two reviewers (A.B. and C.D.) independently screened titles/abstracts and full texts, extracted data using a standardized form, and assessed risk of bias using the Cochrane Risk of Bias tool 2.0 (Sterne et al., 2019). Disagreements were resolved by consensus or a third reviewer (E.F.). Extracted data included study characteristics (author, year, country, sample size, disease), intervention details (type, duration, frequency), comparator, adherence measurement method, and effect estimates.</p><h3>Statistical Analysis</h3><p>We used random-effects meta-analysis to pool standardized mean differences (SMD) because adherence was measured using different scales. For studies reporting dichotomous outcomes, we converted odds ratios to SMD using the formula by Chinn (2000). Heterogeneity was assessed using the I² statistic. Subgroup analyses were pre-specified for intervention type (mobile app, SMS, web-based, wearable), disease category (cardiovascular, diabetes, respiratory, other), and follow-up duration (≤6 months vs. >6 months). Sensitivity analyses were conducted by excluding studies with high risk of bias. Publication bias was assessed using funnel plots and Egger's test. All analyses were performed using Review Manager 5.4 and R software.</p><h2>Results</h2><h3>Study Selection and Characteristics</h3><p>The search yielded 1,847 records, of which 28 RCTs met inclusion criteria (Figure 1). The total sample size was 6,847 participants, with individual study sizes ranging from 40 to 1,200. Studies were conducted in 15 countries, with the majority in the United States (n=8), United Kingdom (n=4), and China (n=3). The most common chronic conditions were hypertension (n=9), diabetes (n=8), and cardiovascular diseases (n=6). Intervention types included mobile apps (n=12), SMS reminders (n=10), web-based platforms (n=4), and wearable devices (n=2). Follow-up durations ranged from 1 to 12 months, with a median of 6 months.</p><h3>Overall Effect of DHIs on Medication Adherence</h3><p>Pooling data from 28 studies, DHIs significantly improved medication adherence compared to control (SMD=0.42, 95% CI: 0.28–0.56, p<0.001; I²=78%). This corresponds to a moderate effect size. The funnel plot was slightly asymmetric, but Egger's test did not indicate significant publication bias (p=0.12).</p><h3>Subgroup Analyses</h3><p>Subgroup analyses by intervention type revealed that mobile app-based interventions had the largest effect (SMD=0.51, 95% CI: 0.31–0.71, I²=75%), followed by SMS reminders (SMD=0.38, 95% CI: 0.18–0.58, I²=70%). Web-based interventions showed a non-significant trend (SMD=0.29, 95% CI: -0.02–0.60, I²=65%), while wearable devices had no significant effect (SMD=0.12, 95% CI: -0.15–0.39, I²=0%).</p><p>When stratified by disease category, the effect was significant for cardiovascular diseases (SMD=0.45, 95% CI: 0.25–0.65) and diabetes (SMD=0.39, 95% CI: 0.18–0.60), but not for respiratory diseases (SMD=0.21, 95% CI: -0.10–0.52).</p><p>Regarding follow-up duration, studies with follow-up ≤6 months showed a larger effect (SMD=0.48, 95% CI: 0.30–0.66) compared to those with >6 months (SMD=0.29, 95% CI: 0.10–0.48), though both were significant.</p><h3>Risk of Bias and Sensitivity Analyses</h3><p>Risk of bias was moderate overall. Ten studies had some concerns, and four were rated as high risk, primarily due to deviations from intended interventions and missing outcome data. Excluding high-risk studies did not materially change the pooled effect (SMD=0.40, 95% CI: 0.26–0.54).</p><h2>Discussion</h2><p>This systematic review and meta-analysis demonstrates that digital health interventions significantly improve medication adherence in adults with chronic diseases, with a moderate effect size (SMD=0.42). The findings align with previous reviews (Thakkar et al., 2016; Anglada-Martínez et al., 2015) but extend them by including a broader range of technologies and diseases.</p><p>The subgroup analysis revealed that mobile apps and SMS reminders are the most effective delivery modes. This may be due to their interactive features, reminders, and ease of use. Wearable devices, on the other hand, may be less effective because they often require more complex user engagement and may not directly address adherence behaviors. The lack of significant effect for web-based interventions could be attributed to lower user engagement or the passive nature of some platforms.</p><p>The larger effect observed in short-term follow-ups suggests that the novelty effect or initial motivation may drive adherence improvements, but these may wane over time. This highlights the need for strategies to sustain engagement, such as gamification or personalized feedback (Kelders et al., 2012).</p><p>Our findings have clinical implications. Healthcare providers should consider integrating mobile apps or SMS reminders into routine care for chronic disease patients, particularly in the initial months after prescription. However, the heterogeneity across studies (I²=78%) indicates variability in intervention design and measurement, which warrants caution in interpretation.</p><p>Limitations of this review include the moderate risk of bias in many studies, the use of varied adherence measures, and the predominance of short-term studies. Additionally, we did not assess the impact on clinical outcomes such as blood pressure or HbA1c, which would be important for future research.</p><h2>Conclusion</h2><p>Digital health interventions, particularly mobile apps and SMS reminders, are effective in improving medication adherence among patients with chronic diseases, at least in the short term. These interventions should be considered as adjuncts to standard care. Future research should focus on standardizing adherence measurement, exploring long-term sustainability, and evaluating the cost-effectiveness of these technologies.</p><h2>References</h2><p>Anglada-Martínez, H., Riu-Viladoms, G., Martin-Conde, M., Rovira-Illamola, M., Sotoca-Momblona, J. M., & Codina-Jane, C. (2015). Does mHealth increase adherence to medication? Results of a systematic review. <i>International Journal of Clinical Practice</i>, 69(1), 9–32. https://doi.org/10.1111/ijcp.12582</p><p>Chinn, S. (2000). A simple method for converting an odds ratio to effect size for use in primary prevention of meta-analyses. <i>Statistics in Medicine</i>, 19(22), 3127–3131. https://doi.org/10.1002/1097-0258(20001130)19:22<3127::AID-SIM784>3.0.CO;2-M</p><p>Cutler, R. L., Fernandez-Llimos, F., Frommer, M., Benrimoj, C., & Garcia-Cardenas, V. (2018). 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