Effective mHealth Interventions to Improve Tuberculosis Treatment Adherence in Asia: A Systematic Review and Meta-Analysis

Effective mHealth Interventions to Improve Tuberculosis Treatment Adherence in Asia: A Systematic Review and Meta-Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Effective mHealth Interventions to Improve Tuberculosis Treatment Adherence in Asia: A Systematic Review and Meta-Analysis Shamal Kankawale, Amruta Chutke, Navpreet Kaur Pannu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8383251/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2026 Read the published version in BMC Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background Tuberculosis (TB) has been one of the most common causes of mortality due to communicable diseases, with an estimated 1.25 million deaths in 2023 worldwide. In Asia, where most new TB cases are reported, treatment non-adherence is affected by complicated factors and practical challenges of implementing directly observed therapy (DOT). Mobile health (mHealth) tools bridge the provider-patient gap and may improve treatment adherence. We aim to systematically evaluate the impact of mHealth interventions compared to standard care on TB treatment adherence by synthesizing data from randomized controlled trials (RCTs) conducted in Asia. Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies were eligible if they were RCTs conducted in an Asian country evaluating mHealth interventions against standard care. Authors searched seven databases, one search engine, and three grey literature sources, with no date or language restrictions. The primary outcome was treatment adherence, and secondary outcomes included type of intervention, income levels, frequency of communication, inclusion of educational component, complexity of technologies used, and the direction of communication. To explore sources of heterogeneity a pre-specified subgroup analyses was conducted. Pooled risk ratios were estimated using random-effects model; heterogeneity was assessed by I 2 , and publication bias by Egger’s test and funnel plots. Results Authors screened 1427 articles, out of which ten trials involving 17,148 participants met the criteria for analysis. mHealth interventions improved treatment adherence compared to standard care (85.6% vs 83.2%; Risk Ratio (RR) 1.09, 95% Confidence Interval (CI) 1.02–1.16; p = 0.01; I² = 94%). Subgroup analysis indicated increased adherence with bidirectional communication (4.9%), daily reminders (7.2%), the inclusion of an educational component (2.7%), and the use of combination technology (13.2%). No significant publication bias was detected (Egger’s p = 0.375). Conclusion mHealth intervention yields a small but meaningful improvement in treatment adherence in Asian settings. Even 2.4% increase in adherence in Asian countries, where TB is a significant burden, could lead to thousands getting cured, decreased relapse rates, fewer drug resistance cases, and decreased transmissions. Asia mHealth Medication monitor Mobile health interventions SMS Smart pillbox Telemedicine Treatment adherence Video-observed therapy (VDOT) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Tuberculosis (TB), an infectious lung disease caused by the bacteria Mycobacterium tuberculosis , has been a public health concern for decades. Despite being curable with a six-month effective treatment regimen, it caused about 1.4 million deaths in 2023 worldwide ( 1 , 2 )( 3 ). The region of Southeast Asia alone accounts for 44% of the total new TB cases ( 4 – 6 ). The global TB burden is disproportionately high in Asia, with half of the top fourteen high-burden countries from this region, namely Bangladesh, China, Democratic People’s Republic of Korea, India, Indonesia, Myanmar, and Pakistan ( 7 ). Maintaining adherence rates above 90% was considered successful treatment adherence ( 4 , 8 ). While non-adherence with treatment is known to cause mortality, morbidity, prolonged treatment duration, and multidrug-resistant TB (MDR-TB) ( 8 – 10 ). It also impacts the healthcare system with increased costs and prolonged hospitalizations ( 4 , 9 ). In response to this challenge the WHO’s End TB Strategy prioritizes treatment supervision to ensure adherence and facilitate desired outcome ( 9 , 11 ). It is important to understand the reasons for non-adherence in addressing the challenges of TB drug resistance. For example, patient-related factors like forgetfulness, insufficient knowledge about the disease and its treatment, the psychological toll, and personal beliefs ( 8 ). Similarly, social factors like lack of support from friends and family, stigma and discrimination, especially with comorbidities like HIV ( 8 , 12 ). Along with economic hardships like financial constraints to acquire medications, travel to the clinics, or nutrition ( 8 , 12 ). Healthcare organizational issues, such as poor provider response or absenteeism, medication unavailability, and inconvenient clinical hours also contribute to non-adherence ( 10 , 13 ). Due to the highly personalized barriers, a one-size-fits-all approach is not favoured ( 5 ). Directly Observed Treatment (DOT) has been recommended internationally for monitoring treatment adherence ( 5 , 14 ). Increased adherence with DOT is highly subjective, depending on the patient population, implementation quality, and factors such as travel and cost to the facility. An Ethiopian study reported that patients travelled 70 hours on average to the DOT facility ( 5 , 14 ). Mobile Health (mHealth) technologies, range from simple Short Messaging Service (SMS) and voice calling to comprehensive ones like Video-Observed Therapy (VDOT) ( 15 ) and smart pillboxes. They are low-cost and more beneficial in resource-limited settings ( 14 , 16 , 17 ). The appeal of mHealth lies in its capacity to overcome geographical barriers, work with the existing healthcare systems, and streamline communication to efficiently deliver incentives ( 10 ). mHealth interventions for TB treatment adherence have shown inconsistent effectiveness, mainly due to variations in study conduction, which makes it challenging to apply them to a broader population ( 18 ). The existing systematic reviews have explored various interventions but the most effective in lower-resource settings is unclear. As mHealth interventions are context-dependent, better clarity is needed to identify and recommend those most suited to lower-income settings ( 16 , 17 ). As most Asian countries are in the low- or lower-middle income group ( 4 , 19 ), interventions tailored to their needs and infrastructure are much needed. To address this gap, we aimed to systematically evaluate the effectiveness of mHealth interventions against standard care in improving treatment adherence among adults undergoing TB treatment in Asian countries. The primary objective of this review was to determine the impact of mHealth interventions compared with standard care on TB treatment adherence using randomized controlled trials (RCTs) from Asian countries. The secondary objectives were to identify factors contributing to differences in effectiveness, including intervention type, country income classification, reminder frequency, educational content, technological complexities, and the direction of communication. Methodology This review used a systematic review methodology. The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the PICO framework guided the design ( 20 ) (Supplementary 1: PRISMA checklist). P opulation included patients 18 years and above with a confirmed diagnosis of TB and residents or those getting treated in Asian countries; I ntervention included mHealth strategies; C ontrol was standard care, and O utcome was adherence to the prescribed treatment regimen until completion. This review has been registered in Prospero with ID CRD420251001579. Eligibility criteria: Only Randomised Controlled Trials (RCTs) conducted in Asian countries on adult patients aged 18 years and above were selected. Studies were eligible if they investigated the effect of mHealth interventions, such as Short Message Services (SMS), telephonic calls, web applications, mobile applications, Video-Observed Therapy (VDOT), medication monitors, Artificial Intelligence (AI), and Machine Learning (ML) based interventions ( 21 ), and smart pillboxes, compared to standard care. Outcome measures included treatment adherence to the prescribed treatment regimen. Articles were excluded if they did not report original data. Searches were not limited by date of publication or language. Databases and search strategy: A comprehensive literature search was conducted across seven databases: PubMed, ScienceDirect, Directory of Open Access Journals, Cochrane Central Register of Controlled Trials, Ovid, Liliacs, and the Clinical Trials registry of the United States National Library of Medicine (US NLM). The first 10 pages of the search engine, Google Scholar, were included. Authors also searched three grey literature databases, Medrxiv, Psyrxiv, and Shodhganga. Snowballing technique was employed to identify more relevant studies, which involved both backward and forward citation searching. Search terms like Telemedicine, Text Messaging, Mobile Applications, mHealth, remote monitoring, Tuberculosis, Medication Adherence, treatment adherence, and RCT, were used as part of the search strategy either alone or in combination. Boolean search operators, truncations, and Medical Subject Headings (MeSH) were used to refine the search. Specific search string for each database is provided in Supplementary Material 2. Selection process: Articles were first screened from titles and abstracts within the database or exported to Zotero by SK and NP. Inclusion and exclusion criteria were used to eliminate irrelevant articles. Studies selected for full-text screening were exported to Rayyan and independently assessed by three reviewers (SK, NK, and AC). Conflicts were resolved through discussion. Data Extraction and Quality Assessment: For feasibility purposes a unique study identification (ID) was assigned to the 10 included studies. An Excel sheet was used to extract data. It included study characteristics such as title, author, publication year, and country and focused on treatment adherence, sample size for both the intervention and control groups, and the number of patients who completed and did not complete the treatment. For secondary analyses type of intervention, income levels, frequency of communication, inclusion of educational component, complexity of technologies used, and the direction of communication were recorded. Data extraction was done by SK and cross-checked by NKP. Any discrepancies were resolved through discussions. The Cochrane Risk of Bias tool 2.0 (RoB 2) was used to evaluate the methodological quality of the included studies. Risk of bias results were summarized using risk of bias graphs ( 22 ), and justification for the same is provided in supplementary material 4. Data analysis: The primary meta-analysis employed a random-effects model to calculate the risk ratios (RR) and 95% confidence intervals (CI) for treatment adherence rates in mHealth interventions compared with standard care. Subgroup analyses were performed as part of secondary analysis. For the income group, the ten RCTs were divided according to World Bank Income classifications ( 19 ). Statistical heterogeneity was quantified using I 2 statistics, with values of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity. A pre-planned sensitivity analysis using a fixed-effects model was conducted to verify the difference between the studies and find the actual reason for heterogeneity. The meta-analyses were performed using SPSS 29.0. Publication bias was checked using Egger’s regression-based test to statistically check for small-study effects, with p < 0.05 indicating possible publication bias. Funnel plots were also used to check visually for any potential gaps in the literature or selective reporting. Results Study selection: The database searches yielded 1250 results, and 197 articles were identified from grey literature and citation searching. After removing 1018 duplicates, 409 articles were selected for abstract and title screening. A total of 44 articles were screened for full texts, of which 34 were excluded (supplementary material 3). Ultimately, ten articles were included for the systematic review and meta-analysis (Fig. 1). Figure 1. PRISMA 2020 Flow Chart of Study Identification, Screening, and Selection for review (n = 44 articles screened for full-text; n = ten included in meta-analysis; n = 34 excluded with documented reasons for exclusion) Footnote: PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses Study characteristics: The ten trials selected were conducted in diverse geographic and socioeconomic settings (Table 1). Out of which, five studies were conducted in China ( 23 – 27 ), two in Pakistan ( 28 , 29 ), and one each in Thailand ( 30 ), India ( 31 ), and the Philippines ( 32 ). These regions represent two socioeconomic groups, with six studies conducted in upper-middle-income countries (UMICs) such as China and Thailand ( 23 – 27 , 30 ), while four studies were conducted in low-middle-income countries (LMICs) like India, Thailand, Pakistan, and the Philippines ( 28 , 29 , 31 , 32 ). Eight trials ( 23 – 25 , 27 – 29 , 31 , 32 ) focused on urban and semi-urban populations, while two trials in China ( 26 ) and one in Thailand ( 30 ) focused on rural populations. Most trials were implemented in the public healthcare sector, except for those conducted by Mohammed et al. ( 29 ), Gupta et al. ( 31 ), and Fang et al. ( 23 ), which collaborated with private sector clinics or used a hybrid setting. Significant heterogeneity was found in the design and delivery of mHealth interventions across the studies. Some studies employed standalone interventions, while others used multi-component interventions. Six studies ( 23 , 25 , 28 , 29 , 31 , 32 )incorporated SMS alone or with another mHealth intervention, with varying frequencies and levels of interactivity. Two studies used web- and application-based VDOT interventions ( 24 , 30 ), while two studies ( 27 , 32 ) used Pillboxes to track medication adherence, one of which provided participants with an SMS option ( 32 ). Two trials only relied on web-based Medication Monitors ( 26 , 27 ). All studies employed computer-generated randomization, and given the nature of the intervention, an open-label design was the predominant approach. All the studies adhered to the six to eight months treatment duration, as per WHO guidelines, except for the trial in Thailand( 30 ), which was observed for four months only. All studies followed the WHO guidelines for treatment( 33 ). The primary outcome in all studies was treatment adherence, which was measured differently, including tracking missed doses, clinical outcomes, and self-reports (Table 1). Table 1: Characteristics of the ten included Randomized Controlled Trials Footnote: Abbreviations: DOT = Directly observed therapy; VDOT = Video-observed therapy; SMS = Short messaging service; EMM = Electronic medication monitor; TB = Tuberculosis; DOTS = Directly observed therapy, short course; Y = Yes; N = No; UMIC = Upper-middle income countries; LMIC = Lower-middle income countries. Risk of Bias assessment: RoB2 assessment concluded that the studies were generally of high methodological quality (Fig. 2, supplementary material 4). Of all the assessed studies, seven demonstrated a low risk of bias across all five domains, indicating that they were well-executed ( 23 – 26 , 28 , 30 , 31 ). Conversely, three studies revealed concerns in specific domains ( 26 , 29 , 32 ). Figure 2: Risk of Bias 2 (ROB 2) Assessment Summary for Ten included RCTs Footnote: RoB 2 = Cochrane Risk of Bias tool version 2.0; seven studies rated low risk across all domains; three studies with concerns in specific domains Primary Outcome Analysis: Treatment Adherence A meta-analysis of the ten studies revealed a small yet statistically significant improvement in tuberculosis treatment adherence with mHealth interventions compared to standard care (p = 0.01). A total of 7,420 patients were enrolled in the control group and 9,728 in the intervention group, among whom 6,176 (83.23%) in the control group completed the treatment, compared to 8,329 (85.61%) in the intervention group. Forest plot including all ten studies showed an overall risk ratio of 1.09 (95% CI: 1.02–1.16), indicating that the patients receiving standard care were 9% more likely to quit treatment before completion (Fig. 3, part (a)). The random-effect model showed heterogeneity (I 2 = 94%), indicating considerable variations in the interventions provided. The sensitivity analysis conducted with the fixed-effects model yielded a lower risk ratio (RR = 1.02, 95% CI = 1.00-1.02, I² = 92%, Fig. 3 part (b)) compared to the random-effects model (RR = 1.08). The high heterogeneity in both models indicates variability across included studies. Figure 3. Random-Effects Meta-Analysis Footnote: (a) Forest plot showing pooled estimate. (b) Funnel plot assessing publication bias Subgroup analysis: 1) Based on the type of intervention: A subgroup analysis was conducted using mHealth intervention technology to investigate variations in intervention effectiveness. Interventions were categorised into three groups – SMS, VDOT, and electronic monitoring, including pillboxes, medication monitors, and application-based technologies. Four studies explored SMS-based interventions with a pooled effect of RR = 1.06 (95% CI = 0.09–1.1, I 2 = 66%, Fig. 4a). Two studies were assessed for the VDOT ap Fig. 4b). Electronic monitoring and pillbox interventions showed the most treatment adherence among the groups at a pooled effect size of RR = 1.12 (95% CI = 0.09–1.28, I 2 = 98%, Fig. 4c). However, this group showed an extremely high heterogeneity (I 2 = 98%), exceeding the overall analysis. While the SMS and VDOT groups revealed I 2 = 66% and I 2 = 34%. In general, all groups based on intervention type indicated high heterogeneity, suggesting variation in the application of the intervention. Also, the improvement in treatment adherence with mHealth interventions compared to standard care was not statistically significant. Figure 4: Subgroup Analysis by Intervention Type Footnote: SMS (n = four studies); VDOT (n = two studies); Electronic monitors and smart pillboxes (n = four studies); Horizontal lines represent 95% confidence intervals; diamond represents pooled effect estimate. 2) Based on the income levels of the countries: Sub-group analysis of UMIC ( 23 – 27 , 30 ) demonstrated a statistically significant improvement in treatment adherence (RR = 1.12, 95% CI: 1.03–1.23; I 2 = 89%, Fig. 5a), while LMIC ( 28 , 29 , 31 , 32 ) showed no significant difference (RR = 1.00, 95% CI: 0.99–1.01; I 2 = 0%, Fig. 5b). Among UMIC, 82.6% of 4935 patients enrolled in the intervention arm completed the treatment, compared to 76.2% of patients enrolled in the control arm, yielding a 6.4% increase in adherence. However, in the LMIC, only a 0.5% increase in treatment adherence was observed in the intervention (88.7% completion rate, n = 4793) compared to the control group (88.2% completion rate, n = 4337). Figure 5: Subgroup Analysis by Country Income Classification Footnote: (a) Lower-middle income countries (n = four studies); (b) Upper-middle income countries (n = studies); 3) Based on the frequency of communication: Eight studies( 23 – 26 , 28 – 31 ) used daily reminders or check-ins, and two ( 27 , 32 ) opted for monthly in-person check-ins. Among the daily frequency group, a 7.2% improvement in treatment adherence rates and statistical analysis showed significant improvement (RR = 1.12, 95% CI = 1.04–1.20, I 2 = 88%, Fig. 6a) was seen in the intervention group (82.48% compared to 75.18% in the control), compared to a negligible 0.2% effect of monthly reminders provided with no significant effect (RR = 1.00, 95% CI = 0.98–1.01, I 2 = 0%, Fig. 6b). Figure 6: Subgroup Analysis by Communication Frequency Footnote: (a) Daily frequency (n = eight studies); (b) Monthly frequency (n = two studies) 4) Based on incorporating an educational component in the intervention: Seven studies ( 23 , 25 , 26 , 28 , 29 , 31 , 32 ) included educational components, such as tuberculosis knowledge, management of side effects, nutritional guidance, and disease awareness content. In these studies, with educational components, the pooled treatment adherence rate in the intervention arm was 85.6%. In comparison, the rates in the control arms was 82.9%, showing a statistically significant improvement (RR = 1.11, 95% CI = 1.02–1.20, I 2 = 94%, Fig. 7a). The three studies ( 24 , 27 , 30 ) lacking educational components demonstrated completion rates of 85.7% in the intervention group and 85.0% in the control group, indicating a mere 0.7% difference that was statistically not significance (RR = 1.01, 95% CI = 0.98–1.04, I 2 = 0%, Fig. 7b). Figure 7: Subgroup Analysis by Inclusion of Educational Component Footnote: (a) With education component (n = seven studies); (b) Without education component (n = three studies) 5) Based on technological complexities: Low-complexity interventions used only simple mobile phones for SMS reminders or automated voice calls, without internet access or special apps, and showed a 2.2% increase in treatment adherence (from 81.5% to 83.7%, RR = 1.06, 95% CI = 0.99–1.14, I 2 = 9%, Fig. 8a) ( 23 , 28 , 29 , 31 ). Medium-complexity interventions utilized smartphones with popular messaging apps, such as WhatsApp, Line, or WeChat, but did not require any special software or web portals ( 27 , 32 ). This only led to a 0.1% improvement in adherence (from 88.8% to 88.9%, RR = 1.00, 95% CI = 0.98–1.01, I 2 = 0%, Fig. 8b). High-complexity interventions incorporated dedicated mobile applications, web dashboards, smart pillboxes, or video-observed therapy platforms with real-time monitoring, achieving significant improvement of 13.2% (from 68.8% to 82.0%) (RR = 1.17, 95% CI = 1.04–1.33, I 2 = 91%, Fig. 8c) ( 24 – 26 , 30 ). Figure 8: Subgroup Analysis by Technological Complexity Level Footnote: (a) Low complexity = Simple SMS or automated voice calls without internet or applications (n = four studies); (b) Medium complexity = Smartphones with standard messaging applications (WhatsApp, WeChat, Line) without specialized software (n = two studies); (c) High complexity = Dedicated mobile applications, web dashboards, smart pillboxes, or video-observed therapy platforms with real-time monitoring (n = four studies) 6) Based on the direction of communication: A total of six studies ( 24 – 27 , 29 , 30 ) using bidirectional communication showed 4.86% more adherence (82.25% in the intervention and 77.39% in the control) compared to a mere 0.74% in unidirectional communication. Bidirectional communication statistically showed a clear advantage over unidirectional communication. (Bidirectional: RR = 1.11, 95% CI = 1.00–1.22, I 2 = 5%, Fig. 9a; Unidirectional: RR = 1.06, 95% CI = 0.98–1.14, I 2 = 76%, Fig. 9b). Notably, baseline treatment rates were higher in unidirectional studies (90% in intervention vs 82.2% in control groups) compared to bidirectional studies (82.25% in intervention vs 77.3% in control groups), suggesting a potential ceiling effect where already high baseline success rates in unidirectional study settings limited the opportunity for intervention benefit ( 23 , 28 , 31 , 32 ). Figure 9: Subgroup Analysis by Communication Direction Footnote: (a) ) Unidirectional communication (n = four studies); (b) Bidirectional communication (n = six studies) Publication Bias Assessment: The funnel plot (Fig. 3 (b)) displayed asymmetry with clustering on the right side (larger effect sizes), suggesting possible small-study effects or missing studies with moderate/null findings. However, Egger's test for the random-effects model showed no evidence of publication bias (intercept = 0.04; 95% CI: -0.06–0.15; p = 0.37). Intervention-specific assessments showed no publication bias for SMS interventions (p = 0.510) or electronic monitoring systems (p = 0.83). However, the evaluation for VDOT was limited by only two studies, which was insufficient for Egger’s test. Overall subgroup analysis by technology type was underpowered to assess publication bias because of small study numbers, such as four for SMS ( 23 , 28 , 29 , 31 ), four for pillbox ( 25 – 27 , 32 ), and two for VDOT ( 24 , 30 ). Discussion Key findings: Despite considerable heterogeneity, this systematic review among studies in Asian countries revealed that mHealth interventions improve TB treatment adherence, with an overall 2.4% increase in completion rates. The intervention’s impact was more pronounced in the upper-income group, particularly due to technological advancements (such as smart pillboxes and smartphone apps) and a more effective healthcare system. Repeated reinforcement of medication-taking behaviour, inclusion of an educational component, and interactive communication also promoted treatment adherence. The interactive communication that allows patients to get personalized feedback and engage with healthcare providers proved more effective. Comparison with previous studies: Conventionally used DOT has been proven effective in one-on-one interactions and home visits, compared to VDOT, mobile calls, and community outreach, especially in low-resource settings among Middle Eastern and South African populations ( 34 , 35 ). However, routine DOT treatment revealed several challenges, such as patient inconvenience and cost, especially in limited resource settings ( 15 , 36 ). On the contrary, telemedicine has been a potential intervention in addressing the challenges of the routine DOT strategy and increasing treatment adherence among TB patients in high-burden countries ( 5 , 37 – 40 ). The WHO's “End TB strategies” also promote patient-centered care and prevention, as well as the use of innovative technology to achieve this goal ( 40 ). Trails conducted in rural Vietnam and Indonesia reported a similar increase in adherence to treatment with daily reminders and multimodal technologies ( 18 , 41 ). Reviews quoting RCTs conducted in the United Kingdom, China, and the United States have shown that VDOT resulted in approximately 80% more adherence to treatment than DOT, which is consistent with studies conducted in Asia ( 4 , 5 , 12 , 42 ). Trials conducted in Moldova using the VDOT intervention also showed a decrease in non-adherence to treatment by four days per two weeks while significantly reducing the travel costs ( 43 ). Another multi-country study, conducted in South Africa, the Philippines, and Ethiopia, concluded that only smart pillboxes reduced missed doses by 42% and by 51% when paired with SMS ( 44 ). Sekandi et al. explored the future of VDOT by training artificial intelligence using 861 video images to automate the classification of medication intake, to enhance efficiency, reduce geographical barriers, and manual labor ( 45 ). To further motivate and enhance the AI-based approach a single-arm intervention trial. This trail conducted with 71 participants in Malaysia gamified real-time video-observed therapy. It observed 90.8% adherence to treatment, which surpassed the standard care rate ( 46 ). Plausible explanations: The success of mHealth interventions mainly depends on contextual factors, including the availability of mobile phones, network coverage, patient literacy, acceptability, cultural and economic concerns. Although the pooled estimate reported positive effects, high heterogeneity was observed. Which is consistent with similar systematic reviews and meta-analyses ( 4 , 5 , 12 , 47 , 48 ). This heterogeneity highlights the difference in outcome definitions, study characteristics, settings, and intervention design. It is also a result of various methodologies in which interventions ranged from simple SMS to mixed medication monitors with applications. This spectrum reflects the differing availability of resources, healthcare infrastructure, and digital literacy across various income groups ( 47 ). High-income countries can afford more robust infrastructure and existing integrated digital services ( 5 ). However, countries with high disease burden often struggle with poor quality of healthcare infrastructure, a lack of adequate and trained healthcare providers, thus limiting the effectiveness of interventions ( 49 ). Moreover, disparities in income levels, education, rural-urban differences, and health literacy affect access and use of mHealth tools ( 9 , 49 ). Non-adherence to treatment is influenced by individual factors, psycho-social aspects, socio-economic status, and the healthcare system ( 50 ). Knowledge about TB has been reported to have a strong positive correlation with medication adherence ( 18 , 51 ). However, stigma around the disease leads patients to hide their illness, delay diagnosis, and discontinue treatment to avoid social isolation ( 52 , 53 ). Telemedicine helps maintain privacy and confidentiality, leading to increased attendance by both patients and care providers ( 54 ). Socio-cultural and demographic factors also influence the patient’s attitude towards medications and technologies. The addition of an educational component tackles the psycho-social aspects that are associated with missed treatment and helps reduce the stigma around TB ( 42 , 50 ). Policy implications and future scope: The findings of this review have significant implications for the TB control program. Asia has the highest number of TB cases globally ( 55 ), and integrating mHealth interventions in the National TB programs will make significant progress towards the WHO's End TB strategy ( 56 ). mHealth can reduce the costs associated with facility-based DOT strategy and maintain patient privacy, reduce stigma, and increase accessibility in underserved areas, while monitoring of the interventions. Intersectoral collaboration through public–private partnerships would ensure the equitable availability and accessibility of digital platforms for patients across various strata. A critical research gap was the need for a standardized measurement tool that can monitor treatment adherence and can be applied across all interventions. Implementation research focusing on adapting successful interventions to different healthcare delivery models and patient populations is needed. Comparative research between the interventions would provide direct evidence of adherence. A longitudinal study is recommended to check the relapse rate and outcomes. Limitations: The limited number of studies restricted the generalizability. High heterogeneity limits the accuracy of the pooled estimate. Variations in adherence measurement, ranging from self-reported to clinical outcomes and electronic monitoring, introduced measurement bias that sensitivity analyses could not address. Subgroup analyses for selected secondary outcomes were conducted using minimal data and may have indicated potential bias due to the significant differences in sample size. Despite a thorough search, the funnel plots indicated potential asymmetry, indicating possible overestimation of the effect. Conclusions This review concludes that mHealth interventions improve TB treatment adherence across diverse Asian healthcare settings. While the high heterogeneity makes it difficult to estimate the exact effect size, the improvements in interventions support the use of these technologies in national TB care programs in Asian countries. Although simpler SMS-based approaches are helpful in limited-resource settings, using multi-component interventions, such as SMS with medication monitors, is advised. Declarations Funding: No funding received Conflict of interest: The authors declare no conflict of interest Data availability: The data supporting the findings of this review are available in the included studies Author Contribution SK led the study design, performed the meta-analyses, and drafted the manuscript. SK and NKP conducted the database searches and title and abstract screening. All authors independently performed full-text screening. All authors reviewed, revised, and approved the final manuscript. References Haley CA, Schlossberg D. Treatment of Latent Tuberculosis Infection. 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Int J Environ Res Public Health. 2024;21(12):1662. Thomas BE, Kumar JV, Periyasamy M, Khandewale AS, Hephzibah Mercy J, Raj EM et al. Acceptability of the Medication Event Reminder Monitor for Promoting Adherence to Multidrug-Resistant Tuberculosis Therapy in Two Indian Cities: Qualitative Study of Patients and Health Care Providers. J Med Internet Res. 2021 June 10;23(6):e23294. 1.1 TB incidence [Internet]. [cited 2025 July 9]. Available from: https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports/global-tuberculosis-report-2023/tb-disease-burden/1-1-tb-incidence The End TB Strategy [Internet]. [cited 2025 July 7]. Available from: https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/the-end-tb-strategy Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table 1: Characteristics of the ten included Randomized Controlled Trials Footnote: Abbreviations: DOT = Directly observed therapy; VDOT = Video-observed therapy; SMS = Short messaging service; EMM = Electronic medication monitor; TB = Tuberculosis; DOTS = Directly observed therapy, short course; Y = Yes; N = No; UMIC = Upper-middle income countries; LMIC = Lower-middle income countries. 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12:31:02","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32654,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/cfb142dfdc02298b18142903.png"},{"id":98763217,"identity":"4da1fa68-de97-42b2-b1c6-0708341ae329","added_by":"auto","created_at":"2025-12-22 10:03:06","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39565,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/8dae93762792d27ac7bf139c.png"},{"id":98780109,"identity":"99015684-0f48-4c05-9aa0-ad73c253c302","added_by":"auto","created_at":"2025-12-22 12:31:03","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29945,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure9.png","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/555a98f4440238f6d03af19b.png"},{"id":98763262,"identity":"4143ae05-94a7-4b11-9bf0-a97e2856bf21","added_by":"auto","created_at":"2025-12-22 10:03:07","extension":"xml","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136892,"visible":true,"origin":"","legend":"","description":"","filename":"bd864507377d47098168ea1006d33f041structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/fe39b51f06156ee068369d64.xml"},{"id":98777850,"identity":"3d42ee22-918e-4538-abfd-1caeef530142","added_by":"auto","created_at":"2025-12-22 12:28:34","extension":"html","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150692,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/5556be6f97d308b446ea245a.html"},{"id":98763184,"identity":"bda36e6a-1505-4f12-85fb-68231487442e","added_by":"auto","created_at":"2025-12-22 10:03:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45663,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA 2020 Flow Chart of Study Identification, Screening, and Selection for review (n = 44 articles screened for full-text; n = ten included in meta-analysis; n = 34 excluded with documented reasons for exclusion)\u003c/p\u003e\n\u003cp\u003eFootnote: PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/9923b719ab4f829b7f3136fc.png"},{"id":98763186,"identity":"17a896aa-c290-4cfd-8142-e2e17caca1ae","added_by":"auto","created_at":"2025-12-22 10:03:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":118457,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of Bias 2 (ROB 2) Assessment Summary for Ten included RCTs\u003c/p\u003e\n\u003cp\u003eFootnote: RoB 2 = Cochrane Risk of Bias tool version 2.0; seven studies rated low risk across all domains; three studies with concerns in specific domains\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/2acf528aa7fb466f8391ef04.png"},{"id":98763189,"identity":"fb14b458-173e-4f2e-afd6-b1d654fd9220","added_by":"auto","created_at":"2025-12-22 10:03:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":143499,"visible":true,"origin":"","legend":"\u003cp\u003eRandom-Effects Meta-Analysis\u003c/p\u003e\n\u003cp\u003eFootnote: (a) Forest plot showing pooled estimate. (b) Funnel plot assessing publication bias\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/484bcad65330503bf3bd6185.png"},{"id":98779911,"identity":"b49e2529-1add-469a-a996-ff926558cd38","added_by":"auto","created_at":"2025-12-22 12:30:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":192012,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis by Intervention Type\u003c/p\u003e\n\u003cp\u003eFootnote: SMS (n=four studies); VDOT (n=two studies); Electronic monitors and smart pillboxes (n=four studies); Horizontal lines represent 95% confidence intervals; diamond represents pooled effect estimate.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/1d78a30628ee9056c0d5a7c9.png"},{"id":98780680,"identity":"d1899c1f-1e2d-49cd-a75a-15a15a61acde","added_by":"auto","created_at":"2025-12-22 12:31:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":122907,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis by Country Income Classification\u003c/p\u003e\n\u003cp\u003eFootnote: (a) Lower-middle income countries (n=four studies); (b) Upper-middle income countries (n= studies);\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/66f437b6201a7140f985d48f.png"},{"id":98779692,"identity":"982359cd-138e-47ee-bad7-d34566820dab","added_by":"auto","created_at":"2025-12-22 12:30:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":136522,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis by Communication Frequency\u003c/p\u003e\n\u003cp\u003eFootnote: (a) Daily frequency (n=eight studies); (b) Monthly frequency (n=two studies)\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/edfdbd2c8a844c34dfd9335e.png"},{"id":98779852,"identity":"b6ab36aa-ad4f-420d-b94e-f375ad2c4bbf","added_by":"auto","created_at":"2025-12-22 12:30:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":169135,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis by Inclusion of Educational Component\u003c/p\u003e\n\u003cp\u003eFootnote: (a) With education component (n= seven studies); (b) Without education component (n= three studies)\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/42208461a12ffa6149f6d822.png"},{"id":98779422,"identity":"ddeb06d6-ea55-4668-8085-0a29fd39f980","added_by":"auto","created_at":"2025-12-22 12:30:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":206107,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis by Technological Complexity Level\u003c/p\u003e\n\u003cp\u003eFootnote: (a) Low complexity = Simple SMS or automated voice calls without internet or applications (n= four studies); (b) Medium complexity = Smartphones with standard messaging applications (WhatsApp, WeChat, Line) without specialized software (n= two studies); (c) High complexity = Dedicated mobile applications, web dashboards, smart pillboxes, or video-observed therapy platforms with real-time monitoring (n= four studies)\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/e63f6d6f9583e09a08a9a7d8.png"},{"id":98778339,"identity":"042404aa-bc76-4471-834a-43b7eb9b2870","added_by":"auto","created_at":"2025-12-22 12:29:10","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":160188,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis by Communication Direction\u003c/p\u003e\n\u003cp\u003eFootnote: (a) ) Unidirectional communication (n= four studies); (b) Bidirectional communication (n= six studies)\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/d43039cb34b3126442ed1a9b.png"},{"id":108437723,"identity":"b2cdcfbc-2b76-4f95-ac93-a487eb74bfeb","added_by":"auto","created_at":"2026-05-04 16:02:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1301089,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/967dde76-57ef-4f32-82c5-18133e68cb5f.pdf"},{"id":98763193,"identity":"93f56354-f8ce-44d6-bfea-9ad17712078a","added_by":"auto","created_at":"2025-12-22 10:03:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18249,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1: Characteristics of the ten included Randomized Controlled Trials\u003c/p\u003e\n\u003cp\u003eFootnote: Abbreviations: DOT = Directly observed therapy; VDOT = Video-observed therapy; SMS = Short messaging service; EMM = Electronic medication monitor; TB = Tuberculosis; DOTS = Directly observed therapy, short course; Y = Yes; N = No; UMIC = Upper-middle income countries; LMIC = Lower-middle income countries.\u003c/p\u003e","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/26faeab9b1a4f1b0193fc447.docx"},{"id":98779181,"identity":"18296cf8-d1cb-4c57-8915-6064662f7227","added_by":"auto","created_at":"2025-12-22 12:30:01","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":277108,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial1PrismaChecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/e14608d148eed0c1c82522d9.docx"},{"id":98779882,"identity":"4f1aaefe-bde1-4fc6-80ac-6837aea1a76b","added_by":"auto","created_at":"2025-12-22 12:30:52","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16026,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2SearchStrategy.docx","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/a843b1b9d8e0163321780884.docx"},{"id":98779201,"identity":"7215d851-2a15-4fea-a233-cf0dfa9adf9a","added_by":"auto","created_at":"2025-12-22 12:30:04","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":21863,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial3fulltextscreening.docx","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/9ed6d65bb6de76ab8da02482.docx"},{"id":98778439,"identity":"5a337f4a-ee04-415d-b255-a694aeaf8a56","added_by":"auto","created_at":"2025-12-22 12:29:15","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":20111,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial4ROB2Explanation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8383251/v1/36a86c239ed556dd779f44c6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effective mHealth Interventions to Improve Tuberculosis Treatment Adherence in Asia: A Systematic Review and Meta-Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB), an infectious lung disease caused by the bacteria \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e, has been a public health concern for decades. Despite being curable with a six-month effective treatment regimen, it caused about 1.4\u0026nbsp;million deaths in 2023 worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The region of Southeast Asia alone accounts for 44% of the total new TB cases (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The global TB burden is disproportionately high in Asia, with half of the top fourteen high-burden countries from this region, namely Bangladesh, China, Democratic People\u0026rsquo;s Republic of Korea, India, Indonesia, Myanmar, and Pakistan (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMaintaining adherence rates above 90% was considered successful treatment adherence (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). While non-adherence with treatment is known to cause mortality, morbidity, prolonged treatment duration, and multidrug-resistant TB (MDR-TB) (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). It also impacts the healthcare system with increased costs and prolonged hospitalizations (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In response to this challenge the WHO\u0026rsquo;s End TB Strategy prioritizes treatment supervision to ensure adherence and facilitate desired outcome (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is important to understand the reasons for non-adherence in addressing the challenges of TB drug resistance. For example, patient-related factors like forgetfulness, insufficient knowledge about the disease and its treatment, the psychological toll, and personal beliefs (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Similarly, social factors like lack of support from friends and family, stigma and discrimination, especially with comorbidities like HIV (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Along with economic hardships like financial constraints to acquire medications, travel to the clinics, or nutrition (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Healthcare organizational issues, such as poor provider response or absenteeism, medication unavailability, and inconvenient clinical hours also contribute to non-adherence (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Due to the highly personalized barriers, a one-size-fits-all approach is not favoured (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDirectly Observed Treatment (DOT) has been recommended internationally for monitoring treatment adherence (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Increased adherence with DOT is highly subjective, depending on the patient population, implementation quality, and factors such as travel and cost to the facility. An Ethiopian study reported that patients travelled 70 hours on average to the DOT facility (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMobile Health (mHealth) technologies, range from simple Short Messaging Service (SMS) and voice calling to comprehensive ones like Video-Observed Therapy (VDOT) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and smart pillboxes. They are low-cost and more beneficial in resource-limited settings (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The appeal of mHealth lies in its capacity to overcome geographical barriers, work with the existing healthcare systems, and streamline communication to efficiently deliver incentives (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). mHealth interventions for TB treatment adherence have shown inconsistent effectiveness, mainly due to variations in study conduction, which makes it challenging to apply them to a broader population (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe existing systematic reviews have explored various interventions but the most effective in lower-resource settings is unclear. As mHealth interventions are context-dependent, better clarity is needed to identify and recommend those most suited to lower-income settings (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). As most Asian countries are in the low- or lower-middle income group (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), interventions tailored to their needs and infrastructure are much needed.\u003c/p\u003e \u003cp\u003eTo address this gap, we aimed to systematically evaluate the effectiveness of mHealth interventions against standard care in improving treatment adherence among adults undergoing TB treatment in Asian countries. The primary objective of this review was to determine the impact of mHealth interventions compared with standard care on TB treatment adherence using randomized controlled trials (RCTs) from Asian countries. The secondary objectives were to identify factors contributing to differences in effectiveness, including intervention type, country income classification, reminder frequency, educational content, technological complexities, and the direction of communication.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e This review used a systematic review methodology. The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the PICO framework guided the design (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) (Supplementary 1: PRISMA checklist). \u003cb\u003eP\u003c/b\u003eopulation included patients 18 years and above with a confirmed diagnosis of TB and residents or those getting treated in Asian countries; \u003cb\u003eI\u003c/b\u003entervention included mHealth strategies; \u003cb\u003eC\u003c/b\u003eontrol was standard care, and \u003cb\u003eO\u003c/b\u003eutcome was adherence to the prescribed treatment regimen until completion. This review has been registered in Prospero with ID CRD420251001579.\u003c/p\u003e \u003cp\u003eEligibility criteria:\u003c/p\u003e \u003cp\u003eOnly Randomised Controlled Trials (RCTs) conducted in Asian countries on adult patients aged 18 years and above were selected. Studies were eligible if they investigated the effect of mHealth interventions, such as Short Message Services (SMS), telephonic calls, web applications, mobile applications, Video-Observed Therapy (VDOT), medication monitors, Artificial Intelligence (AI), and Machine Learning (ML) based interventions (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), and smart pillboxes, compared to standard care. Outcome measures included treatment adherence to the prescribed treatment regimen. Articles were excluded if they did not report original data. Searches were not limited by date of publication or language.\u003c/p\u003e \u003cp\u003eDatabases and search strategy:\u003c/p\u003e \u003cp\u003eA comprehensive literature search was conducted across seven databases: PubMed, ScienceDirect, Directory of Open Access Journals, Cochrane Central Register of Controlled Trials, Ovid, Liliacs, and the Clinical Trials registry of the United States National Library of Medicine (US NLM). The first 10 pages of the search engine, Google Scholar, were included. Authors also searched three grey literature databases, Medrxiv, Psyrxiv, and Shodhganga. Snowballing technique was employed to identify more relevant studies, which involved both backward and forward citation searching. Search terms like Telemedicine, Text Messaging, Mobile Applications, mHealth, remote monitoring, Tuberculosis, Medication Adherence, treatment adherence, and RCT, were used as part of the search strategy either alone or in combination. Boolean search operators, truncations, and Medical Subject Headings (MeSH) were used to refine the search. Specific search string for each database is provided in Supplementary Material 2.\u003c/p\u003e \u003cp\u003eSelection process:\u003c/p\u003e \u003cp\u003eArticles were first screened from titles and abstracts within the database or exported to Zotero by SK and NP. Inclusion and exclusion criteria were used to eliminate irrelevant articles. Studies selected for full-text screening were exported to Rayyan and independently assessed by three reviewers (SK, NK, and AC). Conflicts were resolved through discussion.\u003c/p\u003e \u003cp\u003eData Extraction and Quality Assessment:\u003c/p\u003e \u003cp\u003eFor feasibility purposes a unique study identification (ID) was assigned to the 10 included studies. An Excel sheet was used to extract data. It included study characteristics such as title, author, publication year, and country and focused on treatment adherence, sample size for both the intervention and control groups, and the number of patients who completed and did not complete the treatment. For secondary analyses type of intervention, income levels, frequency of communication, inclusion of educational component, complexity of technologies used, and the direction of communication were recorded. Data extraction was done by SK and cross-checked by NKP. Any discrepancies were resolved through discussions.\u003c/p\u003e \u003cp\u003eThe Cochrane Risk of Bias tool 2.0 (RoB 2) was used to evaluate the methodological quality of the included studies. Risk of bias results were summarized using risk of bias graphs (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), and justification for the same is provided in supplementary material 4.\u003c/p\u003e \u003cp\u003eData analysis: The primary meta-analysis employed a random-effects model to calculate the risk ratios (RR) and 95% confidence intervals (CI) for treatment adherence rates in mHealth interventions compared with standard care. Subgroup analyses were performed as part of secondary analysis. For the income group, the ten RCTs were divided according to World Bank Income classifications (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Statistical heterogeneity was quantified using I\u003csup\u003e2\u003c/sup\u003e statistics, with values of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity. A pre-planned sensitivity analysis using a fixed-effects model was conducted to verify the difference between the studies and find the actual reason for heterogeneity.\u003c/p\u003e \u003cp\u003eThe meta-analyses were performed using SPSS 29.0. Publication bias was checked using Egger\u0026rsquo;s regression-based test to statistically check for small-study effects, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating possible publication bias. Funnel plots were also used to check visually for any potential gaps in the literature or selective reporting.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eStudy selection:\u003c/p\u003e \u003cp\u003eThe database searches yielded 1250 results, and 197 articles were identified from grey literature and citation searching. After removing 1018 duplicates, 409 articles were selected for abstract and title screening. A total of 44 articles were screened for full texts, of which 34 were excluded (supplementary material 3). Ultimately, ten articles were included for the systematic review and meta-analysis (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eFigure 1. PRISMA 2020 Flow Chart of Study Identification, Screening, and Selection for review (n\u0026thinsp;=\u0026thinsp;44 articles screened for full-text; n\u0026thinsp;=\u0026thinsp;ten included in meta-analysis; n\u0026thinsp;=\u0026thinsp;34 excluded with documented reasons for exclusion)\u003c/p\u003e \u003cp\u003eFootnote: PRISMA\u0026thinsp;=\u0026thinsp;Preferred Reporting Items for Systematic Reviews and Meta-Analyses\u003c/p\u003e \u003cp\u003eStudy characteristics:\u003c/p\u003e \u003cp\u003eThe ten trials selected were conducted in diverse geographic and socioeconomic settings (Table\u0026nbsp;1). Out of which, five studies were conducted in China (\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), two in Pakistan (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), and one each in Thailand (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), India (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), and the Philippines (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). These regions represent two socioeconomic groups, with six studies conducted in upper-middle-income countries (UMICs) such as China and Thailand (\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), while four studies were conducted in low-middle-income countries (LMICs) like India, Thailand, Pakistan, and the Philippines (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Eight trials (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) focused on urban and semi-urban populations, while two trials in China (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and one in Thailand (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) focused on rural populations. Most trials were implemented in the public healthcare sector, except for those conducted by Mohammed et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), Gupta et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), and Fang et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), which collaborated with private sector clinics or used a hybrid setting.\u003c/p\u003e \u003cp\u003eSignificant heterogeneity was found in the design and delivery of mHealth interventions across the studies. Some studies employed standalone interventions, while others used multi-component interventions. Six studies (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)incorporated SMS alone or with another mHealth intervention, with varying frequencies and levels of interactivity. Two studies used web- and application-based VDOT interventions (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), while two studies (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) used Pillboxes to track medication adherence, one of which provided participants with an SMS option (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Two trials only relied on web-based Medication Monitors (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll studies employed computer-generated randomization, and given the nature of the intervention, an open-label design was the predominant approach. All the studies adhered to the six to eight months treatment duration, as per WHO guidelines, except for the trial in Thailand(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), which was observed for four months only. All studies followed the WHO guidelines for treatment(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The primary outcome in all studies was treatment adherence, which was measured differently, including tracking missed doses, clinical outcomes, and self-reports (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;1: Characteristics of the ten included Randomized Controlled Trials\u003c/p\u003e \u003cp\u003eFootnote: Abbreviations: DOT\u0026thinsp;=\u0026thinsp;Directly observed therapy; VDOT\u0026thinsp;=\u0026thinsp;Video-observed therapy; SMS\u0026thinsp;=\u0026thinsp;Short messaging service; EMM\u0026thinsp;=\u0026thinsp;Electronic medication monitor; TB\u0026thinsp;=\u0026thinsp;Tuberculosis; DOTS\u0026thinsp;=\u0026thinsp;Directly observed therapy, short course; Y\u0026thinsp;=\u0026thinsp;Yes; N\u0026thinsp;=\u0026thinsp;No; UMIC\u0026thinsp;=\u0026thinsp;Upper-middle income countries; LMIC\u0026thinsp;=\u0026thinsp;Lower-middle income countries.\u003c/p\u003e \u003cp\u003eRisk of Bias assessment:\u003c/p\u003e \u003cp\u003eRoB2 assessment concluded that the studies were generally of high methodological quality (Fig.\u0026nbsp;2, supplementary material 4). Of all the assessed studies, seven demonstrated a low risk of bias across all five domains, indicating that they were well-executed (\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Conversely, three studies revealed concerns in specific domains (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure 2: Risk of Bias 2 (ROB 2) Assessment Summary for Ten included RCTs\u003c/p\u003e \u003cp\u003eFootnote: RoB 2\u0026thinsp;=\u0026thinsp;Cochrane Risk of Bias tool version 2.0; seven studies rated low risk across all domains; three studies with concerns in specific domains\u003c/p\u003e \u003cp\u003ePrimary Outcome Analysis: Treatment Adherence\u003c/p\u003e \u003cp\u003eA meta-analysis of the ten studies revealed a small yet statistically significant improvement in tuberculosis treatment adherence with mHealth interventions compared to standard care (p\u0026thinsp;=\u0026thinsp;0.01). A total of 7,420 patients were enrolled in the control group and 9,728 in the intervention group, among whom 6,176 (83.23%) in the control group completed the treatment, compared to 8,329 (85.61%) in the intervention group. Forest plot including all ten studies showed an overall risk ratio of 1.09 (95% CI: 1.02\u0026ndash;1.16), indicating that the patients receiving standard care were 9% more likely to quit treatment before completion (Fig.\u0026nbsp;3, part (a)). The random-effect model showed heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;94%), indicating considerable variations in the interventions provided. The sensitivity analysis conducted with the fixed-effects model yielded a lower risk ratio (RR\u0026thinsp;=\u0026thinsp;1.02, 95% CI\u0026thinsp;=\u0026thinsp;1.00-1.02, I\u0026sup2; = 92%, Fig.\u0026nbsp;3 part (b)) compared to the random-effects model (RR\u0026thinsp;=\u0026thinsp;1.08). The high heterogeneity in both models indicates variability across included studies.\u003c/p\u003e \u003cp\u003eFigure 3. Random-Effects Meta-Analysis\u003c/p\u003e \u003cp\u003eFootnote: (a) Forest plot showing pooled estimate. (b) Funnel plot assessing publication bias\u003c/p\u003e \u003cp\u003eSubgroup analysis:\u003c/p\u003e\n\u003ch3\u003e1) Based on the type of intervention:\u003c/h3\u003e\n\u003cp\u003eA subgroup analysis was conducted using mHealth intervention technology to investigate variations in intervention effectiveness. Interventions were categorised into three groups \u0026ndash; SMS, VDOT, and electronic monitoring, including pillboxes, medication monitors, and application-based technologies. Four studies explored SMS-based interventions with a pooled effect of RR\u0026thinsp;=\u0026thinsp;1.06 (95% CI\u0026thinsp;=\u0026thinsp;0.09\u0026ndash;1.1, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;66%, Fig.\u0026nbsp;4a). Two studies were assessed for the VDOT ap Fig.\u0026nbsp;4b). Electronic monitoring and pillbox interventions showed the most treatment adherence among the groups at a pooled effect size of RR\u0026thinsp;=\u0026thinsp;1.12 (95% CI\u0026thinsp;=\u0026thinsp;0.09\u0026ndash;1.28, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;98%, Fig.\u0026nbsp;4c). However, this group showed an extremely high heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;98%), exceeding the overall analysis. While the SMS and VDOT groups revealed I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;66% and I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;34%. In general, all groups based on intervention type indicated high heterogeneity, suggesting variation in the application of the intervention. Also, the improvement in treatment adherence with mHealth interventions compared to standard care was not statistically significant.\u003c/p\u003e \u003cp\u003eFigure 4: Subgroup Analysis by Intervention Type\u003c/p\u003e \u003cp\u003eFootnote: SMS (n\u0026thinsp;=\u0026thinsp;four studies); VDOT (n\u0026thinsp;=\u0026thinsp;two studies); Electronic monitors and smart pillboxes (n\u0026thinsp;=\u0026thinsp;four studies); Horizontal lines represent 95% confidence intervals; diamond represents pooled effect estimate.\u003c/p\u003e\n\u003ch3\u003e2) Based on the income levels of the countries:\u003c/h3\u003e\n\u003cp\u003eSub-group analysis of UMIC (\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) demonstrated a statistically significant improvement in treatment adherence (RR\u0026thinsp;=\u0026thinsp;1.12, 95% CI: 1.03\u0026ndash;1.23; I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;89%, Fig.\u0026nbsp;5a), while LMIC (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) showed no significant difference (RR\u0026thinsp;=\u0026thinsp;1.00, 95% CI: 0.99\u0026ndash;1.01; I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%, Fig.\u0026nbsp;5b). Among UMIC, 82.6% of 4935 patients enrolled in the intervention arm completed the treatment, compared to 76.2% of patients enrolled in the control arm, yielding a 6.4% increase in adherence. However, in the LMIC, only a 0.5% increase in treatment adherence was observed in the intervention (88.7% completion rate, n\u0026thinsp;=\u0026thinsp;4793) compared to the control group (88.2% completion rate, n\u0026thinsp;=\u0026thinsp;4337).\u003c/p\u003e \u003cp\u003eFigure 5: Subgroup Analysis by Country Income Classification\u003c/p\u003e \u003cp\u003eFootnote: (a) Lower-middle income countries (n\u0026thinsp;=\u0026thinsp;four studies); (b) Upper-middle income countries (n\u0026thinsp;=\u0026thinsp;studies);\u003c/p\u003e\n\u003ch3\u003e3) Based on the frequency of communication:\u003c/h3\u003e\n\u003cp\u003eEight studies(\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) used daily reminders or check-ins, and two (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) opted for monthly in-person check-ins. Among the daily frequency group, a 7.2% improvement in treatment adherence rates and statistical analysis showed significant improvement (RR\u0026thinsp;=\u0026thinsp;1.12, 95% CI\u0026thinsp;=\u0026thinsp;1.04\u0026ndash;1.20, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;88%, Fig.\u0026nbsp;6a) was seen in the intervention group (82.48% compared to 75.18% in the control), compared to a negligible 0.2% effect of monthly reminders provided with no significant effect (RR\u0026thinsp;=\u0026thinsp;1.00, 95% CI\u0026thinsp;=\u0026thinsp;0.98\u0026ndash;1.01, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%, Fig.\u0026nbsp;6b).\u003c/p\u003e \u003cp\u003eFigure 6: Subgroup Analysis by Communication Frequency\u003c/p\u003e \u003cp\u003eFootnote: (a) Daily frequency (n\u0026thinsp;=\u0026thinsp;eight studies); (b) Monthly frequency (n\u0026thinsp;=\u0026thinsp;two studies)\u003c/p\u003e\n\u003ch3\u003e4) Based on incorporating an educational component in the intervention:\u003c/h3\u003e\n\u003cp\u003eSeven studies (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) included educational components, such as tuberculosis knowledge, management of side effects, nutritional guidance, and disease awareness content. In these studies, with educational components, the pooled treatment adherence rate in the intervention arm was 85.6%. In comparison, the rates in the control arms was 82.9%, showing a statistically significant improvement (RR\u0026thinsp;=\u0026thinsp;1.11, 95% CI\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;1.20, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;94%, Fig.\u0026nbsp;7a). The three studies (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) lacking educational components demonstrated completion rates of 85.7% in the intervention group and 85.0% in the control group, indicating a mere 0.7% difference that was statistically not significance (RR\u0026thinsp;=\u0026thinsp;1.01, 95% CI\u0026thinsp;=\u0026thinsp;0.98\u0026ndash;1.04, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%, Fig.\u0026nbsp;7b).\u003c/p\u003e \u003cp\u003eFigure 7: Subgroup Analysis by Inclusion of Educational Component\u003c/p\u003e \u003cp\u003eFootnote: (a) With education component (n\u0026thinsp;=\u0026thinsp;seven studies); (b) Without education component (n\u0026thinsp;=\u0026thinsp;three studies)\u003c/p\u003e\n\u003ch3\u003e5) Based on technological complexities:\u003c/h3\u003e\n\u003cp\u003eLow-complexity interventions used only simple mobile phones for SMS reminders or automated voice calls, without internet access or special apps, and showed a 2.2% increase in treatment adherence (from 81.5% to 83.7%, RR\u0026thinsp;=\u0026thinsp;1.06, 95% CI\u0026thinsp;=\u0026thinsp;0.99\u0026ndash;1.14, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;9%, Fig.\u0026nbsp;8a) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Medium-complexity interventions utilized smartphones with popular messaging apps, such as WhatsApp, Line, or WeChat, but did not require any special software or web portals (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This only led to a 0.1% improvement in adherence (from 88.8% to 88.9%, RR\u0026thinsp;=\u0026thinsp;1.00, 95% CI\u0026thinsp;=\u0026thinsp;0.98\u0026ndash;1.01, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%, Fig.\u0026nbsp;8b). High-complexity interventions incorporated dedicated mobile applications, web dashboards, smart pillboxes, or video-observed therapy platforms with real-time monitoring, achieving significant improvement of 13.2% (from 68.8% to 82.0%) (RR\u0026thinsp;=\u0026thinsp;1.17, 95% CI\u0026thinsp;=\u0026thinsp;1.04\u0026ndash;1.33, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;91%, Fig.\u0026nbsp;8c) (\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure 8: Subgroup Analysis by Technological Complexity Level\u003c/p\u003e \u003cp\u003eFootnote: (a) Low complexity\u0026thinsp;=\u0026thinsp;Simple SMS or automated voice calls without internet or applications (n\u0026thinsp;=\u0026thinsp;four studies); (b) Medium complexity\u0026thinsp;=\u0026thinsp;Smartphones with standard messaging applications (WhatsApp, WeChat, Line) without specialized software (n\u0026thinsp;=\u0026thinsp;two studies); (c) High complexity\u0026thinsp;=\u0026thinsp;Dedicated mobile applications, web dashboards, smart pillboxes, or video-observed therapy platforms with real-time monitoring (n\u0026thinsp;=\u0026thinsp;four studies)\u003c/p\u003e\n\u003ch3\u003e6) Based on the direction of communication:\u003c/h3\u003e\n\u003cp\u003eA total of six studies (\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) using bidirectional communication showed 4.86% more adherence (82.25% in the intervention and 77.39% in the control) compared to a mere 0.74% in unidirectional communication. Bidirectional communication statistically showed a clear advantage over unidirectional communication. (Bidirectional: RR\u0026thinsp;=\u0026thinsp;1.11, 95% CI\u0026thinsp;=\u0026thinsp;1.00\u0026ndash;1.22, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5%, Fig.\u0026nbsp;9a; Unidirectional: RR\u0026thinsp;=\u0026thinsp;1.06, 95% CI\u0026thinsp;=\u0026thinsp;0.98\u0026ndash;1.14, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;76%, Fig.\u0026nbsp;9b). Notably, baseline treatment rates were higher in unidirectional studies (90% in intervention vs 82.2% in control groups) compared to bidirectional studies (82.25% in intervention vs 77.3% in control groups), suggesting a potential ceiling effect where already high baseline success rates in unidirectional study settings limited the opportunity for intervention benefit (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure 9: Subgroup Analysis by Communication Direction\u003c/p\u003e \u003cp\u003eFootnote: (a) ) Unidirectional communication (n\u0026thinsp;=\u0026thinsp;four studies); (b) Bidirectional communication (n\u0026thinsp;=\u0026thinsp;six studies)\u003c/p\u003e \u003cp\u003ePublication Bias Assessment:\u003c/p\u003e \u003cp\u003eThe funnel plot (Fig.\u0026nbsp;3 (b)) displayed asymmetry with clustering on the right side (larger effect sizes), suggesting possible small-study effects or missing studies with moderate/null findings. However, Egger's test for the random-effects model showed no evidence of publication bias (intercept\u0026thinsp;=\u0026thinsp;0.04; 95% CI: -0.06\u0026ndash;0.15; p\u0026thinsp;=\u0026thinsp;0.37). Intervention-specific assessments showed no publication bias for SMS interventions (p\u0026thinsp;=\u0026thinsp;0.510) or electronic monitoring systems (p\u0026thinsp;=\u0026thinsp;0.83). However, the evaluation for VDOT was limited by only two studies, which was insufficient for Egger\u0026rsquo;s test. Overall subgroup analysis by technology type was underpowered to assess publication bias because of small study numbers, such as four for SMS (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), four for pillbox (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), and two for VDOT (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eKey findings: Despite considerable heterogeneity, this systematic review among studies in Asian countries revealed that mHealth interventions improve TB treatment adherence, with an overall 2.4% increase in completion rates. The intervention\u0026rsquo;s impact was more pronounced in the upper-income group, particularly due to technological advancements (such as smart pillboxes and smartphone apps) and a more effective healthcare system. Repeated reinforcement of medication-taking behaviour, inclusion of an educational component, and interactive communication also promoted treatment adherence. The interactive communication that allows patients to get personalized feedback and engage with healthcare providers proved more effective.\u003c/p\u003e \u003cp\u003eComparison with previous studies: Conventionally used DOT has been proven effective in one-on-one interactions and home visits, compared to VDOT, mobile calls, and community outreach, especially in low-resource settings among Middle Eastern and South African populations (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). However, routine DOT treatment revealed several challenges, such as patient inconvenience and cost, especially in limited resource settings (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). On the contrary, telemedicine has been a potential intervention in addressing the challenges of the routine DOT strategy and increasing treatment adherence among TB patients in high-burden countries (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). The WHO's \u0026ldquo;End TB strategies\u0026rdquo; also promote patient-centered care and prevention, as well as the use of innovative technology to achieve this goal (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Trails conducted in rural Vietnam and Indonesia reported a similar increase in adherence to treatment with daily reminders and multimodal technologies (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Reviews quoting RCTs conducted in the United Kingdom, China, and the United States have shown that VDOT resulted in approximately 80% more adherence to treatment than DOT, which is consistent with studies conducted in Asia (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Trials conducted in Moldova using the VDOT intervention also showed a decrease in non-adherence to treatment by four days per two weeks while significantly reducing the travel costs (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Another multi-country study, conducted in South Africa, the Philippines, and Ethiopia, concluded that only smart pillboxes reduced missed doses by 42% and by 51% when paired with SMS (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Sekandi et al. explored the future of VDOT by training artificial intelligence using 861 video images to automate the classification of medication intake, to enhance efficiency, reduce geographical barriers, and manual labor (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). To further motivate and enhance the AI-based approach a single-arm intervention trial. This trail conducted with 71 participants in Malaysia gamified real-time video-observed therapy. It observed 90.8% adherence to treatment, which surpassed the standard care rate (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlausible explanations:\u003c/p\u003e \u003cp\u003eThe success of mHealth interventions mainly depends on contextual factors, including the availability of mobile phones, network coverage, patient literacy, acceptability, cultural and economic concerns. Although the pooled estimate reported positive effects, high heterogeneity was observed. Which is consistent with similar systematic reviews and meta-analyses (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). This heterogeneity highlights the difference in outcome definitions, study characteristics, settings, and intervention design. It is also a result of various methodologies in which interventions ranged from simple SMS to mixed medication monitors with applications. This spectrum reflects the differing availability of resources, healthcare infrastructure, and digital literacy across various income groups (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). High-income countries can afford more robust infrastructure and existing integrated digital services (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, countries with high disease burden often struggle with poor quality of healthcare infrastructure, a lack of adequate and trained healthcare providers, thus limiting the effectiveness of interventions (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Moreover, disparities in income levels, education, rural-urban differences, and health literacy affect access and use of mHealth tools (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNon-adherence to treatment is influenced by individual factors, psycho-social aspects, socio-economic status, and the healthcare system (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Knowledge about TB has been reported to have a strong positive correlation with medication adherence (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). However, stigma around the disease leads patients to hide their illness, delay diagnosis, and discontinue treatment to avoid social isolation (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Telemedicine helps maintain privacy and confidentiality, leading to increased attendance by both patients and care providers (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Socio-cultural and demographic factors also influence the patient\u0026rsquo;s attitude towards medications and technologies. The addition of an educational component tackles the psycho-social aspects that are associated with missed treatment and helps reduce the stigma around TB (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePolicy implications and future scope:\u003c/p\u003e \u003cp\u003eThe findings of this review have significant implications for the TB control program. Asia has the highest number of TB cases globally (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e), and integrating mHealth interventions in the National TB programs will make significant progress towards the WHO's End TB strategy (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). mHealth can reduce the costs associated with facility-based DOT strategy and maintain patient privacy, reduce stigma, and increase accessibility in underserved areas, while monitoring of the interventions. Intersectoral collaboration through public\u0026ndash;private partnerships would ensure the equitable availability and accessibility of digital platforms for patients across various strata.\u003c/p\u003e \u003cp\u003eA critical research gap was the need for a standardized measurement tool that can monitor treatment adherence and can be applied across all interventions. Implementation research focusing on adapting successful interventions to different healthcare delivery models and patient populations is needed. Comparative research between the interventions would provide direct evidence of adherence. A longitudinal study is recommended to check the relapse rate and outcomes.\u003c/p\u003e \u003cp\u003eLimitations:\u003c/p\u003e \u003cp\u003eThe limited number of studies restricted the generalizability. High heterogeneity limits the accuracy of the pooled estimate. Variations in adherence measurement, ranging from self-reported to clinical outcomes and electronic monitoring, introduced measurement bias that sensitivity analyses could not address. Subgroup analyses for selected secondary outcomes were conducted using minimal data and may have indicated potential bias due to the significant differences in sample size. Despite a thorough search, the funnel plots indicated potential asymmetry, indicating possible overestimation of the effect.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e This review concludes that mHealth interventions improve TB treatment adherence across diverse Asian healthcare settings. While the high heterogeneity makes it difficult to estimate the exact effect size, the improvements in interventions support the use of these technologies in national TB care programs in Asian countries. Although simpler SMS-based approaches are helpful in limited-resource settings, using multi-component interventions, such as SMS with medication monitors, is advised.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNo funding received\u003c/p\u003e \u003cp\u003eConflict of interest: The authors declare no conflict of interest\u003c/p\u003e \u003cp\u003eData availability: The data supporting the findings of this review are available in the included studies\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSK led the study design, performed the meta-analyses, and drafted the manuscript. SK and NKP conducted the database searches and title and abstract screening. All authors independently performed full-text screening. All authors reviewed, revised, and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHaley CA, Schlossberg D. 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Implementation of Alarm and Pill Reminder for Medication Adherence in Tuberculosis Patients. 2025;16(01).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKastien-Hilka T, Abulfathi A, Rosenkranz B, Bennett B, Schwenkglenks M, Sinanovic E. Health-related quality of life and its association with medication adherence in active pulmonary tuberculosis\u0026ndash; a systematic review of global literature with focus on South Africa. Health Qual Life Outcomes. 2016;14(1):42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavenscroft L, Kettle S, Persian R, Ruda S, Severin L, Doltu S, et al. Video-observed therapy and medication adherence for tuberculosis patients: randomised controlled trial in Moldova. Eur Respir J. 2020;56(2):2000493.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTadesse AW, Mganga A, Dube TN, Alacapa J, Van Kalmthout K, Letta T, et al. 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The Impact of Digital Adherence Technologies on Health Outcomes in Tuberculosis: A Systematic Review and Meta-Analysis [Internet]. 2024 [cited 2025 July 7]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://medrxiv.org/lookup/doi/\u003c/span\u003e\u003cspan address=\"http://medrxiv.org/lookup/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2024.01.31.24302115\u003c/span\u003e\u003cspan address=\"10.1101/2024.01.31.24302115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT\u0026aacute;rtaro AF, Silva RV dos, Ara\u0026uacute;jo S, de Ramos JST, Berra ACV, Alves TZ. Digital health for treatment adherence in people with tuberculosis: a systematic review. 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Factors Related to Medication Adherence in Adult Patients with Tuberculosis. Korean J Adult Nurs. 2018;30(5):493.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandes A, Laohaprapanon S, Nam TT, Sequeira EMDC, Le CN. Adherence to Pulmonary Tuberculosis Medication and Associated Factors Among Adults: A Cross-Sectional Study in the Metinaro and Becora Sub-Districts, Dili, Timor-Leste. Int J Environ Res Public Health. 2024;21(12):1662.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas BE, Kumar JV, Periyasamy M, Khandewale AS, Hephzibah Mercy J, Raj EM et al. Acceptability of the Medication Event Reminder Monitor for Promoting Adherence to Multidrug-Resistant Tuberculosis Therapy in Two Indian Cities: Qualitative Study of Patients and Health Care Providers. J Med Internet Res. 2021 June 10;23(6):e23294.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e1.1 TB incidence [Internet]. [cited 2025 July 9]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports/global-tuberculosis-report-2023/tb-disease-burden/1-1-tb-incidence\u003c/span\u003e\u003cspan address=\"https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports/global-tuberculosis-report-2023/tb-disease-burden/1-1-tb-incidence\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe End TB Strategy [Internet]. [cited 2025 July 7]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/the-end-tb-strategy\u003c/span\u003e\u003cspan address=\"https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/the-end-tb-strategy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Asia, mHealth, Medication monitor, Mobile health interventions, SMS, Smart pillbox, Telemedicine, Treatment adherence, Video-observed therapy (VDOT)","lastPublishedDoi":"10.21203/rs.3.rs-8383251/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8383251/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTuberculosis (TB) has been one of the most common causes of mortality due to communicable diseases, with an estimated 1.25\u0026nbsp;million deaths in 2023 worldwide. In Asia, where most new TB cases are reported, treatment non-adherence is affected by complicated factors and practical challenges of implementing directly observed therapy (DOT). Mobile health (mHealth) tools bridge the provider-patient gap and may improve treatment adherence. We aim to systematically evaluate the impact of mHealth interventions compared to standard care on TB treatment adherence by synthesizing data from randomized controlled trials (RCTs) conducted in Asia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies were eligible if they were RCTs conducted in an Asian country evaluating mHealth interventions against standard care. Authors searched seven databases, one search engine, and three grey literature sources, with no date or language restrictions. The primary outcome was treatment adherence, and secondary outcomes included type of intervention, income levels, frequency of communication, inclusion of educational component, complexity of technologies used, and the direction of communication. To explore sources of heterogeneity a pre-specified subgroup analyses was conducted. Pooled risk ratios were estimated using random-effects model; heterogeneity was assessed by I\u003csup\u003e2\u003c/sup\u003e, and publication bias by Egger\u0026rsquo;s test and funnel plots.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAuthors screened 1427 articles, out of which ten trials involving 17,148 participants met the criteria for analysis. mHealth interventions improved treatment adherence compared to standard care (85.6% vs 83.2%; Risk Ratio (RR) 1.09, 95% Confidence Interval (CI) 1.02\u0026ndash;1.16; p\u0026thinsp;=\u0026thinsp;0.01; I\u0026sup2; = 94%). Subgroup analysis indicated increased adherence with bidirectional communication (4.9%), daily reminders (7.2%), the inclusion of an educational component (2.7%), and the use of combination technology (13.2%). No significant publication bias was detected (Egger\u0026rsquo;s p\u0026thinsp;=\u0026thinsp;0.375).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003emHealth intervention yields a small but meaningful improvement in treatment adherence in Asian settings. Even 2.4% increase in adherence in Asian countries, where TB is a significant burden, could lead to thousands getting cured, decreased relapse rates, fewer drug resistance cases, and decreased transmissions.\u003c/p\u003e","manuscriptTitle":"Effective mHealth Interventions to Improve Tuberculosis Treatment Adherence in Asia: A Systematic Review and Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:02:58","doi":"10.21203/rs.3.rs-8383251/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-16T04:18:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-14T18:10:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-06T18:12:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112311346094777127526501314221605813357","date":"2025-12-31T12:44:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205445248352674271357691364794171160619","date":"2025-12-30T16:43:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-30T15:35:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-22T05:21:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-20T03:47:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-20T03:46:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-12-17T08:09:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fc5d7b7f-aa5e-4902-8836-3bfbfed88ffa","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:01:36+00:00","versionOfRecord":{"articleIdentity":"rs-8383251","link":"https://doi.org/10.1186/s12889-026-27448-4","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-04-29 15:58:03","publishedOnDateReadable":"April 29th, 2026"},"versionCreatedAt":"2025-12-22 10:02:58","video":"","vorDoi":"10.1186/s12889-026-27448-4","vorDoiUrl":"https://doi.org/10.1186/s12889-026-27448-4","workflowStages":[]},"version":"v1","identity":"rs-8383251","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8383251","identity":"rs-8383251","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}