We conducted a literature search in the PubMed, Cochrane Library, Ovid Embase, and Scopus databases for relevant studies published between January 2012 and March 2022. A comprehensive search strategy was developed to identify relevant studies, which included but was not confined to the following search string: (“Tuberculosis” OR “TB” OR “Tuberculosis infection”) AND (“RCT” OR “Randomized controlled trial” OR “Experimental study”) AND (“Behavior therapy” OR “Cognitive behavioral treatment” OR “Digital intervention” OR “Digital therapeutics” OR “App-based” OR “Web-based” OR “mHealth” OR “uHealth”) AND (“treatment adherence” OR “medication adherence” OR “selfcare” OR “Management” OR “Persistence” OR “Compliance”). The search terms and strategies are presented in Multimedia Appendix 1.

We included articles that met the following criteria: (1) published in peer-reviewed journals, (2) included TB treatment adherence and health outcomes as part of the study design, (3) written in English, (4) had full text available, and (5) published between January 2012 and March 2022. Studies were excluded if they were published before 2011 or did not focus on DHIs for TB. Reviews, case studies, reports, letters, conference proceedings, and abstract-only articles were also excluded.

Duplicates were eliminated from each database and recorded in the first stage. The second stage involved reviewing study titles and abstracts to ensure that articles were research studies that focused on digital health technology as a main intervention tool to improve the treatment adherence of patients with TB. The full texts of the articles were scrutinized in the last stage to verify whether they satisfied the key requirements.

Data were extracted by 1 reviewer (SL), and 2 independent reviewers (VR and YP) charted the data on different characteristics, including authors, publication year, country, study design, target population, number of participants, type of DHI, duration, follow-up, outcome measures, and major findings.

The retrieved data suggested that the core attributes of digital intervention strategies fell under the following three domains, which were based on the DHIs found in the selected articles: sending reminders via SMS text messages, monitoring progress, and tracking follow-ups for the self-management of TB treatment outcomes.

Two independent reviewers (SL and YP) evaluated the risk of bias as part of the quality assessment, using the Cochrane Collaboration’s tool for assessing the risk of bias (RoB 2 [Risk of Bias 2]; version: August 9, 2019) [14]. The risk of bias was assessed based on 5 domains, and bias scores were assigned (“low risk,” “some concern,” or “high risk”).

The literature search retrieved 305 articles; 72 duplicates were excluded, and 172 did not meet the inclusion criteria, based on the title and abstract review. As a result, 61 articles were screened for the full-text review, and 34 were excluded owing to implications regarding the exclusion criteria and unavailability of full texts. Ultimately, 27 studies were finalized for the data synthesis (Figure 1).

Given the novelty of digital health technology in TB treatment, the number of publications was observed to have increased since 2018. A total of 27 articles [15-41] were selected; their characteristics are described in Multimedia Appendix 2. Most of the studies (19/27, 70%) were published in or after 2019 [21,22,24-31,33-41].

With regard to the study designs, 17 studies were randomized controlled trials (RCTs) [15-17,19-21,24-31,34,37,38], 8 were RCT protocols [22,23,32,33,35,36,39,40], and 2 were quasi-experimental studies [18,41]. Further, 13 studies were published in low- and middle-income countries (LMICs), including countries in Africa [18,20,22,23,28,33,35,36,40] and Asia [17,19,25,41]. Digital health technology for TB is actively used in LMICs due to the high prevalence of TB (Multimedia Appendix 3). The combined study population was aged >18 years and included participants who were diagnosed with TB or were taking TB medication. The average number of participants was 400.

Table 1 and Figure 2 present the most common technologies used in DHIs, including the duration, frequency, and outcomes of interventions. The commonest DHIs were SMS text messages and reminder messages (8/27, 30%) [15-22], DOT (9/27, 33%) [23-31], medication event reminder monitors (MERMs; 6/27, 22%) [32-37], and mobile apps (4/27, 15%) [38-41]. DOT-based DHIs included video observation therapy (VOT) [25], electronic DOT (e-DOT) [31], and wearable bracelet self-DOT [23]. Some studies evaluated a mix of interventions, including mobile app–based video observations [26,29,30,39], a web intervention [24], WhatsApp (Meta Platforms Inc) [38], and WeChat (Tencent Holdings Ltd) [39]. MERMs [32-37] were also used to determine the feasibility of a web-based follow-up [36] and a mobile-based (ie, evriMED1000 [Wisepill Technologies]) follow-up with phone call [34] reminders to enhance treatment adherence.

Table 2 presents the components of DHIs that were derived from the primary and secondary outcomes of the selected articles, including (1) sending reminders for treatment adherence via reinforcement SMS text messages [15-22], (2) monitoring treatment adherence by using digital technology [23-31], and (3) tracking treatment adherence through the use of mobile apps and mobile health (mHealth) technology [38-41] via treatment adherence [42] and modified behavior adherence [43] models. Figure 3 presents a modified adherence model.

A risk of bias assessment was performed to assess the quality of the selected articles. Only 8 of the 27 articles used an RCT design [17,20,24,26,27,29,31,37]. The risk of bias results are shown in Multimedia Appendix 4 [17,20,24,26,27,29,31,37] and Multimedia Appendix 5.

This review aimed to identify DHIs related to TB treatment and management. We retrieved the relevant articles from electronic databases by using standard search terms and identified 27 articles published between 2012 and 2022. DHIs for improving treatment adherence were categorized as DHIs for sending reminders [15-22], DHIs for monitoring [23-31], and DHIs for tracking [38-41]. We identified various types of DHIs, including SMS text messaging [15-22], DOT [23-31], MERMs [32-37], and mobile apps [38-41], which improved the effectiveness of self-management, treatment adherence, and the prevention of TB in clinical and community settings.

A total of 19 studies focused on different types of interventions for reminding patients about treatment adherence and included outcomes such as medication adherence [16,21-23,29,33,35,37,41], self-reported survey satisfaction [17,20,22], and appointment attendance [20]. Treatment adherence was primarily accomplished through daily reminder SMS text messages [15-22] and phone calls [22,27,28,41] that requested confirmation of adherence. Furthermore, additional reminders were sent to patients for encouragement or motivation [15,20] if they did not respond within a given time period [16,17,23,28]. Studies also reported sending compliance reminders through daily quizzes [18]; sending reinforcement SMS text messages twice weekly for 12 weeks [21]; and sending system reminders or additional messages to remind patients about the time of medication use [17], confirm daily doses [28], notify patients about a consultation service for their upcoming monthly visits [32], encourage the use of an app [21], and promote self-satisfaction [17,20,22,27]. Rather than demonstrate treatment efficacy, SMS text messaging–based reminder interventions increased patient satisfaction [17,19]. SMS text messaging–based digital technology supports and helps patients and health care professionals to enhance health practices and clinical outcomes. An interactive reminder, such as an SMS text message or video conversation, should be developed according to the required medical monitoring process and incorporated into clinical practice.

Numerous studies have examined the use of DOT to monitor treatment adherence, including 99DOTS [28], VOT [4,29,39], asynchronous VOT [30], wireless observation therapy [24], and e-DOT [40]. DOT also includes treatment regimen monitoring interventions that are based on technology, such as wearable devices [23], mHealth apps [29], and wireless devices [24]. We identified 8 articles that reported e-DOT interventions for TB treatment adherence. Prior studies reported that participants preferred e-DOT over traditional therapy for supporting daily TB medication use during the long-term phase of TB treatment [24,27,29,30]. e-DOT should be tested in areas with a high risk of TB contraction, as e-DOT could greatly enhance the development of programs for treating the disease in LMICs. In addition, VOT interventions for new TB cases were used in combination with a mobile app [26], WeChat (for education and knowledge) [39], and treatment follow-up (with a maximum follow-up interval of 6 months). Story et al [26] reported that VOT resulted in an 80% medication adherence rate in 2 months when compared to DOT, and Ravenscroft et al [29] reported that VOT resulted in about a 45% decrease in nonadherence, which was statistically significant. Further, smartphone-enabled video surveillance of TB therapy has been proven successful and has many advantages over conventional DOT. Wade et al [44] found that VOT increased the proportion of observed treatment doses when compared to DOT; however, the effect on the treatment adherence rate was not statistically significant. Thus, audio- and video-based DHIs may be useful in reducing attrition and improving treatment adherence and health outcomes in acute care settings.

In this review, 4 RCT protocols for MERM-related monitoring interventions were also included [32,33,35,36] to obtain data on the methodological pattern of treatment adherence. Most MERMs are designed to ensure drug compliance, such as evriMED500 [32,33] or evriMED1000 [35,36]. Maraba et al [35] developed an MERM for the daily monitoring of patients and children with drug-susceptible TB during a 6- to 12-month follow-up. Additionally, Ratchakit-Nedsuwan et al [34] conducted a clinical trial of an MERM for patients with pulmonary TB for approximately 6 months; a total of 54 doses were delivered over 70 days, and the adherence rate was approximately 90%. Further, Acosta et al [37] reported that an MERM was significantly more effective than DOT. Hence, we suggest that further RCTs using MERM-based digital intervention strategies should be conducted to enhance TB treatment adherence and clinical outcomes. Since most outcomes were self-reported, additional trials are recommended to determine the accuracy of MERM system–based adherence rates.

Tracking and guiding patients remain important for the follow-up of treatment adherence in a therapeutic context. We found that 4 smartphone-, mHealth-, and mobile app–based digital devices were used to evaluate TB treatment adherence [34,40,41] and acceptability [38]. Patients with pulmonary TB who received intervention through the WhatsApp TB@Clicks module (an mHealth-based DHI) were approximately 4.1 times more likely to have favorable treatment results than a control group [38]. Another DHI for daily drug tracking resulted in drug adherence rates increasing from 85.5% to 96.4% over time [41], and a health-related VOT resulted in decreased nonadherence rates within 4 days [29]. Some apps were combined with a mobile-based pillbox system for a second consultation, resulting in satisfaction and confidence among patients [34]. These outcomes must be incorporated into future clinical trial designs that adopt trustworthy quantitative methods to determine the relative contribution of each digital health technology component.

This review’s findings revealed that DHIs encouraged self-management among patients with TB and empowered them to participate in collaborative discussions during consultations. However, we found that studies on real-time, conversation-based digital technology are lacking; such technology could improve treatment adherence and foster positive health outcomes in various clinical settings. Due to the rapid development of artificial intelligence technologies, including digital tool kits and generative artificial intelligence, 2-way communication–based chatbots in TB treatment may lead to improved self-management in patients with TB.

This review had some limitations. First, our review included studies that focused on treatment outcome–based interventions rather than health care delivery. Therefore, we did not focus on other details, such as TB prevalence, costs, or health insurance. Second, this study focused on the effects of commonly used DHIs on TB treatment outcomes in clinical and community settings. Further studies should determine how DHIs vary between the two contexts and how they interact with multidomain therapies. Third, this study did not specifically describe treatment adherence and self-management. There are no clear differences between the accurate meaning and measurement of treatment adherence in a clinical trial setting and those of self-management in a clinical or community context, and few studies have attempted to provide answers [45-47]. Fourth, many of the included studies (13/27, 48%) were conducted in LMICs because of the high prevalence of TB cases, even though high-income nations have a considerable number of studies. This could be attributed to our study’s selection criteria, such as our criterion for language. Therefore, additional studies are required to identify DHIs across the entire TB care continuum.

This study examined 27 studies published between 2012 and 2022 and selected the most recent articles. The following three domains were identified from the selected studies: reminding, monitoring, and tracking. The preponderance of treatment adherence was reinforced by mHealth strategies, such as the use of SMS text messaging, mobile apps, mHealth technology, and MERMs. Our findings have implications for TB-related digital health research, which frequently fails to adequately address patients with TB. To preserve treatment adherence and self-care management, patients should have access to real-time, conversation-based interventions (dialogue or communication between patients and health care professionals), such as mobile- or app-based chats, regardless of the restrictions imposed by the COVID-19 pandemic. This scoping review study was conducted before our ongoing chatbot project, which focuses on a mixed methods study on chatbot communication for the treatment adherence of patients with TB. Thus, we emphasize the importance of developing a communication system. DHIs provide several advantages, including improved patient engagement, availability, and accessibility, in addition to lower workloads for practitioners. These results should be considered in the context of national TB control programs and policies to establish a strategy for sustaining TB control and health outcomes. We propose that these developments can significantly improve TB treatment adherence through global collaboration and investment.