TB Treatment Support Tools: Refinement and Evaluation of an Interactive Mobile App and Direct Adherence Monitoring on TB Treatment Outcomes
Phase: NA
Conditions: Tuberculosis, Treatment Adherence
Sponsor: Institute for Clinical Effectiveness and Health Policy

1/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
 
 
 
 
 
TB Treatment Support Tool Interactive Mobile App and Direct 
Adherence Monitoring on TB Treatment Outcomes 
 
ID: 12112019 
 
NCT 04221789 
 
 
Version 2.0: 8/11/2021 
Last IEC approval: 01/07/2022 
 
 
PI: Fernando Rubinstein 
Sponsor: Institute of Clinical Effectiveness (IECS), Argentina 
 
 
 
 
 
 
 
 
 

2/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
 
 
 
 
RESEARCH PROTOCOL 
Summary 
1. TITLE OF THE STUDY .................................................................................................................... 2 
2. DESIGN OF THE PROPOSED STUDY .............................................................................................. 2 
3. RESEARCHERS ............................................................................................................................. 2 
4. FUNDING .................................................................................................................................... 2 
5. SCOPE OF THE STUDY .................................................................................................................. 3 
6. DATE OF SUBMISSION TO THE IEC .............................................................................................. 3 
7. INTRODUCTION ........................................................................................................................... 3 
7.1. Preliminary Investigations Conducted ................................................................................... 6 
7.2. Theoretical Framework for the Proposed Study ................................................................... 8 
8. RESEARCH STRATEGY: INNOVATION ........................................................................................... 9 
9. OBJECTIVES ............................................................................................................................... 10 
9.1. Objective 1 ......................................................................................................................... 10 
9.1.1. Technological refinement of the TB-TST application .................................................... 10 
9.2. Objective 2 ......................................................................................................................... 11 
9.2.1. Design ......................................................................................................................... 11 
9.2.2. Study environment, selection of study sites and population ....................................... 11 
9.2.3. Eligibility criteria for participants ................................................................................ 12 
9.2.4. Standard of care .......................................................................................................... 12 
9.2.5. Statistical power and sample size. ............................................................................... 12 
9.2.6. Recruitment Plan ......................................................................................................... 13 
9.2.7. Randomisation ............................................................................................................ 13 
9.2.8. Procedures .................................................................................................................. 13 
9.2.9. Operationalisation of outcome measures .................................................................... 14 

3/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
9.2.10. Data Management ..................................................................................................... 14 
9.2.11. Data Analysis. ............................................................................................................ 15 
9.3. Objective 3 ......................................................................................................................... 15 
9.3.1. Design and procedures ................................................................................................ 15 
9.3.2. Sampling and sampling................................................................................................ 15 
9.3.3. Qualitative data analysis.............................................................................................. 15 
10. DISSEMINATION OF RESULTS TO STAKEHOLDERS ................................................................... 16 
11. POTENTIAL PROBLEMS AND ALTERNATIVE STRATEGIES .......................................................... 16 
12. EXPECTED IMPACT .................................................................................................................. 17 
13. PUBLIC HEALTH IMPLICATIONS ............................................................................................... 17 
14. ETHICAL CONSIDERATIONS IN RESEARCH ............................................................................... 17 
15. TRIAL REGISTRATION ............................................................................................................... 18 
16. FINANCING .............................................................................................................................. 18 
17. TIMETABLE OF THE STUDY ...................................................................................................... 19 
REFERENCES.................................................................................................................................. 22 
 
 
1. TITLE OF THE STUDY 
TB treatment support tools: design and evaluation of an interactive mobile application for direct 
adherence monitoring of TB treatment outcomes. 
2. DESIGN OF THE PROPOSED STUDY 
Mixed-approach study: Qualitative design in the initial phase of the final development of the 
application and a two-arm randomised controlled clinical trial for the phase of evaluation of the 
impact of the intervention on treatment outcomes, mainly treatment success and drop-out. 
3. RESEARCHERS 
Name 
and 
surname 
Position and 
Institution 
Contact email 
Contact 
telephone 
number 
Principal 
Investigator 
Fernando 
Rubinstein 
Senior Research 
Fellow at the 
Institute for Clinical 
Effectiveness and 
Sanitary 
frubinstein@iecs.org.ar 
114778767 
1134728703 

4/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
Research 
Associate 
Sarah Iribarren 
Researcher at School 
of Nursing at the 
University of 
Washington 
sjhiribarren@gmail.com 
+1 
8014194928 
4. FUNDING 
Proposal for a grant application to the National Institutes of Health (Grant Application: R01AI147129- 
01 NIAID - NIH). 

5/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
5. SCOPE OF THE STUDY 
The study will be carried out in public hospitals in some districts of the conurbation of the 
Province of Buenos Aires (to be designated) where patients with tuberculosis are treated a self- 
administered treatment modality. 
6. DATE OF SUBMISSION TO THE ETHICS COMMITTEE 
Version 1.0: 07/05/2019 
Version 2.0: 08/11/2021 
Last IEC  approval: 
01/07/2022 
7. INTRODUCTION 
Tuberculosis (TB) remains one of the top ten causes of death worldwide.1 The more than 4,500 
deaths per day associated with TB are unacceptable, given that almost all deaths are 
preventable.2 TB patients face multiple barriers to treatment , including stigma and lack of 
education about the disease and its treatment and medication side effects. Poor adherence to 
medication, along with challenges in monitoring patients and re-engaging those who drop out of 
treatment, are important factors contributing to disease persistence and the development of drug 
resistance. If left unchanged, it is estimated that by 2050 multidrug-resistant TB could kill an 
estimated 2.5 million people per year and cost the global economy up to US$ 16.7 trillion.(3) To 
address the global health emergency that TB represents, the World Health Organization's (WHO) 
TB control strategy has set targets to reduce deaths and incidence levels by 95% and 90% by 2035, 
respectively, compared to 2015.5 To support these targets, there is substantial interest in the use 
of mobile health (mHealth) interventions to help reduce the challenges and support patients to 
complete their treatment.6,7 
Of the mHealth approaches being investigated for TB adherence monitoring, drug metabolite 
testing has been identified as the most promising, ethical and accurate, and less intrusive and 
stigmatising strategy compared to other mobile solutions (e.g. video observation, SIM card- 
containing drug vials, ingestible sensors), yet its potential remains largely unexplored.8 Mobile 
applications could provide personalised treatment monitoring, increase patient self-management 
and improve patient-health system communication by offering advanced functionalities for 
treatment support and monitoring. However, available TB-related apps have not focused on 
patients as end-users or been fully evaluated.(9) 
Tuberculosis (TB) remains a global health threat and one of the 's most deadly infectious diseases, 
ahead of HIV/AIDS. Globally, approximately 1.7 billion people are infected with the bacillus, and this 
latent TB infection has a 5-10 percent chance of developing active disease in the course of a 
lifetime.10 In 2017, an estimated 10 million people developed active TB and 1.6 million died from the 
disease.11 The 

6/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
spread of TB is compounded by a myriad of challenges for patients and health systems to adhere 
to prolonged treatment due to social stigma, fear, discrimination, poverty, lack of knowledge 
about the disease and its treatment, drug side effects and lack of system support.12-14 Health care 
systems are overburdened by patient volume, the HIV epidemic, lack of resources and lack of 
advanced technology for monitoring and follow-up.15,16 Treatment abandonment is a known cause 
of adverse individual and societal outcomes, including prolonged infectivity, relapse, increased 
morbidity and mortality, and the development of drug resistance,12,17,18 which threatens to reverse 
the progress achieved by TB eradication efforts to date.19 Resistant tuberculosis (MDR) is more 
contagious, costly and deadly.(20) As a result, preventing the spread of the disease and the 
development of resistance is a global public health priority.21 
To achieve this goal, health systems must ensure completion of treatment. 
A long-standing World Health Organisation (WHO) recommendation to ensure adherence to 
treatment has been the Directly Observed Treatment (DOT) strategy. A key component of DOT is 
the direct monitoring of medication intake by a treatment supervisor.22 While it is an ideal strategy 
to reduce adherence problems, it is also recognised as resource intensive (human and financial) 
for both the patient and healthcare systems.7,23-25 As a result, DOT has been inconsistently 
implemented and, in many settings, self-administered treatment (SAT) remains the most widely 
used standard treatment.23 Incomplete implementation of effective control measures has resulted 
in TB incidence and mortality rates being sustained or declining more slowly than expected in 
recent years.26 The current WHO recommended rate for treatment success is 90% of all identified 
cases.21 The WHO Region of the Americas has the lowest treatment success rate (approx. 75%), 
one of the highest rates of loss to follow-up and a high percentage of TB deaths compared to 
other WHO regions.27 
In Argentina, treatment success rates have been among the lowest in the region, between 44 and 
66% according to WHO reports, and consistently high treatment drop-out rates of close to 20% 
have been reported.(28,29) It is also one of the five countries in the Americas with the highest 
estimated number of MDR-TB cases. Therefore, Argentina is one of the countries where the health 
system needs to implement more effective TB treatment strategies. 
Current strategies are recognised as insufficient to meet the goal of TB elimination in this 
century.21,30 In response to the challenges of DOT implementation, current developments in 
treatment management involve the use of technology (eHealth, mHealth), such as the use of 
mobile phones, tablets, smartphones and other wireless devices, to explore more efficient and 
effective ways to ensure that patients complete their treatment.6 The availability of smartphones 
is rapidly increasing in our setting. Maximising mobile tools for patient support 

7/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
treatment may be a promising alternative to increase patient engagement in care, improve 
adherence, monitoring, communication and delivery of evidence-based interventions.6,8,31 
However, more evidence is needed to develop, adapt and validate these tools in various 
conditions and models of care.(6,7) 
Mobile health approaches to TB treatment monitoring being tested include video-observed 
therapy (VOT), 38,39 and other strategies (e.g. self-reporting, drug vials containing a SIM card, 
direct monitoring via integrated sensors32 or testing for metabolites,33,34 although these 
interventions have been questioned on the basis of their accuracy and costs.8 For example, while 
direct video monitoring may avoid stigmatisation issues, patients may fake ingestion. Drug 
metabolite testing is potentially superior to DOT or VOT because it requires actual drug ingestion 
and has been identified as the most promising, ethical, accurate, least intrusive and stigmatising 
of these mobile strategies; however, its potential remains largely unexplored.8 Preliminary reports 
outside the peer-reviewed literature highlight the potential for drug metabolite testing35, 
however, there is a need for further development of the technology and rigorous research to 
assess its impact on treatment outcomes. On the other hand, as smartphones rapidly replace less 
sophisticated phones, tools such as mobile apps offer more advanced functionalities for patient 
support and monitoring, as they can facilitate real-time adherence monitoring or side-effect 
tracking, more personalised education and two-way communication with care teams.36 In a 
systematic review of TB-related apps, most were geared towards health professionals (e.g. dose 
calculations or treatment recommendations) or provided very general information about TB.9 Few 
apps have been developed directly oriented towards patient use and none were developed to 
support TB patients' engagement in their own care, limiting the potential of these apps to 
facilitate patient-centred care. 
This TB treatment support intervention (TB-TST) will engage and support patients throughout the 
course of treatment and provide a platform through which a treatment coordinator (e.g. a TB 
nurse) can interact with patients, monitor their progress and identify needs for additional support. 
In our previous studies, patients on active treatment identify the following priority components 
for the implementation of a mobile app: simple and reliable TB information, medication 
reminders, interactivity with a treatment coordinator, monitoring of treatment and side effects, 
and the ability to incorporate social networking.37 
The base model of the application includes: medication reporting (self-reporting of intake and 
periodic urine testing (presence of INH metabolite) and monitoring of medication side effects, 
education based on priority patient issues and reliable sources (CDC, WHO). Additional 
modifications will be needed to integrate the 

8/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
patient recommendations, e.g. simplifying reporting steps, improving visualisation of treatment 
progress, and developing a more user-friendly test strip. The refinement will address these issues 
and others based on the initial phase of the study. This development will use non-proprietary 
open source components that can be interoperable with current and future infrastructure and 
integrate data with the health information systems of the national TB programme. 
The intervention is supported by a biochemical test that detects the TB drug metabolite in the 
patient's urine. The paper-based colorimetric test uses a classical chemistry (the Arkansas 
Method) that changes the colour of the strip to purple when an isoniazid drug metabolite (INH) is 
present in the urine.33,38 INH, one of the main first-line TB drugs, is considered an ideal target for 
daily adherence monitoring because it is often combined in the same pill with other anti-TB drugs 
and its pharmacokinetics make it a good biomarker as it is detectable in urine for 24 hours.39 By 
up with bioengineers, work will be done to improve the test (faster, simpler and improved 
sensitivity), which is specifically designed for patient self-assessment and mobile app image 
capture. This innovative work reveals exciting opportunities to greatly improve the test by 
redesigning the strips with features specifically for use in the patient's home. 
7.1. Preliminary Investigations Conducted 
Previous research studies conducted by the authors include: exploration of barriers and facilitators 
to TB treatment success at the individual, societal and system level in regions with high TB burden 
in Argentina16, development and pilot testing of an interactive text message-based mHealth 
intervention;40-42 literature review of mobile device applications used in TB patients;9 an evaluation 
of different treatment modalities and health system and patient factors on treatment success and 
dropout rates,43,44 and development and field usability testing of a functional prototype.37 
A first step to improving consistently low treatment success rates is to identify and understand 
the barriers in this context. To this end, we conducted in-depth interviews and focus groups 
composed of patients, TB care team members, and programme and hospital managers at local, 
regional and national levels.16 Barriers included a shortage of TB-trained staff, lack of trust in local 
health facilities, paper-based follow-up systems that varied across systems, and a lack of 
resources for TB care. Most barriers were concentrated at the health system level. Treatment 
delivery strategies varied, with TSS being the usual strategy in large health facilities, where a large 
number of cases are concentrated. Therefore, there is a need to focus on efforts to support those 
receiving unsupervised treatment in an appropriate manner and to strengthen patient-health 
worker relationships through a strategy that facilitates patient and family interaction with the 
health system. 

9/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
In response, we developed TextTB, a text message-based intervention, with patients and experts 
to help people receiving SAT.40-42 Educational test messages were developed based on patient- 
reported information needs identified in qualitative interviews and guided by the Information- 
Motivation-Behavioural Skills model.45,46 A mixed-methods pilot study was conducted to assess 
the feasibility and acceptability of TextTB with newly diagnosed patients who were 18 years or 
older and had access to mobile phones.41 An open source, text messaging platform 
(FrontlineSMS)47 was used to send, receive and view messages, and turned a computer into a 
'hub' for managing interactions. The final intervention allowed patients to learn about medication 
administration, receive theory-based/appropriate staged educational messages twice a week, and 
receive individualised support remotely. Although successful as a pilot programme, and patients 
reported that they 'provided the necessary support', areas for improvement were identified in a 
larger cohort: developing strategies to reduce manual review of data by the treatment supervisor 
(e.g., increasing automation to alert which patients needed follow-up), expanding educational 
messages, and improving patient adaptation(41,48). In addition, after pilot testing, healthcare staff 
noted an increase in the use of communication apps, such as WhatsApp, rather than text 
messaging as individuals transitioned to smartphones. From this research, primarily, we have 
established that the intervention should be interactive and responsive to the individual, while 
incorporating design efficiencies to support the care team and minimise burden. 
To better understand the situation, we conducted a prospective cohort study of 963 newly 
diagnosed TB patients (R01AI083229, PI: Rubinstein) and identified that 68% received SAT 
compared with 19% who received DOT and 12% mixed treatment monitoring.44 Success rates were 
lower (70.2%, 85%, 84.2%) and default rates were higher (20%, 8.5% and 7%) in SAT, DOT and 
mixed groups, respectively. The high non-compliance rates in the SAT group are a cause for 
concern, as most patients receive this treatment modality. Increasing the level of patient contact, 
even without formal supervised treatment, was associated with a significant improvement TB 
outcomes, particularly among high-risk individuals (drug and alcohol users, young, low-income). 
These findings highlighted the continued need for action to develop tools to support people in 
unsupervised treatment. 
In our current research, we have converted the functionalities of TextTB into a mobile application 
(app) and seek to extend its functionalities based on patient-centred design with input from TB 
experts. User-centred design principles and agile methods will be employed to develop and 
initially pilot test TB-TST to identify refinement needs prior to rigorous testing to assess the impact 
on treatment outcomes (treatment success and dropout rates) in a pragmatic clinical trial. 

10/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
The pilot will also serve to create an actual dataset of patient-created images of metabolite test 
strips needed to build an algorithm for automatic detection of direct adherence results. Further 
refinement is anticipated meet the needs of the TB-TST patient and healthcare team, such as 
expanding functionality, improving customisation, simplifying navigation between screens, and 
making the test strips more user-friendly. The results of the pilot test will be the starting point for 
assessing its potential impact in a clinical trial in the second stage of the study. 
7.2. Theoretical Framework for the Proposed Study. 
Rapid or agile methods for software development and intervention allow for flexibility and 
adaptability, close collaboration between developers and end users, and short iterative product 
cycles to support the development and validation of technology-based tools.49,50 Information 
System Research (ISR) is a framework that applies these user-centred design and system 
development methods in a rigorous and standardised way to incorporate end-user design 
preferences and has been shown to be useful in guiding the design of mHealth applications.51 The 
cycles of relevance (to understand end-user needs), rigour (identifying technology-based 
interventions to meet needs) and design cycle (usability evaluation methods to iteratively develop 
and refine the application). The framework considers the environment, design science and the 
knowledge base that grows through experiences and expertise as a recommended method to 
support the efficient creation and validation of mHealth applications. Considerations for this 
application include interoperability, compatibility with local and remote health data storage, user- 
friendly data visualisation and modular development for future extensible technologies. According 
to this framework, the refinement of the application component (Objective 1) will follow an 
iterative process in which feedback from end-users and stakeholders will be used to optimise the 
intervention. 
The Information, Motivation, Behaviour Change (IMB) model will be used for behaviour change 
content, development of educational material and to guide the coding of qualitative focus group 
data.42,45,46 The IMB model supports the hypothesis that initiating and maintaining a behaviour, 
such as medication adherence, must be provided with adherence-related information and 
motivation, along with appropriate tools and strategies to maintain the behaviour. 
Our proposed study is timely and relevant, as there is currently a paucity of evidence on the use 
of digital health technologies to improve TB adherence and clinical outcomes. In a recent 
systematic review of studies to assess the impact digital health technologies on TB treatment, the 
authors concluded that the evidence remains limited and that better quality studies are needed.52 
Given the lack of evidence for digital technologies in TB research, in particular, patient-centred 
applications for TB treatment adherence53, the need to improve medication adherence, 
recognition of drug metabolite testing, and the need to improve TB adherence, the proposed 
study is timely and relevant. 

11/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
as the most ethical and accurate direct treatment monitoring strategy,8 TB-TST promises to 
improve TB treatment outcomes and have a sustainable public health impact. 
The feasibility of TB-TST is supported by data showing that mobile device use is almost ubiquitous 
and smartphone use is high in Argentina.54 It is important to understand how different strategies 
perform in real-life practice settings, as limitations of health systems and services for TB control are 
often responsible for lack of progress in control efforts.55,56 Our team proposes to revisit a classic 
laboratory test, the Arkansas test, and modify it for use in the patient's home. 
The current trial is important in representing the first study to systematically construct, evaluate 
and apply together with a test strip to monitor and improve anti-TB treatment outcomes. In 
summary, our proposed study to refine and test an interactive mobile intervention (TB-TST) 
builds on the results of our preliminary work,9,16,40-42 a theory-based approach to design51 and 
content construction16 to improve treatment adherence. Our long-term goal is to improve 
treatment outcomes for patients diagnosed with TB, particularly for those in resource-limited 
settings, by developing a tool to support patients, monitor adherence to treatment and identify 
whether they require additional support. This outcome will support global efforts for EndTB by 
2035 and reduce the growth rates of MDR-TB.(2) 
8. RESEARCH STRATEGY: INNOVATION 
There are four innovative aspects of our proposed study to address critical barriers to TB 
treatment adherence. 
1. This study is the first to our knowledge to design a patient-centred mobile app for TB that uses 
an iterative user-centred design, which has been shown to improve quality, functionality and 
engagement with patient health apps. However, this claim has not been tested in an active TB 
population in a low-resource setting. There is a need to provide continuous feedback and support 
for patient-centred approaches. The intervention will provide two-way messages, including 
personalisation based on participants' responses and outcomes (e.g. side effects, compliance 
reports). We will use real-time data collection and provide participants with feedback on their 
compliance performance in graphical and text form. One-way messages may provide reminders or 
motivations for patients to play a more active role in their management, however, bidirectionality 
can maximise the potential of interventions. 
2. Both the re-engineering of a classic paper-based test to detect a drug metabolite in urine for 
home use and the image capture of the results on the mobile phone allow for a new direct 
monitoring of adherence in real time. The test confirms drug intake in approximately the 
previous 24 hours, , 

12/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
avoids the inaccuracy of self-reporting. This may enhance a treatment coordinators ability to 
provide personalised or accurate feedback to promote positive health behaviours and ensure 
adherence to treatment. Self-reporting or other methods of surveillance are considered less 
accurate, increase stigmatisation or are intrusive to the patient.(8) 
3. The collection of repeated, longitudinal data on the side effects of anti-TB drugs by telephone, 
over the course of treatment, is novel in Argentina and may lead to new insights into side effects 
and adherence to treatment over time. 
4. Building the application and provider interface in a modular fashion using open source software 
and open standards allows for future integration into healthcare systems (e.g. electronic health 
records and surveillance systems) and expansion of future functionality based on end-user needs. 
The mobile-optimised web application allows for use on any patient's smartphone rather than 
relying on an operating system or type of phone. The success of this effort could inform 
opportunities for treatment adherence and symptom management for other populations with 
complex care needs. 
9. OBJECTIVES 
9.1. Objective 1 
Refine TB-TST according to the results of the pilot study based on user-centred design principles. 
9.1.1. Technological refinement of the TB-TST application 
For the refinement of the TB-TST application, the findings of the pilot study approved by the 
ethics committee of the Centrándolo Chest Hospital, where the study was conducted, will be 
synthesised. Priority changes will be identified and recommendations will be provided as technical 
requirements to the software engineers. Dr. Iribarren will work with the University of 
Washington's information technology team to convert the recommendations into actionable 
review plans. We will enhance the application's functionalities to meet the needs of the 
healthcare team and the patient, through improvements to interfaces facing both the patient and 
the provider. The developers believe that six months, divided into four 
design/development/testing cycles of six weeks each, will be sufficient to refine this application to 
sufficient levels of function and usability for the ECCA. 
We will refine the home drug metabolite test based on patient feedback. Ideal chemical 
conditions will be evaluated to improve the sensitivity and speed of the test. Design 
considerations will include: ease of use (easy to use by users without prior experience or training, 
quick to perform, easy to understand results); the test should be effective (specificity and 
sensitivity), efficient (the amount of effort and cost required is proportional to the target value 
achieved), and satisfactory (given the 

13/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
context and type of user); using affordable and accessible manufacturing techniques (usable and 
marketable to people with varying abilities, can be manufactured locally using technologically 
appropriate manufacturing (57). 
Further improvements will include automating the reading of test results. We will use the test 
strip images captured during the pilot to implement an algorithm for the application to 
automatically read and send test . Algorithms from open source colourimetric analysis software 
are available, but will require adaptation and testing. Adding image capture guidance within the 
application can improve image quality, complementing the test improvements. 
Once the modifications to the TB-TST components have been completed, we will invite 5-10 
participants from the previous pilot study to review the refined TB-TST application. This step aims 
to ensure that the changes are responsive to patient needs and that usability issues have been 
addressed, test the refined TB-TST functionality, feedback and interactivity of the software, and 
identify any technical bugs. At the end of this technological refinement phase, the application will 
be ready for implementation in the ECCA (Objective 2). 
9.2. Objective 2 
To assess the impact of TB-TST on treatment outcomes compared to usual care. 
9.2.1. Design 
We will conduct a pragmatic, two-branch, randomised controlled clinical trial (RCT) with equal 
numbers of individuals randomised to intervention and control groups. We will patients 
throughout their entire treatment course (6 months) comparing the TB-TST intervention with 
usual care. The unit of analysis will be the participant and randomisation of the intervention will 
be to individual patients seen in hospitals where they receive TB care through self-administered 
treatment. 
We hypothesise that managing adults with drug-sensitive pulmonary TB using the TB-TST 
intervention will result in better clinical and adherence outcomes and improve patient 
satisfaction with care. 
9.2.2. Study environment, selection of study sites and population 
The study will be conducted in hospitals in CABA and the Province of Buenos Aires, Argentina, 
where half of the more than 10,000 cases per year in the country are diagnosed and treated. Most 
patients are concentrated in specialised public hospitals where they receive self-administered 
treatment. We will select public hospitals with different geographical coverage areas in high TB 
burden areas defined by the criteria established by Argentina's National Tuberculosis Programme 
(NTP). NTP reports from the last 5 years and our cohort study showed that more than 60% of 
patients were treated in public hospitals and 65% received self-administered treatment. Of these, 
85% were of low socio-economic status, 

14/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
50% were unemployed, 25% were smokers and 20% reported alcohol or drug use.(28) 
9.2.3. Eligibility criteria for participants 
Participants must be at least 16 years old, have a new diagnosis of TB sensitive to regular drugs, 
have regular access to a smartphone and be able to operate the phone or have someone who can 
help them. It is estimated that more than 75% of the population has smartphones.(59) 
Severely ill patients requiring hospitalisation will be excluded, as well as patients with known 
drug resistance and those with HIV co-infection as their treatment is administered separately. 
Selected patients who do not meet study eligibility will have specific screening data (including 
sex, age and reason for exclusion) entered into the study database to examine reasons for 
exclusion and feasibility of enrolment criteria. 
Case definition: TB confirmed by positive sputum smear or diagnosis of pulmonary TB based on 
radiological findings and clinical signs and symptoms, but with negative sputum smear. The 
diagnosis can be confirmed by other methods such as MGIT960, BACTEC 9000 or MB Bact, 
nucleic acid amplification (PCR) or ELISA. 
9.2.4. Standard of care 
TB treatment and care are provided free of charge in the public health system. The standard of 
care includes routine clinical and laboratory tests. In self-administered treatment patients receive 
a 1-2 month supply of medication and are asked to return monthly for follow-up appointments, 
or earlier if they have problems. Treatment of drug-susceptible TB consists of a 6-month regimen 
consisting of: a two-month intensive phase of four drugs (rifampicin, isoniazid, pyrazinamide and 
ethambutol or streptomycin), followed by a four-month continuation phase of isoniazid and 
rifampin daily or 3 times a week.77 Three of the four drugs are given in a combination pill that 
includes isoniazid, so urine tests for isoniazid metabolites are an indicator that the combination 
pill was taken within the last 24 hours. 
9.2.5. Statistical power and sample size. 
Argentina's WHO report on TB in the country indicates that treatment success rates have ranged 
from 44% to 66%.35 These rates include cases lost to follow-up. For those with known outcomes, 
the Argentina SOP and our previous cohort study estimated the treatment success rate among 
patients on self-administered treatment was approximately 70%, with a dropout or default rate 
of 20%.(28) To detect a success rate of at least 85% in the experimental group with 90% power, we 
will recruit 360 individuals (180 subjects per arm). Power calculations are based on the analysis of 
the primary outcome, the treatment success rate. All calculations are based on a 2-tailed test 
with alpha at the 0.05 level. 

15/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
9.2.6. Recruitment Plan 
The research team and coordinators at each hospital will introduce the study to the members of 
the health care team to whom newly diagnosed patients are referred for registration and 
treatment. Trained clinic staff will notify all newly diagnosed patients who meet the age inclusion 
criteria (16 years or older) the study and refer them to the site study coordinator for further 
information. The site coordinator will: (1) discuss the nature of the study with patients and 
answer questions; (2) review eligibility criteria; (3) obtain informed consent; (4) administer initial 
questionnaires; and (5) collect standard PNT follow-up information from the patient's treatment 
record. 
The research team will conduct training for coordinators to ensure that this process is the same at 
each recruitment site. Signed informed consent will be obtained for all participants. A recruitment 
register will be maintained to document patients selected and reasons for declining participation 
(if willing to share the reason). Patients will be enrolled consecutively and recruitment is 
estimated to be completed within the third year of the trial. 
9.2.7. Randomisation 
Randomisation of the intervention to participants will be done sequentially with a 1:1 allocation, 
using randomised permuted blocks of different sizes between 6 and 10 and stratified by hospital 
to ensure that numbers are balanced by centre and group. Once the patient signs the consent 
form, the study coordinator will call the Institute for Clinical and Health Effectiveness (IECS) to 
receive allocation instructions. The randomisation sequence will be generated using available 
software such as http://www.randomization.com/ or similar. Sequentially numbered and sealed 
opaque envelopes will be used for treatment allocation masking. 
Due to the nature of the intervention, complete blinding of group assignment to research staff or 
patients cannot be achieved. Professionals in charge of patient care will not be informed of the 
group assignment unless their patient informs them. Data managers and analysts will be blinded 
to the Hospital and group assignment of participants, who will be identified by an alphanumeric 
code. To limit contamination bias, all participants, regardless of the group to which they are 
assigned (intervention and control), will receive standard instructions, educational materials and 
booklets on TB treatment from the National Tuberculosis Programme (NTP). Since the primary 
outcomes, treatment success and treatment dropout are objective and not based on self- 
reported adherence, we believe that the risk of bias is minimised. 
9.2.8. Procedures 
Upon enrolment in the study, participants will be asked to complete a baseline questionnaire that 
includes socio-demographic characteristics, habits, self-care measures and a TB knowledge test. After 
completing the baseline survey, all participants will be asked to complete a baseline questionnaire 
that includes socio-demographic characteristics, habits, self-care measures and a TB knowledge test. 

16/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
Study participants will receive written educational materials and will be instructed to follow the 
treatment plan by their primary provider. 
Study participants in the TB-TST intervention group will receive assistance in gaining access to the 
app and will receive verbal and written instructions as well as a personalised demonstration of the 
app features by the study coordinator. Participants in the TB-TST group will also receive drug 
metabolite test strips and verbal and written instructions on how to complete the test and upload 
the image into the app (instructions also within the app). To assess whether patients are taking 
their medications, a request to complete the drug metabolite test will be sent periodically and 
randomly as an app alert 2-3 times per week during the intensive phase and 1-2 times per week 
during the continuation phase. The image will be automatically synchronised with the web 
interface via the application. A member of the TB team (e.g. a TB nurse) from each of the selected 
hospitals will act as study coordinator to interact with patients assigned to TB-TST and monitor 
treatment progress through the provider portal. The coordinator will be trained on the use of the 
application and provider portal, research objectives and protocols by the principal investigator 
and collaborating members of the regional TB programme team. Participants in the intervention 
group will be informed that interaction with the study coordinator through the app will be 
provided within office hours and that any emergencies should be addressed through standard 
routes. 
Study participants in the control group will be asked to complete the Simplified Medication 
Adherence Questionnaire (SMAQ), a validated 6-item questionnaire to assess adherence when 
they return for their monthly visit.60 The control group will not receive the TB-TST (neither 
application nor direct adherence test). 
Compensation for time and expenses: all participants will be given the equivalent in Argentine 
pesos of $20 USD for the baseline survey and $20 USD upon completion of data at the end of 
treatment or at the last follow-up. Patients in the intervention group will be asked to complete an 
exit survey. We will pay for 1 Gb of data per month to cover data usage from the application. 
9.2.9. Operationalisation of outcome measures 
The primary outcome will be treatment success according to WHO definitions.61 Treatment 
outcomes are defined as: success (cure or completion of the 6-month treatment regimen), 
dropout (discontinuation of treatment for at least 2 months), transfer, death, or other. Secondary 
outcomes include dropout rate, technology use as measured by actual use of the application, and 
technology usability as measured by Technology Usability Assessment Scale information. 
9.2.10. Data Management 
The data collection forms will be developed using RedCap, a secure web-based application with 
interactive data capture controls designed to support the 

17/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
data capture for research studies, providing an intuitive interface, audit trails and automated 
export. RedCap is a free service offered through UW SoN and IECS is a Redcap partner through 
Vanderbilt University. 
9.2.11. Data Analysis 
All analyses will be based on intention to treat. Statistical analyses will be performed using STATA 
version 13.1. A t-test or Wilcoxon test will be performed for continuous numerical variables and a 
chi-square test for dichotomous or categorical variables. Standard descriptive statistics of 
frequency, central tendency and dispersion will be used to describe each sample. A p-value of less 
than 0.05 will be set to detect a statistically significant difference for all analyses. We will 
compare baseline characteristics of the group, including age, sex, education, travel time to the 
centre, adherence to medication regimen and basic TB knowledge. The primary endpoints will be 
Although we do not expect differences in the groups, logistic regression analysis can be used to 
adjust for possible confounding factors if necessary. 
9.3. Objective 3 
Assess patient and provider perceptions of facilitators and barriers to TB-TST intervention 
implementation and synthesise lessons learned. 
9.3.1. Design and procedures 
Exploratory qualitative study based on focus groups that will have a duration of 60-90 minutes. 
We will include the equivalent in Argentine pesos of $20 USD to compensate for participants' 
time. Once the informed consent process is completed, all focus group sessions will be audio- 
recorded. Focus group guides will be informed by the Mobile App Rating Scale (MARS) to assess 
acceptability (perceived usefulness and ease of use)62. The MARS has proven to be a highly 
reliable tool for assessing app quality.62-64 The scale includes three sections and a modifiable app- 
specific section. Questions include identification of challenges, bottlenecks, whether the 
intervention meets needs, satisfaction with care, confidentiality concerns, and post-intervention 
perceptions and recommendations. We will use a socio-technical perspective that considers 
Structure-Process-Outcome with a socio-technical perspective to understand, for example, 
workflow issues and altered practice and service delivery.48 
9.3.2. Sampling and sampling 
After the intervention, through non-probability convenience sampling, we will conduct 3 focus 
groups of 8-10 people randomly assigned to the TB-TST group and 2 focus groups with TB care 
coordinators and TB team members to assess their experience using the application and the 
accompanying case management platform. 
9.3.3. Qualitative data analysis 
All focus groups will be transcribed verbatim for coding.65 Transcripts will be entered into 
qualitative data management software, such as Nvivo, to organise and facilitate analysis. We will 
code iteratively using qualitative thematic and thematic methods. 

18/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
66,67 Specifically, we will use an inductive approach that provides a systematic set of procedures for 
analysing and deriving reliable and valid findings from qualitative data.68 The following steps will 
be used for the analysis: 1) Preparation of raw data files: clean data; 2) Thorough reading of the 
text to understand the issues discussed by the participants; 3) Creation of categories (codes): 
identification and definition of categories, themes and sub-themes; 4) Continuous review and 
refinement of the coding scheme: within each category, search for sub-themes, including 
contradictory views and ideas and selected appropriate quotes that conveyed the themes; and 5) 
Application of the final coding scheme to the full data set and assessment of inter-coder reliability. 
10. DISSEMINATION OF RESULTS TO STAKEHOLDERS. 
The findings will be presented and discussed with key stakeholders and participants (patients, TB 
teams, regional and national TB programme officers). The study team will present the findings to 
health professionals at recruitment sites that are open to the community, to disseminate the 
findings, encourage feedback and participation for wide dissemination and use, and further 
adaptation to meet local needs. Members of the study team will attend the annual regional and 
national TB conferences to present the findings and discuss the benefits and limitations of this 
intervention identified in this study. The purpose of knowledge transfer and capacity building of 
mHealth tools is to strengthen local capacity to support leaders who can scale up and adapt these 
interventions to meet the needs of their communities. The team will also disseminate the results 
of the study through traditional academic channels, such as scientific conferences and peer- 
reviewed journal publications. 
11. POTENTIAL PROBLEMS AND ALTERNATIVE STRATEGIES 
Adherence and dropout: We will measure application usage using log files downloaded from the 
provider's web interface and embedded usage analytics (using http://piwik.org, a privacy- 
protective web analytics system run by the University of Washington (UWCIRG) to monitor their 
clinical applications. As part of the algorithms to be established, the study coordinator will 
contact the participant directly. We will request a second phone number of the contact (family 
member, support person) who will be contacted as an alternative. 
In addition to communicating with the patient, personalised/conditional messages, such as: go to 
clinic, inform healthcare provider, will be sent automatically through the app if the patient has a 
problem and sends an alert. Potential IT/app/mobile phone issues: participants may lose or 
change phones, have a flat battery after power outages, or lose service while travelling during the 
intervention, all of which occurred in the TextTB pilot study. These factors will be documented in 
exit surveys and interviews to understand the extent to which they might affect a larger-scale 
intervention. If participants have trouble taking the direct compliance test at home, we will 
conduct the test when the participant comes for their visit. 

19/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
monthly or have a community health worker conduct random inspections and send a photograph 
directly to the intervention coordinator and record it as data collected by a health worker. 
12. EXPECTED IMPACT 
The goal of this study is to improve treatment outcomes by designing and testing tools to support 
patient-centred care and to empower healthcare teams with a strategy to more easily monitor 
and support a cohort of patients. The application is being built using current design and 
interoperability standards and in a modular system. This allows us to modify the application and 
adapt it to user and system needs identified along the way and in the future. As technology is 
changing rapidly, it is important to maintain a flexible development environment to ensure that 
rapid changes can be made while remaining mindful of cost constraints and the particular needs 
of low-resource environments. The system needs to maintain flexibility to reach those most in 
need who may not have access to the most advanced phones or technology. In addition to 
disseminating and presenting the results to key stakeholders and participants in the community 
and national programmes, as described above in Objective 3, our team will also disseminate the 
results of the study through traditional academic channels. We will: 1) seek to publish results in 
high-impact scientific journals; 2) provide results in an annual and final report to NIH; 3) present 
results at major national and international meetings for TB and public health; 4) build code in an 
open source configuration for others to benefit; 5) register and publish findings on 
clinicaltrials.gov. 
13. PUBLIC HEALTH IMPLICATIONS 
The findings have wider implications and will improve our understanding of how to support 
patients on challenging treatment regimens. Poor medication adherence to TB regimens, along 
with challenges in monitoring patients and re-engaging them in treatment, are important 
determinants of poor outcomes and the development of drug resistance, contributing to the 
vicious cycle that makes TB control difficult. The main objective of this study is to refine and 
evaluate the impact of a treatment support tool (TB-TST) that links a user-centred app design to 
improve treatment outcomes in TB patients. We believe that mHealth tools, such as mobile apps, 
hold the promise of providing personalised treatment monitoring, increased self-management of 
care and improved patient-provider communication, as well as a means to record vital 
information across different groups, not only for TB but for a range of other chronic diseases. 
14. ETHICAL CONSIDERATIONS IN RESEARCH 
The fundamental mission of the central ethics committee (CEC) of the Province of Buenos Aires 
will be respected, and this research will be conducted with due respect for the dignity, integrity of 
personal rights and well-being of the participants involved in the research. 

20/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. Last IEC approval: 01/07/22 
 
 
The interests and welfare of each study participant will prevail over the interests of science and 
society. The accepted ethical and scientific principles and the physical and mental integrity of the 
research participants, as well as their privacy and the protection of their personal data in 
compliance with the laws of the Province of Buenos Aires will be honoured. The study will be 
supported by the basic postulates proposed in the Belmont Report. This includes the principles of 
respect for persons, beneficence and justice. 
"The basic principle of research involving human subjects is that the person decides to participate 
in the project voluntarily, by free choice, after being informed and understanding the significance 
of the research, its foreseeable risks and its potential benefits". This principle is to be implemented 
through the Informed Consent Process. In addition, all the administrative documents required by 
the CEC are attached. 
Both the principal investigator and the research associate declare that there is no conflict of 
interest related to the conduct of the proposed study. 
15. TRIAL REGISTRATION 
ClinicalTrials.gov ID: NCT04221789 
Unique Protocol ID: 12112019 
 
16. FINANCING 
Proposal for grant application to the National Institutes of Health (Grant Application: 
R01AI147129-01 NIAID - NIH). 

21/23 
Protocol: Tuberculosis treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring of TB treatment outcomes. 
tuberculosis treatment - Version 2.0 - Date 08/11/21. Researcher: 
Fernando Rubinstein 
 
 
 
17. TIMETABLE FOR THE STUDY 
 
Table 1: Timeline of the study 
Time in months 
Stages 
Activities 
0 
1-6 
7-12 
13-18 
19-24 
25-30 
31-36 
37-42 
43-48 
49-54 
55-60 
01/06/19 
01/11/19 
01/12/19 
01/05/20 
01/06/20 
01/11/20 
01/12/20 
01/05/21 
01/06/21 
01/11/21 
01/12/21 
01/05/22 
01/06/22 
01/11/22 
01/12/22 
01/05/23 
01/06/23 
01/11/24 
01/12/23 
01/05/24 
Preliminary 
Evaluation of the 
protocol by the 
Ethics Committee 
X 
Register on 
clinicaltrials.gov 
X 
High School 
Refinement of 
the TB-TST app 
(Objective 1) 
X 
X 
Preparation of 
materials, 
development 
of technical 
manuals and 
operational. 
X 
X 
Selection of 
hospitals and 
approval by 
each 
institution's 
Ethics 
Committee 
X 
X 
X 
Training of 
coordinators 
of the sites. 
X 
X 

22/23 
Protocol: Tuberculosis treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring of TB treatment outcomes. 
tuberculosis treatment - Version 2.0 - Date 08/11/21. Researcher: 
Fernando Rubinstein 
 
 
Table 1: Timeline of the study 
Time in months 
Stages 
Activities 
0 
1-6 
7-12 
13-18 
19-24 
25-30 
31-36 
37-42 
43-48 
49-54 
55-60 
01/06/19 
01/11/19 
01/12/19 
01/05/20 
01/06/20 
01/11/20 
01/12/20 
01/05/21 
01/06/21 
01/11/21 
01/12/21 
01/05/22 
01/06/22 
01/11/22 
01/12/22 
01/05/23 
01/06/23 
01/11/24 
01/12/23 
01/05/24 
Execution 
Recruitment of 
patients: 
eligibility, 
consents, 
questionnaire 
baseline and 
randomisation 
X 
X 
X 
X 
Implementation 
of the clinical 
trial (objective 
2) 
X 
X 
X 
X 
X 
Data collection 
X 
X 
X 
X 
X 
X 
Trial data analysis 
clinical 
X 
X 
X 
X 
X 
Conducting 
focus groups 
(objective 3) 
X 
X 
Qualitative data 
analysis 
X 
X 
X 
Presentation of 
results to key 
stakeholders 
(Objective 3) 
X 
X 

23/23 
Protocol: Tuberculosis treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring of TB treatment outcomes. 
tuberculosis treatment - Version 2.0 - Date 08/11/21. Researcher: 
Fernando Rubinstein 
 
 
Table 1: Timeline of the study 
Time in months 
Stages 
Activities 
0 
1-6 
7-12 
13-18 
19-24 
25-30 
31-36 
37-42 
43-48 
49-54 
55-60 
01/06/19 
01/11/19 
01/12/19 
01/05/20 
01/06/20 
01/11/20 
01/12/20 
01/05/21 
01/06/21 
01/11/21 
01/12/21 
01/05/22 
01/06/22 
01/11/22 
01/12/22 
01/05/23 
01/06/23 
01/11/24 
01/12/23 
01/05/24 
Monitoring 
Progress reports 
to NIH, the 
Ethics 
committee and 
the 
staff of the 
study. 
X 
X 
X 
X 
Data Safety 
Monitoring 
Board 
meetings 
X 
X 
X 
X 
Preparation and 
submission of 
manuscripts 
X 
X 
X 
Closing 
Submission of 
Final Report to 
NIH, Ethics 
Committee and 
study personnel. 
X 

24/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. 
Researcher: Fernando Rubinstein 
 
REFERENCES 
 
1. World Health Organization. The top 10 causes of death worldwide. Geneva, 2017. 
2. World Health Organization. Global Tuberculosis Report, 2018. 
3. All Party Parliamentary Group on Global TB. The price of a pandemic:counting the cost of MDR- 
TB, 2015. 
5. World Health Organization. End TB Strategy. 2015. 
6. World Health Organization. Digital Health for the End TB Strategy: An Agenda for Action Geneva, 
Switzerland, 2015. 
7. World Health Organization. Digital health for the End TB strategy: progress since 2015 
and future perspectives. Geneva: World Health Organization, 2017. 
8. DiStefano MJ, Schmidt H. mHealth for Tuberculosis Treatment Adherence: A Framework to 
Guide Ethical Planning, Implementation, and Evaluation. Glob Health Sci Pract 2016; 4(2): 211-21. 
9. Iribarren SJ, Schnall R, Stone PW, Carballo-Dieguez A. Smartphone Applications to Support 
Tuberculosis Prevention and Treatment: Review and Evaluation. JMIR Mhealth Uhealth 2016; 
4(2): e25. 
10. Center for Disease Control and Prevention. Tuberculosis Data and Statistics, 2018. 
11. World Health Organization. Tuberculosis: fact sheet no. 104, Reviewed March 2016, 2016. 
12. Maartens G, Wilkinson RJ. Tuberculosis. Lancet 2007; 370(9604): 2030-43. 
13. Osterberg L, Blaschke T. Adherence to medication. The New England journal of medicine 2005; 
353(5): 487-97. 
14. Ray TK, Sharma N, Singh MM, Ingle GK. Economic burden of tuberculosis in patients attending 
DOT centres in Delhi. J Commun Dis 2005; 37(2): 93-8. 
15. Corbett EL, Watt CJ, Walker N, et al. The growing burden of tuberculosis: Global trends and 
interactions with the HIV epidemic. Arch Intern Med 2003; 163(9): 1009-21. 
16. Iribarren SJ, Rubinstein F, Discacciati V, Pearce PF. Listening to those at the frontline: Patient 
and healthcare personnel perspectives on tuberculosis treatment barriers and facilitators in 
high TB burden regions of Argentina. Tuberculosis Research and Treatment 2014: 14. 
17. Hirpa S, Medhin G, Girma B, et al. Determinants of multidrug-resistant tuberculosis in patients 
who underwent first-line treatment in Addis Ababa: a case control study. BMC public health 2013; 
13: 782. 
18. Mitchison DA. How drug resistance emerges as a result of poor compliance during short 
course chemotherapy for tuberculosis. Int J Tuberc Lung Dis 1998; 2(1): 10-5. 
19. World Health Organization. Global Tuberculosis Report, 2016. Geneva: World Health 
Organization, 2016. 
20. Center for Disease Control and Prevention. The costly burden of drug-resistant TB in the U.S., 
2017. 
21. World Health Organization. Global Plan to End TB: The Paradigm Shift 2016-2020. Geneva, 
Switzerland, 2015. 
22. World Health Organization. What is DOTS? A guide to understanding the WHO-recommended 
TB control strategy known as DOTS, 1999. 

25/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. 
Researcher: Fernando Rubinstein 
 
23. Karumbi J, Garner P. Directly observed therapy for treating tuberculosis. The Cochrane 
database of systematic reviews 2015; 5: CD003343. 
24. Pope DS, Chaisson RE. TB treatment: As simple as DOT? Int J Tuberc Lung Dis 2003; 7(7): 611-5. 
25. Volmink J, Garner P. Directly observed therapy for treating tuberculosis. The Cochrane 
database of systematic reviews 2007; (4): CD003343. 
26. Tuberculosis Coalition for Technical Assistance. International Standards for Tuberculosis Care 
(ISTC). Diagnosis, Treatment, Public Health. 2nd ed. The Hague, 2009. 
27. World Health Organization. Global Tuberculosis Report 2015. Geneva, Switzerland: WHO, 
2015. 
28. National Institute of Respiratory Diseases "Dr. Emilio Coni". Tuberculosis case notification 
in the Republic of Argentina 1985-2014 [notificación de casos de tuberculosis en la república 
Argentina]. Santa Fe, Argentina: Departamento Programas de Salud, 2016. 
29. World Health Organization. TB country profile Argentina, 2017. 2018. 
http://www.who.int/tb/country/data/profiles/en/. 
30. United Nations. Sustainable Development Goals. 2015. 
http://www.un.org/sustainabledevelopment/sustainable-development-goals/. 
31. International telecommunication Union. The World in 2016: ICT Facts & Figures. 2016. 
http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2016.pdf. 
32. Belknap R, Weis S, Brookens A, et al. Feasibility of an ingestible sensor-based system for 
monitoring adherence to tuberculosis therapy. PloS one 2013; 8(1): e53373. 
33. Guerra RL, Conde MB, Efron A, et al. Point-of-care Arkansas method for measuring adherence 
to treatment with isoniazid. Respir Med 2010; 104(5): 754-7. 
34. Soobratty MR, Whitfield R, Subramaniam K, et al. Point-of-care urine test for assessing 
adherence to isoniazid treatment for tuberculosis. Eur Respir J 2014; 43(5): 1519-22. 
35. Singer E. TB Drug Compliance. Paper drug test and text messaging could help thwart the most 
deadly strains of tuberculosis. 2009. https://www.technologyreview.com/s/412203/tb-drug- 
compliance/. 
36. Schnall R, Bakken S, Brown Iii W, Carballo-Dieguez A, Iribarren S. Usability Evaluation of 
a Prototype Mobile App for Health Management for Persons Living with HIV. Studies in 
health technology and informatics 2016; 225: 481-5. 
37. Iribarren S, Demiris G, Lober B, Chirico C. What Do Patients and Experts Want in a Smartphone- 
Based Application to Support Tuberculosis Treatment Completion? Studies in health technology 
and informatics 2018; 250: 32. 
38. Kilburn JO, Beam RE, David HL, Sanchez E, Corpe RF, Dunn W. Reagent-impregnated paper strip 
for detection of metabolic products of isoniazid in urine. Am Rev Respir Dis 1972; 106(6): 923-4. 
39. Meissner PE, Musoke P, Okwera A, Bunn JE, Coulter JB. The value of urine testing for verifying 
adherence to anti-tuberculosis chemotherapy in children and adults in Uganda. Int J Tuberc Lung 
Dis 2002; 6(10): 903-8. 
40. Iribarren S, Sward K, Beck S, Pearce PF, Thurston D, Chirico C. Qualitative evaluation of an 
mHealth intervention to support patients with active tuberculosis: Implementation 
considerations. JMIR Mhealth Uhealth 2015; 3(1): e21. 

26/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. 
Researcher: Fernando Rubinstein 
 
41. Iribarren S, Beck S, Pearce PF, et al. TextTB: A mixed method pilot study evaluating acceptance, 
feasibility and exploring initial efficacy of a text messaging intervention to support TB treatment 
adherence. Tuberculosis Research and Treatment 2013; Volume 2013, Article ID 349394(Article ID 
349394): 12 pages. 
42. Iribarren SJ, Beck S, Pearce PF, et al. mHealth intervention development to support patients 
with active tuberculosis. Journal of Mobile Technology in Medicine 2014; 3(2): 16-27. 
43. Rubinstein F, Iribarren, SJ. Effectiveness of different usual regimens treatment delivery 
strategies on Tuberculosis treatment success and default rates in Argentina: A Prospective 
Cohort Study (under review). 2017. 
44. Rubinstein F, Bernachea, MP, Klein K, Zurbrigk F, Iribarren S, Chirico C, Gibbons L. TB treatment 
strategies and risk of default: a quasi-experimental study The International Journal of Tuberculosis 
and Lung Disease; 2016; Liverpool, UK; 2016. 
45. Fisher JD, Fisher WA, Amico KR, Harman JJ. An information-motivation-behavioral skills model 
of adherence to antiretroviral therapy. Health Psychol 2006; 25(4): 462-73. 
46. Munro S, Lewin S, Swart T, Volmink J. A review of health behaviour theories: how useful are 
these for developing interventions to promote long-term medication adherence for TB and 
HIV/AIDS? BMC public health 2007; 7: 104. 
47. FrontlineSMS. FrontlineSMS 1.6.16.3 ed: kiwanja.net; 2011. 
48. Iribarren S, Sward K, Beck S, Pearce PF, Thurston D, Chirico C. Qualitative Evaluation of a Text 
Messaging Intervention to Support Patients With Active Tuberculosis: Implementation 
Considerations. JMIR Mhealth Uhealth 2015; 3(1): e21. 
49. Banos O, Villalonga C, Garcia R, et al. Design, implementation and validation of a novel open 
framework for agile development of mobile health applications. Biomedical engineering online 
2015; 14 Suppl 2: S6. 
50. Flood D, Chary A, Austad K, et al. Insights into Global Health Practice from the Agile Software 
Development Movement. Glob Health Action 2016; 9: 29836. 
51. Schnall R, Rojas M, Bakken S, et al. A user-centered model for designing consumer mobile 
health (mHealth) applications (apps). J Biomed Inform 2016; 60: 243-51. 
52. Ngwatu BK, Nsengiyumva NP, Oxlade O, et al. The impact of digital health technologies on 
tuberculosis treatment: a systematic review. Eur Respir J 2018; 51(1). 
53. Iribarren S, Schnall R. Call for Increased Patient Support Focus: Review and Evaluation of 
Mobile Apps for Tuberculosis Prevention and Treatment. Studies in health technology and 
informatics 2016; 225: 936-7. 
54. eMarketer. Smartphone uptake is on the rise in Argentina. 4G is becoming more common. Aug 
4, 2016 2016. https://www.emarketer.com/Article/Smartphone-Uptake-on-Rise- 
Argentina/1014300 (accessed Aug 17 2018). 
55. Atun R, Weil DE, Eang MT, Mwakyusa D. Health-system strengthening and tuberculosis control. 
Lancet 2010; 375(9732): 2169-78. 
56. Lonnroth K, Jaramillo E, Williams BG, Dye C, Raviglione M. Drivers of tuberculosis epidemics: 
the role of risk factors and social determinants. Soc Sci Med 2009; 68(12): 2240-6. 

27/23 
Protocol: TB treatment support tools: design and evaluation of an interactive mobile application for direct adherence monitoring on TB 
treatment outcomes - Version 2.0 - Date 08/11/21. 
Researcher: Fernando Rubinstein 
 
57. Akraa S, Tran Tama, A, Shena, H, Tang, Y, Tang, B.Z, Li, J, Walker, S. A smartphone-based point- 
of-care quantitative urinalysis device for chronic kidney disease patients. Journal of Network and 
Computer Applications 2018; 115(2018): 59-69. 
58. Schnall R, Cho H, Liu J. Health Information Technology Usability Evaluation Scale (Health-ITUES) 
for Usability Assessment of Mobile Health Technology: Validation Study. JMIR Mhealth Uhealth. 
2018 Jan 5;6(1):e4. doi: 10.2196/mhealth.8851. PubMed PMID: 29305343; PubMed Central 
PMCID: PMC5775483. 
59. Statista. Smartphone users as share of the population in Argentina from 2015 to 2022. 2018. 
https://www.statista.com/statistics/621034/smartphone-user-penetration-in-argentina/. 
60. Knobel H, Alonso J, Casado JL, et al. Validation of a simplified medication adherence 
questionnaire in a large cohort of HIV-infected patients: the GEEMA Study. Aids 2002; 16(4): 605- 
13. 
61. World Health Organization. Treatment for Tuberculosis: Guidelines (4th Edition) 
WHO/HTM/TB/2009.420, 2010. 
62. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating 
scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth 2015; 3(1): 
e27. 
63. Masterson Creber RM, Maurer MS, Reading M, Hiraldo G, Hickey KT, Iribarren S. Review and 
Analysis of Existing Mobile Phone Apps to Support Heart Failure Symptom Monitoring and Self- 
Care Management Using the Mobile Application Rating Scale (MARS). JMIR Mhealth Uhealth 
2016; 4(2): e74. 
64. Mani M, Kavanagh DJ, Hides L, Stoyanov SR. Review and Evaluation of Mindfulness-Based 
iPhone Apps. JMIR Mhealth Uhealth 2015; 3(3): e82. 
65. Lincoln Y, Guba E. Naturalistic Inquiry. Newbury Park, CA: Sage Publications; 1985. 
66. Creswell JW, Klassen, A. C., Plano-Clark, V. L., Smith, K. C., for Office of Behavioral and Social 
Sciences Research. Best practices for mixed method research in the health sciences. 
Washington, DC: NIH National Institutes of Health, 2011. 
67. Johnson RB, Onwuegbuzie AJ. Mixed methods research: A research paradigm whose time has 
come Educational Researcher 2004; 33(7): 14-26. 
68. Thomas DR. A General Inductive Approach for Analyzing Qualitative Evaluation Data. American 
Journal of Evaluation 2006; 27(2): 237-46. 
69. Austral University. Institutional Evaluation Committee (CIE) Operations Manual 2015.