Interactive Mobile Health Intervention to Support Patients With Active Tuberculosis
Phase: NA
Conditions: Tuberculosis, Adherence, Medication
Sponsor: University of Washington

Cover page  
 
Official Title of the study: Development and Evaluation of an Interactive Mobile Health 
Intervention to Support Patients with Active Tuberculosis 
 
NCT number: NCT03544476 
 
Date of the document: 12/7/2017 
 

STUDY PROTOCOL 
 
Aim 1. Convert and expand TextTB into a mobile optimized app based on stakeholder feedback and 
engagement. 1.a Preliminary App version will 
be built with the following components based 
on prior research: educational component, 
communication 
tool, 
side-effect/symptom 
reporting, and self-administration tracking. 
Drug metabolite testing will be added for 
enhanced adherence monitoring. Further 
options for expansion will be explored in Aims 
2 and 3. To improve patient engagement, 
patients will be able to curate their information, 
monitor and share their symptoms, contact 
health care team, tailor reminders or alerts for medication or follow-up appointment. See Appendix B for modular 
feature options. Platform will support texting component for non-smartphone users as done in pilot study. The 
app mPOWEr©22 is an example of a deployed system (led by mentor Dr. Lober) that came from a participatory 
design process (Fig 5) (http://mpowercare.org/). 
1.b Expand educational messages for full treatment course and patient tailoring.  
Educational messages for TextTB focusing on information needs for the intensive 
treatment phase (first 2 months) were developed using patient topic recommendations, 
suggested messages, TB expert input, and by compiling and coding patient-directed TB 
education from WHO, departments of health and Argentina’s patient education material 
and guided by the Information-Motivation-Behavior skills (IMB) model.49,50 Educational 
messages will be expanded ongoing to full treatment course based on user and 
stakeholder feedback throughout Aims 1-3. Educational material can be easily uploaded 
through webpage and notifications can be sent to participants when new and relevant 
material is added. Branching logic will be used to increase individual message tailoring 
based on baseline characteristics/profile conditions (e.g., current smoker) or request for 
further information. 
1.c Add drug metabolite testing component. Photo imaging, similar to that 
used in mPOWEr©22 for surgical wound monitoring, will be added to report 
drug metabolite results. Amlabu et al. reported the Arkansas colorimetric 
test reliably predicted isoniazid ingestion at 4 hours after ingestion.54 Figure 
6 displays purple positive result. After reporting self-administration 
participants will be requested to test and submit photo within 4-6 hours.    
1.c Refine app with information technology (IT) and TB experts. IT and 
TB experts will test and provide critique of the app. IT experts will be 
from the DUB (Design, Use, Build) alliance of faculty and students across the UW exploring Human-Computer 
Interaction and Design; TB experts will be from the Firland Northwest Tuberculosis Center and collaborator Dr. 
Chirico. Dr. Demiris, my primary mentor, is an active member of the DUB alliance. Rapid app refinement will be 
based on written and verbal critiques. All suggestions will be maintained in a study log. 
Aim 2. Refine and enhance product during pilot deployment following principles of iterative design. App 
will be tested in the field with two design iterations aimed to identify technical glitches, potential problems, or 
needed modifications to enhance and prepare app for pilot testing.  
Participants: Five patients and five health care team members will be recruited from the public pulmonary 
specialized hospitals located in Health Region V (see letter of support Dr Chirico). Health Region V serves a 
large geographic region with a population of 3.13 million and accounts for about one third of the TB cases 
(incidence 49.2/100,000) within the province of Buenos Aires.55 Patients will have completed TB treatment or 
are within the last month of completion, have regular access to a smartphone, and be 18 or older. Health care 
team members (eg, nurse, social worker, pulmonologist, nutritionist) will be those who are responsible for care 
or care management of patients with active TB, have regular access to a smartphone, and 18 or older. Potential 
patients will be introduced to the study by clinic staff through collaboration with Dr. Chirico, regional director. 
Informed consent will be obtained at start of testing.   
Procedure. App will be field tested for 10 days. Participants will be asked to use all features and review all app 
content material. This is expected to take 2-3 hours over the course of field testing. After the first five days we 
will conduct focus groups with the end-users. Individual sessions will be conducted for those who cannot join 
Table 5. Base app components and features 
Component 
Features 
Educational 
material 
Educational material can be uploaded through a 
webpage including topics and content 
Application triggers notification of new content  
Communication 
tool 
Encrypted, HIPAA compliant chat between patient and 
health care worker within app 
Side-effect 
reporting 
List of symptoms and checkboxes for users to select and 
report side-effects/symptoms 
Treatment 
administration 
tracking 
Participant confirms self-administration 
Reminders for can be set up 
Screen for program manager to see participants on track 
with treatment and others lacking confirmation (Fig 2) 
Figure 6 Positive result 
Figure 5. Example of 
deployed system 

focus group and will include the “think-aloud” approach where process of use will be recorded and participant 
will be observed using the system to study how easy it is to execute the tasks.  
The following are examples of questions for TB patients:  
- 
What additional functionalities could help you manage your TB treatment? What educational information 
would you have liked to receive throughout treatment (e.g., at initiation, at four months)? What form of 
information do you prefer (e.g., text, video, links to websites)? At what time of day are you most likely to read 
educational messages and what content best matches what time of day? What are your biggest barriers to 
taking medication daily and what features/functions could address them? What are your concerns using an 
app for support (e.g., privacy and confidentiality)? What local resources would you want to have links to or 
more information on? What name would you recommend for the app? 
Examples of provider questions: 
- 
What features and functionalities could better help you support patients throughout their treatment? What 
information do you see as critical to provide to TB patients throughout their treatment? What functions would 
facilitate you managing a large patient cohort effectively? What name would you recommend for the app? 
Method and analysis: All focus group sessions and interviews will be audio-recorded and transcribed verbatim 
for analysis. Identified glitches, feedback, recommendations will be synthesized for programmers. Steps will be 
repeated after refinements made.  
Aim 3. Assess feasibility, usability, perceived usefulness, and initial efficacy of the resulting product in 
a pilot RCT.  By conducting a pilot RCT, mimicking all of the major essentials of definitive trial, I will determine 
the feasibility, usability, perceived usefulness, and further refine intervention in congruence with iterative 
framework and estimate effect size for a future definitive evaluation. The goal is to increase TB treatment success 
rates to WHO minimum 85% target and reduce rates of poor outcomes (e.g., default, drug resistance).   
Sample size: To detect a 15% increase in treatment success with 80% power and an α of 0.05 (two-tailed) a 
sample size total of 348 is required. Based on recommended sample size calculations for pilot RCT,56  9% of 
sample size of main planned trial is needed, 35 participants. The power calculation is only applicable to the 
primary outcome of treatment success and not for the subgroup analyses. To be cautious and account for an 
estimated 20% attrition, the recruitment target was increased to 46. 
Setting: Health Region V described above.  
Recruitment: All patients newly diagnosed with TB meeting inclusion criteria will be notified of the study by clinic 
staff and referred to research staff to receive full explanation and have questions answered. Signed informed 
consent will be obtained for all participants. A recruitment log will be maintained to document screened patients 
and reasons for declining participation. Patients will be enrolled on an ongoing basis, and recruitment is 
estimated to be completed in 4 months after start of recruitment.  
Inclusion/Exclusion criteria: Patients 18 years of age or older starting TB treatment for the first time, with no 
known TB drug resistance, HIV negative, owning or having regular access to a smartphone, and who are able 
to operate the mobile phone to communicate or have someone able to assist. Excluded will be patients who are 
severely ill (i.e., requiring hospitalization) or reside in the same household with another study participant. Patients 
with known drug resistance and those with HIV co-infection will be excluded because their care is managed 
separately and the treatment regimens and duration differ. Screened patients who do not meet study eligibility 
will have specific screening data (including gender, age and reason for exclusion) entered into the study 
database which data will be helpful in examining the patient population and feasibility of enrollment criteria. 
Randomization: Participants will be randomized to usual care and usual care plus app-based intervention at a 
ratio of 1:1 in blocks of 10 to ensure balanced representation in both arms. Random allocation sequence will be 
generated using http://www.randomization.com/. Opaque envelopes sequentially numbered and sealed will be 
used for treatment allocation concealment. Due to the nature of the intervention, blinding to the group allocation 
cannot be achieved for research staff. Clinicians will not be made aware of the group allocation unless their 
patient informs them.  
Usual care: Drug treatment and patient monitoring are provided free of charge in the public health system. Usual 
care consists of outpatient treatment management from the time of diagnosis (unless symptoms are severe and 
hospitalization is recommended), routine clinical and laboratory tests, and follow-up appointments determined 
by the clinician. In general, patients receive 1-2 month’s supply of medication and are asked to return monthly 
for follow-up appointments, or earlier if experiencing any problems. Patients with first treatment receive 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 with isoniazid and rifampin daily or 3 
times a week.57 Three of the four medications are provided in a combined pill, therefore testing isoniazid urine 
metabolite is an indicator of all medications ingestion except for ethambutol.  
Procedures: A local health care professional (i.e. nurse) with expertise of TB treatment protocols and the 
healthcare system will be sought to manage and monitor the messaging system/intervention, and follow-up with 
participants. The health care professional will be trained on app and platform use, research goals and protocols 

by the PI and the regional TB director, Dr. Chirico. Participants will be informed that interaction with the 
healthcare staff through the app is provided within a clinic-based system and available during office hours 
(Monday-Friday) and that emergencies must be directed through standard routes. Participants randomized to 
intervention will receive verbal and written instructions as well as one-on-one demonstration of app features.  
Duration and follow-up period: This study is estimated to take about 1 year and will be conducted during years 
2 to 3 of this award. Participants will be followed for their full treatment course (approximately 6 months) after 
entering into the study. Therefore, data collection will be completed about 6 months after the last participant is 
enrolled into the study.    
Outcomes: Table 5 provides an overview of primary and secondary outcomes, hypotheses, operationalization, 
data collection and analysis methods. Treatment outcomes will be measured using standard definitions set by 
the WHO Standards of TB treatment.58 The primary outcome for initial efficacy will be treatment success based 
on WHO definitions: completed (without bacteriological confirmation) or cured (negative sputum smear at 6 
months and at least once prior to 6 months). Other treatment outcomes include: failed (sputum smear positive 
at 5 months or later), died, defaulted (treatment interruption for ≥ 2 months), lost to follow-up (diagnosed, 
treatment outcome not documented), or transferred out (transferred to another reporting unit and treatment 
outcome is unknown).32 To assess acceptability (perceived usefulness and ease of use), we will use the Mobile 
Application Rating Scale (MARS).59 The MARS has been shown to be highly reliable tool to assess app quality.59-
61 The scale includes three sections and a modifiable app-specific section: classification, quality and satisfaction 
classification section provides descriptive information about the apps. The objective app quality section includes 
19-items divided into four scales: engagement, functionality, aesthetics and information quality. The subjective 
quality section contains 4 items evaluating the user’s overall satisfaction. MARS items are scored using a 5-point 
Likert scale. The final MARS scores include four subscale scores, a total mean score, subjective quality score 
and an app-specific subscale that assesses perceived impact on the user’s knowledge, attitudes, and intentions 
to change as well as likelihood of changing targeted behaviors. The MARS total mean score describes the overall 
quality of an app, while the mean engagement, functionality, aesthetics, and information quality subscale scores 
can be used to describe its specific strengths and weaknesses. Participants will be asked questions similar to 
those in Aim 2 and specifically about urine test strip use. 
Table 5. Overview of variables, hypotheses, operationalization & data collection  
Outcome/Variable 
Hypothesis 
Outcome measure 
Analysis 
Measurement point 
 
T0 
T1 
T2 
T3 
Primary outcomes 
 
 
 
 
     Treatment outcomes 
I > C success 
I < C other 
Treatment success rate (%) 
Other outcomes (failed, default, died, 
lost to follow-up) 
χ2 test 
 
 
 
X 
     Acceptability* 
 
MARS scale (Likert scale) 
Descriptive/ 
qualitative 
 
 
 
X 
     Program evaluation 
 
Feasibility, ease of use, home drug 
metabolite testing, recommendations 
Descriptive/ 
qualitative 
 
 
 
X 
Secondary outcomes 
 
 
 
 
 
 
 
Global Health PROMIS 
SFv1.1 
I>C 
CDE outcome for self-management 
 
X 
 
 
X 
WHOQoL-BREF 
measures 
 
 
 
 
 
 
 
     Level of engagement* 
 
# of messages: notification, side 
effects, questions 
Descriptive 
X 
X 
X 
X 
     TB knowledge 
I > C 
Change in TB knowledge score 
t-test 
X 
 
 
X 
     Appointment attendance 
I > C 
Mean proportion 
χ2 test 
 
X 
X 
X 
Cost accounting 
 
Log of intervention costs 
Descriptive 
X 
X 
X 
X 
   Subgroup analyses 
Gender 
Age 
Mobile phone access type 
Time to clinic 
 
Female>male 
Older>younger 
Personal>shared 
≤30> >30 min 
 
 
χ2 test 
 
 
 
 
Note: I=intervention, C=control; PROMIS= Patient-Reported Outcomes Measurement Information System; CDE=common data elements; 
T0 Screening, T1 Baseline, T2, 2 months, T3 6 months, * = data collected from intervention group only 
Secondary outcomes include the Global Health Patient-Reported Outcomes Measurement Information System 
(PROMIS) short form,62 TB knowledge, level of tool engagement, appointment attendance and cost accounting. 
The Global Health SF is a recommended outcome measure for self-management, includes 10 items, is available 
in the Spanish language, and can be integrated into REDCap. TB knowledge will be assessed using items from 
the Tuberculosis Knowledge Assessment Questionnaire at pre and post-intervention.25 Items include questions 
focused on exploring knowledge about: causes and symptoms of TB (5 items), TB transmission (4 items), TB 
treatment (2 items), and TB prevention (5 items)(Appendix C). The questions are structured to answer (yes, no, 
I don’t know). For those randomized to app, I will evaluate the level of engagement with the intervention (e.g., 
percentage of notification without a reminder, number of questions, number of reported side effects).  

Data collection methods: Baseline survey will use NINR common data elements of main and form administration 
and demographics using the Computer Assisted Interview.62 The web based interface will collect all incoming 
and outgoing messages and capture instances of participants not notifying. Medical records or national 
registry will be reviewed to collect treatment outcomes, including sputum sample results if collected or rationale 
why they samples were not ordered (e.g., patient without cough/sputum). Data convergence will be used to 
assess treatment adherence: notification of self-administration, sputum sample, report of no symptoms, and drug 
metabolite test results. Metabolite tests will be done in home and time-stamped through app. An exit survey will 
be given to all intervention group participants via app survey or text message and will include: Likert scale 
questions on acceptance (e.g., was intervention helpful in you completing your treatment, would you recommend 
to others), open ended questions for problems and recommendations, and 10 item Global Health PROMIS 
SFv1.1. I will conduct in-depth semi-structured qualitative interviews with a minimum of 10 intervention group 
participants and 5 healthcare team personnel (director, clinician, social worker, nurse, and ancillary staff). I will 
attempt to interview participants from each of the following groups: had abandoned treatment, had difficulties, 
and those who completed successfully. Interviews questions include identifying challenges, bottlenecks, if 
intervention meet needs, confidentiality concerns, and recommendations. Interviews will be recorded and 
transcribed verbatim.63  
Data Analysis/Interpretation: All analyses will be based on intention-to-treat. Statistical analyses will be 
performed using IBM SPSS, version 20 (Chicago, IL). Independent-sample t-tests will be conducted for 
continuous outcomes and 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 less 
than 0.05 will be set to detect a statistically significant difference for all analyses. I will compare group baseline 
characteristics including: age, gender, education, travel time to center, take other medication on a regular basis, 
and baseline TB knowledge. For qualitative data, the transcripts will be entered into Nvivo to organize and 
iteratively code using thematic and descriptive qualitative methods.64,65  
Potential Problems and Alternatives: Potential IT issues: Participants may lose or change phones, have 
dead battery after power outages and loss of service while traveling during the intervention, which occurred in 
the pilot study. One participant later notified the study team that his phone was stolen. These factors will be 
documented to understand the degree to which they might affect a larger scale intervention. Access to mobile 
phones: Smartphone access is rapidly increasing globally. Although there are more mobile phone subscriptions 
than people in Argentina, all potential participants may not have access to a smartphone. In our pilot study there 
were only 2 (2.2%) who did not qualify for mobile phone access reason. I do not plan to give potential participants 
mobile phones and will record the reasons for ineligibility into study. Likewise, in a real world situation mobile 
phones would likely not be allocated to newly diagnosed patients. Social desirability bias: Self-reporting 
adherence by texting-in raises concern for assurance of medication adherence and social desirability bias. When 
patients receive treatment by self-administration, treatment completion is extrapolated from returning to 
healthcare visits and picking up ordered medication, conducting sputum smear tests, and reporting of no 
symptoms. To mitigate social desirability of reporting adherence, we will use data convergence of sputum 
samples, report of no symptoms, and metabolite test results. Confounding factors: nonspecific effects of the 
intervention. We considered the option of the comparison group to be attention control one; however, because 
this does not mimic usual care and requires additional resources, we decided against this option.  
Feasibility: Spanish language/Investigator: With my experiences living in South America for extended periods 
(nearly 3 years total) I am proficient in reading, writing, and communicating in Spanish. I will be able to 
successfully communicate with the healthcare team and participants; however, if language issues arise, Dr. 
Rubinstein is bilingual with Spanish as his first language and Dr. Chirico is proficient in reading and 
comprehension of English. The regional TB director and regional TB social worker, with whom I will collaborate, 
will also be available and will serve as local coordinators, TB experts, and Spanish language verifiers. 
Recruitment: There are over 10,000 new TB cases per year in Argentina, half of which are identified in the 
Province of Buenos Aires. Health Region V had 1020 cases in the last analyzed cohort. During recruitment 
phase, in the event that there are insufficient number of subjects, I will enroll additional study sites such as 
another regional public hospital with the assistance of onsite co-mentor Dr. Rubinstein. He recently completed a 
prospective cohort study recruiting from 47 different healthcare facilities from 16 different departments. The 
proposed study will take place at one large public hospitals where there is a higher concentration of cases. IT 
logistics: Refining and adding features to an open-source software is realistic and achievable. Given Dr. Lober’s 
(co-mentor) experiences with successfully implemented apps and international mHealth interventions, he will 
supervise the IT component of app refining, implementation and trialing.  
Timeline: Table 6 provides timeline of the proposed research activities under this award.  
Table 6. Timeline of Proposed Research 
Activities 

Future Directions: If intervention is found to be feasible, 
acceptable, and efficacious in pilot RCT, a subsequent R01 
application will propose a comparative effectiveness evaluation 
comparing app to mixed strategies and directly observed therapy. 
The ultimate goal is to plan for a sustainable, scalable, and easily 
adaptable intervention managed by local healthcare teams. 
Because of a significant percentage of foreign born TB cases in the 
US originated from a Spanish speaking country the intervention 
could be trialed in other Spanish speaking settings where treatment support is limited or could be adapted for 
latent TB infections in the US (with rates of abandonment over 50%)66 or other chronic or long term treatment 
regimens where patients self-administer, have poor adherence, and limited treatment support. 
 
Statistical Design and Power 
Sample size: To detect a 15% increase in treatment success with 80% power and an α of 0.05 (two-tailed) a 
sample size total of 348 is required. Based on recommended sample size calculations for pilot RCT,55  9% of 
sample size of main planned trial is needed, 35 participants. The power calculation is only applicable to the 
primary outcome of treatment success and not for the subgroup analyses. To be cautious and account for an 
estimated 20% attrition, the recruitment target was increased to 46. 
 
Data Analysis/Interpretation: All analyses will be based on intention-to-treat. Statistical analyses will be 
performed using IBM SPSS, version 20 (Chicago, IL). Independent-sample t-tests will be conducted for 
continuous outcomes and 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 less 
than 0.05 will be set to detect a statistically significant difference for all analyses. I will compare group baseline 
characteristics including: age, gender, education, travel time to center, take other medication on a regular 
basis, and baseline TB knowledge. For qualitative data, the transcripts will be entered into Nvivo to organize 
and iteratively code using thematic and descriptive qualitative methods. 
 
Activity 
Year 
1 
Year 
2 
Year 
3 
6month increments 
1 
2 
3 
4 
5 
6 
Coursework 
 
 
 
 
 
 
Aim 1 
 
 
 
 
 
 
Aim 2 
 
 
 
 
 
 
Aim 3 
 
 
 
 
 
 
Analysis 
 
 
 
 
 
 
Submit R01 
 
 
 
 
 
 
Result dissemination 
 
 
 
 
 
 

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