Mobile Health and Oral Testing to Optimize Tuberculosis Contact Tracing in Colombia
Phase: NA
Conditions: Tuberculosis
Sponsor: Yale University

NCT06338462
 Mobile Health and Oral Testing to Optimize Tuberculosis Contact 
Tracing in Colombia
Approved July 1 , 2023

Aim 2: To design and iteratively adapt an mHealth strategy for implementing contact tracing 
optimized for feasibility, acceptability, usability, and appropriateness. We hypothesize that we 
can refine an mHealth contact tracing package over several cycles of community-engaged design. 
We will adapt existing client-centered educational and engagement materials for delivery using 
WhatsApp-embedded 1) interactive client surveys (i.e., “bots”) 2) instructional videos, and 3) 
video calls. We will introduce these in a single-arm pilot implementation trial enrolling 160 
households over one year, with each quarter allowing two months to recruit 40 households and 
one month to refine strategy components. We will assess feasibility, acceptability, usability, and 
appropriateness in quantitative exit surveys and 10 key informant interviews with cases and 
contacts per design cycle. During each round of data collection, we will prepare and distribute 
rapid summaries of interviews and surveys to a stakeholder focus group and then apply nominal 
group technique, a community-engaged design methodology, to elicit and select stakeholder 
modifications to the mHealth contact tracing package. As a secondary outcome, we will quantify 
changes in contact tracing uptake, completion, retention, and yield. 
Introduction. Contact tracing is a complex intervention that is challenging to implement in 
resource-constrained health settings. While cultures and contexts vary, common barriers exist that 
are correctable with streamlined, client-centered approaches to screening, testing, and linkage to 
care delivered in a safe, timely, and convenient location. The pandemic has shifted community 
norms and preferences towards tele-health86, and we now propose to develop and optimize an 
mHealth-facilitated TB contact tracing strategy to improve reach, yield, timeliness, and retention 
in contact tracing. If successful, this research will inform TB contact tracing efforts in similar 
settings in Latin America where marginalized populations cannot access TB services. To achieve 
this, we will test the hypothesis that an optimized mHealth contact tracing package can be 
achieved through cycles of community-engaged design. Specifically, we will determine qualitatively 
if video calls increase opportunity to engage in contact tracing; if instructional videos improve 
motivation of cases and contacts to engaged in tracing and capability of contacts to produce a 
respiratory sample; and, finally, if an automated assistant (“chatbot”) can assist health workers in 
providing more timely and flexible services to households. The expected outcome of this study is a 
feasible, acceptable, usable, and appropriate mHealth implementation package for a future 
household randomized, controlled implementation trial to evaluate implementation effectiveness, 
fidelity, and cost. 
Study Design and Setting. We propose multiple methods studies to achieve our design objectives, 
First, we will carry out an adaptive single-arm preliminary implementation trial within the SPH 
contact tracing program to pilot and adapt mHealth innovations. Second, we will conduct a nested 
qualitative study to collect key informant interviews with cases and contacts on their experiences 
with mHealth tracing. Third will apply nominal group technic (NGT)  with stakeholders to review 
the implementation data and to elicit and vote on suggestions on refining mHealth components. 
We will carry several iterative design cycles, each with one implementation stage and one 
adaptation stage, after a 3-month run-in period. 
Implementation stage: The initial mHealth strategy will be delivered to index patients and 
identified contacts of index patients. We will collect exit surveys on early implementation 
outcomes (feasibility, acceptability, appropriateness, usability) and interview 15 purposively 
selected case and contacts in participating households. 

Adaptation stage. Results from the implementation stage will inform modification and adaptation 
of the mHealth package using NGT87 with stakeholders from the community, households, health 
workers, SPH, and researchers. Outcomes of this phase are modifications of the mHealth package, 
based on stakeholder suggestions and votes. We will document these using the model for 
adaptation design and impact (MADI).88 
Participants. We will include 240 newly diagnosed TB patients (i.e., 20/month) and their 
household contacts reported in the National Epidemiological Surveillance databases (SIVIGILA) 
database and living in Cali. Stakeholders will include SPH personnel responsible for TB contact 
tracing activities (logistics, human resources, coordination, schedulers, visitors), health providers 
(doctors and nurses in PHCs), and TB nurses in PHCs. 
Recruitment. Contact tracing. Cases and enumerated household contacts will be invited to 
WhatsApp chat with the SPH outreach worker using secure customer relations management 
software and invited to a intake call where verbal consent will be obtained. Client Interviews. After 
verbal consent, we will purposively sample 15 index patients and contacts for exit interviews per 
design cycle. Stakeholder Panel. Participants will be formally invited after soliciting names from TB 
advocacy groups, the SPH, and Alianza-TB; we will obtain verbal consent to participate in each 
nominal group. The group consist on members of the academy, public health sector, and members 
o the community. 
 
Procedures. Design and delivery of implementation packages. The proposed components are 
targeted to barriers identified in previous collaborative research with the SPH. SPH will assign two 
TB outreach workers to pilot and adapt existing contact tracing materials for mHealth with the SPH 
communications team. Below, we specify the mHealth intervention components that will be 
delivered as part of routine contact tracing, using the Behavior Change Wheel framework and the 
Template for Intervention Description and Replication. 
Chatbot-assisted client interactions. 
Description 
Design of first version: 
The design of the first version was conducted based on the preliminary results of a systematic 
literature review on outcomes of Automated Conversational Agents on infectious diseases 
healthcare services, the team of researchers joined with the technical local providers develop an 
initial conversational flow that provided the first set of services to the taget users (initialy index 
cases).} 
Capabilities 
Capturing Information:  
Providing Information:  
Decision Making (Automatic scheduling):  
User engagement and support 
Articulation with field activities 

 
Measurement of implementation outcomes. First, after tracing interviews with cases and 
contacts, SPH workers will administer AIM, FIM, IAM, and system usability scale survey 
instruments, all locally adapted and translated.89 Second, research staff with qualitative interview 
expertise will conduct brief key informant interviews with a purposive (age, gender, race/ethnicity) 
sample of ~15 cases and contacts per cycle (45 total) to elicit client perspectives on the mHealth 
strategy. We will record all interviews and have them professionally transcribed. Third, we will 
prepare rapid interview summaries from deductive field notes structured using Proctor’s early-
stage implementation outcomes90 (feasibility, acceptability, appropriateness) plus usability, with 
illustrative quotes. We will share these with the stakeholder panel for review in advance of the 
NGT focus group discussions. 
Adaptation stage procedures. Using NGT, stakeholders will suggest adjustments to the mHealth 
components.91 Briefly, participants will review case and contact interview summaries and, in open 
round-robin discussion, present possible adjustments and rationale. Finally, participants will 
engage in concealed rank-choice voting on the proposed adjustments. The 3 highest-ranked 
innovations will be revealed and implemented during the next design cycle, after they are 
operationalized by the design team.  
Sample Size and Statistical Analysis Plan. For quantitative analyses of implementation outcomes. 
Sample size is based on convenience given our formative objectives. We will include 240 
households over 1 year (20 index TB patients and contacts per month), a feasible target. We will 
summarize quantitative FIM, AIM, IAM, & usability scores by client type (cases, contacts) and 
construct general linear mixed models (GLM) to determine the effect of each design cycle on 
scores, with household as a random effect. As an exploratory analysis, we will construct GLM 
models (logit link, household as a random effect) of tracing uptake, completion, yield, linkage to 
prevention, retention, using prior definitions.12 
For qualitative analyses. Assuming a homogeneous population, we expect to reach code 
saturation with a minimum of 12 interviews92, so we will plan 15 interviews per cycle and adjust 
based on thematic analysis.93 Qualitative recordings of interviews and focus groups, field notes, 
and programmatic materials will be collated and analyzed using RADaR, a rapid analysis 
technique.94 
Potential challenges and alternative approaches. Including 240 households may appear 
ambitious, but since this data will be collected passively during routine tracing, the marginal 
research cost of additional households is small, and more repetition with the mHealth strategy will 
accelerate design insights. In case a smaller target becomes necessary, we anticipate still having 
enough information to meet design objectives. Novel technologies rapidly evolve, and 
adjustments may be necessary, so we have included commercial partners to assist us in rapidly 
adapting to available technologies. Some clients may prefer traditional visits over telehealth visits, 
but our hybrid human-mHealth strategy allows greater flexibility to meet patients where they are. 
Expected Outcomes. The expected outcome of Aim 2 is an optimized mHealth strategy for TB 
contact tracing with enhanced feasibility, acceptability, usability, and appropriateness, to prepare 
for a larger household-randomized, controlled implementation trial. We will also acquire a 
preliminary understanding of the impact and effectiveness of the new strategy. We will have 

protocols for training on and implementing a digital contact tracing strategy, including digitalized 
educational materials and a parametrized, automated chatbot. We will also advance the 
understanding of the ethics and safety of using commercial chat applications for public health data 
on individuals and contribute a model for more equitable design of implementation strategies. 
TIDieR Components Summary 
Component 
Engageme
nt and 
Linkage 
Education 
and 
Information 
Data 
Capture 
Automatic 
Scheduling 
Agent 
Support 
Transfer 
Coordinatio
n with Field 
Activities 
Rationale/G
oal 
Identify 
and link 
TB cases 
and their 
contacts 
for early 
and 
effective 
disease 
manageme
nt. 
Educate TB 
cases and 
contacts 
about 
symptoms, 
prevention, 
and 
treatment to 
improve 
adherence 
and disease 
management
. 
Capture data 
on diagnosis 
perception, 
confirm 
personal 
data, and 
monitor 
treatment 
adherence 
and 
symptoms. 
Facilitate 
the 
scheduling 
of home 
visits and 
calls to 
ensure 
continuity 
of patient 
and contact 
care. 
Provide 
human 
support or 
chatbot 
support 
for 
unresolve
d user 
queries, 
ensuring 
personaliz
ed 
attention. 
Integrate 
chatbot work 
with field 
activities for 
coordinated 
and efficient 
TB care. 
Procedures 
and 
Frequency 
Chatbot 
contacts 
cases and 
contacts 
via 
automated 
messages 
and home 
visits by 
health 
workers. 
Updated 
weekly. 
Chatbot 
provides 
ongoing TB 
education 
on-demand, 
updated 
periodically. 
Chatbot 
collects data 
on diagnosis 
perception, 
personal 
data, 
treatment 
requirement
s, and 
symptom 
tracking. 
Chatbot 
schedules 
visits and 
calls based 
on health 
staff 
availability 
and user 
preferences, 
with 
reminders. 
Chatbot 
transfers 
unresolve
d queries 
to support 
agents in 
real-time 
during 
office 
hours. 
Chatbot 
coordinates 
with field 
staff to 
update case 
and contact 
information 
and schedule 
visits weekly. 
Agent 
Chatbot 
acts as the 
primary 
contact 
agent 
using data 
provided 
by the 
Health 
Chatbot acts 
as an 
educational 
agent with 
approved 
knowledge 
from health 
professional
s. 
Chatbot acts 
as the main 
agent for 
data 
collection, 
transferring 
to health 
staff when 
needed. 
Chatbot 
schedules 
visits using 
a 
synchronize
d database 
with health 
staff 
calendars. 
Chatbot 
acts as the 
first point 
of contact, 
transferrin
g complex 
interactio
ns to 
Chatbot acts 
as a digital 
link between 
patients/conta
cts and field 
staff. 

Component 
Engageme
nt and 
Linkage 
Education 
and 
Information 
Data 
Capture 
Automatic 
Scheduling 
Agent 
Support 
Transfer 
Coordinatio
n with Field 
Activities 
Departmen
t. 
support 
agents. 
Mechanisms 
Automated 
personaliz
ed 
messages 
confirming 
diagnosis 
and 
providing 
initial 
informatio
n. 
Integration 
with data 
manageme
nt systems 
for 
tracking 
responses 
and 
updates. 
Automated 
messages 
and 
responses to 
specific 
queries. 
Chatbot 
provides 
links to 
additional 
resources 
and 
multimedia 
educational 
materials. 
Automated 
questionnair
es and data 
logs. 
Chatbot 
alerts for 
additional 
follow-up 
by health 
staff. 
Integration 
with data 
managemen
t systems 
and 
reminder 
notification
s. 
Real-time 
transfer 
system 
and 
notificatio
ns for 
support 
staff. 
Chatbot 
provides 
interactio
n 
summarie
s to 
ensure 
care 
continuity
. 
Integration of 
databases and 
systems for 
task 
management. 
Real-time 
communicati
on with field 
staff for 
updates and 
coordination. 
Barriers 
Addressed 
by 
Intervention 
Lack of 
timely 
case 
identificati
on and 
difficulties 
in linking 
patients to 
follow-up 
processes. 
Misinformat
ion and lack 
of access to 
reliable TB 
information. 
Inconsistenc
ies in data 
collection 
and 
difficulties 
in 
monitoring 
treatment 
adherence. 
Difficulties 
in 
coordinatin
g and 
scheduling 
appointmen
ts. 
Limitation
s in 
chatbot 
capabilitie
s to 
handle 
complex 
queries 
and user 
needs. 
Lack of 
coordination 
and 
communicati
on between 
digital work 
and field 
activities. 
Possible 
Adaptations 
and 
Improveme
nts 
Adapting 
messages 
for 
different 
languages 
and 
literacy 
levels, 
including 
personaliz
Customizing 
educational 
content 
based on 
user risk 
profiles, 
integrating 
multimedia 
(videos, 
infographics
Incorporatin
g more 
detailed 
surveys, 
personalized 
follow-up 
schedules, 
and user 
interface 
improvemen
Personalizi
ng 
scheduling 
options, 
incorporati
ng real-time 
confirmatio
ns, and 
flexible 
Continuou
s training 
for 
support 
staff, 
improving 
interactio
n transfer 
interfaces, 
and 
Enhancing 
data 
synchronizati
on, providing 
mobile tools 
for field staff, 
and 
conducting 
periodic 
feedback 

Component 
Engageme
nt and 
Linkage 
Education 
and 
Information 
Data 
Capture 
Automatic 
Scheduling 
Agent 
Support 
Transfer 
Coordinatio
n with Field 
Activities 
ed 
reminders 
and direct 
contact 
options 
with health 
staff for 
greater 
support. 
), and 
offering 
information 
in the user’s 
preferred 
language. 
ts to 
facilitate 
data capture. 
reschedulin
g. 
follow-up 
options 
post-
interactio
n. 
sessions to 
improve 
integration.