Using Artificially Intelligent Text Messaging Technology to Improve American Heart Association's Life's Essential 8 Health Behaviors: LE8 Bot + Backup
Phase: NA
Conditions: Cardiovascular Diseases, Lifestyle Factors
Sponsor: University of Colorado, Denver

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Study Protocol 
 
“Using artificially intelligent text messaging technology 
to improve American Heart Association's Life's 
Essential 8 Health Behaviors: LE8 Bot + Backup” 
 
NCT ID not yet assigned, Unique Protocol ID: 22-2097 
 
October 11, 2023 
 
 
 

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Pharmacist 
w 
COMIRB Protocol 
COLORADO MULTIPLE INSTITUTIONAL REVIEW BOARD 
CAMPUS BOX F-490    TELEPHONE:  303-724-1055  Fax:  303-
724-0990 
 
 
 
Protocol #: 22-2097 
Project Title: Using artificially intelligent text messaging technology to improve American 
Heart Association's Life's Essential 8 Health Behaviors: LE8 Bot + Backup 
Multiple Principal Investigators: P Michael Ho, MD, PhD and Pamela Peterson, MD, 
MPH/MSPH 
Version Date: 10.11.23 
 
I. Hypotheses and Specific Aims  
 
Our goal is to improve control of cardiovascular (CV) disease risk factors using a multilevel 
intervention leveraging cellphone-based text messages integrated within health systems to 
improve control of American Heart Association’s Life’s Essential 8 (LE8) lifestyle factors. 
We will test the comparative effectiveness of 3 text messaging delivery strategies (vide 
infra). The findings will provide evidence regarding the best population-based strategy for 
universal delivery to engage all patients with health disparities in self-management to 
improve the AHA’s LE8. We hypothesize that the LE8 text message curriculum will improve 
patient self-management of the LE8 risk factors and there will be a significant change in the 
LE8 composite score between baseline and 12-months following study enrollment. We also 
hypothesize that the optimized AI chatbot text messages with proactive pharmacist 
management arm will show the greatest improvement in the LE8 risk factors compared to 
the optimized AI chatbot text messages alone and generic text messages. 
 
- 
Aim 1 (UG3; Year 1): Iteratively update the infrastructure and expand content for the 
AI text message chatbot with attention to social determinants of health and 
sociocultural contextual relevant to the target population through stakeholder 
engaged N-of-1 and focus group interviews and nominal group sessions. 
- 
Aim 2 (UG3; Year 1): Conduct a randomized pilot to demonstrate feasibility of 
intervention delivery and outcomes data collection to assess preliminary effects and 
to refine the intervention prior to widespread implementation. 
- 
Aim 3 (UH3; Years 2-5): Conduct a pragmatic patient-level randomized intervention 
of 3 text messaging delivery strategies for self-management support of CV risk 
factors. Primary outcome will be change in LE8 health score. Secondary 
effectiveness outcomes will include individual components of the LE8 lifestyle 
factors, Framingham risk score, self-efficacy, medication adherence, clinical 
outcomes (e.g., CV related hospitalizations), and healthcare utilization. 
- 
Aim 4 (UH3; Years 2-5): Evaluate the intervention using PRISM and a mixed 
methods approach to evaluate pragmatic clinical and implementation outcomes 
(reach, effectiveness, adoption, implementation, and maintenance) with an 
emphasis on equity and representativeness, and systematically assess contextual 
influences to inform sustainment and future tailoring, adaptations, and 
dissemination. 
 
II. Background and Significance 

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At least 50% of the US population will develop two or more chronic medical conditions by 
age 45, with the prevalence increasing to >80% for those age 65 years and older. These 
chronic medical conditions include many CV diseases and CV disease risk factors (e.g., 
hypertension, hyperlipidemia, and diabetes). CV disease leads to significant disability, 
health care costs and death. To successfully manage these conditions, patients need 
ongoing care facilitated by health care providers who can help them monitor and manage 
their CV conditions themselves in between episodic health care visits.  
Patients experiencing health disparities, those who are racial and ethnic minorities1; 
people with low income or low socioeconomic status (SES)2; rural residents3 and people 
with limited English proficiency4 are disproportionately affected by these CV conditions and 
suffer greater consequences from these conditions. The risk of diabetes is 77% higher for 
Black and 66% higher among Hispanic/Latino, than for White patients.5 Hypertension 
control rates are lower among Non-Hispanic Black and Hispanic/Latino compared to Non-
Hispanic White patients.6 These differences contribute to disproportionate rates of mortality 
as the attributable risk for hypertension and 30-year all-cause mortality is nearly double for 
Non-Hispanic Black than Non-Hispanic White patients.6 Data show similar disparities for 
people with low SES,7 rural residents and people with limited English proficiency.8,9 These 
statistics highlight an urgent need to address and control these CV risk factors, particularly 
among patients experiencing health disparities. Furthermore, most of the prior interventions 
addressing CV risk factor reduction have generally targeted individual risk factors rather 
than overall CV health as encompassed in the LS7 risk factors. 
 
Self-management (SM) involves focusing on an individual’s role in managing 
chronic disease and has strong evidence of benefit for patients with chronic medical 
conditions.10 SM interventions have demonstrated improved self-efficacy, quality of life, 
health status, chronic disease measures, health behavior change and reduced healthcare 
utilization.9,11–13 The American Heart Association has identified 8 key self-management 
behaviors that when optimized will collectively lead to better CV health, i.e., stopping 
smoking, eating better, being active, sustaining a healthy weight, manage blood pressure, 
control cholesterol and reduce blood sugar.14,15 The LE8 score documents how well patients 
adhere to SM behaviors, with a score that quickly and effectively measures overall CV 
health ranging from 0-14, where 0-4 is considered “inadequate” 5-9 “average” and 10-14 
“optimum” CV health.16  
As healthcare becomes increasingly complex, alternative team-based approaches 
to chronic care that include clinical pharmacists, are becoming common.17 Clinical 
pharmacists have advanced training in chronic disease management that includes both 
non-medication behavioral interventions (e.g., motivational interviewing) and all aspects of 
medication management (e.g., selection, monitoring, adjusting). There is clear evidence of 
their positive impact on patient outcomes, spanning from smoking cessation to glycemic 
control and blood pressure control across various care settings (e.g., Federally Qualified 
Health Centers (FQHC), academic health centers).18–22 These benefits have led to 
widespread integration and reliance on clinical pharmacists, but with added health system 
costs associated with paying another doctoral-level health care provider.23–30  
 
Mobile telephones are common with 96% of US adults owning a cellphone.31 Use of 
text messaging to communicate is also common with 81% of cellphone owners using their 
phones to text messages.32 Text messaging is used by people across the age spectrum, 
among racial and ethnic minority populations, rural populations3, people with low (SES) as 
well as people with limited English proficiency.4,33,34  Meta-analyses of text messaging 
interventions have demonstrated improved health behaviors including physical activity35, 
weight loss, chronic disease control (i.e., glycemic control and BP)36–38 and medication 
adherence. However, evidence such as optimal message content, conversational 

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approaches that facilitate bidirectional messaging, timing and dose of messages is limited, 
and it is unknown if patients experiencing health disparities benefit similarly.39 
The use of text-message based artificially intelligent (AI) conversational chat bots is 
emerging as the next generation for technology-based health behavior interventions.40,41 An 
AI conversational chatbot utilizes natural language processing (NLP) to classify the intent 
of a user-initiated question on specific topics and machine learning (ML) to continually 
update and refine the precision in offering a response that correctly addresses the intent of 
the question. This allows patients to initiate and direct organic text message 
communications to a specific phone number in support of self-management. Using a priori 
libraries focused on specific health behaviors that anticipate the intent of patient text-
message queries, an AI chatbot can continuously use NLP to process questions and ML to 
update and refine messages to train the system to increase the precision in matching the 
correct response to user queries. A well-functioning AI chatbot using NLP and ML will return 
answers that are appropriately matched to user queries 80% of the time or more.42 As of 
now, we have little understanding of the incremental benefits of this nascent tailored and 
user-centric approach compared to standard text message systems.  
 
Tailoring SM interventions meets patient identified needs and increases the level of 
intervention effectiveness. A prior study43 found that White patients had the lowest physical 
activity and highest adherence to insulin therapy whereas Hispanic patients were more 
interested in improving self-management behaviors, suggesting that targeted support to 
meet patient needs may be important. As another example, a tailored self-management 
intervention for Black patients with diabetes improved diabetes related clinical 
measures.44,45 A systematic review of SM support interventions in low income and low health 
literacy patients showed that they were generally resource intensive and had inconsistent 
benefits.  
Behavioral “nudges” from the fields of behavioral economics and cognitive 
psychology have the potential to augment the impact of text messaging interventions to 
support patient behavior change. The Dual-Process Theory of decision-making (one of two 
foundational theories supporting Dan Kahneman’s 2002 Nobel prize in economics) states 
that people make decisions either ‘intuitively,’ quickly drawing on emotion and past 
experiences or ‘reasonably’ using a thoughtful, analytic approach. Nudges take advantage 
of the intuitive aspects of decision-making. 46,47 A nudge is defined as a small change in 
choice framing or choice architecture that “alters people’s behavior in a predictable way 
without forbidding any options or significantly changing their economic incentives.”48 
Behavioral nudges are more personalized and resonate better with patients, and have 
demonstrated impact on healthy eating49,  smoking50, and physical activity51,52. A systematic 
review demonstrated the benefit of nudges to improve SM activities for patients with chronic 
conditions.53 Use of persuasive message strategies can further impact message 
engagement. Theories in Health Communication emphasize the need to provide a message 
frame (e.g., with a positive or negative tone) to provide opportunities for bidirectional 
engagement that allow for senders to demonstrate pro-social characteristics, evoke an 
emotional response or include a narrative in order for audiences to resonate with and 
internalize message content.54  
Interventions for SM are more likely to have a greater impact when addressing 
multilevel contributors to health inequities.55 Per the social-ecological model, to effectively 
reduce CV risk, patients must have the knowledge and skills to adopt healthy behaviors; 
communities must have resources that align with cultural norms of the patients at risk; and 
health systems must have resources to identify and treat risk in an integrated, patient-
centered manner.56 SM interventions are primed for multi-level components that facilitate 
greater engagement with and support from interpersonal connections, health organizations, 

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communities and environments to facilitate health. The “Social Ecological Model, Inside 
Out” proposed by Golden et al.,57 explicitly emphasizes an approach to health equity 
through conceptualizing how individuals, their personal social networks and group 
affiliations co-create the context that drives policy development and supportive physical and 
structural environments to support health. A key factor in this model is understanding how 
social determinants such as access to safe places to exercise, reliable transportation and 
food insecurity are considered in the intervention development and implementation. We 
attend to multiple levels of the model by making our intervention fairly and equitably 
distributed, by fostering interpersonal connections between patients and pharmacists, by 
automating identification of eligible patients through the EHR; by linking patients to 
community resources that support improvements in social determinants of health and 
through identification of infrastructural supports needed to replicate, sustain and scale the 
LE8 Bot + Backup intervention. As an example, an intervention that encourages participants 
to eat fresh vegetables and fruits but does not access whether this is feasible for participants 
or provide resources to find these foods at free or reduced costs will not equitably benefit 
all people.  
 
 
 
IV. Research Methods 
 
A.  Outcome Measure(s) 
 
Improvement in LE8 risk factor (Primary Outcome): The objective of this study is to 
determine the impact of the different text message delivery strategies on self-management 
support and subsequent change in the LE8 risk factors. We hypothesize that the LE8 text 
message curriculum will improve patient self-management of the LE8 risk factors and there 
will be a significant change in the LE8 composite score between baseline and 12-months 
following study enrollment. The LE8 score assesses how well patients’ CV risk factors are 
controlled with a score that quickly and effectively measures overall CV health ranging from 
0-14, where 0-4 is considered “inadequate” 5-9 “average” and 10-14 “optimum” CV health.29 
We also hypothesize that the optimized AI chatbot text messages with proactive pharmacist 
management arm will show the greatest improvement in the LE8 risk factors compared to 
the optimized AI chatbot text messages alone and generic text messages. The primary 
outcome will be improvement in initial qualifying LE8 components (those categorized as 
intermediate or poor at baseline and observable in the EHR, including blood pressure, total 
cholesterol, blood sugar and weight) between baseline and 12-months. We will obtain these 
measures from the EHR and take the measurement closest to baseline of those between 
3-month prior to enrollment date and 1-month post enrollment date. Study inclusion criteria 
requires identification of at least one LE8 EHR component as poor or intermediate, thus all 
patients will have at least one qualifying LE8 component obtained from the EHR. We will 
encourage patients to talk to their physician about obtaining a measure (i.e., blood draw or 
BP measurement) close to 12 months consistent with LE8 recommendations. For the 12-
month measurement, we will take the value closest to the 12-month post enrollment date 
with a 3-month window prior to and after the 12-month enrollment date. As a sensitivity 
analysis, we will also identify the lowest score within this window and the highest score then 
repeat the analysis. 
 
Individual LE8 components (Secondary Outcome): Secondary outcomes will include 
change in the individual risk factors of the LE8, including change in blood pressure, total 

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cholesterol, blood sugar, weight, physical activity, health diet pattern and smoking between 
baseline and 12-months following enrollment. For patients without a baseline measure for 
an LE8 component derived from the EHR, we will encourage patients to talk to their 
physician about obtaining a measure (i.e., blood draw or BP measurement) consistent with 
LE8 recommendations. For the 12-month measurement, we will take the value closest to 
the 12-month post enrollment date with a 3-month window prior to and after the 12-month 
enrollment date. Since physical activity, health diet pattern and smoking are not observable 
in the EHR, we will ask patients via text to self-report their status at baseline and 12-months 
following enrollment via Qualtrics Survey. Qualtrics will send a push message three times, 
if patients not respond research staff will contact them by phone up to three times. We will 
use evidence-based practices143 for text message survey completion, including pre-survey 
reminder notification and 2 follow-up reminders. We will review the patient response data 
weekly to ensure data validity. We will call patients if they do not complete the surveys and 
for any data discrepancies. 
 
Self-Efficacy for Managing Chronic Diseases (Secondary Outcome): The Self-Efficacy 
for Managing Chronic Disease Scale is a valid and reliable instrument available in English 
and Spanish. The English version is made up of 6-items on a visual analog scale, ranging 
from 1 (not at all confident) to 10 (totally confident). The psychometric properties of the 
scale include Cronbach's alpha of .88 across all studies, minimal floor and ceiling effects, 
sensitive to change, and moderate and significant correlations provide convergent validity 
evidence when measured against selected health indicators. Baseline higher self-efficacy 
was associated with lower health distress, illness intrusiveness, activity limitation, 
depression and fatigue; improvements over 4 to 6-months in self-efficacy scores was 
associated with lower levels of the same health indicators.  
 
Medication adherence (Secondary Outcome): We hypothesize the intervention will 
improve medication adherence by reducing the number of gap days between medication 
refills given that patients will be provided educational messages about the importance of 
medication adherence to help treat uncontrolled CV risk factors. We will measure 
medication adherence by identifying the number of gaps (frequency) and the length of each 
gap (severity) for every patient and medication. The gap days will be determined using 
pharmacy refill data based on the date of refill, the number of days supplied, and the 
subsequent refill date during the 12-month intervention period. Worse medication 
adherence will be identified as an increase in either the frequency of gaps or the length 
(severity) of the gaps. We are currently using this same methodology in the Nudge study. 
 
Framingham CV disease risk score (Secondary Outcome): We will use the Coronary 
Heart Disease (2-year risk) – First Event or the Recurrent Coronary Heart Disease, for those 
with established coronary heart disease or ischemic stroke risk calculator. Both risk scores 
use similar risk factors to calculate risk including systolic blood pressure, Cigarette smoking 
status, Fasting lipid level (totals and HDL Cholesterol), diagnosis of diabetes, and use of 
antihypertensive medication. We will have already obtained these measures as part of our 
assessment of the LE8. We hypothesize that the self-management support intervention will 
lower the calculated Framingham risk score between baseline and 12-months of follow-up.  
 
Clinic events (Secondary Outcome): We will also assess for clinical events defined by 
emergency department (ED) visits or hospitalizations. Our hypothesis is that improved LE8 
risk factor control will lead to decreased ED visits and/or hospitalizations. We will assess 
specific clinical events we would expect improved LE8 risk factors could have an impact 

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upon and conversely where poor adherence can lead to clinical deterioration necessitating 
additional care. For example, poor adherence to antihypertensive medications can lead to 
uncontrolled blood pressure leading to hospitalization for heart failure or stroke. We will 
assess clinical events via the EHR within each health system.  
 
Healthcare utilization (Secondary Outcome): In addition to the clinical events and 
adverse clinical events, we will also measure healthcare utilization defined by routine clinical 
visits and/or other procedures associated with the clinical condition. We hypothesize 
patients with more uncontrolled CV risk factors may be more likely to have clinic visits due 
to uncontrolled clinical conditions. For example, a patient with hypertension may not take 
their medications and therefore have uncontrolled blood pressure. They may have more 
clinic visits and have their medication uptitrated for better blood pressure control. It is also 
possible that non-adherent patients may be less likely to follow-up with clinic visits and they 
will have less healthcare utilization. Accordingly, it will be important to measure healthcare 
utilization to assess the impact of improved LE8 risk factor control as part of the study. 
 
Assessment of patient perspectives: In Year 5, after the intervention and follow-up 
period has ended, we will survey all patients via text messaging using a previously 
developed text messaging survey58 (Figure 1). In a random sample of 80 patients who 
respond to the survey, we will also contact them via telephone to get more in-depth 
feedback through qualitative interviews on the intervention. The sample will be stratified 
evenly across patients in the 3 intervention arms and prioritize representativeness of 
diverse patients. We have conducted similar interviews with patients following adherence 
interventions. 
These 
interviews 
will 
evaluate issues such as ease of use and 
acceptability and help inform future 
adaptation of the interventions59 as we 
plan for broader dissemination of the 
intervention 
(if 
demonstrated 
to 
be 
effective) to more clinics and patients with 
other chronic conditions.  
 
Follow-up assessment of clinician 
and health system organization/setting 
perspectives: 
We 
will 
conduct 
key-
informant interviews with up to 2-3 
providers (6-9 across the 3 health 
systems) from each setting whose patients 
have received the intervention to get their 
feedback about the intervention and the intervention effects on their patient’s self-
management behaviors related to CV risk factors. For some providers, they may have 
received a note from the study pharmacist informing them of changes in clinical status with 
their patients and we will also interview the providers on their perceptions of that process. 
 
 
B. Description of Population to be Enrolled:   
 
N-of-1 interviews, nominal groups, and focus groups 
10 patients from each healthcare system (30 total) will be recruited to take part in N-of-1 
interviews. We will also convene one nominal group with 6-8 participants in each health 
system (18-24 total). Each group will be held via synchronous Zoom video conference and 
Figure 1. Patient Feedback Survey 

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last up to 90 minutes. Patients for N-of-1s and nominal group sessions will be recruited from 
designated clinics and will be a balance of older/younger patients, men/women, those with 
one versus multiple chronic CV conditions and native Spanish/English speakers.  
 
We also will conduct at least 3 focus groups with multilevel stakeholders (patients, 
providers/pharmacists, community advocates, health system leaders) using purposive 
sampling to increase representation from diverse perspectives including those across the 
spectrum of health disparities. 
 
Inclusion criteria for qualitative interviews: 1) diagnosis of >=1 of the following CV risk 
factors: hypertension, diabetes or hyperlipidemia, 2) poor medication adherence defined as 
a refill gap of >=7 days within 6 months, to 1 at least one of the CV medications outlined in 
Table 1; and 3) 1 or more of the risk factors in categorized as poor or intermediate health 
as defined by LE8 as outlined in Table 2. Exclusion criteria: 1) patients who do not have 
cellphone; 2) enrolled in hospice or palliative care; 3) Non-English/Spanish speaking; 4) 
enrolled in another clinical trial if denoted in the EHR.  
 
Table 1: CV Medication eligibility 
Condition 
Classes of medications 
Hypertension 
Beta-blockers (B-blockers), Calcium Channel Blocker (CCB), Angiotensin converting 
enzyme inhibitors (ACEi), Angiotensin Receptor Blockers (ARB), Thiazide diuretic 
Hyperlipidemia 
HMG CoA reductase inhibitor (Statins) 
Diabetes 
Alpha-glucosidase inhibitors, Biguanides, DPP-4 inhibitors, Sodium glucose transport 
inhibitor, Meglitinides, Sulfonylureas, Thiazolidinediones, metformin 
 
Table 2: Life’s Essential 8 Health Categories for Study Inclusion 
 
Definition 
Level of health for each metric 
 
 
Poor 
Intermediate 
Ideal 
Blood lipids 
Metric: Non–HDL 
cholesterol (mg/dL) 
≥220 mg/dl 
130-189 mg/dl or 
treated to goal 
<130 mg/dl 
Blood 
pressure 
Measurement: 
Appropriately measured 
systolic and diastolic BPs 
SBP>140 mm Hg or 
DBP >=90 mm Hg 
Subtract 20 points if 
treated level 
SBP 120-139 or 
DBP 80-89 mm Hg 
or treated to goal 
<120 mm HG/<80 
mm Hg 
Blood glucose Measurement: FBG or 
casual HbA1c 
Example tools for 
measurement: Fasting 
(FBG, HbA1c) or non- 
fasting (HbA1c) blood 
sample 
Diabetes with 
HbA1c ≥8.0 
No diabetes and 
FBG 100–125 or 
HbA1c 5.7–7.9 
 
No history of 
diabetes and 
FBG <100 (or 
HbA1c <5.7) 
 
 
For all qualitative interviews, participants will be consented via postcard consent due to the 
minimal risk presented. 
 
Intervention 
We will identify eligible patients using EHR data with the same inclusion and exclusion 
criteria as N-of-1 interviews and nominal groups. There will be minimal exclusions criteria: 
1) patients who do not have cellphone; or 2) enrolled in hospice or palliative care; or 3) Non-
English or Spanish speaking; or 4) enrolled in another clinical trial if denoted in the EHR.   
 

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The intervention will include patients based on the following: 1) diagnosis of one or more of 
the following CV risk factors (i.e., hypertension, diabetes or hyperlipidemia), 2) poor 
medication adherence defined as a refill gap of >=7 days within 6 months, to 1 at least one 
of the CV medications outlined in Table 1; and 3) 1 or more of the risk factors in categorized 
as poor or intermediate health as defined by LE8 as outlined in Table 2.  While not part of 
the eligibility criteria, we are partnering with 3 safety-net health systems (Denver Health and 
Hospital Authority, Salud Family Health Centers, and STRIDE Community Health Centers) 
to further focus enrollment on Black, Hispanic/Latino, rural, low income and Spanish-only 
speaking patients. 
 
Stakeholder Panel 
The Stakeholder Panel will have 1 patient, 1 pharmacist or physician, and 1 administrative 
leader from each healthcare system. The panel will also include 3 representatives from local 
organizations, such as a food bank or other services. Members will be recruited through 
relationships of the investigators at the 3 sites. Stakeholder members will be consented via 
postcard consent due to the minimal risk presented. 
 
Stakeholders will meet monthly during the UG3 (Y1) year and then quarterly during the UH3 
(Y2-5) years. Meetings will be held virtually via Zoom or in person, as decided by the Panel. 
Participants will be reimbursed $50 per meeting in the form of a gift card. 
 
 
C. Study Design and Research Methods   
 
Aim 1 (UG3; Year 1): Iteratively update the infrastructure and expand content for the 
AI text message chatbot with attention to social determinants of health and 
sociocultural contextual relevant to the target population through stakeholder 
engaged N-of-1 and focus group interviews and nominal group sessions. 
 
Data infrastructure development 
The first step for Aim 1 will be to develop the technology platform to facilitate error free 
delivery of messages via text to user cell phones and to program our AI chatbot to use NLP 
and maximize the chatbot precision so that users are more often sent a response from our 
system that matches the intent of their query. We previously built a technologically current 
AI NLP chatbot system that operates via short message service (the textbot).113 We will first 
develop and categorize anticipated “intents”—i.e., the specific anticipated topics patients 
will want to learn or ask about LE8. To generate a comprehensive list of intents, we will 
review topics of frequently asked questions about the  topics in LE8 from reputable clinical 
websites. We will also build intents that anticipate questions about addressing, managing 
or overcoming social factors demonstrated as common moderators of healthy behavior for 
patients114. Once we have an initial set of intents, we will generate multiple variations on 
questions that users could ask related to that intent so the system could be “trained” to infer 
the intent of a query based on many possible ways of asking a query. For example, one 
user may ask “When do I call my doctor if my blood pressure is high?”, while another might 
ask, “what do numbers on my blood pressure mean?”, and both queries would be matched 
to an “understanding blood pressure” intent. Our AI chatbot system can facilitate branch 
logic conditioning by branching to provide responses based on patient specific queries. This 
infrastructure provides flexibility to facilitate tailoring of content to be responsive to 
individually specific preferences for information. 
 
 

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Generating message library intents. To generate an initial library of question variations for 
each intent, we will rely on the Amazon Mechanical Turk (MTurk), a crowdsourcing platform 
where one can offer a small incentive for users to complete tasks. We will ask 50 MTurk 
participants to generate 3 to 5 variations of questions with the same intent for each of the 
LE8 topics and social factor intents, randomly assigning topics and factors until we have 25 
variations on queries for each intent. This allows the system to have enough initial data to 
learn how to interpret user questions, tolerate misspellings, and recognize the underlying 
intent of each question. Although the crowdsourcing activity allows us to develop a robust 
set of question variations, there is still the likelihood that we will not anticipate every possible 
variation on questions. When the system does not match a response to the question intent, 
it reverts to the fixed choice (also called a “pick list”) set of responses, e.g., “I think you are 
asking about one of these 4 topics: (a) healthy eating, (b) cost of healthy food, (c) how to 
access a food pantry, (d) how much you can eat in a day. Please type the letter 
corresponding to the topic you wish to explore or try your question again.” Our goal is to 
correctly match the response to the intent of the question ≥85% of the time. As more users 
engage with the system, we can review logs and re-classify content that resulted in a pick 
list to match an intent daily, which will increase the precision of the system. 
 
Technology. The platform that will send text and chatbot messages will be built by Clinic 
Chat, LLC. Clinic Chat is hosted in a scalable cloud environment using Amazon Web 
Services. The NLP pipelines for textbot are built using Python 3.8 with NumPy, Pandas, 
and scikit-Learn, flask, npm, pm2 Python modules.  
 
N-of-1 interviews  
We will ask participants during synchronous sessions to react to content presented during 
a live demonstration of the message content using an interactive AI chatbot text messaging 
platform through multiple N-of-1 (i.e. within subject) assessments that conform to evidence-
based strategies for persuasive message design. A priori messages presented to N-of-1 
participants will represent theoretical constructs intended to (1) increase norms, 
commitment, and salience, key components of behavioral nudge messages; and (2) 
facilitate a sense of autonomy, competence and relatedness, key components of the Theory 
of Self-Determination. The messages and system combine to create the mechanism 
through which patients will develop greater SM autonomy, competence and relatedness. All 
content will be translated into Spanish and reviewed by our Health Equity and Engagement 
core to ensure relevance of content and that it is appropriate for Spanish speakers. Because 
messages will include assessments of social factors that can modify behavior, we will 
explicitly ask participants to comment on how best to assess these topics through the AI 
chatbot text messages to minimize concerns about confidentiality, privacy and relevance. 
We will deploy a content analysis of N-of-1 data and update our a priori library for initial text 
and chatbot messaging to identify the range of popular approaches for communication of 
the LE8 intervention content. 
 
Nominal Groups 
After completing N-of-1 interviews, we will convene up to three virtual nominal group 
sessions in each health system to further refine content and develop a final library of 
messages. The nominal group technique has been used in health promotion and in the 
design of mobile and digital health interventions119,120 to facilitate the free exchange of ideas 
in a structured but non-hierarchical manner.121 The nominal group is structured like a 
focused group discussion, where 6-8 participants are invited to react to and offer opinions 
on a series of topics. In a focused group discussion, the emphasis is on exploring a full 

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range of ideas, including outliers. In contrast, the nominal group is focused on generating 
consensus. In a nominal group session, there are multiple rounds of engagement, beginning 
with an initial round explaining a goal and answering clarifying questions. In subsequent 
rounds participants identify their preference for message content and discuss their 
preferences with the moderator with a goal to gain consensus across diverse participants. 
In this round, we will pay particular attention to message content that resonates for specific 
racial/ethnic groups and is relevant for low-income and rural communities; we will also 
review modifiable social determinants of health to consider if message content appropriately 
recognizes variable experiences with housing, income, employment, etc. that will influence 
self-management behaviors. It is beneficial if participants in a nominal group have different 
demographic characteristics so all can hear and contemplate diverse perspectives in 
working towards consensus on messages. This effort will also allow us a deeper 
understanding of how we can use message tailoring to maximum effect by asking patients 
both during the N-of-1 and Nominal group sessions to react to message tailoring examples.  
 
Each group will be held via synchronous Zoom video conference and last up to 90 minutes. 
We will review findings to determine if a second group in each setting would be needed to 
gain a higher degree of consensus on the message content. Aim 1 will yield a library of 
contextually relevant messages to be deployed for the pilot and pragmatic trial. The library 
will be designed to be delivered over an 8 week period (consistent with one week for each 
of the LE8 topics; for people who are non-smokers, we will offer a week on a self-
management topic of their choice; in the eighth week, the topic will focus on medication 
adherence and its importance given that all patients randomized to the study will have 
already demonstrated poor medication adherence). Each week for 8 weeks patients will be 
sent four messages that are specific to the topic for that week and with each message, they 
will be invited to engage with the chatbot to ask more questions about that topic.  
 
Focus groups 
We will conduct at least 3 focus groups with multilevel stakeholders (patients, 
providers/pharmacists, community advocates, health system leaders) using purposive 
sampling to increase representation from diverse perspectives including those across the 
spectrum of health disparities. A semi-structured moderator guide will be informed by 
PRISM and the Health Equity in Implementation Framework and will be reviewed by the 
Health Equity and Engagement and Implementation Science cores to guide a systematic 
evaluation of contextual determinants that positively and negatively influence the success 
of text messages (content, dose, access to community resources). We will also ask these 
stakeholders to help identify resources available locally to address social determinants of 
health which we will be able to incorporate into our educational material for patients.  
 
We will capture audio recordings of focus groups and interviews and will transcribe these 
recordings. We will analyze these data using a thematic content analysis facilitated by use 
of Atlas Ti, enabling the investigators to code, index and retrieve participant responses 
containing key themes, concepts or events, and group them into larger categories. Coding 
and analysis of data will be facilitated by the use of a codebook that will be created prior to 
data collection, containing codes and categories (groups of codes) of themes, concepts, 
events, people, actions and things that may be encountered in the data (e.g., oral history 
“vignette” or “soap-opera” styles to convey preferences for structure of messages). These 
a priori codes will be based on what the investigators may expect to find based on the 
literature and what the investigators hope to find based on the research questions. Coding 
strategies will be based on the grounded theory techniques of open and axial coding, as 

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described by Strauss and Corbin.60 Open coding is used to categorize key concepts, 
categories and patterns of experience. Axial coding is used to specify the relationship of 
categories to the phenomenon under study. Summary coding will synthesize the 
relationships across themes to generate actionable responses, such as ensuring all 
messages are branded with a clinic name, or all communication with the chatbot about a 
risk event has to happen within one hour. 
 
Pharmacist training 
We will develop a training and capacity building effort to support pharmacists from each 
health system who will be integral to arm 3 of our pragmatic trial that links users of our AI 
chatbot text messaging to pharmacists for additional self-management support. Dr. Katy 
Trinkley will lead this effort and will use an optimized instructional design method to create 
a brief online training program and a series of resources (e.g. Frequently asked questions; 
community resources with links and contacts to provide patients; templates for reporting 
patient concerns in the EHR) for pharmacists. The training program will include access to 
three 1.5-hour training modules on Motivational Interviewing via telemedicine offered by the 
University of Colorado School of Nursing Continuing Education program.125 Motivational 
Interviewing (MI) is an evidence-based approach for eliciting intrinsic motivation to change 
using open ended questions, reflective listening and decisional balancing that has been 
shown in systematic reviews to be superior to more traditional methods of supporting patient 
health behavior change.126 We will also include an orientation to LE8 and resources from 
the American Heart Association that offer specific details on each of the 8 self-management 
components of LE8,127 strategies to improve any of them, and articles providing further 
information. The training will also include explicit skills building in soliciting detail about 
patient contextual factors that impact self-management, including social determinants of 
health. Pharmacists will be oriented to resources such as the American Association of 
Family Practitioners website128 that offers local resources such as food banks, housing, 
transportation vouchers, access to good such as medical supplies, access to information 
on financial assistance programs, educational programs, jobs training programs and legal 
assistance as well as information of resources from our health systems and stakeholder 
groups. The training will offer Pharmacists guidelines and templates for engaging with 
patients and to document and log each engagement. The training will be designed as a self-
paced, fully asynchronous online module and will be housed on the Canvas Learning 
Management System.61 We will ask each health system partner to identify the pharmacist(s) 
they will dedicate to the patient support tasks for arm 3 of our pragmatic trial and will ask 
them to complete the training during the first year of the award. 
 
Aim 2 (UG3; Year 1): Conduct a randomized pilot to demonstrate feasibility of 
intervention delivery and outcomes data collection to assess preliminary effects and 
to refine the intervention prior to widespread implementation. 
 
We will conduct the trial at 3 health systems that care for large patient populations affected 
by health disparities, including Black, Latino/Hispanic, Spanish speaking, low-income and 
rural patients. In Year 1, we will conduct stakeholder engagement (patients, providers, 
community advocates, and health system leaders) guided by PRISM and the Health Equity 
Framework12 to further understand the context of the optimized patient communication, and 
to generate input on the sustainable design of automated communications that include 

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attention to social determinants of health and ensure linguistic and community relevance. 
We will also engage these implementation partners to obtain feedback on intervention 
design, and outcomes as well as throughout the study to help address potential barriers to 
implementation, make necessary adaptations, help ensure sustainability of the program and 
plan for dissemination. Following refinement of the automated patient communication 
content, we will pilot the intervention at each health system to ensure that all aspects of the 
protocol have been operationalized and refine any potential barriers. 
 
We will update the infrastructure for our text messages for the Nudge study, expanding the 
content beyond medication refill adherence to incorporate LE8 topics and providing 
structure to facilitate pharmacist support. Health Equity and Engagement scientists will work 
to ensure robust participation of patients, providers, community advocates, and health 
system leaders to provide feedback on the messages, intervention and implementation 
strategy design, and outcomes. We will also solicit routine feedback from these groups 
during the study to help address potential barriers to adoption and implementation and help 
ensure program sustainability. These activities will be guided by PRISM and the Health 
Equity in Implementation Framework to systematically assess the dynamic interactions of 
contextual factors (including SDoH and indices of health disparities) that influence success 
and sustainability of the implementation and its generalizability across populations 
experiencing inequities. To achieve the contextual assessment with a health equity lens, 
we will prioritize diverse representation across patients, providers (including pharmacists), 
community advocates and health systems. 
 
 
Aim 3 (UH3; Years 2-5): Conduct a pragmatic patient-level randomized intervention 
of 3 text messaging delivery strategies for self-management support of CV risk 
factors. Primary outcome will be change in LE8 health score. Secondary 
effectiveness outcomes will include individual components of the LE8 lifestyle 
factors, Framingham risk score, self-efficacy, medication adherence, clinical 
outcomes (e.g., CV related hospitalizations), and healthcare utilization. 
 
The objective of this study is to conduct a pragmatic patient level randomized trial to 
evaluate the implementation and effectiveness of 3 different automated patient 
communication approaches for self-management support to improve control of CV disease 
risk factors defined by AHA’s Life Essential 8 risk factors.  
 
In Years 2-5, we will conduct a patient level randomized pragmatic trial comparing the 
following strategies (study arms): 1) generic text messages; 2) interactive AI chatbot text 
messaging incorporating tailoring to increase message relevance and address social 
context; behavioral nudges to facilitate intuitive decision-making; and persuasive 
messaging to increase motivation to change over time; or 3) interactive AI chatbot text 
messaging plus proactive pharmacist management. We have not included an usual care 
group because prior studies have generally found that control of the LE8 factors are not 
ideal and generic text messages have generally been more effective than usual care for 
behavior change. The study will randomize at the patient level rather than a cluster level 
because: 1) our intervention uses automated and interactive text messages that are 
delivered directly to patients greatly reducing the risk of intervention contamination; and 2) 
we will include all patients who meet eligibility criteria into the study with an opt-out option 
for patients who do not wish to participate due to the low risk nature of the study intervention, 
consistent with the Nudge study. 

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Opt out process 
Among patients who fulfil the eligibility criteria, we will send them an opt out packet. The 
packet will include an introductory letter signed by the Site PI, information about the study, 
the opt out consent form, and a self-addressed, stamped envelope. A member of the study 
team will remove the participant from the potential list if an opt out form is received; forms 
will be saved in a secure, locked cabinet, as noted in the IRB application. Should an opt-
out package be returned as undeliverable, the study team will remove the patient from the 
study. All materials will be available and sent to patients in both English and Spanish. If they 
have previously specified in their contact preferences that they prefer English or Spanish, 
we will send materials in their preferred language. Upon the deadline for response to the 
opt-out consent has expired, patients that have not opted-out will be randomized 
accordingly. 
 
Upon the start of the intervention, there will be a secondary opt out option, which patients 
can opt out at any time by the patient responding to a message with “STOP”. The first 
message sent to patients will inform patients they can opt out this way. 
 
Message arm descriptions 
Once randomized, we will send patients an introductory text message about the study. In 
the message, we will briefly share LE8 risk factors and elicit baseline information via text 
messaging on lifestyle factors that are not available in the EHR, including current physical 
activity, healthy diet as defined by the LE8 categories and smoking status (if not available 
within the EHR). For those with missing baseline values for blood pressure, weight, total 
cholesterol or blood glucose, we will recommend that they get the appropriate testing or 
measurement as recommended in LE8. Finally, we will also assess via a text message 
survey the 6-item patient self-efficacy for managing chronic conditions. Self-efficacy for 
Managing Chronic Conditions is defined as an individual's confidence in his/her ability to 
successfully perform specific tasks or behaviors related to one's health in a variety of 
situations. 
 
1) Generic text messages: The information content for these messages will be derived from 
trusted sources of medical information and contain links to websites such as American 
Heart Association. An example of such a message would be: Remember to take your blood 
pressure today! You can find more information from the American Heart Association by 
clicking here. Patients will be able to return texts with questions which will be addressed by 
the study team, including a clinical pharmacist if needed.  
 
2) AI chatbot text messages: This AI system will utilize NLP and ML to facilitate bi-directional 
system-patient dialogue with messages that incorporate content utilizing tailoring, 
behavioral nudges and persuasive messaging as described above. An example message 
would be: Make a promise to yourself to check your blood pressure today! Your goal is to 
have the top number at 120 or lower and the bottom number at 80 or lower. Each message 
will end with a question for the participant that will encourage engagement with the AI 
conversational chatbot that allows greater opportunity to use theoretical content to engage 
patient autonomy, competence and relatedness, the mechanisms through which we will 
impact behaviors. 
 
3) AI chatbot text messages plus proactive pharmacist management: The AI chatbot will be 
the same as arm 2. In arms 1 and 2, pharmacist will respond to clinical questions from 

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patients in a reactive manner. In arm 3, pharmacists will review patient’s baseline LS7 risk 
factors and proactively contact patients via telephone and/or the EHR patient portal to 
address any risk factor that is in poor/intermediate health categories. We are proposing 
proactive pharmacist involvement as a population-based approach to address patients with 
uncontrolled CV risk factors. Prior studies131 including meta-analyses and systematic 
reviews have demonstrated the effectiveness of pharmacist management to reduce CV risk 
factors (i.e., blood pressure, cholesterol and smoking). The proactive pharmacist 
involvement is increasingly common but our proposal is to actively link them to our AI 
chatbot to better facilitate tailored SM support. Pharmacists will proactively manage these 
patients and will be able to identify their specific SM needs through a review of the AI chatbot 
logs prior to engagement with patients using telephone or EHR portal. 
 
Plan for responding to text messages from patients 
In arms 1 and 2, a PRA or pharmacist will respond to clinical questions from patients in a 
reactive manner. A PRA will check the patient responses in regular intervals throughout the 
day and either 1) respond to questions regarding the study or 2) triage clinical questions to 
the pharmacist. 
 
Aim 4 (UH3; Years 2-5): Evaluate the intervention using PRISM and a mixed methods 
approach to evaluate pragmatic clinical and implementation outcomes (reach, 
effectiveness, adoption, implementation, and maintenance) with an emphasis on 
equity and representativeness, and systematically assess contextual influences to 
inform sustainment and future tailoring, adaptations, and dissemination.  
 
We will use PRISM for evaluation with RE-AIM outcome measures and consideration of 
health equity. PRISM pragmatically focuses on four categories of contextual factors that 
influence implementation success: 1) organizational and participant characteristics; 2) 
organizational (health system and providers) and participants’ perspectives (i.e., patients) 
on the intervention; 3) implementation and sustainability infrastructure (e.g., resources and 
support processes); and 4) external environment. These four elements will be assessed 
both qualitatively and quantitatively and will be critical to understanding how to sustain and 
further disseminate the intervention if demonstrated to be effective. The component RE-
AIM (i.e., Reach and Effectiveness, Adoption, Implementation, and Maintenance) outcomes 
informs the development of pragmatic outcomes important to different stakeholder 
perspectives (e.g., executive-level decisionmakers, clinicians, patients). PRISM focuses on 
health equity by emphasizing both representation in terms of the persons involved in 
planning 
and 
evaluation 
for 
each 
outcome 
dimension, 
and 
especially 
the 
representativeness (equity) of outcomes across different groups or types of settings. Below 
we highlight the measures that we will assess a part of the RE-AIM outcomes evaluation. 
 
 
D.   Description, Risks and Justification of Procedures and Data Collection Tools 
The study team believes this project poses minimal risk to all subjects involved, based on 
the study team’s previous work (COMIRB Protocol #18-2779). There is no clinical 
intervention being proposed, and no deeply personal matters will be discussed. The goal of 
this study is to ensure that patients increase healthy behaviors. 
 
Study information including risks of data, confidentiality, breach of confidentiality, and other 
risks will be addressed in the opt out packet. Consent will be assumed unless the postcard 

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opting the study subject out of the study is returned. Study subjects can also opt out of the 
study if they wish at any point by replying with a text message with the word “STOP.” 
Alternative treatment in this study is the standard care with no texting communication. 
 
We do not anticipate substantial risks to be associated with participation in this study. As 
with any study involving participants with chronic disease, however, there is some risk of 
psychological discomfort related to discussing disease management.  Participants will be 
informed that if they choose to discontinue the study at any time, this will not interfere with 
their usual medical care. 
 
Trained and certified professional staff will obtain all data according to detailed study 
protocols. Phone numbers and patient information including eligibility criteria will be 
obtained through EHR. Only IRB-approved individuals on the study team will have access 
to individually identifiable information about human subjects. This will include the PIs, co-
investigators, project coordinator, statisticians, database/programming team, and research 
assistants. Some of the data above will be accessed from information already collected as 
part of usual care. Depending on the study arm they are randomly assigned, study subjects 
will receive, generic messages or interactive AI chatbot messages that incorporate English 
or Spanish-language versions depending on patient choice. 
 
Minimal risks to human subjects are anticipated for this study. Nevertheless, remaining risk 
is expected to include: 
• 
Behavioral incentives (nudges) will be administered to the participants as a method 
to reinforce lifestyle factors related to their risk factors of cardiovascular disease. 
The nudges themselves will reinforce healthy behaviors and therefore will not 
introduce any new instructions/treatments. However, there is some risk that patients 
will misinterpret the text message information that is sent to them in text messages. 
• 
Data Confidentiality. As with all research, there is also a slight risk of loss of 
confidentiality and/or anonymity, especially for participants being interviewed. We 
have standard operating procedures for data acquisition and data management 
designed to protect against data loss and maintain patient confidentiality. These 
procedures have been developed and used in many studies and we will adhere to 
these procedures for the proposed study. Paper files related to the study (i.e., 
consent forms) will be stored in a secure location in a locked file cabinet accessible 
only to study personnel who must access these files. Computer files will be 
password protected. Files containing names, addresses, or other personal 
identifiers will have a separate password and will be accessible only to personnel 
who need to contact subjects. 
• 
Breach of confidentiality. All participant information collected in the context of this 
research study, and even the fact that an individual is participating in the study, will 
be considered confidential. This confidentiality will be assured through several 
mechanisms. First, each participant will be assigned an anonymous study ID. 
Second, all study forms and paper records that contain participant information will 
be kept in secured, locked areas when not in use. In addition, such materials, when 
in use, will be kept away from public scrutiny. Third, access to all participant data 
will be restricted to authorized personnel. In the case of computerized study data, 
access to data will be password protected, and staff members will be assigned 
individualized passwords that allow them access to only those elements of the data 
management system to which they are authorized. In addition, all study personnel 

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will maintain certification with training in research ethics, which includes training on 
confidentiality. Finally, participants will not be identified by name in any reports or 
publications, nor will data be presented in such a way that the identity of individual 
participants can be inferred. All information obtained in the course of the study that 
identifies an individual will be treated as confidential in accordance with section 
903C of the Public Health Service Act (42 U.S.C.299a-1). We will strip all identifiers 
from analytic data sets after data merging and keep all personal identifiers in a 
separate location from the analytic data. Database files needed to generate mailing 
labels will contain no research data. All research data files are organized by study 
ID number and have no names or other identification attached. This ID number links 
all computerized research records. 
• 
Financial, Legal, or Other Risks. There are no financial or legal risks associated with 
this study. Since personal data will be collected as part of this study, there is a risk 
of loss of confidentiality. However, we will take several measures to minimize this 
risk. First, we will only collect the data necessary for the study. Second, all electronic 
data will be stored on a secure server, rather than on individual desktop or laptop 
computers. Third, electronic study data will be kept in folders and databases that 
are only accessible to key personnel who are IRB-certified and whose job functions 
require access to these data. Fourth, all paper copies of data (i.e., consent forms) 
will be stored in locked cabinets and offices. Any paper documents that must be 
transported to/from clinic enrollment sites will be carried in a locked briefcase. 
  
Adequacy of Protection of Risk  
To mitigate the above risks, the text message nudges will contain a secondary opportunity 
for opt-out, and will contain pharmacy contact information, should the patient decide to 
discontinue their participation or want to contact the pharmacist with questions or need more 
information about the intervention or their prescribed medications. Contact information for 
those opting out of the study will be maintained in a separate file and deleted as soon as 
recruitment is complete. The opt out consent will also state that the text messages are not 
from their doctor. 
  
As is the standard, all staff participating in the project will complete compliance and human 
subject research training and all recruitment materials and consent forms will be approved 
by the Colorado Multiple Institutional Review Board (COMIRB).  
  
Potential Benefits to Subjects 
No claim is made that subjects will benefit from participation in this project. However, the 
results of the study may improve care to the extent that our study improves medication 
adherence. 
 
    
E.   Potential Scientific Problems:   
 
What if the text messages are not delivered as planned? For Aims 2 and 3, we anticipate 
there may exist system failures, e.g., messages sent multiple times or incorrect branching. 
To ensure minimal disruption in message delivery, we will conduct system alpha testing at 
each health system to ensure correct message distribution and branching prior to 
conducting the trial. It is possible that even once we implement a pilot trial there could be a 
system failure, such as sending three versions of a single message or sending a message 
in the middle of the night. We will minimize the impact of such an occurrence by having 

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designated staff at each participating health system that can shut down the system and 
reboot as needed. We will continuously monitor for potential systems failures and deviations 
and implement standardized processes to correct them immediately. 
 
Is there potential for contamination in the proactive pharmacist arm? It is possible that 
if we train pharmacists in motivational interviewing and proactive case management to 
support SM for patients they will engage with all patients in this manner, regardless of study 
arm. We are reducing the likelihood of this happening by sharing with them the specific list 
of patients randomized to the “proactive pharmacist” arm and will share with them the logs 
documenting chatbot engagement by each patient in that arm so the pharmacist will have 
a priori information on each patient’s questions and SM experiences that can offer context 
for why they are struggling with SM outcomes. None of the settings where we are deploying 
the intervention are currently using a proactive population-based approach so we think it 
unlikely they will begin doing this for everyone. However, we will monitor the patient portals 
for all patients enrolled to determine if pharmacists are proactively engaging with patients 
outside the “proactive pharmacist” arm and document this for our analysis. 
 
What is the likelihood of successfully changing health behaviors or addressing 
social determinants? We are proposing a population-level intervention that uses 
ubiquitous technology (e.g., cellphones). We hypothesize that arm 3 (interactive AI text 
messages with proactive pharmacist management) will have the greatest effect and that 
the combination of the AI chatbot and pharmacist management will be able to successfully 
address medication adherence, SDoH and change health behaviors. We acknowledge that 
this intervention is not appropriate for more challenging cases or situations and these 
patients will need more intensive support from providers and the health system than can be 
provided by the various intervention arms. This intervention is not meant to supplant those 
resources but provide health systems with a low-cost, generalizable population-based 
intervention that can address a majority of patients who need reminders and support that is 
light touch while they focus their resources on the most difficult cases. 
 
F.   Data Analysis Plan:   
 
Sample size calculations using the Life’s Essential 8 (LE8): 
We conducted a simulation-based power analysis for the LE8 outcome to address 
feasibility. Using LE8 as the outcome and conservative assumptions about effect size, we 
will have adequate power (>90%) if we analyze 5,059 subjects at the conclusion of the 
study. If we enroll 6,070 patients and assume a ~20% loss to follow-up, we will still have > 
90% power to detect a small change in the proportion of patients with an improvement in 
one of the LE8 risk factors. We define improvement if a patient moves from one category to 
another as defined by each of the individual LE8 risk factors. For example, the following 
clinical scenario would be defined as an improvement in a LE8 risk factor: patient starts with 
non-HDL cholesterol of 200 mg/dL (equivalent to 20 points) and improves to a non-HDL 
cholesterol of 160 mg/dL (equivalent to 40 points) at the 1-year follow-up.  
  
The above is based on the following assumptions: 1) 25% of the patients in the generic text 
arm will have an improvement in a LE8 risk factor (lipids, glucose or BP); 2) 28% of patients 
in the AI chatbot text message group will have an improvement in a LE8 risk factor (3% 
more than the generic text group; and 3) 31% of patients in the AI chatbot plus proactive 
pharmacist support will have an improvement in a LE8 risk factor (6% more than the generic 
text group). We will have adequate patient populations across the 3 proposed health 

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systems (Denver Health, Salud, and STRIDE) in which to enroll 6000 patients with 1 or 
more uncontrolled LE8 risk factor.  
  
Further details of this power analysis are summarized below: 
We will target for enrollment patients with the following abnormal values of LE8 measures 
observable in the medical record: 
- 
Lipids: >= 190 
- 
Glucose: HbA1c >= 8.0 
- 
BP: systolic >= 140 or diastolic >= 90 
Using the qualifying measures from above, we will calculate a baseline LE8 measure (single 
value of 0-100; mean of qualifying measures at baseline) and at follow-up LE8 measure 
(single value of 0-100; mean of qualifying measures at follow-up). The difference between 
baseline and follow-up will be the primary outcome (for LE8).  
  
Primary hypothesis comparison: There will be three comparisons for the primary 
hypotheses, thus we adjust for multiple comparisons with alpha = (0.5/3) in these power 
estimates.  
- 
Group 1 (generic text) versus Group 2 (AI chatbot text messages) 
- 
Group 2 versus Group 3 (AI chatbot text messages plus proactive pharmacist 
support) 
- 
Group 1 versus 3 
Small (conservative) effect size assumptions:  
- 
All groups: patients have a 5% chance of decreasing one LE8 category on a 
qualifying health state category.  
- 
Group 1: patients have a 25% chance of increasing one LE8 category on a qualifying 
health state category. 
- 
Group 2: patients have a 28% chance of increasing one LE8 category on a qualifying 
health state category. 
- 
Group 3: patients have a 31% chance of increasing one LE8 category on a qualifying 
health state category. 
Moderate effect size assumptions:  
- 
All groups: patients have a 5% chance of decreasing one LE8 category on a 
qualifying health state category.  
- 
Group 1: patients have a 25% chance of increasing one LE8 category on a qualifying 
health state category. 
- 
Group 2: patients have a 30% chance of increasing one LE8 category on a qualifying 
health state category. 
- 
Group 3: patients have a 35% chance of increasing one LE8 category on a qualifying 
health state category. 
  
Effect Size 
Power 
Total 
Sample 
size 
across 
3 
arms 
Sample 
in 
each 
arm 
Moderate 
80% 
1,605 
535 
Moderate 
85% 
1,824 
608 
Moderate 
90% 
2,085 
695 
Small 
(conservative) 
80% 
4,032 
1,344 

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Small 
(conservative) 
85% 
4,461 
1,487 
Small 
(conservative) 
90% 
5,508 
1,836 
 
 
G.  Summarize Knowledge to be Gained:   
 
Summary of evidence and gaps in knowledge: (1) We lack interventions that 
successfully address multiple CV conditions, particularly for racial/ethnic minorities, poor, 
rural, and non-English speaking patients, all of whom face disparities in chronic CV health 
outcomes. (2) SM can be successful but often fails to consider the complex social 
determinants of health on behaviors that can be amplified for persons experiencing 
disparities in chronic CV conditions. (3) One way to improve SM programs is to design 
interventions that acknowledge and support patients in addressing influences on their SM 
behaviors at the interpersonal, organizational and community level. (4) Interventions that 
are successful in facilitating SM for persons experiencing disparities can likely suffer from 
being too resource intensive or too complex for delivery. Therefore we must consider 
approaches that are easy for health systems to adopt, implement and maintain. (5) Using 
technology that relies on cellphone based text messaging is one such approach. Although 
we know text messaging can be effective to facilitate healthy behavior, we have not fully 
integrated emergent systems that utilize artificial intelligence in combination with strategic, 
evidence-based messaging to increase the impact of low intensity interventions, or 
evaluated the incremental benefits of adding health system-level, proactive pharmacist 
engagement..  
 
How our intervention addresses these gaps: Our goal is to improve control of CV 
disease risk factors by engaging patients experiencing CV disparities with “LE8 Bot + 
Backup,” an innovative technology-based SM intervention with linkages to health system 
providers focusing on control of the American Heart Association’s Life’s Essential 8 (LE8) 
lifestyle factors (blood glucose, cholesterol, blood pressure, physical activity, weight, sleep, 
diet, and smoking). Using a patient level randomized pragmatic trial design, we will test the 
comparative effectiveness of 1) generic unidirectional text messages; 2) theory-based, 
tailored and socially contextualized communications using an artificially intelligent (AI) text 
messaging chatbot for self-management support; or 3) Optimized AI chatbot messages with 
proactive pharmacist management for self-management support. We plan to enroll 6000 
patients with sub-optimal control of their CV risk factors and poor adherence with 
medications to treat the CV risk factors since they are more likely to benefit from a SM 
support intervention. Further, given that Black patients, Hispanic/Latino patients , Spanish-
speaking only patients, rural residents, and low-income patients experience disparities in 
CV outcomes, we will target enrollment to include these groups from clinics within 3 health 
systems that care for large populations of patients experiencing health disparities: 1) Salud 
Family Health Centers, an FQHC with 13 clinics including clinics serving rural Colorado 
residents, 2) Denver Health and Hospital Authority, a safety net health system for Denver 
county with 9 FQHCs, and 3) STRIDE Community Health Centers, a FQHC with 18 
locations surrounding Denver County.  
 
If the proposed intervention proves successful, it may lead to improved cardiovascular 
health as measured by the Life’s Essential 8 score for each study subject as well as 

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decreases in other cardiovascular risk factors and increased engagement with their health 
care system. If the intervention does not improve patients’ LE8 score, investigators will know 
to direct the attention to the evaluation of other, potentially more fruitful interventions. The 
results of the work may lead to new knowledge and guidance for medical institutions to 
utilize the accessible and inexpensive service of text messaging. 
 
 H. References: 
 
1. Healthcare Delivery and Disparities Research. PCORI. Published March 29, 2017. Accessed 
April 2, 2021. https://www.pcori.org/about-us/our-programs/healthcare-delivery-and-disparities-
research 
2. Schultz William M., Kelli Heval M., Lisko John C., et al. Socioeconomic Status and 
Cardiovascular Outcomes. Circulation. 2018;137(20):2166-2178. 
doi:10.1161/CIRCULATIONAHA.117.029652 
3. Harrington RA, Califf RM, Balamurugan A, et al. Call to Action: Rural Health: A Presidential 
Advisory From the American Heart Association and American Stroke Association. Circulation. 
2020;141(10). doi:10.1161/CIR.0000000000000753 
4. Rawal S, Srighanthan J, Vasantharoopan A, Hu H, Tomlinson G, Cheung AM. Association 
Between Limited English Proficiency and Revisits and Readmissions After Hospitalization for 
Patients With Acute and Chronic Conditions in Toronto, Ontario, Canada. JAMA. 
2019;322(16):1605. doi:10.1001/jama.2019.13066 
5. Thorpe K, Chin K, Cruz Y, Innocent M, Singh L. The United States Can Reduce 
Socioeconomic Disparities By Focusing On Chronic Diseases | Health Affairs Blog. Health 
Affairs Blog. Published August 17, 2017. Accessed April 2, 2021. 
https://www.healthaffairs.org/do/10.1377/hblog20170817.061561/full/ 
6. Lackland DT. Racial Differences in Hypertension: Implications for High Blood Pressure 
Management. The American Journal of the Medical Sciences. 2014;348(2):135-138. 
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