A Motivational Interviewing Intervention Customized by Patient Adherence Patterns
Phase: NA
Conditions: Diabetes Mellitus, Type 2, Hypertension
Sponsor: University of Houston

A motivational interviewing intervention customized by patient adherence patterns 
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PRINCIPAL INVESTIGATOR: 
Name: Susan Abughosh, PhD  
Department: College of Pharmacy, Department of Pharmaceutical Health Outcomes and Policy 
Telephone Number: 832-842-8395 
Email Address: smabughosh@uh.edu 
 

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Table of Contents 
1.0 
Objectives ............................................................................................................................ 3 
2.0 
Background .......................................................................................................................... 3 
3.0 
Inclusion and Exclusion Criteria .......................................................................................... 4 
4.0 
Vulnerable Populations ........................................................................................................ 4 
5.0 
Number of Subjects.............................................................................................................. 4 
6.0 
Recruitment Methods ........................................................................................................... 4 
7.0 
Study Timelines ................................................................................................................... 5 
8.0 
Study Endpoints ................................................................................................................... 6 
9.0 
Procedures Involved............................................................................................................. 6 
10.0 Setting .................................................................................................................................. 8 
11.0 Risks to Subjects .................................................................................................................. 8 
12.0 Potential Benefits to Subjects .............................................................................................. 9 
13.0 Withdrawal of Subjects ........................................................................................................ 9 
14.0 Costs/Payments to Subjects ................................................................................................. 9 
15.0 Confidentiality ..................................................................................................................... 9 
16.0 Provisions to Protect the Privacy Interests of Subjects ........................................................ 9 
17.0 Informed Consent Process ................................................................................................. 10 
18.0 Process to Document Consent in Writing .......................................................................... 10 
19.0 HIPAA ............................................................................................................................... 10 
20.0 Data Management .............................................................................................................. 10 
21.0 Sharing of Results with Subjects ....................................................................................... 11 
22.0 Resources ........................................................................................................................... 11 
 
 
 

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1.0 
Objectives 
1.1 Purpose, specific aims, and/or objectives. 
This proposed study aims to develop group-based trajectory models to 
identify patients with similar medication filling behavior among patients 
with co-morbid hypertension (HTN) and diabetes (DM) using angiotensin 
converting enzyme inhibitors (ACE) or angiotensin receptor blockers 
(ARB). A motivational interviewing (MI) pharmacy student telephone 
intervention will then be customized by the identified trajectories and tested 
to demonstrate effectiveness in improving ACE/ARB adherence.   
1.2 Hypothesis 
It is our central hypothesis that the customized MI based pharmacy student 
intervention will be beneficial in improving medication adherence to 
ACEI/ARBs. 
 
2.0 
Background 
2.1 Diabetes (DM) and hypertension (HTN) are independent risk factors for 
cardiovascular (CV) disease and major public health issues in the United 
States (US). The two conditions frequently coexist, with 70-80% of DM 
patients also having HTN. The combination significantly increases the risk 
of macrovascular and microvascular DM complications. Controlling high 
blood pressure (BP) can greatly reduce these complications including the 
CV risk which accounts for 50-80% of DM fatalities. Angiotensin 
converting enzyme inhibitors (ACE) and Angiotensin receptor blockers 
(ARB) are highly recommended for patients with DM and HTN, with a 
well-documented benefit in reducing DM complications. 
The project will have 2 phases: Phase 1 will be a retrospective analysis of 12 
month refill data to determine patient past adherence behavior patterns. 
Phase 2 will entail a prospective randomized design among non-adherent 
patients to evaluate the effectiveness of the customized intervention. The 
students will contact patients assigned to the intervention group and follow a 
protocol using the Ask-Provide-Ask approach of MI given the patient 
adherence pattern for a tailored education. Monthly follow-up calls for 6 
months will be carried out. Adherence during the 6 and 12 months post-
intervention, will be evaluated for the intervention and control groups. 
Findings are expected to demonstrate the effectiveness of an innovative low 
cost intervention in improving adherence and have a positive impact in 
implementing tailored interventions leading to more desirable long term 
health outcomes and considerable cost savings in our financially 
overwhelmed healthcare system. 
2.2 The primary investigator has conducted prior research collaborating with 
Cigna Healthspring using motivational interviewing techniques over phone 
calls. The IRB reference of the last study is: 16410-01-(7926)  

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3.0 
Inclusion and Exclusion Criteria 
3.1 
Inclusion Criteria: Patients over the age of 18, with continuous 
enrollment in the Cigna Medicare Advantage health plan in Texas starting July, 
2016 until January 2018 with a diagnosis of comorbid DM and HTN and having 
a prescription filled for an ACEI or ARB medication during the month of 
January, 2017. The patient identification period will be included in the study. 
Patients who disenroll or die before the study ends will be excluded from the 
study.. Exclusion criteria include having a diagnosis (identified by ICD-10 
codes) for dementia, or an ACEI/ARBS contraindication like angioedema, 
hyperkalemia, renal artery stenosis. 
3.2 Describe how potential subjects will be screened for eligibility based on 
these criteria: The retrospective analysis will apply these exclusion and 
inclusion criteria during the data analysis stage to give the final cohort of 
patients. 
3.3 This study will not include any vulnerable population, mentioned below: 
1. 
Adults unable to consent 
2. 
Individuals who are not yet adults (infants, children, teenagers) 
3. 
Pregnant women 
4. 
Prisoners 
5. 
Students for whom you have direct access to/influence on grades 
6. 
Economically and/or educationally disadvantaged persons 
 
4.0 
Vulnerable Populations 
4.1 N/A 
5.0 
Number of Subjects 
5.1 This study will have two phases. In the restrospective analysis stage, Cigna 
Healthspring will provide data on patients who fulfill eligibility criteria 
mentioned above. It is expected that this data will have approximately 
22,000 patients with DM and HTN.  
5.2 In the intervention phase, 250-300 patients will be included in the 
intervention phase. If 1 control is used for each patient in the intervention 
group, the total sample of phase 2 is expected to be 500-600.  
6.0 
Recruitment Methods 
 
LOCAL: 
6.1 This study will start with the retrospective phase which does not involve 
recruitment of patients, but involves analysis of existing data. De-identified data 
will be obtained from Cigna-Healthspring.  
6.2 The project will be implemented in collaboration with Cigna-Health 
Spring, a Medicare Advantage Plan in Texas. The health plan has several 
electronic data files available for analysis including membership file, member 

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summary file, institutional claims file, professional claims file, quest lab, Cigna-
Health Spring’s medical claims file (CCMS) and pharmacy file. Pharmacy files 
contain Part D pharmacy data provided by pharmacy benefits manager 
including patient/ drug identifying information, fill dates, days of supply, 
quantity dispensed and dosing for each prescription filled. The refill data will be 
used to determine the non-adherent patients. ICD-10 codes will be used to 
determine other exclusions using professional claims data. 
Phase 2 will involve project intervention which will be conducted over the phone. 
A list of actively enrolled members, taking the medication who were a part of 
phase 1 will be created by Cigna HealthSpring to be used for intervention 
implementation at their premises. The phone call will be made by University of 
Houston pharmacy students who will be on rotation at Cigna-HealthSpring. The 
form of informed consent which will be read to the patient is attached to the IRB. 
If the patient consents to receive the intervention over the phone, and completes 
the phone call, the patient forms a part of the intervention group. Five follow-up 
phones will be attempted one month apart for the patient after the initial call.  
7.0 
Study Timelines 
7.1 The duration of the total study will be 3 years. Retrospective analysis of 
phase 1 and intervention development will require 12 months. The 
retrospective analysis will use data from July2016-January 2018. July – 
December 2016 will be used for baseline covariate measurement. January 
2017- January 2018 will include adherence measurement. However this is 
de-identified patient data used for retrospective analysis and does not 
require patient participation. 
7.2 The phase 2 of the study will involve the intervention call provided in the 
day of consent followed by monthly follow-up calls for six months. The 
patient however can refuse to participate in any of the calls, even if the 
patient has completed the primary phone call 
7.3 The number, frequency and length of study visits: One intervention call 
lasting 12-15 minutes on average. Monthly follow up calls to the patients 
who received the initial call for six months lasting on an average for 5-10 
minutes.(First call + 5 follow-up calls) 
7.4 The duration anticipated to enroll all study subjects: The enrollment will 
continue for a period of 8 months (equivalent to an 2 active semesters) 
7.5 The estimated date for the investigators to complete this study: 3 years 
Timeline details below 
 
 
 
 
 
 

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Pre-
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Study Period: 36 months 
  
  
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Phase 1 trajectory modeling 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 Intervention Development 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
MI training and randomization 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 Intervention implementation 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
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Manuscript / Final report prep  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
8.0 
Study Endpoints 
8.1 Develop group-based trajectory models to identify patients with similar 
medication filling behavior patterns among patients utilizing ACEI/ARBs with 
co-morbid HTN and DM enrolled in a MAP and examine predictors associated 
with having different trajectories. 
8.2 Customize a pharmacy student telephone intervention incorporating MI by 
patient adherence behavior patterns identified under aim 1. 
8.3 Demonstrate the benefit of the customized intervention on ACEI/ARB 
adherence among non-adherent patients with co-morbid HTN and DM enrolled 
in a MAP. 
9.0 
Procedures Involved 
9.1 Phase 1: Retrospective data analysis  
Patients that used a study medication during January 2017 will be identified and 
the first prescription date during this time will be considered the index data. A 
dummy variable will be created to indicate prevalent user vs incident user by 
evaluating the 6 month data prior to index date (July – December 2016). Patient 
adherence will be assessed during the one year following the index date. 
Adherence will be measured as proportion of days covered (PDC). PDC rates 
over the entire 360-day assessment period will be determined. PDC will also be 
calculated separately for each month during the one year following the index date. 
During each of the 12 consecutive 30-day periods of adherence assessment, a 
binary indicator for “full adherence” each month, defined as PDC= 0.8 vs. non-
adherence will be created. The 12 binary indicators of full adherence will then be 
modeled during each 30-day period as a longitudinal response in a logistic group- 
based trajectory model. Patients will be assigned to a trajectory on the basis of 
these models. Several logistic regression models will then be carried out to 
identify predictors associated with specific trajectories for adherence. 
9.2 Covariates: Demographics (age, gender, and language), physician 
specialty (primary care vs. specialist), health plan (low income subsidy vs. 

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other), prevalent vs. incident user, Centers for Medicare and Medicaid CMS 
Risk Score, Charlson comorbidity index (CCI) and number of distinct 
medications. Additionally, selected co morbidities will be identified by ICD-10 
codes during the one year prior to index date including congestive heart failure, 
coronary artery disease, peripheral vascular disease, myocardial infarction, 
diabetes, stroke hypertension, end stage renal disease (ESRD), chronic 
obstructive pulmonary disease, asthma, and depression. To ensure appropriate 
risk adjustment in the models, additional covariates which will be added to the 
model include, regimen complexity, class of antihypertension medication used, 
baseline adherence measured as PDC, previous hospitalization and patient’s 
trajectory prior to intervention. 
Customization of intervention: After completion of the retrospective analysis 
phase and identifying the major trajectories in this patient population, the team 
will meet to discuss the patterns identified, the potential barriers likely to be 
associated with each trajectory, and strategies that can help patients within each 
trajectory based on their behavior pattern. The team will then develop customized 
education specific to each identified trajectory. For example: some patients can 
have  high medication use in the early part of the follow up period but poor after, 
others may have low initial use and improved subsequent. Some may have 
intermittent adherence throughout the follow up time, others may simply 
discontinue. The expected barrier and potential solutions that should be discussed 
with the patient using MI strategies will vary accordingly.  Patients past 
adherence patterns will guide the team in determining probable barriers and what 
can potentially help the patient. For example, patients who discontinue the 
medication may have side effects concerns or may view the medication as 
unnecessary. Patients who continue to take medication but have less than 0.8 PDC 
throughout may be forgetting to take the medicine and need help remembering. 
The team will create a code for each trajectory identified to categorize the 
patients.  A protocol for each trajectory will be developed to summarize the 
education that is likely to benefit patients with the specific behavior pattern given 
the code. All the developed protocols will be based on the Ask-Provide-Ask 
approach, a pharmacist adaptation for the Elicit-Provide-Elicit approach of MI 
(attached) 
9.3 Phase 2: intervention implementation 
Patients that are still enrolled in the health plan during 2017-2018 will be included 
in phase 2. Patients will be categorized by their adherence trajectory identified in 
phase 1 and will be randomized to intervention vs. not within each category. The 
proposed intervention will be a telephone call by a pharmacy student participating 
in rotations at Cigna-Health Spring Plan and trained in MI and 6 monthly follow 
up calls. During implementation, students will first examine the patient adherence 
pattern that was identified in phase 1 which will be coded. The student will then 
confirm the diagnosis, medication name, and dose with the patient answering the 
call and will follow the outlined protocol. Using MI, students will assess readiness 
for change, help patients identify potential barriers in accordance with patient past 
pattern of use, and guide them to develop a plan to deal with these barriers 

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Additionally, the students will ask the 2 questions from the Patient Health 
Questionnaire (PHQ)-2, a validated screening tool for depression (attached).If the 
patient responds positively to either question, the student will suggest that the 
patient schedules an appointment with his/her healthcare provider to discuss these 
symptoms. The Supervising pharmacists will be available to assist the students in 
answering any medication related questions that may arise during the calls and 
arrange for a diabetes educator to contact the patient at a later time if needed. 
Follow up phone calls will be performed each month for a total of six calls. 
Students will be supervised by the pharmacist preceptors and managed care 
pharmacy residents throughout the calls and intervention implementation. The 
students who will be implementing the calls will attend a three day MI training 
facilitated by Dr. Abughosh, who is experienced in utilizing motivational 
interviewing to improve adherence. The students will practice calls as a part of 
their training. They will also complete Human Subjects Research training before 
their rotation at Cigna Healthspring, and the certificates will be uploaded to the 
IRB application.  After each initial call, students will record the date and write a 
summary of the interaction with the patient documenting the main barrier/s 
identified in the conversation and the plan made with the patient. During the 
follow up calls, students will emphasize the information, confirm problem solved, 
provide further encouragement and reinforce adherence.  
Control group: For every patient that received a call, a control will be randomly 
selected from the list of patients randomly assigned to the control group. 
10.0 Setting 
10.1 The retrospective data analysis will be conducted in building HSBS2 using 
secured computers provided by UH-College of Pharmacy. The PI and 
research assistant will have access to the data. 
10.2 The phone calls will be made on the premises on Cigna HealthSpring under 
the supervision of the research team at Cigna HealthSpring. The data will be 
de-identified at Cigna Healthspring and shared with UH through secured 
servers. 
10.3 A letter of support from Cigna HealthSpring is attached to the email. 
11.0 Risks to Subjects 
11.1 This study does not involve more than Minimal Risk for the safety of 
patients. 
11.2 The intervention participation is completely based on the patients consent, 
and they are made aware of the same. They can withdraw or choose to stop 
the call at any point, which is also read out to them. The students making the 
calls are pharmacy students who have completed human subjects’ research 
training as well as training for motivational interviewing. Additionally, the 
research team members closely supervise the pharmacy students to ensure 
the quality of the calls. Thus, it is not anticipated that the intervention can 
harm any patient in any way 

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12.0 Potential Benefits to Subjects 
12.1 Describe the potential benefits that individual subjects may experience 
from taking part in the research. Include as may be useful for the IRB’s 
consideration, the probability, magnitude, and duration of the potential benefits. 
12.2 As mentioned in the initial sections of this IRB protocol, medication 
adherence is very important for patients with comorbid conditions and the lack 
thereof is an important problem being faced in the real world settings. This 
intervention uses motivational interviewing, which has successfully been used 
to improve patient adherence in various other chronic diseases, by helping the 
patients find their own internal motivation to change and find ways to address 
their adherence barriers. The intention of this study is to bring about a change in 
the behavior of patients and to improve their adherence, with a goal to improve 
the outcomes associated with the disease. 
 
13.0 Withdrawal of Subjects 
13.1 There is no more than minimal risk for the subjects. There are therefore, no 
foreseeable circumstances which require withdrawal of subjects. However, 
the participant can voluntarily refuse to participate in the follow up calls 
even if they have participated in the first call.  
14.0 Costs/Payments to Subjects 
N/A. No compensation to the participating patients 
15.0 Confidentiality 
The intervention will occur on the premises of Cigna Healthspring. No identifiable data 
will leave the premises. Only encrypted, de-identified data will be available to Dr. Susan 
Abughosh at UH. There will be no audio recording of the intervention. Only the research 
team operating from Cigna Healthspring will have access to identifiers. 
The UH Pharmacy students who will conduct the intervention will only interact with the 
patients to conduct the intervention over the phone and will not be able to access any 
information on the patients otherwise. The students will collect notes about the phone 
calls to identify number of successful calls, number of attempts made to contact each 
patient, and barriers to adherence for the successful calls. These notes will be made in a 
single excel sheet which will be available only at Cigna – HealthSpring. The students will 
have no access to the notes otherwise. After the completion of the intervention phase, the 
excel sheet will be deidentified, and will contain a de-identified ID and corresponding 
barrier to adherence. This information will be provided to UH for understanding the 
success of the intervention process. 
 
16.0 Provisions to Protect the Privacy Interests of Subjects 
16.1 The retrospective data analysis will be completely de-identified. For phase 
two, the personnel making the phone calls will be provided the list of names 
and numbers to contact at the premises of the health plan for intervention 

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implementation. During the follow up calls, the patients would be asked if 
they are willing to participate in the follow up. The students are trained to 
conduct training using MI to promote behavioral change in a collaborative 
supportive manner. 
16.2 After the completion of the intervention stage, post intervention data will be 
de-identified and the analysis completed. Data will be stored on a secured 
server for a period of 3 years after completion of the study. The attached 
script is an example of how MI has been used previously by pharmacy 
students at UH, in research led by the PI. 
17.0 Informed Consent Process 
The intervention is conducted on the phone, and therefore the consent will be 
obtained orally on the phone. The pharmacy student will read the consent letter 
(attached) to the patient, and once the patient says that he or she agrees to 
participate in the study, does the student proceed with the intervention. The 
patient can refuse, or ask the pharmacy student to call another time. 
18.0 Process to Document Consent in Writing 
The research involves no more than minimal risk of harm to subjects and involves 
no procedures for which written consent is normally required outside of the 
research context. The intervention involves only a phone call to educate the 
patient about adherence. A waiver of obtaining consent in writing is being sought. 
19.0 HIPAA 
The direct identifiers will be removed before providing any information to 
University of Houston. All information will be analyzed and results reported 
in aggregated form. Thus, the protected health information of patients will 
not be accessed by UH, and therefore we will not be obtaining, creating, 
using, and/or disclosing individually identifiable health information 
associated with a HIPAA-covered component or entity in the course of the 
research.  
20.0 Data Management 
20.1 The data which will be provided by Cigna will contain data with encrypted 
patient IDs. The electronic data files for this study will be processed on a 
dedicated, layered-security server, which can be accessed only by Dr. Susan 
Abughosh and designated project staff members who are under the direct 
supervision of Dr. Susan Abughosh. Since the system is behind multiple 
firewalls, is monitored regularly, and is accessible only to key personnel, the 
risk of unlawful penetration is not a significant data safeguard concern. Data 
will be stored on the secure server for 3 years following the completion of 
the study. All analysis will be conducted on the desktop at UH College of 
Pharmacy. The data will not be accessible through any personal computers. 
SAS 9.4 is the statistical software, which will be used for the analysis.  

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Baseline characteristics: Comparisons in baseline characteristics will be 
made between the 2 study groups (intervention vs. control) using t-tests for 
continuous variables and chi-square tests for categorical variables. The 
characteristics evaluated will include predisposing factors: demographics, 
and number of other medications used; enabling factors: health plan (LIS vs. 
not), prescriber specialty (specialist vs. primary care); need factors: co-
morbidities (CHF, MI, CAD, stroke, ESRD, depression), previous 
hospitalization and CMS risk score.   
Baseline PDC in the 12 months prior to intervention and patient past 
adherence trajectory will also be compared between 2 groups. Because of 
the randomization, we do not anticipate any significant differences between 
the two study groups on these characteristics.  
Outcome analysis: Refill data during 2 time periods -- 6, and 12 months, 
following the initial call of the intervention will be evaluated to calculate 
adherence rates for both the intervention and control groups. The two 
periods were chosen to evaluate the intervention effect during the 6 months 
when the follow up calls were ongoing as well as after these calls had 
stopped. The adherence rate will be defined as proportion of days covered 
(PDC) for six and twelve month follow up period following the intervention. 
An intent to treat analysis will be utilized.  
To examine the effect of study group (intervention vs. control) on 
adherence, study group PDC means for each follow up time, 6 and 12 
months, post intervention will be calculated and compared using a t-test.  In 
the event that significant differences in baseline characteristics were 
observed, multiple regression will be utilized to adjust for the imbalance. 
The outcome variable will be PDC during each follow up period, 6 and 12 
months, following the intervention initial call. The primary exposure 
variable will be study group, and other covariables will include any baseline 
characteristics not equally distributed.  
Dichotomization of the outcome: Patients in both groups will also be 
categorized as adherent vs. not during the two follow up periods based on 
PDC≥80% in each time period, 6 and 12 months, to create 2 categorical 
outcome variables. The effect of study group on adherence will be evaluated 
for these categorical outcomes using chi-square tests. If significant 
differences in baseline characteristics were observed, logistic regression 
models will be utilized to adjust for imbalances 
21.0 Sharing of Results with Subjects 
21.1 N/A.  It is impossible to contact or identify any subject as all direct 
identifiers that might lead to identification will be omitted. 
21.2  During the consent, Patients will be informed that they can check the 
progress and results of the study on www.ClinicalTrials.gov 
22.0 Resources  

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22.1 The PI has successfully completed several studies using motivational 
interviewing based phone calls. Collaboration with Cigna healthSpring has 
also been successful in the past projects and the team at Cigna Healthspring 
is extremely experienced as well. Everyone has completed human subhects 
research training. The research assistant is trained to analyze data and 
efficiently use research design and analytical techniques at UH- Department 
of Pharmaceutical health Outcomes and Policy.  
22.2 Infrastructure at both Cigna HealthSpring as well as University of Houston, 
is updated to be able to conduct the study.  
22.3 Research Team: 
1.Susan Abughosh1 - Principal investigator 
2.Michael L. Johnson1 - Co-investigator 
3.Marc L Fleming1 - Co-investigator 
4.James Essien1 - Co-investigator 
5.5. Dr. Kamalasanthi Masilamani1 – Co-investigator 
6.Dr. Omar Serna2 - Co-investigator 
7.Dr. Tara Esse2 – Co-investigator 
8.Aisha Vadhariya1- UH Student, research assistant 
9.Rutugandha Paranjpe1 - UH Student, research assistant 
 
 
1= University of Houston, Department of Pharmaceutical Health Outcomes and 
Policy 
2= Cigna HealthSpring