Treatment Phenotypes for Adolescents With Asthma
Phase: NA
Conditions: Asthma in Children, Adherence, Treatment
Sponsor: University of Colorado, Denver

Clinical Trials Cover Page
Treatment Phenotypes for Adolescents with Asthma
Document date 09/22/2023
NCT04228107

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COMIRB Protocol 
COLORADO MULTIPLE INSTITUTIONAL REVIEW BOARD 
CAMPUS BOX F-490    TELEPHONE:  303-724-1055  Fax:  303-724-0990 
Protocol #:19-1861 
Project Title: Treatment Phenotypes For Adolescents With Asthma 
Principal Investigator:  Heather De Keyser, MD 
Version Date:  
22-SEP-2023
I.
Hypotheses and Specific Aims:
The goal of this proposal is to better characterize medication use patterns 
among children with asthma and to develop provider and adolescent-focused 
interventions to respond to poor adherence and poor control. Asthma is the 
most common chronic disease in children, with a huge impact on healthcare 
outcomes and expenditures in the US1. Inhaled controller medications are the 
cornerstone of asthma therapy and adherence to these medications is critical 
to effective asthma management. However, commonly used techniques to 
measure adherence to daily medications are insufficiently accurate2-6. Reliance 
on patient reported measures of adherence tends to overestimate true 
adherence when compared to more objective measurements6-9. Previous 
evaluations of adherence using electronic monitoring devices have shown 
lower adherence rates to be correlated with higher levels of healthcare 
utilization10, and worsened levels of asthma control11,12. One study found that 
nearly ¼ of asthma exacerbations are attributable to inhaled corticosteroid 
non-adherence13. Some estimates indicate that even achieving high adherence 
for 40% of children with asthma would result in cost savings of $8.2 
million/year14. Additionally, biologic medications are now available for the 
treatment of refractory asthma in children, however the high cost of these 
medications and potential for side effects makes the objective distinction of 
non-adherence from treatment refractory asthma of paramount importance15. 
Medication monitoring technology allows for more accurate measures of 
medication adherence. Technology for monitoring adherence is expected to be 
widely available to providers within the next 5-10 years, and providers will need 
to know how to use patient adherence data to improve their patient outcomes. 
Therefore, what is needed is better understanding of medication use 
patterns and methods of intervening to increase adherence that are 
feasible to implement in the clinical setting.  
Specific Aim 1) To better characterize adherence trajectory phenotypes, and to 
combine adherence with rescue medication use patterns to create defined asthma 
treatment phenotypes. 
Hypothesis: we will be able to obtain distinct treatment phenotypes 
utilizing medication monitoring sensors.  
Specific Aim 2) Use qualitative methods among adolescents and providers to 
evaluate reasons for adherence/nonadherence and motivating factors for 
adherence, feelings about device monitoring and suggested strategies for 

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intervention among each of the highest risk treatment phenotypes using a self-
determination theory framework. 
Hypothesis: Qualitative methodology will yield a number of 
intervention strategies that can be subjected to expert opinion 
 
 
II. Background and Significance:  
 
a. Asthma morbidity is a significant public health problem, with 7-9 
percent of children in the US having a diagnosis of asthma in 2016, and 
54% of those children having an asthma exacerbation within that year16. 
Asthma exacerbations/hospitalizations remain a significant source of 
healthcare expenditure1, with an average inpatient hospitalization in 
children costing $360017, and an overall cost to the United States of $81.9 
billion per year1. Asthma reduces productivity, resulting in approximately 
14.41 million lost work days and 3.68 million lost school days per year.18 
Across the lifespan, a history of a previous exacerbation is a significant risk 
factor for a future exacerbation,19,20 and has been found to be more 
predictive of an exacerbation than other variables including race/ethnicity 
and insurance status19.  
b. Inhaled corticosteroids have been shown to significantly improve 
asthma control. The use of inhaled corticosteroids (ICS) in asthmatic 
children and adults has been shown to significantly decrease the rate of 
asthma-related hospitalization21 and asthma related death22. Inhaled 
corticosteroids have been shown in clinical trials to reduce asthma 
exacerbations and improve daily asthma control23-25. Recurrent 
exacerbations are also known to decrease lung function over time26, so 
improving adherence to decrease exacerbations could have very important 
long-term implications for asthmatic children.  
c. Adherence has been correlated with improved asthma outcomes. 
Previous evaluations of adherence using electronic monitoring devices 
have shown lower adherence rates to be correlated with higher levels of 
healthcare utilization10, and worsened levels of asthma control11,12. One 
study found that nearly ¼ of asthma exacerbations are attributable to ICS 
non-adherence13. Some estimates indicate that even achieving high 
adherence for 40% of children with asthma would result in cost savings of 
$8.2 million per year14. For all diseases, non-adherence to medications is 
estimated to cost $100 billion every year in avoidable hospitalizations27.  
d. Adherence to asthma medications is poor28, and even with monitoring 
has been shown to worsen over time 29. The large Childhood Asthma 
Management Program study found that 75% of children studied had 
adherence levels of less than 80% when measured objectively4. In fact, 
one review of previous attempts to measure adherence to asthma 
medications utilizing electronic monitoring devices showed adherence rates 
between 28-73%, with only one study showing an adherence rate above 
90% 30.  

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e. There is a group of children who are highly adherent to medications 
but remain poorly controlled and require escalation to biologic therapy. 
These therapies are expensive31, with one model suggesting that 
omalizumab may cost as much as $117,000 per quality adjusted life year15, 
so rapid and accurate identification of the appropriate children for this 
therapy is of high importance. The ability to accurately monitor adherence 
would permit identification of children whose poor control is related to poor 
adherence, potentially sparing them these extremely expensive therapies. 
However, there is a gap between the medications we know will work to 
control childhood asthma, and the behavior changes that are needed to 
encourage medication use.  
f. Use of theory-based approaches to behavior change is widely 
recognized as key to developing interventions with demonstrated 
effectiveness and relevance to real-world practice 32. Self-determination 
theory (SDT) is a theory of human development and motivation that has 
been broadly applied to health behavior change. According to SDT, human 
behavior is regulated along a continuum of motivation ranging from more 
autonomous (self-determined) to more controlled; greater environmental 
support for an individual’s need for autonomy, competence, and belonging 
lead to more autonomous forms of motivation (e.g., intrinsic, integrated and 
identified motivation). Research consistently shows that interventions that 
support such needs are especially effective for enhancing the motivation 
and behavior of children and adolescents, including in the health domain 32. 
Through application of principles and evidence-based behavior change 
strategies from the SDT literature, in addition to medication use monitoring, 
there is untapped potential for improving both initial and long-term 
maintenance of adherence to asthma medications.  
 
g. Potential Impact of the proposed study: This study has the potential to 
significantly benefit children with asthma by developing the best strategies 
to sustain high levels of monitored adherence and identify those medication 
treatment phenotypes most in need of clinical intervention. Previous 
studies, including my previous work on adherence, have shown that 
adherence levels decline over time, even with monitoring and reminders. A 
better understanding of why patients follow the specific adherence 
trajectories that they do is key for sustained success of adherence 
improvement programs. Understanding adherence patterns could lead to 
more personalized approaches to asthma medication use, including 
tailored interventions addressing mechanisms that lead to poor asthma 
control. The issues studied here could potentially be extrapolated to many 
other chronic health conditions in children where adherence is also 
problematic.  
 
h. Innovation 1-Methodology: Utilizing group based trajectory modeling 
to place patients into adherence trajectory groupings. Group based 
trajectory modeling has been utilized in evaluations of medication 
adherence in a number of other disease states including psoriasis33, cystic 
fibrosis34, epilepsy35, glaucoma36, and heart disease37 (among others). 
These evaluations have shown similar adherence trajectory patterns to 
those seen in our pilot study, with small variations based on the number of 

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trajectories chosen for the models. We will extend our previous work to 
evaluate adherence trajectories in asthmatic children, and couple these 
with rescue medication use measures in order to implement treatment 
phenotype specific interventions, which has not previously been attempted 
in asthmatic children. 
  
i. 
Innovation 2-Extend the knowledge base about adherence by using 
mixed methods pairing qualitative data collection with objective 
measures of adherence. Previously identified barriers to asthma 
medication adherence include lack of routine, forgetfulness and competing 
demands on time38,39. However, participants in these studies did not have 
objective measures of adherence to correlate with specific barriers and 
facilitators. The proposed study will allow for the delineation of barriers and 
facilitators that are particular to groups with high, low or decreasing 
adherence, which has not been assessed in children with asthma.  
  
j. 
Innovation 3-Programatic development that is tailored to each 
individual patient. Individualized management programs have been 
shown to attenuate drops in medication adherence in adult asthma patients 
using sensor monitoring40 and in pediatric patients using medication fills41. 
However, such programs have not yet been attempted in children using 
adherence monitoring. 
 
k. Innovation 4-Ability to accurately identify patients who are highly 
adherent and poorly controlled, and therefore, good candidates for 
expensive biologic therapies. New, but expensive therapies such as the 
monoclonal antibodies omalizumab and mepolizumab are viable options for 
patients who have poor control but good adherence to traditional therapies. 
The type of monitoring technology we propose to study could allow for 
quicker and more accurate characterization of this patient type before they 
endure exacerbations potentially leading to hospitalizations and irreversible 
loss of lung function.  
 
 
III. Preliminary Studies/Progress Report:   
Pilot program to evaluate the usability and feasibility of the Propeller Health 
adherence monitoring device-through this work I gained skills working with this 
technology and population of asthmatic children 
a. 25 children with high risk asthma (at least one 
hospitalization or 2 ED visits within the last year), 
monitored for 3 months. 
b. Devices were well accepted by parents, children 
and providers, with little to no concerns from 
families about ease of use.  
c. Average adherence rate of about 56% over the 3 
month time period studied, however average 
adherence started at 78% and dropped to 36% 
by the end of the study42. 
Breathing Counts Program-through this work I 
gained skills of working with this technology and this population, as well as 
beginning to address adherence barriers 
Figure 1 Adherence trajectories after 3 months 
of monitoring in asthmatic children (n=65) 

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d. Adherence measured plus an asthma health educator who contacted 
families at regular intervals to discuss adherence and address barriers.  
e. 65 patients monitored for a total of 6 months per patient 
f. Has added significantly to our knowledge, by identifying likely barriers to 
adherence, identifying adherence trajectories after 3 months of monitoring 
(Fig 1), confirming that 61% of patients fell into a suboptimal adherence 
category, and dealing with technical support issues that might arise with 
the devices. 
Propeller Health database evaluation-through this work I have continued to 
evaluate the adherence trajectories over time in a diverse population of 
asthmatics. 
g. Initial data presented at the 2018 American Thoracic Society meeting 
showing stability of initial adherence trajectories over 20 weeks of 
monitoring in 1,234 patients. Manuscript under review 
Financial Incentives for medication use 
h. Evaluating the use of financial incentives for asthma medication use in 
asthma 
i. 
43/50 patients enrolled to date.  
In the current proposal, I have decided to focus the evaluation on adolescents with 
asthma as they are a group at high risk for non-adherence, and they are often responsible 
for their own medication use, as opposed to younger children who depend on their 
parents for medication administration. I have enrolled a significant number of adolescent 
asthmatics in the previous two studies discussed, and I have experience in working with 
this age group. Additionally, I have extensive experience using the adherence monitoring 
devices and I am well equipped to address any technical issues that may arise.  
 
 
IV. Research Methods 
 
Specific Aim 1) To better characterize adherence trajectory phenotypes, and to 
combine adherence with short acting beta agonist (SABA) use patterns to create 
defined asthma treatment phenotypes. 
Hypothesis: We will be able to identify distinct treatment phenotypes during the 12 months 
of monitoring.  
Study Design: Longitudinal, prospective, cohort study of the medication adherence 
patterns of children with high risk asthma during which time they will receive adherence 
monitoring and notifications. 
Study Setting Patients who have been seen within the last 2 years by the Pulmonary, 
Allergy and/or Child Health/Adolescent Medicine clinics at Children’s Hospital Colorado 
(CHCO) Main Campus, and network-of-care sites including: Briargate, North Campus, 
South Campus, Parker, and CHCO in Colorado Springs. These clinics treat a variety of 
patients; however, a significant proportion of these patients are asthma sufferers, and the 
pulmonary clinic alone sees over 3000 asthma visits per year. Patients who are currently, 
or have been, admitted to CHCO for an asthma exacerbation may also be approached.   
Study Population The study population will include children with a diagnosis of asthma, 
either in their “Problem List” or “Visit Diagnosis”, aged 12-16 years old at the time of 
consent, on an inhaled controller and rescue medication that can fit the Propeller Health 
devices. For patients who follow the new NIH-approved SMART (single maintenance and 
reliever therapy) approach, their inhaled controller medication and rescue medication will 

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be the same.  Exclusion criteria will include co-morbidities such as other significant 
chronic lung disease (cystic fibrosis, chronic respiratory failure, tracheostomy status, 
interstitial lung disease, restrictive lung disease, bronchopulmonary dysplasia, pulmonary 
hypertension, bronchiectasis, chronic lung disease of prematurity) on their problem list, 
significant developmental delay on their problem list, taking more than 3 asthma related 
medications (other than their quick relief medication), or primary language other than 
English, Spanish, Catalan, Dutch, French, German or Italian (the languages currently 
supported for the monitoring application), or if patients do not have a smartphone that can 
download the Propeller Health app. We plan to enroll 100 patients using a rolling 
enrollment plan over a period of 6-12 months. 
Outcome Measures The primary outcome for this Specific Aim will be the medication 
treatment phenotype as determined by Group Based Trajectory Modeling (GBTM)43. We 
will analyze trajectories at the completion of 2, 4, 6, 8 and 12 months of monitoring for all 
patients in order to determine how rapidly the treatment phenotypes are established, as 
well as how stable they are over time.  
Study Procedures We will screen for eligible participants by reviewing clinic schedules 
for the Pulmonary, Allergy, Child Health/Adolescent Medicine clinics, and recent 
admissions on a regular basis.  Additionally, we may utilize EMR tools to identify all 
patients seen in these clinics in the past 2 years to reach out to families directly.  Due to 
current practice changes during the COVID-19 pandemic, patients and parents will be 
consented either in clinic, while admitted to CHCO, or by Telehealth after a clinic visit by a 
member of the study team. Remote consent will be done via Vidyo on the MyChart 
platform, and participant ability to login to the MyChart application will be considered 
verification of identity for the purposes of consenting.  We will use REDCap to house 
electronic consent documentation.  If the consent occurs in person, a copy of the signed 
consent form will be given to the family. If the consent occurs via Telehealth, the REDCap 
consent responses will be emailed or mailed via USPS standard mail to the participant 
after the consent visit is complete.  After consent is obtained and upon receipt of the 
Propeller Health device either in the clinic, inpatient room, or in their home, patients will be 
enrolled in medication use monitoring with mobile sensor technology. Participants will be 
given sensors to attach to their controller and rescue medications. For participants who 
have one medication for both their controller and rescue inhaler, they will only receive one 
sensor. If the consent happens in clinic or while admitted, the patients will be enrolled in 
the Propeller Health system at the same time.  If consent occurs remotely, a second 
telehealth visit may be scheduled after the consent has been signed if the devices have 
not been received by the participant. If the patient has already received the devices, they 
will be enrolled in the Propeller health system immediately after consent. The sensors will 
sync via Bluetooth to an application on the participant’s or parent’s phone (or both based 
on participant preference) to provide feedback and reminders regarding medication 
adherence (push notifications will be sent if medications have not been used 15 min after 
the time specified by the participant on enrollment as the time they normally take their 
medications). Patients will be given the option to share their data with their healthcare 
provider, however providers will not have direct access to this data.  Data will be collected 
regarding the exact time of inhaler actuation indicating a dose of medication being 
delivered. Additionally, at study entry, we will collect information regarding basic baseline 
demographic and clinical information (including age, medications prescribed, allergic 
history, lung function and secondhand smoke exposure). Participants will be given the 
option to receive text message reminders from the study team regarding sensor inactivity. 
Participants will be asked to sync their sensors to their phone application prior to 
disposing of the Bluetooth devices in order to remove data from the device memory.  If 

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there are problems syncing the data, we will ask participants to mail the sensors back to 
the research team in order to sync the devices and obtain/remove the data.   
Analytic Approach/Data Analysis Plan  
Adherence trajectory modeling 
Group Based Trajectory Modeling (GBTM) will be used to explore subgroups of 
adherence trajectories. This methodology has been previously used in medication 
adherence phenotyping (see innovation above), and we have previously studied this 
modeling technique and found good model agreement with 3 subgroup trajectories 
identified. We will collect data continuously and evaluate at 2, 4, 6, 8 and 12 months of 
monitoring for all patients, as data from the entire group is needed in order to place 
patients in the appropriate group trajectory. We will compare results from 2, 4, 6, 8 and 12 
months of monitoring in order to determine how quickly patterns are established after the 
initiation of monitoring and the initial Hawthorne effect, as well as if they are stable over 
time. We will model average weekly adherence as a function of cubic and quadratic time 
and compare modeling approaches by Bayesian information criterion, Akaike information 
criterion, number of groups, and proportion of patients per group. For a set of trajectories 
to be considered reasonable, we will require that estimated group proportions remain 
greater than 5%. The GBTM analysis will be run in SAS software 9.4 (SAS Institute Inc, 
Cary, North Carolina) and the PROC TRAJ macro 
(http://www.andrew.cmu.edu/user/bjones/) a freely available program (Copyright (c) 2017 
Bobby L. Jones). We anticipate that we will see 3 adherence trajectories based on our 
previous studies (high, moderate and poor adherence) (Figure 1). Additionally, we will 
plan to evaluate mean level of adherence over the 12 month monitoring period (>80% 
adherence will be considered high adherence, 30-80% will be considered moderate 
adherence, and less than 30% will be considered poor adherence), to compare to the 
GBTM modeling and provide an alternative means of assessing adherence.  
Asthma Control 
We will determine levels of asthma control based on NHLBI EPR-3 guidelines defining 
asthma impairment44. Asthma control will be defined on a weekly basis, with albuterol use 
<2 times per week labeled as good control, albuterol use daily labeled as poor control, 
and albuterol use multiple times per day labeled as very poor control. When defining a 
treatment phenotype, the poor control and very poor control groups will be combined as 
‘poor control’. All albuterol actuations occurring within 5 minutes will be considered a 
single use event. Albuterol actuations occurring outside this 5-minute window from the first 
actuation will be considered a separate albuterol use event. When defining a treatment 
phenotype at the end of the monitoring period, we will consider a patient to have ‘poor 
control’ if they demonstrate poor or very poor control based on the parameters above for 
>50% of the measured weeks of monitoring, or if they have an exacerbation requiring oral 
steroids during the monitoring period.  
Potential Problems and Alternative Approaches  
Inability to confirm adherence trajectories. The null hypothesis is that we will not be able to 
identify defined adherence trajectories and therefore treatment phenotypes. Traditional 
power analysis techniques do not currently exist for the group based trajectory modeling 
approach and this is an active field of current methodologic research. While we are 
reasonably confident that we will be able to successfully determine adherence trajectories 
based on our previous research with smaller groups of patients as well as the work in 
other disease states, there is a small chance that we will not be able to confirm the group 
based adherence trajectories. However, we will be able to evaluate medication use 
patterns per the other methods described above (average adherence over time). 
Additionally, if adolescents shift their adherence trajectories over time, we will do an in-

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depth analysis to evaluate possible reasons for the change (including provider 
interventions and significant exacerbations). 
Technical Difficulties with adherence sensors There is a risk with any technology that 
technical difficulties will arise that make completion of the project problematic. However, 
we have been working with Propeller Health for more than 3 years on 3 projects that have 
enrolled more than 120 patients. From this experience we have learned how to deal with 
sensor failures (troubleshooting along with the manufacturer, providing alternative 
devices) and issues with Bluetooth syncing (troubleshooting the phones, turning Bluetooth 
back on, alternative means of obtaining data including use of Bluetooth hubs or a provider 
application). We feel very confident in the technology and our ability to handle any 
technical support challenges that arise.  
  
Specific Aim 2) Use qualitative methods among adolescents and providers to 
evaluate reasons for adherence/nonadherence and motivating factors for 
adherence, feelings about device monitoring and suggested strategies for 
intervention among each of the highest risk treatment phenotypes using a self-
determination theory framework.  
Study Setting and population: See Specific Aim 1 
Study procedures: All Aim 1 participants will be considered for participation in Aim 2 
qualitative analysis.  Participants will range in age from 12-17 and will be recruited from 
Aim 1 participants. Participants who have reached age 18 prior to interview selection will 
not be contacted for interviews.  Selected participants will be called at home to participate 
in a phone or remote video interview within 1 year of completing the 12-month monitoring 
period. A semi-structured interview guide45 will be developed for this study by Dr. Holtrop 
and myself, and will be based on findings from aim 1, existing literature and expert 
opinion. Questions in the interview guide will explore patients’ perceptions of the factors 
that influence medication use (including barriers and facilitators) and strategies that 
adolescents perceive might be helpful in improving medication adherence. Self-
determination theory (SDT) will be use as a framework to provide more specific insight to 
factors that may influence motivation to participate in medication adherence. The most 
likely behavior change techniques in this case which we will explore will be 1. 
Implementation intentions (plans that specify the context in which a behavior will be 
performed), 2. Monitoring and feedback, and 3. Support for overcoming failure (i.e., 
contingency planning, goal revision, and enhancing recovery self-efficacy).Therefore, the 
interview guide will also include questions to address SDT components which include 
competence, relatedness and autonomy32. Second, clinical providers have a unique 
perspective on patient adherence/non-adherence issues. Providers (n=up to 30), including 
physicians, advanced practice providers and nurses will be grouped by role and be asked 
to participate in a focus group interview once during the study period. Providers will be 
contacted by the study team via email and asked to participate, and then scheduled based 
on available time.   Interview questions and probes will include provider perceptions of 
what interventions are needed to best address patient medication use as well as the types 
of treatment phenotypes that providers think would benefit most from active intervention. 
To do this, we will present our proposed phenotypic groupings to the providers and ask 
probing questions regarding their completeness, meaning, and use in practice. The phone 
or video interviews will last 30-60 minutes; focus groups will last 1 hour. All will be audio 
recorded and transcribed verbatim. 
Data analysis: To analyze the patient interview data, we will use both inductive and 
deductive approaches. First, using a grounded theory hermeneutic editing approach46, the 
qualitative PRA and I will inductively code the interview transcripts. We will initially embark 

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on an open coding process in which codes are developed based on 
reading the text. Second, using SDT as a guide, we will deductively 
code the data for SDT components. Next, we will use an axial coding 
process to determine if this theory is a good fit for the data and if 
additional findings emerge from a theoretical approach. Transcribed 
data will be entered into the Atlas.ti qualitative software analysis 
program (version 8; Scientific Software Development, GmbH, Berlin, 
Germany) and utilized for coding and analysis. We will examine the data for overall 
themes, SDT components, and in a matrix approach47 by participant groups (e.g., which 
treatment phenotype the patient was in). The results generated from these processes will 
be used to generate a framework for evaluation of patient behavior and strategies for 
individualized approaches to medication adherence. The provider focus group data will be 
analyzed similarly as noted directly above, both separately and then together with the 
patient interview data. Once these data are analyzed, they will be combined with the 
quantitative data using a concurrent mixed methods approach48 (figure 2), utilizing 
quantitative data gathered from the monitoring devices themselves, and merging that with 
the qualitative data obtained from the qualitative interviews and focus groups, allowing for 
triangulation of results across data types. Each data set will be obtained separately. 
Tables describing the interplay of the quantitative results in light of the qualitative results 
will be created. 
Potential Problems and Alternative Approaches: GBTM strategy: As described above, 
there is a small chance that the patients studied may not fall into clear adherence 
trajectories. However, the GBTM will not affect the questions asked, nor will the interviews 
preclude this change in protocol. Bias: As Dr. Hoch is an MD pulmonary provider, there is 
a possibility that her patients will show evidence of desirability bias in their interactions 
with her. Therefore, a research assistant will conduct these interviews to minimize this 
bias, and she will be very clear in indicating that there will be no judgement on behaviors.  
Thematic saturation: While we expect to reach thematic saturation in the two highest risk 
groups by interviewing up to 50 patients, we recognize that we may not reach thematic 
saturation with only 30 providers. If that is the case, we will expand these focus group 
numbers as needed. 
Participant ages: We recognize that children at the lower end of our age spectrum (age 
12) may have less autonomy in medication usage than those at the upper end. Therefore 
we will purposefully select a broad range of ages within each treatment phenotype in 
order to capture age related differences in behavior.  
 
Treatment effect in diverse populations-there is some evidence that diverse populations 
may require different cut points for markers of asthma control52. We plan to evaluate 
asthma control both by Asthma Control Test and objectively measured rescue medication 
use to help ameliorate this effect.  
 
 
G.  Summarize Knowledge to be Gained:   
We will be able to evaluate adolescent medication use patterns across a full year 
to determine medication use phenotypes.   We will then be able to utilize a mixed 
methods approach to develop and pilot test a novel strategy to improve medication 
adherence in this high risk population.   
 
 H. References: 
 
Table 2-RE-AIM data evaluation 
Figure 2 Concurrent mixed methods approach 

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