Integration of mHEALTH Into the Care of Patients With Sickle Cell Disease to Increase Hydroxyurea Utilization- mESH Study
Phase: NA
Conditions: Sickle Cell Disease
Sponsor: St. Jude Children's Research Hospital

Official Study Title: Integration of mHEALTH Into the Care of Patients with Sickle Cell 
Disease to Increase Hydroxyurea Utilization - mESH Study 
 
CTG Number: NCT04080167 
 
Document Date: 07/22/2020 

Sickle Cell Disease Implementation
Care Redesign Protocol Study
INTEGRATION OF mHEALTH INTO THE CARE OF 
PATIENTS WITH SICKLE CELL DISEASE TO INCREASE 
HYDROXYUREA UTILIZATION – mESH study
Sickle Cell Disease Implementation Consortium (SCDIC)
June 5, 2020
Version 1.2
Principal Investigators:
Jane Hankins, MD, MS (PI)
Hayden Bosworth, PhD (co-PI)
Nirmish Shah, MD (co-PI)
Co-Investigators:
Nicole Alberts, PhD
Kenneth Ataga, MD
Ana Baumann, PhD
Cecelia Calhoun, MD
Nancy Crego, PhD, RN, CCRN, CHSE
Lisa DiMartino, PhD, MPH
Christian Douglas
Jeremie Estepp, MD
Maria Fernandez, PhD
Robert Gibson, PhD
Jeff Glassberg, MD
Victor Gordeuk, MD
R. Ward Hagar, MD
Lewis Hsu, MD
Sara Jacobs, PhD
Allison King, MD, PhD, MPH
Lisa Klesges, PhD
Abdullah Kutlar, MD
Richard Lottenberg, MD
Cathy Melvin, PhD, MPH
Sarah Norell, MDes + MFA
Shannon Phillips, PhD, RN
Jerlym Porter, PhD, MPH 
Michael Potter, MD
Jena Simon, MS, FNP-BC, RN-BC
Sharon Smith, PhD
Paula Tanabe, PhD, RN, MPH, FAEN, FAAN
Marsha Treadwell, PhD
Ted Wun, MD
Elliott Vichinsky, MD
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Revision History 
Version 
Date
Comments
1.0 
12 November 2019
Initial Release 
1.1 
03 January 2020
Section 1.4: Study Design and Implementation 
Conceptual Framework
Section 2.1: Study Setting
Section 2.2: Eligibility Criteria,
Section 2.4: Data Collection, Measures, and Outcomes 
Section 2.7: Recruitment 
Section 5.3: Consent or Assent
Minor grammatical and formatting changes throughout.
1.2
05 June 2020
Section 2.4: Data Collection, Measures, and Outcomes
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Table of Contents
Abstract....................................................................................................................................1
1. Introduction .........................................................................................................................3
1.1
Background and Rationale [SPIRIT 6a-6b, StaRI 3-4] .............................................3
Sickle cell disease and hydroxyurea therapy............................................................3
Hydroxyurea utilization is low in SCD care ...............................................................3
Mobile health (mHealth) technology and its potential for SCD care and 
hydroxyurea utilization ..................................................................................4
Why test an intervention to increase adherence to hydroxyurea within an 
implementation science research framework?..............................................4
Hydroxyurea utilization barriers within the SCDIC....................................................5
Logic model of change to increase hydroxyurea utilization ......................................5
Intervention to increase hydroxyurea prescribing habits and improve patient 
adherence .....................................................................................................5
Hydroxyurea adherence behaviors for patients ........................................................6
Behavioral model for mHealth utilization among providers.......................................7
Provider prescribing hydroxyurea according to guidelines .......................................8
Preliminary data for the efficacy and process development of mHealth 
Interventions................................................................................................8
RE-AIM as the evaluation framework for mHealth intervention impact ..................10
1.2
Primary Hypotheses................................................................................................10
1.3
Aims and Objectives [SPIRIT 7, StaRI 5] ...............................................................10
Overall objective of proposed research ..................................................................10
1.4
Study Design and Implementation Conceptual Framework [SPIRIT 8, 
StaRI 6]...................................................................................................................12
2. Methods: Participants, Interventions, and Outcomes...................................................16
2.1
Study Setting [SPIRIT 9, StaRI 7-8]........................................................................16
2.2
Eligibility Criteria [SPIRIT 10, StaRI 8]....................................................................17
Eligibility criteria for patient participants..................................................................17
Eligibility for provider participants ...........................................................................18
2.3
Interventions and Enrollment Strategy [SPIRIT 11a, StaRI 9-10]...........................18
Patient-level intervention ........................................................................................18
Provider-level intervention ......................................................................................19
2.4
Data Collection, Measures, and Outcomes [SPIRIT 12, 18a-18b, StaRI 11-
13]...........................................................................................................................20
Mixed-method evaluation of the facilitators and barriers in adopting and 
implementing the mHealth interventions................................................26
2.5
Participant Timeline [SPIRIT 13].............................................................................27
2.6
Sample Size [SPIRIT 14, StaRI 14] ........................................................................28
2.7
Recruitment [SPIRIT 15].........................................................................................30
3. Methods: Data Management and Analysis .....................................................................31
3.1
Data Management [SPIRIT 19]...............................................................................31
3.2
Methods of Analysis [SPIRIT 20a-20b, StaRI 15-16]..............................................31
Definition of the clinical influence outcomes ...........................................................33
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4. Methods: Monitoring.........................................................................................................37
4.1
Data Monitoring and Quality [SPIRIT 21a-21b] ......................................................37
4.2
Harms [SPIRIT 22] (Adverse Event, Unanticipated Problem Reporting)................37
Adverse events and unanticipated problems..........................................................37
4.3
Auditing [SPIRIT 23] ...............................................................................................37
5. Ethics and Dissemination ................................................................................................39
5.1
Research Ethics Approval [SPIRIT 24]...................................................................39
Risks and benefits ..................................................................................................39
Unbiased recruitment..............................................................................................39
5.2
Protocol Amendments [SPIRIT 25].........................................................................39
5.3
Consent or Assent [SPIRIT 26a-26b] .....................................................................39
5.4
Confidentiality [SPIRIT 27]......................................................................................40
5.5
Access to Data [SPIRIT 29] ....................................................................................40
5.6
Dissemination Policy [SPIRIT 31a-31c] ..................................................................40
6. References.........................................................................................................................42
Appendix
Appendix A Informed Consent Materials
Appendix B Patient Feedback on InCharge Health App
Appendix C App Descriptions
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Acronyms List
ANOVA
analysis of variance
CCNC
Community Care of North Carolina 
CMS
Centers for Medicare & Medicaid Services 
CONSORT
Consolidated Standards of Reporting Trials
ED
emergency department
HbF
fetal hemoglobin
HbS
mutant haemoglobin
HbSS
People who have this form of SCD inherit two sickle cell genes (“S”), one from 
each parent. This is commonly called sickle cell anemia and is usually the most 
severe form of the disease.
HSD
hydroxysteroid dehydrogenase deficiency
LDH
lactate dehydrogenase
LMM
linear mixed model
MARS
mobile app rating scale
MCC
medical coordinating center
MCV
mean corpuscular volume
MPR 
medication possession ratio 
MTD
maximum tolerated dose 
NC
North Carolina
NHLBI
The National Heart, Lung, and Blood Institute
NIH 
National Institutes of Health
NP
nurse practitioner 
NPT
Normalization Process Theory 
PA
physician’s assistant
PCP
primary care physician
PDC
proportion of daily coverage
PHI
protected health information
PI
principal investigator 
PROMIS
Patient-Reported Outcomes Measurement Information System
RA
research assistant
RE-AIM 
Reach, Effectiveness, Adoption, Implementation and Maintenance
SCD
sickle cell disease 
SCDIC
Sickle Cell Disease Implementation Consortium 
SMART
Sickle Cell Disease Mobile Application to Record Symptoms via Technology
TAM
technology acceptance model 
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Abstract
The National Heart, Lung, and Blood Institute (NHLBI) created the Sickle Cell Disease 
Implementation Consortium (SCDIC) to apply implementation science methods to identify and 
address barriers to guideline-based care in sickle cell disease (SCD) and promote evidence-based 
treatment for SCD patients between ages 15 to 45 years. The SCDIC conducted a systematic 
literature review and a comprehensive needs assessment among the eight participating centers. A 
major conclusion was that care redesign to support better hydroxyurea utilization would likely 
improve clinical outcomes for patients with SCD. Hydroxyurea therapy has been shown to improve 
patient outcomes and reduce disease complications and is endorsed by the NHLBI. SCDIC now 
proposes to develop, test and evaluate targeted interventions to improve clinical provider prescribing 
of and patient adherence to hydroxyurea.
The overall purpose of this proposed project is to address barriers identified by the needs 
assessment to improve adherence with hydroxyurea therapy. Multiple approaches for improving 
adherence with pharmaceutical regimens have been studied and demonstrate a need to address 
barriers that both providers and patients face. This project aims, via a stepped-wedge design, to test 
two innovative interventions utilizing mobile health (mHealth), to address both patients’ and 
providers’ needs: 1) an mHealth application for patients (InCharge Health app) that includes multi-
component features to address the memory, motivation, and knowledge barriers to hydroxyurea use, 
and 2) an mHealth toolbox application for providers (HU Toolbox app) that addresses the clinical 
knowledge barriers in prescribing and monitoring hydroxyurea use. These two interventions will be 
tested through the following aims:
Aim 1. Improve Patient Adherence to Hydroxyurea: Addressing Memory, Motivation, and 
Knowledge Barriers to Hydroxyurea Use. Primary hypothesis: We hypothesize that among 
adolescents and adults with SCD, the adherence to hydroxyurea, as measured by proportion of daily 
coverage (PDC), will increase by at least 20% at 24 weeks after receiving the InCharge Health app, 
compared to their hydroxyurea adherence at baseline.
Sub-Aim 1.a. To examine and assess both patient engagement and behaviors related 
to use of the InCharge Health app, we will evaluate consistent use of the app among 
enrolled patients, patient satisfaction, and continued use of the app beyond the study period.
Sub-Aim 1.b. To examine the clinical influence of the use of the InCharge Health app 
on PDC, patients’ clinical outcomes, perceived health literacy, health related quality of life, 
and perceived self-efficacy between baseline and 24 weeks.
Aim 2. Improve Provider Hydroxyurea Awareness, Prescribing and Monitoring Behaviors. We 
will examine among providers using the HU Toolbox app if there is an increase in reported 
awareness of hydroxyurea benefits and risks, accurate prescribing of hydroxyurea, and perceived 
self-efficacy to correctly administer hydroxyurea therapy between baseline and after 9 months of 
using the HU Toolbox app.
Sub-Aim 2.a. To examine and assess provider engagement and behaviors related to 
use of the HU Toolbox, we will evaluate consistent use of the app among enrolled 
providers, providers’ satisfaction, and continued use of the app beyond the study period.
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1. Introduction
1.1
Background and Rationale [SPIRIT 6a-6b, StaRI 3-4]
Sickle cell disease and hydroxyurea therapy
Sickle cell disease (SCD) is a chronic disorder affecting approximately 100,000 Americans,1 many of 
whom are economically disadvantaged. The effects of SCD are devastating: most patients 
experience one or more complications, including chronic severe pain, cognitive disability, renal 
failure, and lung disease. Although medical advances have reduced mortality of children with SCD, 
most adults with SCD die before age 45.1-3 Over 30 years of rigorous investigation has proven that 
hydroxyurea reduces disease complications, health care utilization, and costs for patients with 
SCD.4-7 Consequently, the National Institutes of Health/National Heart, Lung, and Blood Institute 
(NIH/NHLBI) released a guideline recommending that hydroxyurea be offered to symptomatic adults 
and all children with SCD (HbSS and HbSβ0-thal genotypes) when they are 9 months or older and 
consult with a hematologist if other SCD genotypes (e.g., HbSC, HbSβ+-thal) have sufficient disease 
severity prompting this therapy.8 Hydroxyurea induces fetal hemoglobin (HbF) production, thereby 
decreasing erythrocyte HbS polymers, hemolysis, and vaso-occlusion. In addition, hydroxyurea 
reduces inflammation through HbF-independent mechanisms. In uncontrolled population studies, 
hydroxyurea reduces hospitalizations and mortality, supporting its effectiveness outside of clinical 
trials.9-14 Hydroxyurea is given as a once-daily oral dose that costs less than $1 per day in the United 
States. Hydroxyurea is initiated and monitored in medical settings (e.g., outpatient clinics) by health 
care providers (e.g., physicians, advanced care practitioners, qualified nurses, and clinical 
pharmacists). During hydroxyurea therapy, blood counts are monitored every 1-3 months with 
titrated dose escalation to reach a maximum tolerated dose (MTD) defined by mild, reversible 
myelosuppression.
Hydroxyurea utilization is low in SCD care
Despite overwhelming evidence for positive 
effects, hydroxyurea is vastly 
underutilized.19,20 Given the relative ease of 
its administration, low cost, and safety 
profile, barriers to hydroxyurea utilization 
are primarily constrained by the health 
system as well as provider and patient 
determinants. Although we do not examine 
them here, system-level barriers include 
access to SCD-specific care and loss of 
health coverage. Provider-level barriers 
include providers’ reluctance in prescribing 
due to lack of knowledge about the drug 
and appropriate dosing. Patient-level 
barriers include low acceptance due to 
insufficient knowledge or misconceptions 
about risks and benefits, and forgetfulness 
leading to poor adherence.19-27 Negative 
perceptions toward hydroxyurea are 
strongly associated with lower adherence to this medication.28 Forgetfulness related to daily 
hydroxyurea use may be exacerbated by the known cognitive dysfunction, including working 
memory deficits that result from brain insults from SCD.29,30 In analyses conducted through Medicaid 
Figure 1. Hydroxyurea utilization among dults 
with SCD. Estimated hydroxyurea utilization in three 
U.S. states using Medicaid claims due from Florida, 
Maryland, and North Carolina and survey of adult 
providers.15-18
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claims data, fewer than 50% of adults were prescribed or initiated hydroxyurea and only about 30% 
of those who initiated treatment achieved adequate adherence levels, as defined by medication 
possession ratio (MPR) ≥80% (Figure 1).15-18 Among children, adherence was higher, but less than 
50%.21,31 Anticipation of known poor patient adherence dissuades medical providers from prescribing 
hydroxyurea.18,21,32 Improving adherence to hydroxyurea would achieve higher HbF levels, fewer 
disease complications, and reductions in health care costs, resulting in a major improvement in 
overall clinical outcomes.
Mobile health (mHealth) technology and its potential for SCD care and hydroxyurea utilization
mHealth refers to the practice of medicine and public health supported by mobile devices. Short 
message service (SMS) text messaging (through cell phones) is a widespread means of 
communication, particularly among adolescents and young adults,33,34 and an emerging intervention 
modality to improve medication adherence. The existing body of research provides support for 
mHealth interventions to improve treatment adherence across a variety of chronic conditions, 
including SCD.35-38 For example, structured text message interventions have been shown to improve 
medication adherence by 15.3–17.8% and improved clinical outcomes in patients with HIV, 
hypertension, diabetes, and epilepsy.39-42 Text message interventions also have been found to 
improve patient-provider relationships.43 In the most recent and also largest systematic review of 
mHealth applications for outpatient cardiovascular therapies, the effectiveness of mHealth was 
summarized from randomized trials involving approximately 2,500 patients. All interventions aimed 
at increasing medication adherence and showed modest, but significant reductions in cholesterol 
levels and blood arterial pressure.35 Similar benefits were observed in patients with asthma who 
received mHealth interventions, who experienced improved quality of life and reduced pulmonary 
exacerbations.36 Among patients with SCD, approximately 84–92% own smart phones and 91% use 
SMS regularly for communication;44-46 most SCD patients (87%) already accept and use this 
technology to monitor pain.47,48 Since the first reports of text messaging in the SCD population, 
mHealth applications have been developed to increase patient engagement and symptom 
tracking.38,49 Preliminary studies also suggest that mHealth interventions can specifically be used to 
improve hydroxyurea utilization. In one study, 14 children with SCD received text message 
reminders combined with a video recording to verify therapy and a financial incentive, which 
improved hydroxyurea adherence by 18% after 6 months.50 
Acceptance and adherence to hydroxyurea for SCD patients is impeded by mistrust of the medical 
establishment and misperceptions about relative risks and benefits.51 mHealth can address patient 
behavioral barriers, such as forgetfulness, and enhance communication with SCD providers who can 
use this intervention to educate patients on the benefits of hydroxyurea and improvement in 
hydroxyurea adherence. mHealth is also increasingly used to aid providers in their medical decision-
making and to facilitate consultations with other providers and experts in their areas,52,53 highlighting 
the broad applicability of mHealth, not only for patients, but for providers.
Why test an intervention to increase adherence to hydroxyurea within an implementation 
science research framework?
Enhancing the implementation of SCD evidence-based care guidelines can lead to population-wide 
improvements, but requires that contextually relevant findings be evaluated for future translation to 
diverse patients, clinics, and communities.54,55 Current evidence about beneficial effects of 
hydroxyurea is based primarily on efficacy trials, where eligibility screening criteria and low 
participation rates may lead to narrowly selected patients and settings. This limited evidence 
impedes our ability to generalize findings to the full spectrum of SCD patients, leading to limited use 
of hydroxyurea among providers and patients—and overall suboptimal effectiveness. Expanding 
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evidence-based use of hydroxyurea in SCD requires a multi-level systems perspective, evaluation of 
generalizability, and inclusion of practical measures and participatory approaches.55
Hydroxyurea utilization barriers within the SCDIC
In Phase I of the needs assessment, we conducted a mixed-methods analysis of hydroxyurea 
barriers using surveys, interviews, and focus groups across all eight clinical sites of the SCDIC. We 
identified obstacles at many levels of care, with salient barriers at the patient and provider levels. In 
qualitative analyses, patients reported the following as barriers to hydroxyurea use: it was not 
recommended or offered by their provider, difficulty remembering to take the medicine, perceived 
lack of benefit, and side effects. Related to these barriers, patients (both adolescents and adults) 
expressed the need to have greater communication with the providers, access to other patients 
(e.g., communication forums), and information about hydroxyurea and SCD. Among providers, lack 
of dosing and monitoring support in the electronic medical record were all reported as barriers, while 
the use of mHealth was identified as a potential source of support. Consistent with qualitative 
findings, the survey indicated that among a sample of 165 providers, nearly 30% reported they did 
not prescribe hydroxyurea, 39% reported patient anticipation of side effects influenced their 
prescribing of hydroxyurea, while 34% said patient adherence was important in their decision to 
prescribe the medicine. 
Logic model of change to increase hydroxyurea utilization
Our logic model combines established behavioral models, including the Health Belief Model, as a 
framework for understanding patient hydroxyurea utilization. The technology acceptance model 
(TAM)56,57 is used to understand uptake of the mHealth apps. These behavioral models and the 
knowledge gained from the needs assessment phase helped conceptualize a logic model that 
guided the development of the interventions to positively change hydroxyurea utilization and improve 
provider prescribing behaviors. This logic model used Intervention Mapping methods to develop and 
adapt the behavioral models for testing mHealth as the intervention to increase hydroxyurea use. 
Intervention Mapping is a systematic framework for developing, implementing, and adapting theory- 
and evidence-based interventions.58 Using the knowledge of barriers to using hydroxyurea, we 
mapped the determinants of hydroxyurea utilization (Figure 2). These determinants are 
hypothesized to drive the behaviors involved in patients’ and providers’ use of hydroxyurea. 
Furthermore, these determinants correspond to the barriers of hydroxyurea use that were identified 
through literature review and the results of the needs assessment analysis. Importantly, the 
interventions were developed and aimed at the determinants that could affect the behavior involved 
in taking and prescribing hydroxyurea; the ultimate goal (the behavioral outcome) is to foster greater 
patient adherence to hydroxyurea.
Intervention to increase hydroxyurea prescribing habits and improve patient adherence
We used Intervention Mapping to systematically develop and adapt two mHealth interventions 
addressing the barriers and determinants of hydroxyurea utilization: 1) a patient phone app, 
InCharge Health, and 2) a provider phone toolbox app, HU Toolbox app. Informed by the needs 
assessment analysis and patients’ preferences, the InCharge Health app incorporates features that 
address each determinant of the behavior in taking hydroxyurea, including reminders (determinant: 
cue to action), tracking of progress (determinant: motivation), and education bank (determinant: 
disease and hydroxyurea knowledge and perceptions). The InCharge Health app went through an 
iterative process during its development, including extensive feedback by stakeholders in different 
regions (Appendix B). Informed by the providers’ desire to have greater and easier access to SCD 
management, the HU Toolbox app gives providers direct access to experts (determinant: perceived 
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peer support), and care flowcharts (determinant: disease and hydroxyurea knowledge), among other 
features.
Figure 2. Logic model of change to increase hydroxyurea utilization. This logic model maps all 
barriers identified by literature review and needs assessment analysis, and focuses on the determinants 
of the behaviors in hydroxyurea use. The intervention addresses the determinants of hydroxyurea use at 
both the patient and provider levels. If this two-level intervention is successful, hydroxyurea utilization will 
increase, as reflected by increased hydroxyurea adherence, resulting in improved health-related quality 
of life and reduction in acute health care utilization. 
Hydroxyurea adherence behaviors for patients 
To guide the development of the mHealth intervention for patients, we used the Health Belief Model 
as the framework for the behavioral change necessary to increase hydroxyurea acceptability and 
use (Figure 3). The Health Belief Model is a widely used theoretical model that attempts to explain 
and predict health behaviors and focuses on these individuals’ attitudes and beliefs. The health-
related action driving the increased use of hydroxyurea include five constructs: perceived 
susceptibility, perceived severity, perceived benefits, perceived barriers, and self-efficacy. Individual 
factors including socio-demographics (including financial barriers and social support), 
clinical/medical status, cognitive functioning and emotional functioning, may influence these 
perceptions that ultimately drive the behavior of reducing the threat of disease complications by 
increased use of hydroxyurea. Notably, these five constructs represent modifiable factors that, 
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together, can be influenced to increase use of hydroxyurea. Cues to action represent prompts that 
trigger an individual to utilize hydroxyurea. The patient intervention focuses on these five constructs 
to help identify the mechanisms the patient intervention addresses to change behavior. 
 
Figure 3. Health belief model as the behavioral theory for increased hydroxyurea utilization.
Behavioral model for mHealth utilization among providers
Users’ acceptance of new technology, including new mHealth innovations, determines its successful 
adoption and, therefore, its downstream effects. TAM56,57 is a conceptual model that explain the 
intent to use a new information technology (including mHealth) or information science among users, 
including medical providers. TAM has five constructs, but perceived ease of use and the perceived 
usefulness are the two dominant determinants of technology use. Mobile health care systems self-
efficacy is the health care professional’s perception of her or his ability to use mobile health care 
systems to accomplish the health care task. Mobile health care systems self-efficacy is an important 
construct that should also be accounted for when new technology is implemented. When combined, 
these two models explain 70% of the behavior of intent to use a new mobile technology: perceived 
usefulness, perceived ease of use, compatibility, and also mobile health care systems self-efficacy 
are the most important determinants of the behavior intent (Figure 4).59 The compatibility construct 
was the strongest driver to directly affect the behavior intent of using mobile technology.
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Developers of the mHealth intervention for the providers considered all of these drivers. The new 
intervention for providers accounted for:
•
Perceived usefulness: SCD providers’ needs to receive more information about hydroxyurea, 
to improve their knowledge of prescribing this medication
•
Compatibility: SCD providers’ prior experience in using mobile technology (i.e., the HU 
Toolbox) to acquire general SCD knowledge
•
Perceived ease of use: SCD providers’ perception that mobile technology can be integrated 
with their electronic medical record and their clinical daily routine
•
Mobile health care systems self-efficacy: SCD providers’ perception that mobile health could 
help with the task of caring for patients with SCD
Figure 4. Conceptual model for mobile health care. Solid arrows denote direct significant effect. 
Dashed arrows indicate no significant direct effect. * denotes path significant at the 0.05 level, ** 
denotes path significant at the 0.01 level, and *** denotes path significant at the 0.001 level, and 
n.s. denotes nonsignificant (modified from Wu et al.59)
Provider prescribing hydroxyurea according to guidelines
The HU Toolbox app will detail clinical guidelines for prescribing hydroxyurea to SCD patients, but 
behavioral processes will influence provider’s change in practice methods. Factors such as 
awareness, familiarity, agreement and self-efficacy impact physicians’ following guidelines and were 
identified in the needs assessment as potential barriers.
Preliminary data for the efficacy and process development of mHealth Interventions
mHealth for Patients with SCD
In a study conducted at St. Jude Children’s Research Hospital, 81 patients with SCD and who were 
treated with hydroxyurea (with variable adherence levels), received a text messaging application to 
improved hydroxyurea adherence.60 In this study, 97% of the population owned smart phones. 
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9
Messages were customizable for content, delivery time, frequency and duration of delivery. Children 
with HbSS or HbSβ0-thalassemia age <19 who had been on hydroxyurea therapy at MTD, and who 
had utilized the text messaging for at least 12 months were retrospectively analyzed. Significant 
increases in hematological indices (HbF, MCV, Hb), and significant reduction of hemolysis markers 
(absolute reticulocyte count, bilirubin, and lactate dehydrogenase) were seen. These findings are 
proof of principle in SCD that: 1) text messages are feasible when used with the intent of enhancing 
hydroxyurea adherence, and 2) hydroxyurea effect can be improved with the use of text messages, 
denoting improved adherence. A systematic review of mHealth applications for SCD has confirmed 
these findings but observed that the sample size of most studies was not large, and the studies were 
mostly observational or retrospective.38
These preliminary findings served as the basis to develop a more robust and multi-component 
mHealth intervention to increase hydroxyurea adherence, the InCharge Health app. Our approach 
was to build on the prior experience and develop an intervention that would not only increase 
memory by sending text messages to patients, but that would also affect the other determinants of 
hydroxyurea utilization, namely motivation, knowledge and self-efficacy. This mHealth application 
was developed using a patient-centered design, in which the users (patients) input was obtained 
through an iterative process that started with a design-thinking session with adult patients and the 
investigators. Following the design-thinking session, 100 adolescents and adults with SCD were 
surveyed and 20 participated in semi-structured interviews for their interest and desire in using an 
mHealth application to help with hydroxyurea use. An app developer (Drawn, LLC) was hired to 
develop the app. Data summarizing hydroxyurea barriers, facilitators, and patients’ preferences, 
including the results of the surveys and interviews, were analyzed in partnership with Drawn. Drawn 
developed the app prototype that was further refined through focus groups in 17 patients in 
Memphis, TN and 10 more in Chicago, IL (Appendix B) where the app was tested in patients’ 
phones. Following the positive feedback of focus groups, Drawn finalized the app prototype adding 
features suggested by the patients (e.g., partner to remind of hydroxyurea doses, switch off during 
admissions), which is now ready to be used in this study (Appendix C). 
mHealth for Providers of Patients with SCD
An earlier version of the HU Toolbox app, called SCD Toolbox, was built based on a collaborative 
effort by the Community Care of North Carolina (CCNC). Beginning in 2016, CCNC led efforts to 
develop primary care physician (PCP) directed guidelines, based on the new NHLBI guidelines for 
SCD. Surveys of 53 PCPs around NC found 73% were comfortable with the number of patients they 
had in their clinics; however, the majority did not communicate with a SCD specialist (67%) and 
were not aware of the 2014 NHLBI guidelines (66%). Additionally, the majority (76%) did express 
interest in having the guidelines provided to them and most (51%) were interested in accessing the 
guidelines via a mobile app.61 
Due to need to provide PCP specific guidelines and algorithms for patients with SCD, stakeholders 
were brought together through CCNC. Stakeholders included pediatrician, internists, and 
hematologists from all academic centers in NC and members of CCNC. Guidelines were grouped by 
age for pediatric patients and by history, exam, and labs for adult patients. In early 2018, the newly 
developed SCD Toolbox was then provided to PCPs in NC via a paper, web-based link, and mobile 
app. Although dissemination efforts (through flyers, talks, and word of mouth) were only focused to 
NC, the SCD Toolbox has been downloaded over 1,000 times, in four countries (USA, Canada, 
China, and Brazil), and it is currently in use by dozens of providers in NC. Feedback for the use of 
the SCD Toolbox by PCPs is currently being assessed via follow up surveys and qualitative 
interviews. Based on feedback by the Care Redesign co-investigators, additional revisions were 
made to the SCD Toolbox. 
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10
RE-AIM as the evaluation framework for mHealth intervention impact
Key considerations to begin implementing mHealth for hydroxyurea utilization include recruitment in 
diverse care settings and estimating the reach, effectiveness, adoption, implementation, and 
maintenance (RE-AIM) of the intervention, elements that comprise the RE-AIM framework.54 RE-AIM 
is used in many areas of clinical investigation,62-64 and is a useful framework to evaluate the utility of 
mHealth to foster hydroxyurea utilization and to broaden the applicability of this intervention. RE-AIM 
will be used in this study to evaluate the overall robustness of interventions at achieving patient 
adherence to hydroxyurea in real-world clinical settings. Secondary objectives of the study include 
additional clinical outcomes and reach, adoption, implementation, and maintenance to better 
understand the context for implementation to facilitate the spread of the interventions. The measures 
of RE-AIM are outlined in Tables 5 and 6 and address the secondary implementation aims of this 
proposal.
In summary, we are conducting a pragmatic trial,65 which informs a clinical or policy decision by 
providing evidence for adoption of the intervention into real-world clinical practice as opposed to an 
explanatory trial, which confirms a physiological or clinical hypothesis. The requirements for 
pragmatism were loosened substantially in PRECIS-2,66 and a pragmatic extension to the 
CONSORT statement has been proposed.67 We will use the PRECIS-2 as outlined by Ford and 
Norrie.68 
1.2
Primary Hypotheses
Among individuals with SCD ages 15 to 45 years who initiate or already receive hydroxyurea 
therapy, adherence, as measured by proportion of days covered (PDC), will be increased by 20% at 
24 weeks after receiving the InCharge Health app intervention, compared to their hydroxyurea 
adherence measured at baseline before the InCharge Health app is initiated. This analysis is a 
pre/post analysis of individuals receiving the patient intervention. PDC is a proxy measure of 
adherence and the metric used by CMS as the process measure of adherence.69 It best reflects the 
“real world” setting, as opposed to the use of electronic bottles (e.g., MEMS CAP), or video-recorded 
daily dose ingestion (i.e., directly-observed adherence measure). A 20% increase in PDC is a 
clinically meaningful change, because it represents an increment of approximately 1.5 additional 
days of hydroxyurea use in a week’s period and is associated with improved clinical outcomes.60  
Twenty-four week (6 months) is the interval were an increase in hydroxyurea adherence promote 
changes in clinical and laboratorial outcomes, as it takes an average of 4 to 6 months to observe full 
hydroxyurea effects. Furthermore, our estimated increase of 20% is conservative and based on the 
increase in prior studies that used text message to increase hydroxyurea adherence that observed 
adherence increases as high as 60%.70
1.3
Aims and Objectives [SPIRIT 7, StaRI 5]
Overall objective of proposed research
We propose to overcome the barriers to hydroxyurea utilization by using a two-level mHealth 
intervention: the InCharge Health app for patients, and the HU Toolbox app for providers. While 
acknowledging the multi-factorial barriers to hydroxyurea utilization, our approach will address the 
main barriers affecting hydroxyurea adoption and use among patients, while focusing on improving 
prescribing practices among providers who prescribe this treatment. This multi-prong approach will 
allow us to demonstrate the clinical effect of mHealth intervention to improve adherence among 
patients, while addressing and evaluating other barriers to optimal care among providers. Our 
findings will enhance subsequent implementation of mHealth into diverse settings and populations, 
as the participating sites are substantially different in geographical, setting (e.g., urban, suburban, 
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11
and rural) and population characteristics. The study will provide data on the efficacy of integrating 
mHealth into clinical care, its clinical influence, and evaluate how well this strategy is accepted, 
adopted, and sustained in diverse clinical settings.
Aim 1. Improve Patient Adherence to Hydroxyurea: Addressing Memory, Motivation and 
Knowledge Barriers to Hydroxyurea Use. Primary hypothesis: We hypothesize that among 
adolescents and adults with SCD, the adherence to hydroxyurea, as measured by proportion of daily 
coverage (PDC), will increase by least 20% after 24 weeks of use of the InCharge Health app, 
compared to their hydroxyurea adherence measured at baseline. 
Sub-Aim 1.a. To examine and assess both patient engagement and behaviors related to use 
of the InCharge Health app, we will evaluate consistent use of the app among enrolled 
patients, patient satisfaction, and continued use of the app beyond the study 
period.  Specifically, we will assess:
1) InCharge Health reach (proportion of patients approached and enrolled in the study 
among all patients who receive treatment with hydroxyurea at each site) 
2) InCharge Health adoption (proportion of patients who initiate the use of the app but then 
later discontinued or completed the study at each site) 
3) InCharge Health implementation (consistency with which sites are able to implement the 
app as planned)
4) InCharge Health maintenance (extent to which program leaders express a desire or intent 
to continue providing the app to patients at the conclusion of the study) 
Sub-Aim 1.b. To examine the clinical influence of the use of InCharge Health app on 
“adequate” PDC (proportion of patients with PDC>80%), patients’ clinical outcomes (e.g., 
differences in hematologic indices, acute healthcare utilization), perceived health literacy, 
health-related quality of life, and perceived self-efficacy between baseline and 24 weeks 
among adolescents and adults with SCD after receiving the InCharge Health app. Patients 
will also be stratified into their low and high intervention uptake groups and compared 
between baseline and 24 weeks on all the clinical outcomes listed above.
Aim 2. Improve Provider Hydroxyurea Awareness, Prescribing and Monitoring Behaviors.
We will examine whether, among providers using the HU Toolbox app, reported awareness of 
hydroxyurea benefits and risks, accurate prescribing of hydroxyurea, and perceived self-efficacy to 
correctly administer hydroxyurea therapy will increase between baseline and after 9 months of using 
the HU Toolbox app.
Sub-Aim 2.a. To examine and assess both provider engagement and behaviors related to 
use of the HU Toolbox, we will evaluate consistent use of the app among enrolled providers, 
providers’ satisfaction, and continued use of the app beyond the study. 
Specifically, we will assess: 
1) HU Toolbox reach (proportion of eligible providers approached and enrolled in the study 
among all providers at each site)
2) HU Toolbox adoption (characteristics of the clinics that choose to adopt the app) 
3) HU Toolbox implementation (consistency with which sites are able to implement the app 
as planned) 
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13
Figure 5. Study time periods and staggering of sites using the stepped-wedge design.
•
T0 baseline evaluation. Baseline measures and observations of provider prescribing 
practices and overall patient adherence to hydroxyurea. Total duration of this phase is 1 
month. 
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14
•
T1 enrollment of providers. This phase includes enrolling providers and introducing the 
provider toolkit with education support; training staff on using the HU Toolbox app; and 
measuring (with chart audit) prescribing practices and population patient adherence. All 
providers will be enrolled within 60 days. Total duration of this phase is 2 months.
•
T2 enrollment of patients. This phase includes enrolling patients and introducing the patient 
InCharge Health app with education support. All patients will be enrolled over a period of 6 
months. Total duration of this phase is 6 months.
•
T3 data collection. During this phase, all enrolled patient and provider participants are 
followed as active study participants. Following enrollment, each provider will be followed for 
9 months and each patient for 24 weeks. Each participant receives study evaluations as 
outlined on the schedule of evaluations (Tables 3 and 4). Total duration of this phase is 6 
months for patients and 9 months for providers.
•
Post-intervention data collection. This phase reflects the sustainability of the interventions. 
We will continue to provide technical support for both patient and the provider apps and 
measure continued utilization of the apps and the long-term adherence to hydroxyurea. Total 
duration of this phase is 3 months.
By the end of the study, all sites will have received the intervention. This design offers a number of 
opportunities for data analysis, particularly for modeling the effect of time on the effectiveness of the 
interventions, and may conserve resources, as not all sites will be actively enrolling and testing 
participants at a time. It would not be possible to initiate the intervention at all sites simultaneously 
given existing resources. In addition, implementing the interventions at the first two sites will allow us 
to determine any challenges and adapt to ensure increased uptake and implementation for the 
following four sites. The order of site participation is as follows: 
1.
Sites 1 and 2: St. Jude and Duke University
2.
Sites 3, 4, and 5: Augusta University, Mount Sinai and University of Illinois at Chicago
3.
Sites 6, 7, and 8: MUSC, UCSF and Washington University 
To promote uptake of both the patient and provider mHealth apps into practice, we will employ 
multiple implementation strategies. Sites will be provided with a list of discrete strategies (Table 1), 
based on the most recent compilation in the field.71 Each principal investigator (PI) and his/her team 
will be able to select the strategy(ies) that best fit their context, providing a small rationale for 
selection based on the needs assessment, literature review, and/or feasibility of the strategy. 
Because we are also using a stepped-wedge design and not all sites will have access to the 
interventions at the same time, information and experience from previous sites will help inform the 
implementation of the next sites and allow sharing of local knowledge. As sites implement their 
strategies, centralized technical assistance, provided by St. Jude for InCharge Health and Duke for 
HU Toolbox, will help identify potential barriers to the use of the apps and ensure a high level of 
fidelity in their implementation. All new strategies that are introduced at each site will be collected 
and tracked. 
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15
Table 1. Example Implementation Strategies to Promote Uptake of the mHealth Apps 
(modified from Powell et al.71,72)
Plan Strategies
•
Conducted a local needs assessment
•
Assessed for readiness and identified barriers
o
Surveyed providers that will test the mHealth interventions and ensured their level of interest in 
testing the apps was high and that they recognized the need for an intervention for both patients 
and providers
o
Consulted with patients and providers regarding relevance and interest in having mHealth 
interventions to improve hydroxyurea use
•
Tailored strategies to overcome barriers and honored patients’ preferences
•
Built a coalition within the SCDIC that will implement the mHealth interventions
•
Identified the providers that will test/champion the mHealth applications
•
Obtained input from patients and providers regarding features of the mHealth interventions. Used this 
input to develop the apps
•
Beta tested both the patient and the provider apps for functionality 
Educate Strategies
•
Distribute educational materials (both through the apps and in person)
•
Conduct ongoing training
•
Conduct regular check-ins with patients and providers regarding app functionality
•
Sharing of local knowledge across sites
Quality Management Strategies
•
Audit and provide feedback
•
Deliver centralized technical assistance to identify implementation issues. 
o
St. Jude will provide technical assistance for InCharge Health; Duke for HU Toolbox.
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16
2. Methods: Participants, Interventions, and Outcomes
2.1
Study Setting [SPIRIT 9, StaRI 7-8]
Table 2 describes the setting of each participating site, including the total population, academic or 
community and urban or rural settings, and the type of health professionals comprising the provider 
staff. The context for the program is diverse and presents an opportunity to test mHealth in different 
settings, with not only geographical but also structural differences using the RE-AIM evaluation 
framework.
Table 2. Study Site Characteristics
Estimated Population
Number of Providers in 
the Practice Caring for 
SCD Patients
Site
City
Pediatric 
(15.0-
17.9yrs)
Adults
(18.0-
45.0yrs)
Type of 
Community
Setting
Academic
Setting
Physicians
Advanced 
Practitioners
St. Jude
St. Jude Children’s 
Research Hospital
Memphis
140
0
Urban
Yes
3
10
Methodist University 
Hospital
Memphis
0
350
Urban
No
2
1
Baptist Health Care
Memphis
0
100
Suburban
No
3
3
Duke University
Duke Adult Sickle Cell 
Clinic 
Durham
0
450
Suburban
Yes
5
4
Duke Pediatric Sickle 
Cell Clinic
Durham
80
0
Suburban
Yes
3
2
University of Illinois
UI Hospital & Health 
Sciences System,
Sickle Cell Center
Chicago
0
600
Urban
Yes
6
3
UI Hospital & Health 
Sciences System,
Pediatric Department
Chicago
20
0
Urban
Yes
2
1
OSF 
Healthcare/Children’s 
Hospital of Illinois
Peoria
9
20
Rural
Yes
2
1
Sinai Health System
Chicago
14
141
Urban
No
3 
3
Lawndale Christian 
Health Center 
Chicago
3
13
Urban
No
7
6
UCSF
UCSF Benioff 
Children's Hospital 
Oakland
Oakland
50
286
Urban
Yes
3
2
Mount Sinai
Mount Sinai Hospital
New York
15
175
Urban
Yes
1
2
Mount Sinai St. Lukes 
Hospital
New York
0
30
Urban
No
1
0
Washington University
St. Louis Children’s 
Hospital Pediatric
St. Louis
55
20
Urban
Yes
4
2
Barnes Jewish 
Hospital Hematology
St. Louis
0
300
Urban
Yes
4
2
Christian Hospital 
Northeast- 
Hematology
St. Louis
0
87
Suburban
Yes
2
2
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Estimated Population
Number of Providers in 
the Practice Caring for 
SCD Patients
Site
City
Pediatric 
(15.0-
17.9yrs)
Adults
(18.0-
45.0yrs)
Type of 
Community
Setting
Academic
Setting
Physicians
Advanced 
Practitioners
Augusta University
Augusta University 
Adult Center for Blood 
Disorders
Augusta
0
358
Urban
Academic
1
1
AU Pediatric 
Hem/Onc
Augusta
123
0
Urban
Academic
4
0
AU Macon Outreach 
Clinic
Macon
0
87
Rural
Community
1
1
AU Sylvester 
Outreach Clinic
Sylvester
0
137
Rural
Community
1
1
AU Savannah 
Outreach Clinic
Savannah
0
65
Urban
Community
1
1
MUSC
Adult sickle cell clinic
Charleston
0
520
Urban
Academic
11 (both 
physicians 
and APs)
Pediatric sickle cell 
clinic
Charleston
350
0
Urban 
Academic
6 (both 
physicians 
and APs)
2.2
Eligibility Criteria [SPIRIT 10, StaRI 8]
Eligibility criteria for patient participants
A total of 46 patients per site will be enrolled to allow for a 25% patient attrition (total of 368 patients 
enrolled in all 8 sites). Women and children will be included to the extent that they exist in the 
population being studied and meet eligibility criteria for study participation. In addition to 
parental/guardian consent, adolescent assent will be obtained from children 15–17 years old, or as 
determined by the Institutional Review Board (IRB). Because SCD disproportionally affects 
individuals of African descent in the United States, we expect enrollment to consist primarily of 
African American participants.
Inclusion criteria:
•
Age 15 years up to and including 45 years
•
Treated at or affiliated with one of the SCDIC sites
•
English speaking
•
Confirmed SCD diagnosis. An SCD diagnosis is defined as Hb fractionation test (e.g., high- 
performance liquid chromatography or another technique) that is diagnostic of one the 
following: Hb SS, Hb SC, Hb Sβ-thalassemia, Hb SO, Hb SD, Hb SG, Hb SE, or Hb SF.
•
Willing and cognitively able to give informed consent
•
Access to a cellular/mobile smart phone (either Android or iPhone are acceptable)
•
Hydroxyurea therapy:
o
Already receiving hydroxyurea therapy: defined as at least one prior prescription to 
hydroxyurea in the past 3 months and no plans to escalate the dose by more than 5 
mg/kg/day
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18
o
Initiating hydroxyurea therapy: defined as at least one prescription written at the time 
of study enrollment (the first prescription must be written on the same day as study 
enrollment)*
Exclusion criteria:
•
Current pregnancy
•
On a chronic transfusion program in which they receive more than 8 erythrocyte transfusions 
in a 12-month period. This exclusion is necessary, since transfusions will mask laboratorial 
changes and will contaminate clinical outcomes.
•
A red blood cell transfusion in the past 60 days
•
Currently using another phone application or an online-based tool (e-health tool) to increase 
hydroxyurea adherence
*Patients who initiate hydroxyurea on the same day of study enrollment will not contribute to the total 
of 46 patients target accrual for the site. A max of 30 patients who are initiating hydroxyurea can be 
enrolled per site.
Patients who become pregnant during the study will have discontinued hydroxyurea; therefore, we 
will withdraw these participants from the study. This does NOT apply to those who discontinue 
hydroxyurea during the study for other reasons as patients may decide to restart hydroxyurea.
Eligibility for provider participants
Inclusion criteria:
•
Physician or advanced practice provider (NP or PA) who care for at least one patient with 
SCD for an anticipated minimum of 12 months from study enrollment
•
Willing to provide informed consent
•
Access to a cellular/mobile smart phone (either Android or iOS) or access to a computer with 
internet connectivity (a version of the HU Toolbox app can be accessed via internet on any 
device)
Exclusion criteria:
•
Currently using another phone application to increase hydroxyurea adherence for patients 
with SCD in his/her practice
2.3
Interventions and Enrollment Strategy [SPIRIT 11a, StaRI 9-10]
Patient-level intervention
We will approach patient participants who meet inclusion and exclusion criteria. Potential 
participants will be approached while they are not experiencing an acute complication of the 
disease. The setting of patient enrollment may be the clinic or a venue outside the hospital, but with 
adequate patient privacy and with IRB knowledge and approval.
A member of the research team (e.g., nurse assistant, research assistant, research coordinator) will 
verify that the participant meets study eligibility and will approach the subjects. Alternatively, clinic 
staff will be trained to also approach eligible participants, and will be encouraged to do so, if clinic 
flow allows. Eligible participants will be asked to sign the informed consent form (or we will ask the 
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19
legally authorized caregiver, if a minor is involved). Once informed consent is signed, a clinic staff or 
a member of the study team (a nurse, research assistant, or other trained investigator) will help 
install the InCharge Health app on the patient’s mobile phone. The study team will provide input on 
the settings into the app, explain how to use the app, and answer any questions. Before participants 
leave the clinic, patients will be asked to demonstrate their knowledge and ability to use the app. We 
will provide supporting material to reinforce what was discussed. In addition, a number to call with 
questions regarding the app or the study in general will be provided. 
All features of the InCharge Health app were developed with patient input through surveys, 
interviews, and focus groups. This iterative process addressed all performance objectives identified 
during the process of developing the logic model for increased hydroxyurea utilization (Figure 5). To 
address all behavior determinants and process objectives, four main features were incorporated into 
the app: daily medication reminders, motivational tools, disease education, and communication tools 
(see Appendix C for more details about the app). The app data will be stored under the study 
provider’s unique ID (study ID number), and the InCharge Health app will not collect any protected 
health information (PHI).
Provider-level intervention
A member of the study team will ask provider participants who care for patients with SCD to sign an 
informed consent form. All providers within each practice will be approached and invited to 
participate. All clinics will have each provider register within the app to allow provider-specific data. 
The app data will be stored under the study provider’s unique ID (study ID number), and the HU 
Toolbox app will not collect any PHI. The following app-related data will be collected: features of the 
app used, frequency of each feature used, and number of times specialists are contacted via the 
app. We will encourage providers to use the app for all of their SCD patients for at least 9 months, 
however, they will have the option to keep it in their phones (or computers) for an indefinite period. 
All providers will be given an email address and phone number to contact should any technical 
problems occur related to the use of the app. Data related to technical problems related to the app 
will also be tracked evaluate its functionality.
The HU Toolbox app contains NHLBI guidelines adapted for pediatrics (guidelines/ 
recommendations separated by age) and for adults (guidelines/recommendations separated by 
organ system, laboratory, or physical exam finding) providers (Appendix C). It was developed with 
provider input and contains algorithms guiding the clinician on how to prescribe hydroxyurea and 
monitor its effect. The HU Toolbox also guides clinicians on how to recognize hydroxyurea side 
effects and how to manage them. Finally, a contact list of local SCD specialists and important 
contacts is included, so providers can contact SCD experts and expect an answer in 24 hours or 
less.
The InCharge Health and HU Toolbox apps will both comply with Health Insurance Portability and 
Accountability Act (HIPAA) requirements, because: (1) participants will electively enroll into the 
program and sign informed consent; (2) messages will contain no PHI; (3) messages will be 
delivered to private cellular telephones, (4) no personal information will be shared with third parties, 
and (5) usability data stored in secure sites (e.g., firebase or Mixed Tables software) and will contain 
no PHI, because all participants’ data will be stored under a study number assigned upon study 
enrollment.
Figure 6 shows the introduction and investigation of each intervention sequentially. Four possible 
intervention combinations will be evaluated and compared: provider and patient use the intervention, 
neither provider nor patient uses the intervention, provider uses the intervention but patient does not, 
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20
and patient uses the intervention but provider does not. Comparisons within and across groups will 
be conducted as shown.
Figure 6. Study groups comparisons according to each aim.
2.4
Data Collection, Measures, and Outcomes [SPIRIT 12, 18a-18b, StaRI 11-13]
Patient participants will be enrolled while they are not experiencing an acute event. During 
enrollment, the patient participant will receive the instructions about app use and undergo a baseline 
evaluation. Patient participants will then return every 12 weeks for study visits, where study-related 
procedures will be conducted (Table 3). The visit window for the follow-up visits is +/- 4 weeks. 
Follow-up visits that occur outside the visit window will be considered a protocol deviation.
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21
Table 3. Schedule of Evaluations for Patient Participants
Measures
Definition
Pre-Baseline
(Retrospectively 
Collected up to 
12 months prior 
to baseline)
Baseline 
Week 
12 
Week 
24 
(Study 
Exit)
Week 
36 
(Post-
Study) 
Socio-
demographic
Age, sex, race, ethnicity, marital 
status, educational attainment, 
health insurance type, income, 
occupation
x
Informed 
consent
x
Aim 1. Patient Adherence to hydroxyurea
Aim 2b. Combined effects of the patient and provider mHealth Interventions
Proportion of daily coverage (PDC)
x
x
x
x
x
Hydroxyurea 
adherence
App daily adherence statistics and 
7-day recall measure using the Brief 
Medication Questionnaire 
(BMQ)[65]
x
x
Sub-Aim 1b. Clinical Influence of the InCharge Health app
Aim 2b. Combined effects of the patient and provider mHealth Interventions
Date hydroxyurea initiated
x
MTD dose (mg/kg/day) and date 
reached
x
Current dose (mg/kg/day and 
mg/day)
x
x
x
x
Hydroxyurea 
effect
Biomarkers of hydroxyurea effect 
(HbF%, Hb, MCV, ANC, ARC, 
indirect bilirubin, LDH)
x
x
x
x
x
Health care 
utilization
Date and discharge diagnosis of ED 
visits, acute care/infusion visit 
hospitalizations
x
x
x
x
x
PROMIS self-efficacy for medication 
short form
x
x
Self-efficacy 
and health 
literacy
Perceived Health literacy[66]
x
x
Health-related 
quality of life 
and pain report
ASCQ-Me Pain Impact,
ASCQ-Me Pain Episode Frequency 
and Severity, PROMIS Pain Quality
x
x
Sub-Aim 1a. Engagement of patients related to the use of InCharge Health app
Implementation 
Measures
See RE-AIM tables for a full 
description of measures
x
mHealth 
satisfaction
Perceived usability and acceptability 
of mHealth intervention (MARS 
scale)[67]
x
x
Aim 3. Evaluation of facilitators and barriers to implementation of the mHealth app
Barriers and 
facilitators to 
implementation
Qualitative interviews†
x
x
Notes: MTD: maximum tolerated dose, HbF: fetal hemoglobin, Hb: hemoglobin, MCV: mean corpuscular volume, ANC: 
absolute neutrophil count, ARC: absolute reticulocyte count, LDH: lactate dehydrogenase, ED: emergency department, 
ASCQ-Me: adult sickle cell quality of life measurement information system. MARS: mobile app rating scale. †Conducted at 
end of implementation at each study site.
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22
Provider participants (i.e., physicians and advanced practice providers who care for individuals with 
SCD) will consent to use the HU Toolbox app and will use this tool for at least 9 months. During 
these 9 months, providers will provide feedback on the HU Toolbox app’s clinical usefulness and its 
usability and impact on clinical care provided. (Table 4). The visit window for the follow-up visits is 
+/- 4 weeks. Follow-up visits that occur outside the visit window will be considered a protocol 
deviation.
Table 4. Schedule of Evaluations for Provider Participants
Measure
Definition
Baseline
Week 36 
(Study Exit)
Week 48
(Post-Study)
Socio-
demographics
Age, sex, race, ethnicity, type of 
professional (physician, nurse 
practitioner, physician assistant), 
years in practice
x
Informed consent
x
Aim 2. Improve provider hydroxyurea awareness, prescribing and monitoring behaviors
Self-efficacy and 
hydroxyurea 
knowledge
Perceived confidence in prescribing 
hydroxyurea to patients with SCD, 
including correct daily dosing
x
x
Sub-Aim 2.a. Engagement of providers related to the use of the HU Toolbox app
See RE-AIM tables for a full 
description of measures
x
Implementation
and mHealth
satisfaction
Perceived usability and acceptability 
of mHealth intervention (MARS 
scale)[67].
x
Total number of patients with SCD
x
x
x
Number of patients eligible to 
receive hydroxyurea therapy at 
provider participant’s site*
x
x
x
Hydroxyurea 
prescribing 
practices (clinic 
level measures)
Number of hydroxyurea-eligible 
patients who are prescribed 
hydroxyurea (all sickle genotypes)*
x
x
x
Aim 3. Evaluation of facilitators and barriers to implementation of the mHealth app
Barriers and 
facilitators to 
implementation
Qualitative interviews†
x
x
Notes: *Hydroxyurea eligibility will follow the 2014 NHLBI guidelines as follows: hydroxyurea should be offered to all 
children with HbSS/HbSβ0-thalassemia age ≥ 9 months (regardless of clinical severity) and prescribed to all symptomatic 
adults with HbSS/HbSβ0-thalassemia, i.e., >3 episodes of severe vaso-occlusion in the preceding 9 months.8 # 
hydroxyurea indication according to the NHLBI guidelines. †Conducted at end of implementation at each study site.
Tables 5 and 6 summarize how the RE-AIM framework will be used to evaluate the effectiveness 
and implementation of the patient and provider mHealth applications. Timeline for measurement can 
be found in Tables 3 and 4 schedules of evaluations.
Table 5. Patient App (InCharge Health): RE-AIM Evaluation Measures
Domains
Measures
Data Sources
Data Collection 
Instrument Mapping
Reach
Socio-demographic 
characteristics of patients at 
each site
Clinic administrative data
Clinic data collection forms
Qualitative interviews 
with patients 
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23
Domains
Measures
Data Sources
Data Collection 
Instrument Mapping
Proportion and 
representativeness of patients 
screened for the study 
(numerator) among all patients 
who receive hydroxyurea 
treatment (denominator) at each 
site
Proportion and 
representativeness of patients 
eligible for the study 
(numerator) among all patients 
who receive hydroxyurea 
treatment (denominator) at each 
site
Proportion and 
representativeness of patients 
participating/enrolled in the 
study (numerator) among all 
patients who receive 
hydroxyurea treatment and 
were eligible (denominator) at 
each site
Screening log
Qualitative interviews
Patient Reported 
Outcome Form, all of 
Demographic Section
Clinic Data Collection 
Form, all of Sections B 
(patient population), C 
(patients who meet NHLBI 
criteria to receive HU), G 
(Patients who are 
prescribed HU), E (case 
mix), 
Case/Screening log 
Effectiveness
Primary outcome
>20% improvement in refill for 
hydroxyurea among those 
receiving the intervention
Secondary outcomes
Change in Quality of life, self-
efficacy, perceived health 
literacy
Change in Percentage of 
patients with ED visits, 
hospitalizations since last study 
visit
Change in biomarkers of 
hydroxyurea effect (MCV, ANC, 
ARC, indirect bilirubin, HbF, Hb, 
LDH)
Prescription drug claims
Surveys (ASCQ-Me, PROMIS, 
Perceived Health Literacy, 
Electronic health record
Qualitative interviews
Examine hydroxyurea 
Rx data from pharmacy 
records
Qualitative interviews 
with patients
Patient Reported 
Outcome Form, all of 
Your Pain History, 
Medication Self-
Efficacy, Hydroxyurea 
History, Hydroxyurea 
adherence 
Patient Medical Record 
Abstraction Form (acute 
care visits)
Patient Lab Reporting 
Form (biomarkers)
Adoption 
Clinic-Level
Provider-Level
Proportion and description of 
clinics in each site agreeing to 
support InCharge Health
Proportion and description of 
providers in each clinic agreeing 
to support InCharge Health 
Institutional data to describe 
clinics (e.g., size, case mix, 
yrs. in service, regional socio-
demographics of SCD 
patients)
Qualitative interviews 
with administrators 
Clinic Data Collection 
Form: Sections A (clinic 
name and address), B 
(patient pop), E (case 
mix), F (provider and clinic 
setting description)
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Domains
Measures
Data Sources
Data Collection 
Instrument Mapping
Implementation
Consistency with which sites 
are able to implement the app 
as planned 
Qualitative assessment of any 
adaptations or enhancement to 
recruitment strategies needed 
to meet enrollment by clinic, by 
site
Assess adaptation of training 
needed to improve InCharge 
Health implementation at each 
clinic
Percentage, number and 
representativeness of patients 
who used InCharge Health app 
during the study period (in the 
entire practice) (low, medium-
low, medium-high, or high use – 
see pg. 33)
Proportion, number, and 
characteristics of patients who 
complete the study among 
those who initiate the use of the 
app but then later discontinue at 
each site, and by provider
Percentage and characteristics 
of patients who reported 
satisfaction with the InCharge 
Health app
Clinic/provider assessment of 
perceptions of the InCharge 
Health app for further scale-up 
or sustainability – ease of use, 
preferred features, etc.
InCharge Health app data use 
and (e.g., daily clicks) and 
percentage of features of the 
InCharge Health app used on 
a daily basis (e.g., pain score, 
adherence documentation).
Survey (MARS scale)
Qualitative interviews 
with administrators and 
patients 
App usage statistics (not 
captured in CRF – this 
data will be transferred 
directly to RTI)
Clinic Data Collection 
Form, all of Sections B 
(patient population), C 
(patients who meet NHLBI 
criteria to receive HU), D 
(Patients who meet 
criteria and prescribed 
HU), E (case mix) 
Patient Reported 
Outcome Form, all of 
Engagement with the 
InCharge Health app 
Section
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25
Domains
Measures
Data Sources
Data Collection 
Instrument Mapping
Maintenance
Extent to which program 
leaders express a desire or 
intent to continue providing the 
app with patients at the 
conclusion of the research
Percentage of patients who 
continue to use the app beyond 
the study period and their 
representativeness
InCharge Health app data use 
3 months after end of study
Clinic data collection forms
Qualitative interviews
Pharmacy claims data
Qualitative interviews 
with patients 
 
App usage statistics (not 
captured in CRF – this 
data will be transferred 
directly to RTI)
Patient Reported 
Outcome Form: Section 
Engagement with the 
InCharge Health app, 4.f 
(plan to continue to use 
app)
Patient Medical 
Abstraction Form 
Question #16-#19 (HU 
refills) 
Clinic Data Collection 
Form, all of Sections B 
(patient population), C 
(patients who meet NHLBI 
criteria to receive HU), D 
(Patients who meet 
criteria and prescribed 
HU), E (case mix), 
Table 6. Provider App (HU Toolbox): RE-AIM Evaluation Measures
Domains
Measures
Data Sources
Data Collection 
Instrument Mapping
Adoption – 
Provider 
Characteristics of providers at each site 
(e.g., specialty, yrs. in practice, socio-
demographics, level of expertise) 
Proportion and representativeness of 
eligible providers approached in the 
study (numerator) among all providers 
(denominator) 
Proportion and representativeness of 
enrolled providers in the study 
(numerator) among all eligible providers 
(denominator) at each site  
Survey
Clinic population 
demographics and 
treatment data, study 
database (CRFs)
Qualitative interviews 
with providers 
Provider Data Collection 
Form: Demographics 
Section
Clinic Data Collection 
Form: Section F (provider 
and clinic setting 
description)
Case/Screening log
Effectiveness
(see Table 4 for 
a complete 
listing of 
measures) 
Number and proportion of providers 
demonstrating improved knowledge and 
self-efficacy in hydroxyurea 
administration
Percentage of patients who were 
prescribed hydroxyurea per provider
Self-efficacy survey 
Chart audit
Provider Data Collection 
Form: HU self-efficacy, 
Experiences Providing 
Care to Patients with SCD 
Patient Medical 
Abstraction Form: HU 
refills
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26
Domains
Measures
Data Sources
Data Collection 
Instrument Mapping
Adoption – 
Clinic 
Proportion and representativeness of 
clinics that agree to support the HU 
Toolbox
HU Toolbox
Institutional data to 
describe clinics (e.g., 
size, case mix, yrs. in 
service, regional 
socio-demographics 
of SCD patients)
Qualitative interviews 
with administrators 
Clinic Data Collection 
Form: Sections A (clinic 
name and address), B 
(patient pop), E (case 
mix), F (provider and clinic 
setting description)
Implementation 
Consistency with which sites are able to 
implement the use of the HU Toolbox 
app as planned
Engagement with the app: Percentage 
of participating providers that used the 
provider HU Toolbox app (in the entire 
practice) (low vs high use – see pg. 34)
Percentage of providers who reported 
satisfaction with HU Toolbox app
Percentage of patients whose provider 
used the HU Toolbox (per practice site)
HU Toolbox app data 
(e.g., monthly clicks) 
and features of the 
HU Toolbox app used 
(e.g., monthly 
consultations with 
SCD experts)
Provider Survey 
HU Toolbox app data
Qualitative interviews 
with administrators and 
providers 
App usage statistics (not 
captured in CRF – this 
data will be transferred 
directly to RTI)
Provider Data Collection 
Form: MARS Scale (HU 
Toolbox App Quality 
Ratings)
Clinic Data Collection 
Form: Sections A (clinic 
name and address), B 
(patient pop), E (case 
mix), F (provider and clinic 
setting description)
App usage statistics (not 
captured in CRF – this 
data will be transferred 
directly to RTI)
Maintenance/
Sustainability
Extent to which program leaders 
express a desire or intent to offer or 
encourage the use of the HU Toolbox 
app by their clinical providers at the 
conclusion of the research
Percentage of providers who continue to 
use the provider app beyond the study 
period, and representativeness 
Percentage of providers who continue to 
prescribe hydroxyurea to their patients 
HU Toolbox app data 
3 months after end of 
the study
Clinic administrative 
data
Qualitative interviews 
with providers
App usage statistics (not 
captured in CRF – this 
data will be transferred 
directly to RTI)
Clinic Level Form: 
Section D (patients who 
are prescribed HU) 
Mixed-method evaluation of the facilitators and barriers in adopting and implementing the 
mHealth interventions 
Sufficient understanding of the contextual factors in implementation of mHealth interventions is 
critical to ensuring future scale-up and translation of study findings to other institutional settings 
outside the SCDIC.76 As such, for Aim 3, we will elaborate on the RE-AIM quantitative findings with 
qualitative inquiry at study midpoint and end of implementation at each study site to continuously 
identify and address barriers as the study progresses and provide an understanding of the 
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27
contextual factors at each site that may have influenced how and why results of individual RE-AIM 
domains occurred and variations in implementation across the sites. Qualitative methods can also 
help to understand disparate patterns across RE-AIM domains (e.g., high reach but low rates of 
adoption).77 We will use the Normalization Process Theory (NPT) to systematically assess barriers 
and facilitators that affect RE-AIM domains.78  NPT comprises four core constructs that are 
concerned with identifying and understanding the ways that people make sense of the work of 
implementing and integrating a new technology (coherence); how they engage with it (cognitive 
participation); enact it (collective action); and appraise its effects (reflexive monitoring). Each NPT 
construct has four specific components (e.g., coherence – differentiation, cognitive participation – 
initiation, collective action –relational integration, reflexive monitoring – systematization) that can be 
used to develop a detailed understanding of the data and the factors that influence the process by 
which mHealth interventions become integrated into practice. What we learn about barriers and 
facilitators under Aim 3 could also be used to develop specific implementation strategies for use in 
other settings.  
Data would be collected and analyzed concurrently using a quantitative+qualitative approach, where 
qualitative data will be secondary to the quantitative assessment.79 We will plan key-informant 
interviews (60–90 minutes) with multiple stakeholders at the patient-, provider-, and clinic-level.  
Questions will be developed using the broader RE-AIM domains and NPT constructs as a guide. For 
example, to further understand adoption, providers may be asked to describe what they thought 
when they first heard about or used the SCD HU Toolbox app. We will purposively sample and 
interview patients and providers (physicians, NPs, PAs) from each site according to mHealth 
intervention adoption (low uptake vs high uptake), and plan interviews with clinic administrators to 
gain a clinic-level perspective on factors that influenced implementation. Example topic areas are 
provided in Table 7.  
Table 7. Sample Qualitative Evaluations
RE-AIM Domain
Potential Interview Topics
Example
Reach
•
Why do patients/providers choose to participate in 
the app intervention?
• Interviews with patients/providers at study 
midpoint to understand contributors to 
intervention participation
Effectiveness
•
Do providers find the effectiveness results 
meaningful? If unanticipated negative results are 
found, why are they observed?
• Interviews with providers at end of 
implementation
Adoption
•
Why do different clinics—and providers within 
those clinics—use the app intervention or not? 
•
What factors contribute to patients’ initial app use 
but later discontinuing or completing the study?
• Interviews with patients/providers at study 
midpoint and end of implementation
Implementation
•
What strategies influence implementation of the 
apps?
•
How are the app interventions modified or adapted 
over time?
• Interviews with patients/providers at study 
midpoint and end of implementation
Maintenance
•
What existing infrastructure could support the 
ongoing use of the app interventions? 
• Interviews with providers at end of 
implementation 
2.5
Participant Timeline [SPIRIT 13]
Please see Tables 3 and 4.
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28
2.6
Sample Size [SPIRIT 14, StaRI 14]
The linear mixed model proposed by Hussey and Hughes80 was adapted for the Care Redesign 
Study. If Yijt is PDC in subject i at site j provider sites and time t, then the modified model is
Yjti = μ +  αj +  Xjtθ +  αjXjtθ +  νji +  εjti
where  is average baseline hydroxyurea adherence,  is a random site effect; Xjt is a treatment 
μ
αj
indicator (=1 if intervention is present in site j at time t and =0 prior to implementing the intervention), 
θ is the fixed treatment effect, 
 is a random subject effect and 
  is an error term. The interaction 
νji
εjti
term 
 captures the heterogeneity in treatment response across sites. It is assumed that 
 
αjXjtθ
αj,νji
and 
 are normally distributed with mean zero and zero correlation, given the model fixed effects.  
εjti
Hussey and Hughes did not include the random subject effect but did include a fixed time effect in 
their model. Their model was intended for a study that involved repeated cross-sectional sampling of 
the study population. The observations at different time points were assumed to be independent. In 
Care Redesign Study, we will instead follow a cohort longitudinally. The random site effect was 
added to account for the correlations between repeated observations of the same subjects.   
The time effect in the Hussy and Hughes model is not included in the model for they Care Redesign 
Study. The time effect accounted for background variation in adherence during a study. Accounting 
for this variation made sense in the context of repeated cross-sectional sampling. The Care 
Redesign Study involves repeated observations on the same subjects. The treatment effect is 
measured by within-subject changes in adherence, rather than differences in mean adherence 
between samples taken at different times. The interpretation of the time effect in the context of 
within-subject changes is less clear than it is with repeated cross-sectional sampling. In fact, with the 
planned study design and the model above, it is difficult or impossible to separate the time effect 
from the treatment effect.          
A simulation study was conducted to evaluate the statistical power of a study with 8 study sites and 
46 subjects per site. The first step in developing the simulations was to model the distribution of 
baseline values of PDC. Candrilli,17 reported a mean of 60 and standard deviation of 30 for the MPR, 
with is a slightly more liberal measure of hydroxyurea adherence than PDC. Both MPR and PDC are 
constrained to be >0 and <100. These constraints, combined with the mean and standard deviation 
reported by Candrilli, point to a left skewed distribution for baseline values. Therefore, baseline PDC 
was modeled as PDC=100*X where X follows a beta distribution. The beta distribution has two 
parameters, A and B, mean A/(A+B) and variance 
. If A=1 and B=0.6667, 
AB [(A + B)2(A + B + 1)]
then the distribution has mean 0.6 and standard deviation 0.3; i.e., PDC has mean 60 and standard 
deviation 30.  
Site-to-site variation in the distribution of baseline values was expected. Therefore, a site-specific 
random variable r, drawn from a normal distribution with mean 0 and standard deviation 0.2, was 
added to A and B, converting the parameters to A+r and B+r. In a simulation study, 95% of the 
resulting site means were between 56 and 69. The overall baseline mean remained close to 60.  
Several additional assumptions were made in conducting the simulations:
1) The subjects at each site will be recruited at a constant rate over a six-month interval.
2) The 8 sites will be divided into three groups of size 2, 3 and 3. Recruitment in the second 
group will start 6 months after recruitment begins in the first group and recruitment in the 
third group will start 6 months later. 
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29
3) Adherence was expected to increase 20% as a result of the intervention so θ was set at 12% 
(i.e. at 20% of 60%).
4) A two-sided p-value < 0.05 is assumed for rejecting the null hypothesis.
5) Finally, the simulation assumes that about 25% of the participants will have no data at the 
24-week follow-up, a conservative estimate given the interactive intervention.
To account for variation in treatment response among sites, a site-specific random variable, drawn 
from a normal distribution with mean zero, was added to the expected treatment difference of 12%.  
Missing values at the 24-week follow-up point were generated by assigning a uniform random 
variable U to each post-treatment observation and deleting the observations for which U<0.25. To 
account for the residual errors that are represented by 
 in the linear model, a separate random 
εjti
variable, drawn from a normal distribution with mean zero, was added to each pre-treatment and 
post-treatment observation. Finally, some of the values for PDC that were generated in the 
simulations were <0% or >100%. Values <0% were set to 0% while those >100% were set to 100%.      
With these assumptions, 368 total subjects will be recruited and an average of 276 subjects will 
provide post-treatment values. Given the design of the study, site-to-site variation in the treatment 
response and residual errors in the pre- and post-treatment measurements will both contribute to the 
noise against which the treatment response will be measured. The power of the study was evaluated 
by varying the standard deviations for the error term and site-to-site variation in treatment response 
to determine the conditions under which the study will have power of at least 80% to reject the null 
hypothesis of no treatment effect in favor of a positive treatment effect. A total of 1,000 simulated 
data sets were generated under each set of assumed conditions. Power was estimated as the 
percentage of the simulations in which the null hypothesis was rejected and the estimated treatment 
effect indicated an increase in adherence. 
Although site-to-site variation in treatment response was included in the simulated data, the 
interaction between site and treatment was not included in the analytical model used in the initial 
simulations. The goal of these simulations was to evaluate the power of the study to identify an 
overall treatment effect, not the power to evaluate differences in the treatment effect among sites.
It is expected that those with relatively low pre-treatment adherence will still tend to have lower post-
treatment adherence than those who start with higher values. This expectation implies a moderate to 
strong correlation between pre- and post-treatment values for PDC. The correlation varies inversely 
with the variance of the observations. Thus, a higher correlation implies lower variance of the 
observations and higher statistical power. The simulations indicate that the study will have >90% 
power to reject the null hypothesis under the assumed conditions. To illustrate this, consider an 
extreme case in which the error variance is 2,100 (SD=45.83) and the site-to-site variance is 30 
(SD=5.48). The resulting correlation between pre-treatment and post-treatment PDS is only 0.29, 
which is much lower than expected. Even so, the study would have 80% power to detect the 
treatment effect. Experience indicates that adherence in one interval is a reasonably good predictor 
of adherence in another interval, which leads to the expectation that the correlation will be much 
higher than 0.29. If this expectation is correct, then the study will have power much greater than 
80% to detect the expected treatment effect. For example, cutting the error variance in half while 
maintaining the site-to-site variance at 30 boosts the correlation to a modest 0.48 but increases 
statistical power to >90%. Thus, even with conservative assumptions about the variance structure of 
the data, the planned sample size will provide more than adequate statistical power for the study.
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2.7
Recruitment [SPIRIT 15]
Recruitment for this study will occur at each participating clinical center as well as within its 
respective geographic catchment area. Enrollment will be restricted to the SCDIC participating sites 
and affiliated institutions within each site, which are representative of academic and community 
centers.
All recruitment materials will be approved by the IRB, as appropriate, prior to implementation. The 
SCDIC Clinical Center infrastructure allows efficient access to the proposed study population. 
Eligible subjects may be identified and recruited in different ways. People may be contacted in 
person (e.g., in clinic, emergency department), by phone, or via electronic media (e.g., chat rooms, 
text) about enrolling in the study. Informed consent for patient-app participants only will occur in the 
following ways, with appropriate IRB approval:
•
In person (e.g., in clinic or hospital, at SCD community events)
•
Telephone
The SCDIC participating Centers will enroll both patient and providers. For the provider app, all 
centers will request a waiver of consent since these participants will all be adult clinical providers 
and downloading the app will imply consent to participate. All providers within a clinic will be 
approached. Patients treated by these providers will be approached, that is, at least 46 eligible 
participants per Center who meet inclusion criteria for this study. A participant will be considered 
enrolled when consent is obtained, and inclusion criteria have been confirmed. The SCDIC enrolling 
Center will maintain a local log of consented participants and will also confirm enrollment status in 
the data management system (DMS). SCDIC clinic staff will identify eligible patients using the 
eligibility criteria developed and approved by the Steering Committee. The research team will both 
screen their current patient population as well as identify new patients that attend the clinic. Eligible 
patients will be solicited during clinic visits according to the protocol approved by the IRB. 
Depending on the geographic area covered by the SCDIC, patients may also be recruited during 
outreach visits to outlying areas or through other outreach efforts within the community. This 
flexibility on the part of the SCDIC will insure inclusion of the greatest number of eligible patients for 
the study.
Designated study staff will screen, approach, consent, and verify eligibility of potential study 
participants. If the patient (and parent/guardian of minors) agrees, the designated study staff will 
meet with the patient for a more comprehensive explanation of the study. If there is continued 
agreement, the designated study staff will proceed with the consent and enrollment process. Signed 
informed consent will be obtained prior to uploading application on participants’ phones and any 
data collection. Patients will receive a hard copy of the completed and signed consent form to keep. 
Patients will be able to ask questions at any time. The consenting patient or parent/guardian must be 
literate in English. Adolescent assent will be obtained from children 15–17 years old, or as 
determined by the IRB. If a minor becomes a legal adult during the 6-month study participation, an 
age of majority consent will be obtained from the participant. All participants must also sign a HIPAA 
Research Authorization form.
All sites will compensate $25 to each patient participant upon each study visit completed (max $75) 
and $25 to each participant upon completion of an interview. 
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3. Methods: Data Management and Analysis
3.1
Data Management [SPIRIT 19]
RTI will develop an electronic data capture (EDC) system for this study. eCase Report Forms will 
reflect the data elements to be collected at each visit. Data will be entered into the EDC and stored 
in the study database in REDCap. A research assistant at each site will collect study data and enter 
it into the EDC. Different data sources will be used, such as pharmacy records for PDC, laboratory 
results from local electronic medical records and patient-reported outcomes that can be completed 
in electronic format (e.g., tablets) or paper. Figure 7 depicts the flow of data from multiple sources.
Figure 7. Flow of data from patients and providers (participants) into study database.
*RA denotes research assistant
3.2
Methods of Analysis [SPIRIT 20a-20b, StaRI 15-16]
Aim 1. Improve Patient Adherence to hydroxyurea: Addressing Memory, Motivation, and 
Knowledge Barriers to Hydroxyurea Use.
Characterization of study patients: A characterization of the study’s patients by their demographic 
characteristics, baseline hydroxyurea adherence, hydroxyurea dosage, laboratory values, measures 
of self-efficacy, health literacy and quality of life, as detailed in Table 3, will be provided for all 
patients combined and across sites.
Primary outcome: The primary outcome is the proportion of days covered (PDC) of hydroxyurea 
change from baseline (prior the intervention) to week 24. For the PDC, the pharmacy that fills the 
most prescription claims within the target therapeutic category for a specific patient within the 
calendar range will be assigned responsibility for the patient. The pharmacy where hydroxyurea is 
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32
filled will be verified by the research coordinators at each study visit and refill information will be 
obtained from this(es) pharmacy(ies). All prescription drug claims, regardless of dispensing 
pharmacy, will be counted towards the patient’s PDC threshold.  
PDC is calculated as follows:
1) Determine the number of days in each individual’s treatment period.
2) Within the treatment period, count the days each individual was covered by hydroxyurea 
based on the prescription fill dates and days of supply of each prescription.
a.
If multiple prescriptions for hydroxyurea are dispensed on the same day, count the 
number of days covered using the prescription with the longest days of supply.
b.
If multiple prescriptions for hydroxyurea are dispensed on different days with overlapping 
days of supply, count each day covered by the medication only once within the treatment 
period. For example, if a prescription A and a prescription B are filled 5 days apart and 
each has a 30-day supply, then the total days covered are 35.
c.
If multiple prescriptions for hydroxyurea are dispensed on the same day or different days 
where the days of supply overlap, adjust the prescription start date to be the day after the 
previous fill has ended. For example, if three prescriptions for hydroxyurea are dispensed 
on the same day, each with a 30-day supply, then a total of 90 days are covered.
d.
Any days of supply that extend beyond the end of the measurement period are not 
included when calculating the total number of days covered.
3) Calculate PDC: Divide the number of covered days found in Step 2 by the number of days 
found in Step 1 and multiply this number by 100 to obtain the PDC (as a percentage) for 
each individual.
As noted in the power calculations, the assumed data generating process is considered a linear 
mixed model (LMM) as expressed in the equation in section 2.6, where the primary outcome of PDC 
is a continuous outcome denoted Yijt and is measured in i individuals located in j provider sites at t 
time periods. Given the normality and independence assumptions of the variance components αj, νji 
and εjti, the impact of the intervention on the primary outcome (Xiθ), controlling for variation across 
sites (αj) and time periods (βt), can be estimated using standard statistical software for mixed effects 
models such as PROC MIXED in SAS. A LMM model with a random effect parameter for subject 
nested within site, a fixed intervention effect parameter, a fixed effect for time interval, a fixed effect 
interacting the intervention effect with time (βtXjtθ), and five fixed effect dummy parameters (for all 
but one site) will be specified and interacted with the intervention parameter (αjXjtθ). 
The fixed effect intervention parameter (θ) specified in the LMM model will test the primary 
hypothesis by estimating the change in hydroxyurea adherence, as measured with PDC, at 24 
weeks after introduction to the InCharge Health app compared to their measured adherence at 
baseline. The fixed effect parameter βtXjtθ will test the hypothesis that the intervention effect 
changes over time as implementation of the intervention may improve over time periods. The fixed 
effect parameter will test the hypothesis that the intervention effect differs across sites. The initial 
LMM will include all fixed effects listed above and if either of the two interaction terms have p-values 
greater than 0.2, they will be removed to create a parsimonious LMM that will be compared to the 
full model with a likelihood ratio test to test the null hypothesis that the parsimonious model fit to the 
data is not different than the full model. If the null hypothesis holds (p-value ≥ 0.05) in the 
parsimonious model will be used. 
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After 40 patients are enrolled, the Care Redesign workgroup will verify their baseline PDC to ensure 
that there is diversity in the PDC distribution. The DCC (i.e., RTI) will provide this data and report it 
to the workgroup for review. If after this initial review, if >80% of the patients have PDC of 85% or 
higher, we will amend the protocol to restrict participants with PDC of 80% or lower. 
Sub-Aim 1.a. To measure implementation of the InCharge Health app among 
adolescents and adults with SCD. The assessment of implementation of the InCharge 
Health app among patients after 24 weeks will be assessed using the measures listed in 
Table 5 (i.e., use of the apps features measured by daily clicks, symptom and adherence 
tracker app outputs, satisfaction with the MARS scale, use after 24 weeks, measured by 
clicks in the app). Counts and scores of these measures will be graphed with box plots by 
month. Using the box plots from the last month, patients will be classified into four levels of 
app implementation: “low” (<25% of the days use of the app), “medium-low” (25 to 49% of 
the days use of the app), “medium-high” (50 to 74% of the days use of the app) or “high” (75 
to 100% days use of the app) by initially using quartiles of the implementation measures, 
then examining the box plots of the measures and adjusting as needed to create four 
clinically meaningful groupings of app users. As no specific hypothesis regarding app 
implementation has been specified a priori, no statistical tests will be conducted. App uptake 
will be computed at the end of the study at each site.
Sub-Aim 1.b. To examine the clinical influence of the use of the InCharge Health app, 
the clinical influence outcomes listed below will be compared in patients at baseline and 24 
weeks for all subjects together and stratifying by the four levels of implementation as defined 
in sub-aim 1.a. 
Definition of the clinical influence outcomes
•
Daily recorded adherence on the app
•
Proportion of patients with PDC ≥80%
•
Hematologic indices (blood tests) MCV, HbF, Hb, Absolute reticulocyte count, ANC, Bilirubin 
(indirect), LDH (this will be calculated separately for those on previous and newly started 
hydroxyurea therapy, and stratified by genotype)
•
Health care utilization—incidence of hospitalizations and ED visit rates
•
Quality of life—pain score and pain interference as measured by PROMIS and ASCQ-Me
•
Health literacy scores with one question perceived health literacy
•
PROMIS medication self-efficacy short form (for patient participants only)
The continuous clinical influence outcomes measured at baseline and 24 weeks, including MCV, 
HbF, Hb, Absolute reticulocyte count, Bilirubin (total and indirect), LDH, quality of life, pain and pain 
interference, health literacy, self-efficacy, and satisfaction with care scores will also be analyzed 
using the LMM analysis listed above. The LMM will initially include the dichotomous variable Xi to 
test intervention effects as was done for the primary outcome, but a second analysis that uses a 
categorical parameter labeled Zi, denoting the level of app uptake, will replace Xi to determine if 
there is a change in the effect of the intervention on the clinical influence outcomes at different levels 
of InCharge Health app usage. The parameter is found to have a p-value less than 0.05, pairwise 
comparisons among groups will be using Tukey’s HSD test control for potential type I error.
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34
For the laboratory outcomes of MCV, MCV, HbF, Hb, Absolute reticulocyte count, bilirubin (total and 
indirect) and LDH, the LMM analysis will be conducted as stated above but will include variables to 
control for variation in when hydroxyurea was initiated and sickle cell phenotype. Models including 
site level characteristics of urban versus rural and academic versus community will also be created 
to determine if heterogeneity in the site characteristics impact intervention effectiveness. 
The categorical clinical influence outcomes measured at baseline and 24 weeks include daily 
recorded adherence, the proportion of patients with PDC adherence above 80%, whether patients 
are prescribed and initiate hydroxyurea and if the patient experiences an ED visit. For these 
outcomes, generalized linear mixed models (GLMM) will be used, where the outcome is specified as 
a dichotomous variable modeled with a logit function as indicated.81 The GLMM models would follow 
the same format as linear mixed model above with fixed effects for time, intervention/app use and 
random effects subjects within sites. Such models can be fit with standard statistical software like 
PROC GLMMIX.
Aim 2. Improve Provider Hydroxyurea Awareness, Prescribing and Monitoring Behaviors. The 
analysis specified below for Aim 2 seeks to understand how providers utilize the HU Toolbox app, 
whether the app improves providers’ provision of hydroxyurea therapy to SCD patients, and their 
perceived self-efficacy to correctly administer hydroxyurea therapy between baseline and after 9 
months of using the HU Toolbox app. Given the limited number of providers expected to enroll in the 
study (no more than 40 per site) many the analysis conducted below are simplified and do not 
account for the across site and across time complexities of the study design. As such, the results 
should be considered exploratory.
Using baseline data, providers will be classified into 4 categories, according to the level of comfort 
and expertise in caring for patients with SCD (Figure 8). In the analysis specified below, we will 
attempt to evaluate the implementation and effectiveness outcomes stratified by this provider 
categorization to better understand how expertise impacts the implementation and effectiveness of 
the HU Toolbox app.
Figure 8. Categorization of providers according to expertise level.
• May be primary care provider or hematologist/oncologist.
• SCD patients not sought by provider.
• Provider doesn’t prescribe hydroxyurea for SCD patients.
• Provider doesn’t feel comfortable with SCD management.
I. Unengaged SCD 
Provider
• Primary care provider or hematologist/oncologist willing to care for SCD 
patients.
• Willing to learn to prescribe hydroxyurea.
• Unfamilliar or unaware of evidence-based prescribing for SCD.
• Frequently refers to or consults SCD experts.
II. Willing SCD Provider
• Primary care provider or hematologist/oncologist.
• Accepts and tries to attract SCD patients; feels comfortable prescribing 
hydroxyurea.
• Is aware of evidence-based prescribing for SCD.
• Cares for ≤25 SCD patients.
III. Willing high-volume 
SCD Provider
• Primary care provider or hematologist/oncologist.
• Accepts and tries to attract SCD patients.
• Prescriber and caregiver for >25 SCD patients.
• Often sought for SCD management decisions by other providers.
• Usually at an academic medical center.
IV. SCD Expert
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The Normalization Process Theory (NPT) will be combined with RE-AIM to guide the coding 
structure for the qualitative analysis. A common codebook will be used for the deductive coding of 
the interviews, and further expanded and refined once the interviewing begins. Using this deductive 
approach, the codebook will create an initial list of codes to be used in the analysis and include 
operationalized examples on how to apply the code. We will revise the codebook as necessary to 
hone definitions to increase consistency in coding across the research teams. We will use qualitative 
software to code the transcripts and identify emergent patterns and themes in the data. Any 
discrepancies in coding and analysis will be identified and resolved. Interview data will be analyzed 
by different stakeholder groups both within and across the study sites. Data integration will occur by 
embedding the qualitative data within the quantitative outcomes data (for example, using a matrix 
where sites are organized from low to high levels of adoption) to understand why and how outcomes 
were obtained and contextual factors related to the mHealth interventions. 
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4. Methods: Monitoring
4.1
Data Monitoring and Quality [SPIRIT 21a-21b]
The RTI International Data Coordinating Center (DCC) will manage a central web-based EDC of all 
data collected by SCDIC Care Redesign research sites. RTI will monitor the ratio of enrolled patients 
throughout the duration of recruitment. In this way we will ensure a final enrollment ratio at the 
desired target.
4.2
Harms [SPIRIT 22] (Adverse Event, Unanticipated Problem Reporting)
This study does not involve a drug intervention, device intervention, or highly invasive data collection 
procedure. However, recognizing that unanticipated events can occur during any study, even a 
minimal risk study, the following reporting protocols will apply. The site principal investigator (PI) or 
designee will assess the event to evaluate whether it is unanticipated (i.e., unexpected), related to 
the study, places the participant or others at risk, and/or is serious to determine whether it should be 
reported to the IRB and DCC.
Adverse events and unanticipated problems
This study will collect the following information:
•
unanticipated (i.e., unexpected) and related adverse events (possibly related, probably 
related, or definitely related to study participation), and
•
unanticipated problems that may involve risk to participants or study staff, but do not 
necessarily result in an adverse event (i.e., harm).
Unanticipated adverse events are new or greater than previously known events in terms of nature, 
severity, frequency, or occurrence, as documented in the protocol, consent, or other study 
documents approved by the IRB.
An example of an unanticipated problem that may not result in an adverse event (i.e., harm) is 
misplacement of a participant’s research record containing PII such that the risk of loss of 
confidentiality is introduced. This event is reportable regardless of whether the confidentiality is 
breached or not breached. If the PI or designee identifies the adverse event or unanticipated 
problem as meeting the following criteria, it will be reported to the IRB within 5 business days and to 
DCC within 10 business days:
•
involves substantive harm (or genuine risk of substantive harm) to the safety, rights, or 
welfare of the site’s research participants, research staff, or others
Otherwise, the site will report the events to the IRB and Steering Committee (if applicable) on an 
annual basis at the time of continuing review. It is expected that patient participants will have acute 
disease complications (e.g., pain crises). Acute events will be treated as per each site’s standard of 
care. If they lead to a visit to the clinic/hospital, they will be captured in the study dataset.
4.3
Auditing [SPIRIT 23]
Clinical research monitors will review up to 10% of the study participants annually for 
appropriateness of the informed consent process, eligibility, serious adverse event reporting and 
patient protocol status. Additional information may be monitored at the request of the NHLBI, the 
IRB, or other institutional administration. The monitor will generate a formal report, which is shared 
with the PI, study team, and the NHLBI.
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Continuing reviews by the IRB will occur at least annually. In addition, SAE reports in are reviewed 
in a timely manner by the IRB and NHLBI. Monitoring of this protocol is considered to be in the “low 
risk” risk category.
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5. Ethics and Dissemination
5.1
Research Ethics Approval [SPIRIT 24]
No data collection activities will begin at an individual SCDIC participating clinical center until 
approvals from the IRB have been granted. The IRB will focus on data security (receipt, storage, 
sharing, protection of breach) and defer to the Center IRBs for procedures related to direct patient 
interaction and those conducted locally. All participating SCDIC clinical centers and RTI have a 
Federal Wide Assurance issued through the U.S. Office of Human Research Protections which 
assures that the organizations are complying with all Federal regulations to protect research 
subjects.
Risks and benefits
The data collected for this study may come from medical record abstraction, self-reported 
information, and application intervention. The patient surveys are not considered greater than 
minimal risk but may trigger uncomfortable feelings about one’s lifestyle, quality of life, or personal or 
family history of disease. Some patients may benefit from participating in the study through improved 
understanding of hydroxyurea and/or increased adherence to hydroxyurea and the associated 
health benefits. Some patients may benefit from knowing that they are helping to advance 
knowledge for future patients with their condition.
Unbiased recruitment
All eligible participants will be recruited without bias. Adolescents, women, and minorities will be 
included as they represent the patient population of each Center.
5.2
Protocol Amendments [SPIRIT 25]
Modifications to the protocol or consent form that impact eligibility criteria, outcomes, or analyses will 
be submitted to the RTI and IRB for approval prior to implementation. Protocol modifications and 
consent form changes will be submitted to the appropriate oversight committees according to the 
timetables set forth by those committees.
5.3
Consent or Assent [SPIRIT 26a-26b]
Participants will be recruited by local clinical staff from all eight participating Centers (University of 
Illinois at Chicago in collaboration with Sinai Health System, Washington University School of 
Medicine, Augusta University, St. Jude Children's Research Hospital, Duke University Medical 
Center, University of California Benioff Children's Hospital Oakland, Medical University of South 
Carolina), who will consent participants at the community clinic sites. If a minor is involved in this 
study, the minor’s guardian will be approached during regular clinic visits and invited to have his/her 
child participate in the study. If a participant is eligible, the family will be informed and given detailed 
information about the study, including the risks and benefits of study participation. An informed 
consent session will take place in which patients and guardians will have the opportunity to ask 
questions regarding participation in the study, as well as to learn the risks and benefits of 
participation.
After detailed discussion of the protocol, participants will be given a copy of the informed consent 
document for review. Participation will be voluntary, and patients may withdraw from the study at any 
time. Families will receive a set amount for reimbursement per study visit to help offset the costs for 
parking, food, and travel. Subjects and families will be informed of any information that becomes 
available during the study that might impact their continued participation.
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There will be a consent form specific to the protocol:
•
Study consent
o
To recruit 368 participants, 15-45 years old, diagnosed with SCD
o
Adult participants (ages 18 and older) will sign informed consent, adolescents ages 
15 to 17 will sign informed assent and their legal guardians will sign the informed 
consent
5.4
Confidentiality [SPIRIT 27]
All study data will be collected by local study coordinators with the supervision of the local study 
leaders and sent via EDC to the study database, which RTI manages. Full names and other 
identifying information, excluding date of birth, will be retained only by the Centers. Participants’ data 
will be labeled and stored with coded identification numbers that can be linked to names only by the 
corresponding Center. Access to the database will be restricted to the local study leaders, PIs, and 
designated research staff and will be password-protected. Each study participant will have a study 
identification (ID) number to protect patient identity. App data will be collected and stored under this 
subject ID. RTI will receive data coded with subject ID numbers for tracking and linkage only with no 
identifying information. Once data collection is complete, an analysis file will be provided to begin 
analysis. Data will be encrypted to protect against loss of confidentiality. Study coordinators will 
maintain a list to allow linkage to subject identity; this list will be restricted to designated study staff 
(PIs and research coordinators) and to entities that may need access to verify accuracy and 
completeness of data (IRB and study monitors). All collected data will be kept confidential to the 
extent permitted by law. The DCC will not be able to link an individual to their identifying information.
5.5
Access to Data [SPIRIT 29]
Data will be entered into a password-protected, secure web-based data management (DMS) 
system. Within this system, the DCC will build in edit, range, and validity checks on the data as they 
are entered. In addition to data entry, the DMS will allow SCDIC staff to produce data management 
reports to monitor their performance. The DCC will train Center staff in data collection and 
management in accordance with the protocol and manual of operations.
To monitor enrollment, data flow, delinquent data, and data quality, the DCC project managers will 
run reports that monitor the performance of the individual Centers. These reports will also be 
distributed and reviewed regularly by the Center staff and the Executive and Steering Committees. 
The reports will show the number of patients enrolled, the number and type of forms submitted 
through the DMS, the number of incomplete and delinquent forms, and the number of unresolved 
data edits. The DCC will collaborate with the Center staff to design reports that are helpful in 
monitoring the conduct of the study and producing high-quality data for analysis.
5.6
Dissemination Policy [SPIRIT 31a-31c]
The primary goal of the SCDIC Care Redesign study is to increase the appropriate use of 
hydroxyurea among patients and providers. Data collected from this study will be shared with SCDIC 
investigators and qualified researchers outside of SCDIC interested in studying additional aspects of 
SCD that are not being addressed by this protocol. Results will be published in peer-reviewed 
journals and presented at national and international conferences, community and professional 
meetings. De-identified patient-level data will be made available to researchers outside the SCDIC 
through an application and approval process as part of the SCDIC’s Ancillary Studies Policy and 
Data Dissemination Plan. To protect the confidentiality and privacy of the subjects, investigators 
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41
granted access to the limited access data and biologic specimens must adhere to strict requirements 
incorporated into a standard Data Use Agreement. In accordance with NHLBI policy, outside 
researchers will also be required to submit an approval from their IRB. Dissemination of the app will 
be done upon a larger-scale study, upon completion of the current study. 
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42
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A-1
Appendix A
Informed Consent Materials
Please refer to documents entitled informed consents. 
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B-1
Appendix B
Patient Feedback on InCharge Health App
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B-2
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B-3
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C-1
Appendix C
App Descriptions
InCharge Health app
The InCharge Health app features include several features to increase patient engagement and 
motivation. Below is a detailed description of the app features.
•
Daily reminders will be sent to the participants’ phone. These daily messages will involve a 
customized push notification with medication reminder (Figure C1A).
•
The ability to customize the content of the message and time of the day when the message 
comes (Figure C1B).
•
Symptom tracker to monitor daily pain and mood (Figure C1C).
•
7-day streak that tracks daily adherence (Figure C1D).
•
Graphing adherence against pain symptoms (Figure C1D).
•
Communication feature that allows the patient to connect to the clinic and to a “health 
partner” (Figure C1E).
•
Link to discussion forum where communication to other patients can occur (Figure C1E).
•
Education bank that provides information about SCD and hydroxyurea (Figure C1F).
Participants will be encouraged to use the app daily by documenting when they take hydroxyurea 
once the push notification arrives. On any given day, participants will have the option to delay the 
daily push notification; however, the notification will occur again between 1 and 12 hours later (the 
participant chooses, that timing when the app is set up). This feature accommodates the scenario 
when a participant is occupied with other activities but wishes to be reminded once activity is 
completed (e.g., delay message while driving). In addition, if they are hospitalized, participants may 
stop push notifications. Participants will be encouraged to visit other components of the app. A 
special feature of the app, optional for the participant, is to set up an “health partner” who will follow 
the participant’s progress. The participant may choose a person from his/her contact list (e.g., family 
member, friend) who will receive notifications if it had been <4 hours since not documenting the use 
of hydroxyurea. The “app accountability partner” will be encouraged to message the participant to 
remind him/her to take the medication, if he/she received the message of “participant failed to take 
medication today”. The accountability partner will not have access to the data the patient inputted in 
the app, however he/she will receive a notification if the patient was hospitalized.
Use statistics of the InCharge Health app will be collected and stored in the Mixed Tables software 
in an Enterprise account housed at St. Jude and then transferred to RTI for analysis. The use 
statistics (app usability data) will be stored under the study participant’s number, and the InCharge 
Health app will not collect any protected health information (PHI). The following app-related data will 
be collected: features of the app used, frequency of each feature used, daily adherence, daily pain 
scores, daily mood scores. We will encourage each participant to use the InCharge Health app for 
the duration of the individual patient participation, i.e., 24 weeks. At each study visit (12 and 24 
weeks), the participant will meet with a study member who will review the app use, functionality, 
encourage him/her to continue using it, and answer any questions related to its use. Upon study 
enrollment, participants will be given an email address and phone number to call should any 
technical problems occur related to the use of the InCharge Health app. They will receive a local 
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C-2
number in addition to a central number from the study sponsor for any technical difficulty during the 
study. Data related to technical problems related to the app will also be tracked to evaluate its 
functionality. 
Figure C1. Features of the InCharge Health app for patients. A) Push notifications will come daily 
and will prompt the patient participant to mark if dose was taken, not taken, or be reminded later, B) 
customization of the push notification messages, time of the day, choice of “app buddy” are available 
featuers, C) daily pain and mood tracker are available and will capture pain level and mood changes, D) 
graphing of pain level versus mood and pain is available for the past 7 days or longer, E) a link to the 
patient porter (EMR), clinic numbers, and patient-led discusson forums are available, and F) a large 
resource bank is available with links to vetted educational websites, educational material, and 
educational videos and is included. 
HU Toolbox app
The HU Toolbox app is an updated version of the SCD Toolbox mobile application released 
approximately 1 year ago. It has been modified to emphasize algorithms (Figure C2, C3, and C4) for 
appropriate hydroxyurea use and is ready for immediate use on Apple and Android operating 
systems (i.e., iPhones and Android phones). In addition, it has the NHLBI guidelines adapted for 
pediatrics (guidelines/recommendations separated by age) and for adults (guidelines/ 
recommendations separated by organ system, laboratory, or physical exam finding) (Figure C2). 
The guidelines and algorithms are the consensus of U01 medical providers as adapted versions of 
the NHLBI guidelines. The HU Toolbox app includes the ability to search guidelines for key words 
and add notes. Algorithms are also included as PDF documents that can be printed out or emailed. 
Finally, a contact list of local SCD specialists and important contacts is included, so providers can 
easily contact SCD experts and expect an answer in 24 hours or less. The HU Toolbox app is easily 
updated with all data and resources stored on a cloud-based server that can provide instant up-to-
date information to those using the app. All updates and maintenance of the HU Toolbox app will be 
provided by SickleSoft, LLC, which developed the mobile app and has agreed to continue its support 
of this effort.
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C-3
Figure C2. Features of the SCD Toolbox app for providers. NHLBI guidelines and associated 
algorithms are presented in simplified and ready-for-consumption format. 
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C-4
Figure C3. Algorithm for managing hydroxyurea therapy in children with sickle cell disease. 
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C-5
Figure C4. Algorithm for managing hydroxyurea therapy in adults with sickle cell disease. 
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