Development and Testing of a Behavioral Activation Mobile Therapy for Elevated Depressive Symptoms
Phase: NA
Conditions: Depressive Symptoms, Depression
Sponsor: Medical University of South Carolina

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PROTOCOL TITLE: Development and Testing of a Behavioral Activation Mobile Therapy for 
Elevated Depressive Symptoms  
 
PRINCIPAL INVESTIGATOR: Jennifer Dahne, Ph.D. 
 
 
 

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1.0 
Objectives / Specific Aims 
Via a successfully completed Phase I STTR, our investigative team developed and preliminarily clinically 
evaluated Moodivate, a self-help mobile app informed by Brief BA to be utilized for depression treatment 
among adult primary care patients. Results of our Phase I study indicate that Moodivate is feasible and 
acceptable to primary care patients and that use of the app relative to TAU is associated with significantly 
greater decreases in depressive symptoms over time. This Phase II STTR will allow us to refine, improve, 
and rigorously evaluate Moodivate.  
 
Aim 1: Refine Moodivate and develop the EHR provider portal (months 1-12). Iterative usability 
testing with patients (n=10) and providers (n=10) will be utilized to refine both the app and the portal.  
Milestones: 1) Development of a HIPAA-compliant Moodivate EHR portal, 2) Android expansion of 
Moodivate, and 3) completed app modifications consistent with feedback from Phase I participants. 
 
Aim 2: Via a three-arm randomized controlled trial (N=695), examine the efficacy of 1) Moodivate 
vs. 2) Moodivate + EHR integration vs. 3) treatment as usual (TAU) for the treatment of depressive 
symptoms within primary care (months 12-30). Main outcomes include: 1) change in depression over 
time as a function of treatment and 2) patient- and provider-level treatment feasibility and acceptability. 
Milestones: 1) Determination of clinical efficacy of Moodivate and Moodivate + EHR relative to standard 
practice for depression treatment and 2) demonstrated treatment acceptability and feasibility, defined as 
>70% app retention across follow-up and patient and physician self-reported benefit of utilizing 
Moodivate. 
 
Aim 3: Conduct a cost-effectiveness analysis of implementing Moodivate and Moodivate + EHR 
integration within primary care practices relative to TAU (months 30-36).  
Milestones: Derivation of incremental cost-effectiveness of Moodivate and Moodivate + EHR vs. TAU.  
 
2.0 
Background 
Depression is a Major Public Health Problem and the Leading Cause of Disability Worldwide. 
Depression is the leading cause of disability worldwide, with more than 300 million people affected1,2. In 
the United States alone, 16.2 million adults report a past year major depressive episode (MDE)3 and an 
additional 38 million experience subsyndromal depression4. Individuals with depressive symptomatology 
experience significant suffering, high morbidity and mortality, and considerable social and occupational 
impairment5. As such, efficient and effective identification and treatment of depression is of critical public 
health importance. 
 
Primary Care Offers a Ripe Opportunity for Depression Treatment. Most adults with depressive 
symptoms make at least one annual medical visit to a primary care physician (PCP)2. Thus, primary care 
offers a ripe opportunity to identify and treat adults with depressive symptoms. This is consistent with 
guidelines from the United States Preventive Services Task Force, which specify that depression screening 
should be implemented in the general adult primary care population with systems in place to ensure 
appropriate diagnosis, treatment, and follow-up6. Despite these recommendations, depression has 
historically been undertreated in primary care5,7 and more than two-thirds of U.S. adults who screen 
positive for depression do not receive treatment2. Lack of depression treatment via primary care results 
in negative outcomes for patients, including prolonged depressive episodes, decreased recovery incidence, 
and heightened impairment and suffering8. 
 
Table 1: Current Primary Care Psychotherapy Market2 

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Evidence-based depression treatments 
that can be delivered via primary care 
include 
medications 
(e.g., 
antidepressants) and psychotherapy6. 
Although primary care patients with 
depressive symptoms tend to prefer 
psychotherapy9,10, the vast majority 
(87%) of patients treated are prescribed 
medications2 without therapy offered 
even as an option11-13. Of the 21.2 
million U.S. adults who visit a PCP 
annually and screen positive for 
depression, only 6.6% receive therapy 
(Table 1)2. One key barrier is limited 
access to mental health specialty care14. 
Within one nationally representative 
survey, 66.8% of PCPs reported they were unable to get high-quality outpatient mental health services for 
their patients15. From a patient perspective, therapy barriers include problems of transportation, childcare, 
lack of time, stigma, and lack of available services16,17. There is clear need for a disseminable, evidence-
based psychotherapy treatment option to meet the need for depression treatment via primary care. Such a 
product would not only address a public health need but would also be commercially viable.  
 
Mobile Health Technologies can be Leveraged to Deliver Evidence-Based Depression Treatment via 
Primary Care. Mobile health (mHealth) technologies offer an ideal strategy to meet the need for evidence-
based psychological depression treatment via primary care. Several computerized psychological 
interventions for depression treatment via primary care have previously been developed and have 
demonstrated success18,19. However, mobile technologies, such as smartphones and mobile apps may be 
superior treatment delivery platforms as they allow continuous, patient-driven access to treatment materials 
while also eliminating barriers to computerized treatment (e.g. the need to have a computer at home or go 
to a clinic to access the treatment). Smartphone ownership in the U.S. has grown year over year and now 
77% of adults own smartphones20, with high smartphone ownership rates across age groups—92% of 
Millennials, 85% of Gen Xers, and 67% of Baby Boomers own smartphones21. Moreover, the majority of 
physicians report utilizing mobile apps in their clinical practices22. Thus, treatment access barriers could be 
reduced if evidence-based psychotherapy could be efficaciously delivered via smartphone. Apps delivered 
directly within primary care settings could provide an immediately actionable, evidence-based treatment 
for patients in need of services for depressive symptoms.  
 
Brief Behavioral Activation is an Evidence-Based Depression Treatment Well-Suited for mHealth 
Delivery via Primary Care. To be an attractive treatment option for PCPs to recommend to their patients, 
a mobile app-based depression treatment must be strongly grounded in evidence-based treatment and have 
a research base supporting primary care implementation. Among evidence-based depression 
psychotherapies, brief Behavioral Activation Treatment for Depression (Brief BA)23,24 has shown great 
promise and is particularly amenable to mHealth delivery within primary care. Brief BA is grounded in 
behavioral principles, which suggest that depression is caused by a lack of reinforcement in the environment 
for positive, non-depressed behaviors. Thus, the goal of Brief BA is to help the patient identify, schedule 
and reengage in positive activities. Brief BA accomplishes this via the following components, delivered 
across five to ten sessions: 1) Psychoeducation: Introduction to the Brief BA model; 2) Identification of life 
areas, values, and associated activities: Identification of values and goals within a variety of life areas 
important to the patient, including relationships, education, career, recreation, and health; 3) Daily 
monitoring and activity planning: Selection of activities that allow the patient to live according to his/her 
 
U.S. adults 
327.2 
million 
U.S. adults who visit a PCP > 1x/year 
251.9 
million 
U.S. adults who visit a PCP > 1x/year and 
screen + for depression 
21.2 million 
U.S. adults who visit a PCP > 1x/year, screen + 
for depression, and receive treatment 
6.1 million 
U.S. adults who visit a PCP > 1x/year, screen + 
for depression, and receive antidepressants  
5.3 million 
U.S. Adults who visit a PCP > 1x/year, screen + 
for depression, and receive psychotherapy 
1.4 million 
Current untapped market for psychotherapy 
via primary care within the U.S. 
19.8 million 

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values and incorporation of the activities into the patient’s daily schedule; and 4) Contracts: Identification 
of a supportive individual to facilitate completion of difficult activities.  
 
Brief BA was originally developed by Lejuez (study consultant) and colleagues in 200125 and several large-
scale randomized controlled trials (RCTs) since that time have demonstrated the efficacy and cost-
effectiveness of the treatment for depressive symptoms. Across studies, Brief BA is associated with 
reductions in depressive symptoms26,27 including among those with Major Depressive Disorder (MDD)28. 
Brief BA treatments have been delivered successfully within primary care clinics both by mental health 
nurses29 and psychologists30. Further, Brief BA has shown promise in a variety of other health care settings, 
including oncology31,32, the Veteran’s Administration health system33, and substance use treatment34-36. 
Across studies, effect size estimates for Brief BA are in the medium to large range indicating meaningful 
clinical impact37,38. A key strength of Brief BA that makes it fitting for mobile app delivery is that it is 
simple, straightforward, and easily understood by patients39. 
 
Progress Report. The overarching goal of our Phase I STTR was to tailor the delivery of Brief BA for 
mHealth within primary care. We developed “Moodivate,” a self-help patient-facing mobile app informed 
by Brief BA (Figure 1). Moodivate development began with a diverse team of PCPs, psychologists, and 
app developers (all investigators herein) to ensure psychological treatment components were faithfully 
translated to a mobile platform in a manner fitting the primary care environment. We utilized an agile 
development approach, which promotes continued collaboration across the software development lifecycle 
between the entire team40. 
 
When users first download Moodivate, they complete a psychoeducational interactive tutorial illustrating 
the connection between thoughts, feelings, and behavior. This tutorial highlights that Moodivate will focus 
on increasing activity to improve mood and decrease the incidence of negative thoughts (Figure 1). Users 
practice generating values and activities and receive instruction regarding app use. Users are then taken to 
the “Life Areas” screen, where they select from five life areas (Relationships, Daily Responsibilities, 
Recreation, Career and Education, Health) to develop values. A value is defined as an ideal, quality, or 
belief in a certain way of living. For example, within the relationships life area, a user might generate a 
value of “Be a trustworthy friend.” This value-driven framework ensures that activities will be positively 
reinforced over time, by virtue of being connected to what the user values as important24. Users generate 
several values within each life area. Values are then used as a framework to generate activities. For the user 
who generated the value of “Be a trustworthy friend”, activities might include going to dinner with a friend 
once a week or calling a friend for 20 minutes twice a week. Users generate several activities for each value. 
Figure 1: Moodivate Screenshots 

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Once a user has generated activities across values, activities are scheduled and monitored via the Calendar. 
After completing an activity, the user receives reinforcement (e.g., “Nice going, you did it!”). Sometimes 
activities are difficult to complete individually and require help from others. For example, a user might 
schedule an activity to grocery shop weekly but might not have transportation. In these instances, users can 
enlist social support via the “Assists” screen through which they identify an individual who could help them 
complete the scheduled 
activity and schedule a 
time to ask that individual 
for help. Moodivate users 
can track daily mood to 
monitor progress. Calendar 
prompts remind users to 
rate their mood each day. 
Users can view a graph of 
fluctuations 
in 
mood 
overlaid upon a graph of 
the number of completed 
activities, illustrating the 
connection between activity and mood. Moodivate users are reinforced for treatment engagement via 
badges. When a user earns a badge, a pop-up displays and congratulates the user while explaining why the 
badge was earned. Users can view earned badges on the User Info page41. 
 
To examine the feasibility and preliminary efficacy of Moodivate for depression treatment within primary 
care, we conducted a pilot randomized feasibility trial as part of our Phase I STTR42. Participants (N = 52; 
Table 2) were recruited from one primary care clinic affiliated with the Medical University of South 
Carolina (MUSC) and were randomized 2:2:1 to receive: 1) Moodivate, 2) an active control Cognitive 
Behavioral Therapy mobile app (“MoodKit”), or 3) Treatment As Usual (TAU; no app). Because clinical 
data are limited on other available mobile apps that purport to treat depression43-46, at the suggestion of NIH 
program staff, we opted to include the active control comparator (MoodKit) to extend the literature on 
efficacy data for other clinical apps. Participants completed assessments of depressive symptoms weekly 
for eight weeks. App analytics data were captured to examine Moodivate feasibility (similar analytics data 
not available for MoodKit as it was not developed by our team). Moodivate participants on average had 
46.8(SD = 30.1) app sessions throughout the trial duration, spent 3.5(2.8) minutes using the app per session, 
and spent 120.8(101.0) minutes using the app in total throughout the trial. Participants on average generated 
6.1(3.2) unique values, 14.7(10.2) unique activities, and completed 52.2(89.3) activities. Nearly 70% of 
Moodivate participants continued to use the app one month after trial enrollment and 50% at the end of the 
eight-week follow-up period. A generalized estimating equation (GEE) model examining change in 
depressive symptoms over time by treatment indicated a significant interaction between time and treatment 
(2=42.21, df=14, p<.001). As compared to TAU, Moodivate participants evidenced significantly greater 
decreases in depression over time. Moreover, these gains were sustained across the trial period. Moodivate 
participants had an average(SD) 12.2(13.2) point decrease on the Beck Depression Inventory-II (BDI-II47; 
a 10-point decrease is clinically significant) from pre- to post-treatment. At study end, 52.6% of Moodivate 
participants (vs. 25.0% of TAU) had less than minimal symptoms of depression. Differences between the 
two app conditions were nonsignificant, as might be expected from a pilot feasibility study not powered to 
detect small effects. In sum, this trial demonstrated that: 1) our team can feasibly recruit patients with 
depressive symptoms from primary care, 2) participants randomized to Moodivate utilized the app 
regularly throughout an eight-week period, and 3) Moodivate use was associated with clinically 
significant decreases in depression over time relative to TAU48. 
 
To Enhance Commercialization, Moodivate Must Be Refined, Expanded, and Further Evaluated. 
Although these preliminary data are promising, several key issues must be resolved before Moodivate can 
Table 2: Phase I STTR Participant Demographics 
 
 
All 
(N=52) 
Moodivate 
(n=24) 
MoodKit 
(n=19) 
TAU 
(n=9) 
Age (M(SD)) 
43.8(13.3) 
44.7(14.0) 
43.0(13.6) 
43.1(11.9) 
Gender (% Female) 
84.6% 
83.3% 
78.9% 
84.6% 
Race (% White) 
40.4% 
41.7% 
36.8% 
44.4% 
Ethnicity 
(% 
Hispanic) 
3.8% 
0.0% 
10.5% 
0.0% 
Education (% > HS) 
84.6% 
91.6% 
78.9% 
77.8% 
Income (% <$50k) 
61.5% 
54.1% 
68.4% 
66.6% 

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be commercialized. First, the patient is not the only customer. Providers and healthcare systems can benefit 
from more effective, efficient depression treatment49,50. To systematically deliver Moodivate to all patients 
with depressive symptoms and provide comprehensive follow-up, Moodivate must be integrated with the 
healthcare system (i.e., with the Electronic Health Record (EHR)) in a manner that will facilitate provider 
referral and treatment monitoring. Prior studies of such EHR integration for the management of other 
chronic diseases including diabetes and obesity indicate that integration of patient data captured via 
consumer technologies with the EHR facilitates more efficient workflow, improves patient-provider 
communication, and ultimately improves efficacy51,52. Herein, we will integrate the patient-facing 
Moodivate mobile app with the EHR. This integration will be developed in partnership with PCPs to reduce 
workflow burden and promote feasibility and acceptability. Via the Moodivate EHR portal, providers will 
be able to track their patient’s Moodivate treatment utilization and response. This portal could also be used 
to provide additional support to patients not adherent to Moodivate, to reinforce those using the app and 
responding, and to track clinical outcomes across patients at the provider- and clinic-levels. Beyond 
potential patient-level benefits of Moodivate (i.e., improved health), systems-level integration of Moodivate 
with the EHR will allow clinics and health-care systems to improve rates of depression treatment, a key 
metric for Accountable Care Organizations (ACOs) from the Centers for Medicare and Medicaid Services 
(CMS). Reimbursements for ACOs by CMS are directly linked to the percent of adult patients screened and 
treated for depression53. As such, Moodivate integrated with the EHR could help rapidly disseminate 
depression treatment to patients who screen positive and subsequently increase reimbursement rates. 
 
Second, our experience shows 
that 
minor 
product 
modifications are needed prior 
to additional clinical evaluation 
and commercialization. In Phase 
I, we developed Moodivate for 
iOS only to rapidly develop and 
test a minimum viable product 
(MVP). During our Phase I trial, 
we found that 56% of primary 
care patients enrolled were 
Android 
owners. 
For 
the 
purpose of the trial, these 
individuals were allowed to 
borrow a study iPhone to use 
throughout the trial. However, 
to commercialize Moodivate, the app must be expanded to Android. Additionally, as part of Phase I, 
participants randomized to Moodivate completed a qualitative interview at study end to identify 
modifications that would promote engagement (Table 3). These refinements cluster around: 1) improving 
the Assists component, 2) incorporating tailored suggestions for values and activities, 3) developing 
notifications for when activities are scheduled, and 4) offering tailored support when users record low mood. 
 
Third, in Phase I we primarily focused on the feasibility and acceptability of Moodivate. To advance toward 
commercialization and collect data that may be necessary for the FDA regulatory path, a larger-scale clinical 
trial is needed in which the efficacy and safety of Moodivate are comprehensively evaluated.  
 
Fourth, any downstream commercialization of Moodivate within healthcare systems requires that we first 
quantify its cost-effectiveness, both as a stand-alone app and as integrated with the EHR. Via our clinical 
trial and subsequent cost-effectiveness analyses we will determine: 1) whether there is added clinical benefit 
of Moodivate EHR integration and 2) the incremental cost of any added clinical benefit from a) Moodivate 
Table 3: Suggested App Refinements as a Result of Phase I  
 
App Feature Participant Suggestion 
Assists 
“It was difficult to understand what I was supposed 
to do with the Assist.”  
Activities 
“It might be nice to have a bank of activities and 
values. It was hard to come up with things I wanted to 
do. Like be a better friend, I had that value, and having 
suggested activities would have been helpful.” 
Calendar 
“I would like to get reminders to do things 15 
minutes before I scheduled an activity to occur.” 
Mood Rating 
“When I rate my mood low, it would be helpful to 
offer suggestions of activities I completed in the 
past that helped improve my mood. When you’re 
feeling down it’s difficult to think of what to do to feel 
better.” 

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+ EHR integration vs. sole provision of Moodivate, b) Moodivate + EHR integration vs. TAU, and c) 
Moodivate vs. TAU.   
 
Summary of Scientific Premise and Potential to Lead to a Marketable Product. Via a successfully 
completed Phase I STTR, our investigative team developed and preliminarily clinically evaluated 
Moodivate, a self-help mobile app informed by Brief BA to be utilized for depression treatment among 
adult primary care patients. Results of our Phase I study indicate that Moodivate is feasible and acceptable 
to primary care patients and that use of the app relative to TAU is associated with significantly greater 
decreases in depressive symptoms over time. This Phase II STTR will allow us to refine, improve, and 
rigorously evaluate Moodivate. Activities proposed via Phase II will move us toward Phase III and 
commercialization. Outcomes of this Phase II clinical trial will position us well in the current movement 
across healthcare in which traditional care delivery is being replaced with patient-centric care, facilitated 
by digital health tools46,54,55. This Phase II STTR is wholly consistent with PA-18-579 (Complex 
Technologies and Therapeutics Development for Mental Health Research and Practice) as well as with 
NIMH’s strategic priorities to “support development, refinement, and implementation of evidence-
supported digital health technology into routine practice.” Most importantly, commercialization of 
Moodivate will help to address the public health need for psychological depression treatment via primary 
care. 
 
3.0 
Intervention to be studied  
Moodivate. Details of Moodivate are provided in the “Progress Report” section above. 
Participants randomized to the Moodivate condition will be instructed to utilize Moodivate 
regularly, at least once per day, for the treatment of depressed mood.  
 
Moodivate + EHR Provider Portal. Participants randomized to the Moodivate + EHR condition 
will receive similar instructions as those randomized to Moodivate, but will also be instructed that 
their PCP will have access to metrics related to their app utilization and may choose to follow-up 
with them regarding treatment utilization and response. The PCP for each participant randomized 
to this condition will be provided EHR access to Moodivate metrics. Portal design will be 
developed iteratively with providers as part of Aim 1, but we expect that it will include metrics 
related to change in mood, frequency of app utilization, and frequency of activity completion. 
 
TAU. TAU is designed to mimic existing standard treatment for depression. Participants in all 
conditions, including TAU, will be provided educational material about mood management 
available via the EHR with the suggestion to discuss questions with their PCP. Across conditions, 
participants will not be precluded from obtaining additional treatment (medication or 
psychotherapy), which will be tracked as a study outcome. 
 
4.0 
Study Endpoints (if applicable) 
Primary outcome variables include: 
Depression. The clinic-administered PHQ-256 will be used for preliminary EHR-based screening 
and the full PHQ-957 will be administered at final eligibility screening to assess depressive symptoms. 
Across all assessment timepoints (including baseline) depressive symptoms will also be assessed via the 
BDI-II47. The BDI-II is a well validated assessment of depressive symptoms and is our primary outcome 
measure47.  
 
Treatment Utilization and Retention. Moodivate utilization and utilization of the provider portal 
will be tracked via analytics data. As in our prior trials, Moodivate analytics will be accessed via Yahoo’s 
Flurry Analytics system. Specifically, we will examine 1) number of app sessions, 2) average time per 
session, 3) total time using the app, 4) number of values created, 5) number of value-driven activities 

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created, 6) number of activities scheduled, 7) number of activities completed, 8) number of times mood is 
rated, 9) number of badges earned, 10) number of assists created, and 11) number of assists completed. 
Analytics will be examined within each follow-up week as well as across the entire trial. Retention will be 
defined as any app use within each week following enrollment. EHR portal analytics data will be accessed 
via reports in Epic. We utilize a similar approach in an ongoing trial of a smoking cessation electronic 
visit delivered to patients and reviewed by providers in Epic. This report will include: 1) whether the 
provider accessed the portal (yes/no) and 2) time spent reviewing the portal. Additional analytics data 
may be built into the report depending on the design of the portal.  
 
Moodivate Feasibility and Acceptability. Moodivate participants will self-report metrics related to 
feasibility and acceptability at all study follow-ups. As in Phase I, participants will self-report: 1) ease of 
Moodivate use, 2) continued desire to use Moodivate, 3) perceived benefits of using Moodivate, and 4) 
suggested improvements. 
 
Provider Portal Feasibility and Acceptability. Providers of patients in the Moodivate + EHR 
condition will be invited to complete a questionnaire following their patient’s 12-week study follow-up. 
This questionnaire will assess: 1) provider experiences utilizing the portal with their patient and 2) 
suggested modifications to the portal.  
 
We will also assess: 
Depression Treatment Practices. All PCPs affiliated with MUSC’s Primary Care ICCE will be 
invited to complete the Views about Depression Treatment Questionnaire58 pre- and post-study, which 
includes four subscales assessing: 1) attitudes about depression prevalence and treatment effectiveness 
within primary care, 2) their skills in recognizing and treating depression, 3) the specific behaviors they 
implement in depression treatment, and 4) satisfaction, compensation, and adequacy of time to treat 
depression.  
 
Satisfaction with Care. At baseline and Week 12, participants will self-report satisfaction with 
their primary care via the 10-item Patient Satisfaction Scale59. Questionnaire items were adapted from the 
Adult Primary Care Questionnaire developed by the Consumer Assessment of Healthcare Providers and 
Systems. All items are scored on a 7-point Likert scale and are summed, with a higher score indicative of 
higher patient satisfaction59.  
 
Treatment Mediators. Consistent with NIMH’s experimental therapeutics approach, we will assess 
whether Moodivate engages mechanisms presumed to underlie Brief BA effects. Theorized mechanisms 
include hedonic capacity (i.e., ability to experience pleasure), environmental reward (i.e., increased 
behavior and positive affect due to rewarding experiences), and behavioral activation (i.e., goal-directed 
behaviors)60,61. Assessments will be administered at baseline, Week 4, and Week 12. Hedonic capacity 
will be assessed via the 14-item Snaith-Hamilton Pleasure Scale62 (SHAPS), which has adequate 
psychometric properties in clinical and non-clinical samples, including convergent validity with hedonic 
tone and anhedonic depression63. Environmental reward will be assessed via the 10-item Environmental 
Reward Observation Schedule (EROS)64, which measures the magnitude of reinforcing events, 
availability of environmental reinforcement, and ability to elicit positive reinforcement. The EROS has 
strong internal consistency and excellent test-retest reliability64. Change in behavioral activation will be 
assessed via the 25-item Behavioral Activation for Depression Scale (BADS)65. The BADS measures 
activation and avoidance behaviors and is valid in clinical and community samples65,66.  
 
 
 
 
 
 
 
 

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Assessment Grids 
 
Questionnaires 
Screening 
Final 
Eligibility 
Baseline 
Week 1 
Week 2 
Week 3 
Week 4 
Week 5 
Week 6 
Week 7 
Week 8 
Week 12 
Screening 
Questionnaire 
x 
 
 
 
 
 
 
 
 
 
 
 
PHQ-9 
x 
x 
 
x 
x 
x 
x 
x 
x 
x 
x 
x 
BDI-II  
 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
App Utilization 
Questionnaire 
(Analytics data) 
 
 
 
x 
x 
x 
x 
x 
x 
x 
x 
x 
Moodivate 
Usability Survey 
(Feasibility/ 
Acceptability) 
 
 
 
x 
x 
x 
x 
x 
x 
x 
x 
x 
System Usability 
Scale 
 
 
 
x 
 
 
x 
 
 
 
x 
x 
Patient 
Satisfaction Scale 
 
 
 
x 
 
 
 
 
 
 
 
 
x 
Current Use of 
Psychotropic 
Meds 
 
 
x 
 
 
 
x 
 
 
 
 
x 
SHAPS 
 
 
x 
 
 
 
x 
 
 
 
 
x 
EROS 
 
 
x 
 
 
 
x 
 
 
 
 
x 
BADS 
 
 
x 
 
 
 
x 
 
 
 
 
x 
 
 
Provider Questionnaires 
 
 
 
 
 
5.0 
Inclusion and Exclusion Criteria/ Study Population 
Inclusion criteria: 
a) Elevated depressive symptoms, defined as a score of > 10 on the Patient Health Questionnaire-9 
(PHQ-9)57 and a score of > 10 on the Beck Depression Inventory-II (BDI- II).  
b) Age 18+ 
c) Currently own an iOS- or Android-compatible smartphone and runs the most up to date operating 
system (or willingness to update to the most current operating system) 
d) Report willingness to utilize a mobile app for the treatment of depressed mood (response of “yes” 
on yes/no item) 
Questionnaires 
Pre-study 
Post-study 
PCP Demographics 
x 
x 
PCP Questionnaire  
(Views About Depression Treatment) 
 
x 
x 
AIM/IAM/FIM  
 
x 
EHR Usability Survey 
 
x 
System Usability Scale 
 
x 

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e) Have a current, valid e-mail address that is checked at least once per day or have regular access to 
text messages (to access follow-up assessments) 
f) Enrolled in Epic’s MyChart program 
g) English fluency 
Exclusion criteria: 
a) Current suicidal ideation at study screening, defined as a response of ≥ 2 on item nine (“I have 
thoughts of killing myself but I would not carry them out”) of the BDI-II 
b) If another household member is currently enrolled in the study 
c) Severe visual impairment, which may limit ability to utilize an app   
 
6.0 
Number of Subjects 
We will recruit up to 20 participants (n=10 providers and n=10 patients) for usability testing and 695 
subjects for the RCT. 
 
7.0 
Setting 
Study participants will be recruited from MUSC’s Primary Care Integrated Center of Clinical Excellence 
(ICCE), which is comprised of 20 unique clinics across the Department of Family Medicine (DFM), 
University Internal Medicine (UIM) and Carolina Family Care (CFC) with a total patient count of 
~30,000 patients treated annually. Research will be conducted remotely via REDCap and MyChart. 
Participants will be recruited remotely and will be MUSC patients.  
 
8.0 
Recruitment Methods 
We will utilize proactive recruitment prior to primary care visits to give patients time to utilize their 
assigned treatment prior to their next primary care visit. For those randomized to Moodivate + EHR, 
proactive recruitment will ensure there is data available within the EHR for the PCP to review. Our team 
has previously implemented similar proactive recruitment approaches with success, utilizing EHR fields 
to identify intended patient populations. Herein, we will utilize available EHR data to identify Primary 
Care ICCE patients with depressive symptoms. As part of routine clinical care, all patients complete the 
Patient Health Questionnaire-2 (PHQ-2) to screen for depression during each primary care visit. Because 
all participants will be current Primary Care ICCE patients, they will all have completed the PHQ-2 
during their last primary care visit. Information on depression is also available via the EHR within each 
patient’s problem list and billing codes. Adult (age 18+) patients will be identified who have an upcoming 
visit scheduled within the next four weeks and have either: 1) a score of > 3 on their last PHQ-2 
assessment (the optimal cut-point to identify those symptomatic56), 2) depression listed on their problem 
list, or 3) a depression-related billing code associated with their last visit. In the last year alone, 6,102 
patients have been treated via Primary Care ICCE clinics that would meet these enrollment 
criteria. With the permission of their PCP, these patients will be contacted by the study team via the 
patient EHR portal (MyChart) with an invitation to participate in the study. If participants do not respond 
to this invitation within 72-hours, we will contact them via phone, e-mail, and/or text-message (based on 
preferences within the EHR) to ensure they received the message. We currently implement this 
recruitment approach in an ongoing trial of an electronic visit for smoking cessation, and our experience 
has been that it is feasible and efficient to generate a recruitment report via the EHR and that all PCPs 
associated with the MUSC Primary Care ICCE have agreed to have their patients contacted for the 
purpose of our studies. If interested, participants will complete an eligibility screening via 
MyChart/REDCap. This eligibility screening will assess: age, sex, e-mail access, MyChart enrollment, 
English fluency, smartphone ownership, willingness to use a mobile app for the treatment of depressed 
mood, and depressive symptoms via the PHQ-9. This eligibility screening will be used solely for 
screening purposes, not for research purposes. Within our informed consent documents, if a participant 
provides informed consent to participate in the study, they will agree that their screening information can 

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be used for research purposes. After completing determination of eligibility, a member of the research 
team will complete remote electronic informed consent with the participant (see details below).  
 
PCPs will be recruited for participation in usability testing via an email message that will be sent to all 
MUSC Primary Care ICCE providers.  
 
9.0 Consent Process 
Signed informed consent will be obtained from study participants. The consent process will take place via 
one of the following modalities: 1) Remote or in person electronic consent (e-consent) via REDCap (if 
remote, e-consent will be facilitated with a discussion over the phone or via video), 2) Remote consent via 
doxy.me facilitated with either a discussion over the phone or video connection via doxy.me, 3) Mailed 
(paper) consent facilitated with a discussion over the phone, or 4) in person consent (e.g., in clinic, in the 
investigator’s lab space for usability testing).  
 
All participants will be provided with a hard copy and/or an electronic copy of the consent form. 
Participants will be informed that participation in this research is strictly voluntary. Informed consent will 
include a detailed description of the purpose and the procedure of the study emphasizing our policy 
regarding privacy and confidentiality and an opportunity for the individual to ask any questions or voice 
concerns. Signatures on the consent form may be obtained with paper and pen OR electronically via 
REDCap/doxy.me. Participants who do not have access to the required technology to complete consent 
remotely via REDCap or doxy.me will be given the option to complete consent via mail facilitated with a 
discussion over the phone.  
 
10.0 Study Design / Methods 
 
Aim 1: Development of an EHR Provider Portal and Moodivate Refinements. 
EHR Provider Portal Development and Usability Testing 
 
Development of the Moodivate EHR provider portal will take place in two steps: 1) portal 
development, and 2) usability testing to determine refinements needed to promote feasibility and 
acceptability. PCPs will be included in usability testing if they are family medicine or primary care 
providers who treat adults.  
Features identified in usability testing will form the basis of EHR provider portal development. As 
in prior projects, to develop the Moodivate EHR provider portal, we will follow an agile software 
development approach40. Key phases of development will include whiteboard design to outline all 
features, wireframing of user stories, graphic design, programming, and deployment. Development will be 
led by Mr. Kustanowitz, implemented by MPT, with iterative testing completed by the PI and co-Is. 
 
A key consideration as we develop the Moodivate-Epic integration to promote future 
dissemination and commercialization will be to ensure this functionality is based on standard interfaces 
portable to other institutions that utilize Epic or other EHRs. As such, EHR integration will use existing 
connection points provided by Epic via the standard HL7 Fast Healthcare Interoperability Resources 
(FHIR) interface. MPT will be granted appropriate access to MUSC’s Epic instance under a business 
associate agreement with strict security assurances. This Epic access will include appropriate credentials 
as well as firewall and other security access support. Mr. Bryan Rogers, Director of the Epic-EHR 
Research Operations Core within the Biomedical Informatics Center (BMIC) at MUSC will serve as the 
technical resource at MUSC to assist with Epic access. This will include assisting with setup, serving as 
an institutional contact for questions, and addressing any connectivity issues. MPT will coordinate with 
Mr. Rogers and his team to determine APIs needed, to implement a proof of concept to confirm 
information flows as expected, and then to proceed with full development.  

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Following initial portal development, we will complete usability testing with PCPs. Planned 
enrollment for this step is n=10, as is standard for usability testing, though we will continue testing until 
saturation is reached68,69. Usability testing procedures will largely follow the cross-sectional usability 
testing procedures outlined below for Moodivate refinements, although here providers will test the EHR 
portal rather than the patient-facing app.  
  
Moodivate Refinements and Usability Testing 
 
Mr. Kustanowitz and MPT will lead Moodivate refinements. These refinements will center 
around: 1) expansion to Android and 2) incorporation of features determined during Phase 1 qualitative 
interviews. Refinements will be implemented prior to usability testing which will commence during 
month eight. We will conduct longitudinal (i.e., field testing; n=10) usability sessions. Eligibility criteria 
for Moodivate usability testing will be identical to eligibility criteria for the RCT.  
 
The goal of longitudinal usability testing is to address barriers participants may experience when 
utilizing the intervention in their usual environments. During longitudinal usability testing, participants 
will be recruited, enrolled, and provided with Moodivate in the same manner as participants who will be 
enrolled in the RCT. Participants will be instructed to utilize Moodivate at least once per day and that the 
goal of this testing stage is to provide feedback regarding barriers to utilizing Moodivate during normal 
life. Following longitudinal usability testing, necessary refinements will be identified  and implemented 
by MPT to ready the product for RCT evaluation.  
 
HIPAA Compliance 
 
For Moodivate to be a viable commercial treatment option, it must be Healthcare Insurance 
Portability and Accountability Act (HIPAA) compliant. Mr. Kustanowitz has expertise in HIPAA-
compliant software development and will oversee all aspects of HIPAA compliance within Phase II. It 
should be noted that HIPAA compliance adds measurable overhead to both the time and cost of 
development and operations. For example, activities that for a non-HIPAA project would be trivial (e.g., 
granting a developer access to the production environment) require additional controls for a HIPAA 
project (e.g., background check, training, access form completion, and a sanity check for “minimum 
required access”). Developers will not be able to simply pull production data locally to test, and scripts 
will be written to anonymize data for offline debugging. Operationally, server cost is higher at data 
centers that support HIPAA compliance, and HIPAA introduces technical requirements that surpass 
operational ones. For example, for a small application (<10k users), a single server could suffice, but 
HIPAA requires separating the database server from the front-end web server, and possibly a 3rd “hot 
failover” server as part of a Disaster Recovery plan. HIPAA also requires administrative activities, 
including but not limited to: 1) creation of a policies and procedures document and subsequent 
enforcement which will include staff training materials, setup of access control forms, breach report 
documents, and onboarding/termination procedures, 2) HIPAA-compliant server hosting, 3) enterprise 
antivirus protection, 4) a third-party compliance gap assessment report, 5) background checks for all 
employees, 6) monthly review of security reports and systems checks, and 7) an annual full-system 
security review and Risk Analysis. 
 
Moodivate will use the existing standard HL7 FHIR (Fast Healthcare Interoperability Resources) interface 
supported by Epic. MountainPass Technology will be granted appropriate access and credentials to 
MUSC’s Epic instance, under a business associate agreement (BAA) and strict security assurances. Mr. 
Buck Rogers from MUSC’s BMIC Epic-EHR integration team will serve as the technical resource at 
MUSC to assist with Epic access. This will include assisting with initial setup, serving as an institutional 
point of contact for questions as they arise, and addressing connectivity issues if/when they occur. 
MountainPass will coordinate with Mr. Rogers and his team to determine APIs that will be needed, to 
implement a proof of concept, to confirm that information is flowing as expected, and then to proceed to 
full scale development. 

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Aim 2: RCT of Moodivate vs. Moodivate + EHR Provider Portal vs. TAU within Primary Care.  
Procedures 
 
After completing determination of eligibility and consent, the participant will complete baseline 
assessments remotely and be randomized via REDCap 1:1:1 to Moodivate, Moodivate + EHR, or TAU. 
Participants will be randomized via a mixed block design stratified by baseline depressive symptoms and 
concurrent depression treatment. If randomized to either of the Moodivate conditions, study staff will 
provide the participant with an app download code and will ensure successful download. Study staff will 
then give the participant a brief, scripted overview (as utilized in our Phase I trial) regarding app 
utilization and will provide the participant with 10 minutes to use the app and ask questions. Participants 
randomized to the Moodivate + EHR condition will be told that their PCP will be provided information 
regarding their Moodivate utilization and may discuss use and response during their next visit. Providers 
of Moodivate + EHR patients will be granted access to the EHR provider portal for those patients and it 
will be suggested that they review the portal during the patient’s next visit. The provider portal will be 
developed such that it is available for providers to view only if the patient is randomized to the Moodivate 
+ EHR condition.  
 
Assessments 
 
All participants will be text messaged and/or emailed a REDCap link, accessible via smartphone, 
to complete follow-up assessments weekly for 8 weeks, with a final follow-up at Week 12 (Figure 3). 
We utilized a similar assessment approach in the Moodivate Phase I feasibility trial with success—80% of 
participants completed at least half of all follow-up assessments and >50% completed all follow-up 
assessments. Assessments are estimated at 20 minutes each and participants will be compensated $10 for 
completion of each. Participants can earn an additional $40 for completing all follow-up assessments. All 
PCPs affiliated with the MUSC Primary Care ICCE will be invited to complete a brief study 
questionnaire at the beginning and end of the trial. Providers of patients enrolled in the Moodivate + EHR 
condition will be invited to complete an additional questionnaire following their patient’s 12-week study 
follow-up assessment.   
 
 
 
 
Aim 3: Cost-Effectiveness. The economic evaluation of Moodivate and Moodivate + EHR relative to 
TAU will be conducted from the budgetary perspective, as the primary focus herein is on the financial 
attractiveness of Moodivate for commercial launch. Health systems and payors in particular may be more 
likely to adopt Moodivate if it is established as either 1) cost-saving or 2) providing a clinical benefit cost-
effectively. To examine cost-saving, we will first conduct a cost-benefit analysis by comparing changes in 
BDI-II scores and any reduction in all-cause and mental health-related healthcare expenditures prior to 
and following Moodivate implementation (or Moodivate + EHR) relative to TAU. Cost data for all 
inpatient, outpatient and emergency department (ED) care will be obtained from the MUSC Primary Care 
ICCE billings. As study participants may seek care outside of the MUSC system, we will also obtain all-
payors’ claims data from South Carolina’s Revenue and Fiscal Affairs Office (RFA). RFA maintains data 
on inpatient and ED utilization across the state regardless of patients’ point of care. The case for 
Carolina 
Family Care
Dept of Family 
Medicine
Univ Internal 
Medicine
Clinical Partners
0
Research Assessments, Week #
1
2
3
4
5
6
7
8
12
Moodivate
Moodivate + 
EHR Provider 
Portal
TAU
Treatment
Remote 
recruitment, 
screening, 
consent, 
enrollment, 
and 
randomization 
Enrollment
EHR list of 
patients with 
appointments in 
< 4 weeks and :
•
PHQ-2 > 3 or
•
Depression 
on problem 
list or
•
Depression 
billing code
Recruitment
Figure 3: RCT Study Design (Aim 2) 

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Moodivate (with or without EHR) adoption and commercialization will be the strongest if the product 
reduces healthcare expenditures while also improving depressive symptoms.  
 
11.0 Data Analysis and Data Management  
 
Data Analytic Plan for Aim 1 Qualitative Data 
 
Usability testing data will be analyzed using grounded theory analytical techniques71, including inductive 
code identification and development, as well as associated documentation through marginal remarks and 
memos. Inductive coding (“open coding”) involves a data-based approach to the identification of key 
themes (codes), their development through further exploration of the data, and systematic application. 
Following grounded theory analyses, we will develop a coding system for the key concepts or problems, 
and the key points with similar concepts will be grouped together into a category if they are raised by 
different participants. Each category will then be discussed and underlying themes from the coded data will 
be generated. Written guidelines for category classification will be used to create a response coding system. 
After themes are extracted, classic content analysis will be used to identify quotes and content from the 
feedback that fit each of these themes in the classification system72. The PI and two independent raters 
trained to criterion on the classification system will independently sort quotes by theme and then together 
reach a consensus on any discrepancies. Average inter-rater reliabilities will be computed prior to the 
resolution of coding differences. Codes developed following patient and PCP usability testing will be used 
to identify modifications necessary to Moodivate and to the provider portal. These modifications will be 
implemented by MPT prior to the RCT. 
 
Sample Size Estimation 
 
There are three pairwise comparisons of interest (1: Moodivate vs. Moodivate + EHR, 2: Moodivate vs. 
TAU, and 3: Moodivate + EHR vs. TAU), but we focus on two (each Moodivate group vs. TAU) and set α 
to 0.025 for each, resulting in an overall significance level of 0.05. We view the direct comparison of the 
two Moodivate groups a priori as exploratory. Although based on prior literature we expect that the provider 
portal addition will bolster treatment efficacy, we cannot yet estimate the magnitude of this effect. Within 
our Phase I trial, Moodivate participants experienced a 12.2(13.2) point decrease on average in depressive 
symptoms on the BDI-II, while TAU had a 7.8(15.0) point decrease42. Assuming, similar results here, a 
sample of 158 participants per group will provide 80% power to detect this difference, with a two-sided 
α=0.025 using a two-sample t-test. Generalized estimating equation (GEE) modeling will have higher 
power given the repeated nature of observations. Assuming 20% attrition, the sample size is inflated to 200 
(for each group) for a total sample size of N=695.  
 
With a sample size of 200 per group, a two-sided 95% confidence interval (CI) for binary 
feasibility/acceptability outcomes such as retention will extend a maximum of 0.069 in each direction from 
the observed proportion. For example, if the observed proportion is 50%, the 95% CI will range from 0.43 
to 0.57.  As the observed proportion gets further from 50%, the width of the confidence interval will 
decrease, providing even more precision on the observed estimate. For this study, 70% of participants are 
expected be in the retention group, providing a 95% CI of 0.64 to 0.76 with n=200. A sample size of n=200 
in each of the Moodivate groups will provide 80% power to detect the difference between the expected 
proportion of 0.70 and an alternative proportion of at least 0.79, when the sample size is 200, using a one 
sized chi-square test of proportions with α=0.025. 
 

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For continuous measures of feasibility/acceptability, a two-sided 95.0% confidence interval for a single 
mean will extend 0.14 from the observed mean, assuming that the standard deviation is known to be 1.0, 
with a sample size of n=200.   
 
Data Analytic Plan for Aim 2 
  
Efficacy. Descriptive statistics (means, SDs, frequencies, percentages) will be calculated and compared for 
overall group differences via chi-square tests, ANOVA models, or nonparametric equivalents, as necessary, 
for baseline variables. Pairwise differences will be explored for variables that show a significant overall 
group difference. Significant baseline demographic differences between groups will be included as 
covariates in regression analyses. Generalized Estimating Equation (GEE) modeling will be used to 
examine group differences (Moodivate, Moodivate + EHR, TAU) in depression (BDI-II) over time along 
with the interaction between group and time adjusting for baseline differences and baseline depression. All 
models will include a clustering effect to account for the clinic from which the participant was recruited. 
Sex will be included as a covariate in all models. GEE affords examination of specific contrasts (i.e., 
Moodivate vs. TAU at a specific time point) as well as overall effects of group, time, and the interaction 
between group and time, while also accounting for repeated measures within participants and any clustering 
within clinics73,74.  
 
Missing Data. Missing data can be a concern in longitudinal studies, and GEE modeling can assist by 
including participants with partially complete follow-up data instead of requiring complete follow-up data 
from all participants. All randomized participants will be included in primary analyses, and imputation 
methods will be used to accommodate missing data if missing data is >10%. For small amounts of missing 
data (<10%), these data points will be excluded from modeling, as the sample size has been inflated to 
account for some attrition. Additionally, sensitivity analyses will be done using only participants with 
complete follow-up data, with results compared to primary analyses. Attrition will also be assessed to 
determine whether dropout is differential by group to identify any potential bias in group assignment.  
 
Feasibility and Acceptability. Within both the Moodivate and Moodivate + EHR groups, retention will be 
defined as the proportion of participants who use the app at least once within each week following 
enrollment. Retention point estimates and 95% confidence intervals (CIs) will be calculated for each group 
separately. A one-sided, one-sample proportion test will be used to determine whether retention is at least 
70% (or higher) within each group. Other metrics of feasibility/acceptability (ease of use, continued desire 
to use, etc.) for both patients and providers will be summarized within group via summary statistics and 
95% CIs, as appropriate. 
 
Secondary Analyses. Secondary mediation analyses will examine whether changes in hedonic capacity, 
environmental reward, and/or behavioral activation mediate the relationship between treatment and change 
in depression. Following recommendations by Preacher and Hayes75, bootstrapping will be used to examine 
the indirect effect of treatment on depression via change in proposed mediators. CIs around the indirect 
effect estimate that do not contain zero will be considered significant. Fit indices including 2, the 
comparative fit index (CFI), the Tucker-Lewis index (TLI) and the root mean square error of approximation 
(RMSEA) will be examined. Model fit will be acceptable when 2 is nonsignificant, CFI and TLI are > 
0.976,77 and RMSEA is < 0.0578.     
 
Adverse Events (AEs). AEs will be defined as a clinically significant (i.e., 10-point) increase in depressive 
symptoms on the BDI-II from baseline or any incidence of suicidality (response of “I would like to kill 
myself” or “I would kill myself if I had the chance” on the BDI-II suicidality item).  We will determine the 

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incidence of AEs and associated 95% CIs. A chi-square test will determine if the rate of AEs is greater than 
5% in any group. 
 
Data Analytic Plan for Aim 3 
 
The economic evaluation of Moodivate and Moodivate + EHR relative to TAU will be conducted from the 
budgetary perspective, as the primary focus herein is on the financial attractiveness of Moodivate for 
commercial launch. Health systems and payors in particular may be more likely to adopt Moodivate if it is 
established as either 1) cost-saving or 2) providing a clinical benefit cost-effectively. To examine cost-
saving, we will first conduct a cost-benefit analysis by comparing changes in BDI-II scores and any 
reduction in all-cause and mental health-related healthcare expenditures for a 12-month period before and 
after Moodivate implementation (or Moodivate + EHR) relative to TAU. Cost data for all inpatient, 
outpatient and emergency department (ED) care will be obtained from the MUSC primary care ICCE 
billings department. As study participants may seek care outside of the MUSC system, we will also obtain 
all-payors’ claims data from South Carolina’s Revenue and Fiscal Affairs Office (RFA). RFA maintains 
data on inpatient and ED utilization across the state regardless of patients’ point of care. The case for 
Moodivate (with or without EHR) adoption and commercialization will be the strongest if the product 
reduces healthcare expenditures while also improving depressive symptoms.  
 
Even if the app is not cost-saving, there is a compelling case for adoption if the intervention improves 
outcomes cost-effectively. For cost-effectiveness analyses, we will follow gold standard procedures79 and 
calculate the incremental cost effectiveness ratio (ICER), defined as the additional cost per additional 
outcome desired. Because there are three groups, we will order interventions from most to least costly, 
likely to be (a) Moodivate + EHR, (b) Moodivate, and (c) TAU. We will then calculate the ICERs for (a) 
vs. (b) and (b) vs. (c). Because this is an RCT, our ICER is straightforward: (cost of Moodivate + EHR – 
cost of Moodivate) / (BDI-II score of Moodivate + EHR – BDI-II score of Moodivate). This formula is 
replaced with corresponding variables when the comparison is Moodivate vs. TAU. We will assume (but 
verify) that the three groups have similar characteristics so that observed differences in outcomes reflect 
differences in the intervention. If group differences are evident, a generalized linear model will be used to 
adjust for between-groups differences. Any intervention that is “dominated” (i.e., is more expensive but 
yields less desirable result) will be eliminated from consideration. Likewise, we will also eliminate any 
intervention that is “extendedly dominated” (i.e., an intervention that has an ICER higher than the next most 
effective intervention). For example, if the ICER of Moodivate vs. TAU is higher than the ICER of 
Moodivate + EHR vs. Moodivate, we will eliminate Moodivate as being extendedly dominated and 
calculate the ICER of Moodivate + EHR versus TAU directly. All costs will be converted to net present 
value at standard discount rates (3% and 5%). Probabilistic sensitivity analyses using Monte Carlo 
microsimulations will test the robustness of results with differing ranges of costs and treatment 
effectiveness80. Ranges will be based on confidence intervals estimated in Aim 2 outcomes. For cost data, 
MPT will estimate ranges of app development and distribution costs. An acceptability curve of the cost-
effectiveness of any non-dominated intervention will demonstrate the probability of app cost-effectiveness 
under different levels of willingness to pay. 
 
 
Data Management 
 
Regarding questionnaire data, data will be obtained for research purposes only. All data will be collected, 
stored, and managed via REDCap, which is a secure, web-based application designed exclusively to support 
data capture for research studies. REDCap provides secure, web-based flexible applications, including real 
time validation rules with automated data type and range checks at the time of entry. The underlying 
database is hosted in a secure data center at MUSC, a secure environment for data systems and servers on 

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campus, and includes redundancy, failover capability, backups and extensive security checks. The system 
has several layers of protection including user/group account management, "Data Access Groups" which 
allow data to be entered by multiple groups in one database with segmented user rights for entered data, 
audit trails for all changes, queries and reports, and Secure Sockets Layer (SSL) encryption. Name and 
relevant contact information will be obtained to provide compensation and every effort will be made to 
maintain subject confidentiality, in accordance with HIPAA. All data will be identified only by code 
numbers (participant IDs). Participant IDs will be linked to participants’ names in a password-protected file 
that is accessible only to the PI and trained research staff.  
 
Deidentified data from this study will be submitted to the National Institute of Mental Health Data Archive 
(NDA) at the National Institutes of Health (NIH). 
 
12.0 Provisions to Monitor the Data to Ensure the Safety of Subjects (if applicable) 
 
This section is based on the recommendations in NIDA’s “Guidelines for developing a Data and Safety 
Monitoring Plan” as well as NCI’s “Essential Elements of a Data and Safety Monitoring Plan for Clinical 
Trials Funded by the National Cancer Institute”.  
 
Summary of the Protocol 
This application consists of a 3-Aim proposal. In Aim 1, both providers (n=10) and adults with depressive 
symptoms (n=10) will be recruited for iterative usability testing of Moodivate and the Moodivate EHR 
provider portal. In Aim 2, we will conduct a three-arm randomized controlled trial (n=695) to examine the 
efficacy of 1) Moodivate vs. 2) Moodivate + EHR integration vs. 3) TAU for the treatment of depressive 
symptoms within primary care. In Aim 3, we will conduct a cost-effectiveness analysis of implementing 
Moodivate and Moodivate + EHR integration within primary care practices relative to TAU. 
 
Trial Management 
The study will be managed from the Addiction Sciences Division within the Department of Psychiatry and 
Behavioral Sciences at the Medical University of South Carolina (MUSC). Recruitment, data collection, 
data management, and treatment provision will be coordinated and centrally managed at our research lab at 
MUSC and will be implemented within local Family Medicine/Primary Care clinics that are part of MUSC’s 
Primary Care ICCE.  
 
Data Management and Analysis 
Participants will enter data in REDCap, a secure, web-based application designed exclusively to support 
data capture for research studies. REDCap provides: 1) an intuitive interface for data entry (with data 
validation); 2) audit trails for tracking data manipulation and export procedures; 3) automated export 
procedures for seamless data downloads to common statistical packages (SPSS, SAS, Stata, R); 4) 
procedures for importing data from external sources; and 5) advanced features, such as branching logic and 
calculated fields. These procedures are effective in minimizing data entry errors (e.g., missing or errant 
data). All data from Moodivate will be stored on a HIPAA-compliant server and data will be transmitted 
securely via industry standard protocols to Epic (MUSC’s EHR). Data analytic plans are outlined above. 
 
Quality Assurance 
Accuracy and completeness of the data collected will be ensured by weekly review. The REDCap system 
does not accept outliers, illogical response patterns, etc. The PI and research assistant will have weekly 
meetings to discuss any qualitative comments received during data collection and any problems in data 
collection. The PI will examine the database for potential irregularities monthly. Initial data analyses will 

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examine distributions of variable scores and comparability of baseline characteristics across conditions (for 
Aims 2 and 3) in case analyses need to be adjusted for these. Confidentiality procedures are outlined above.  
 
Regulatory Issues 
This study will be registered on clinicaltrials.gov. The study does not require an IND from the FDA. All 
serious AEs will be reported to the MUSC Committee on Human Research within 48-hours. Follow-up of 
all unexpected and serious AEs will also be reported. All AEs will be reviewed weekly by the PI and yearly 
by the IRB. Any significant actions taken by the local IRB and protocol changes will be relayed to the 
funding agency. We estimate the significant AE rate to be very low (< 5%). If monthly monitoring indicates 
the rate is above this, we will convene a meeting of the DSMB. Potential conflicts of interest (COI) will be 
reported using the rules of MUSC’s COI committee. 
 
Trial Safety 
The potential risks and benefits and methods to minimize these risks are outlined in the “Protection of 
Human Subjects” section. We will determine if any AEs result in dropouts or are serious according to FDA 
guidelines. The PI (Dr. Dahne) will serve as the Program Manager for AEs. All unexpected AEs will be 
monitored while they are active to determine if treatment is needed. We anticipate that AEs will be rare 
given that the Moodivate app is non-invasive and that all participants will be engaged with current 
healthcare. Nonetheless, any AEs will be coded on a weekly basis using the FDA’s COSTART rules81 and 
entered into a database. For each weekly study meeting, the research assistant will prepare a summary of 
all AEs, including their severity, whether they caused a dropout, required treatment, and presumed relation 
to app utilization. The PI will review this at the weekly study meeting (or before if more urgent). At the 
weekly meeting (or before if urgent), the research assistant will report any premonitory symptoms to suggest 
emergence of a serious psychiatric condition (e.g., suicidality). Drs. Diaz and Player, board-certified Family 
Medicine physicians, will be available on an ad-hoc basis for on-site medical supervision for any issues that 
cannot be resolved by Dr. Dahne.  
 
Study procedures will follow as much as possible the FDA’s Good Clinical Practice Guidelines and our 
research team has found Spilker’s comprehensive text on conducting clinical trials to be useful82. We will 
encourage participants to notify their physicians that a) they are in a randomized controlled research study 
examining a treatment for depressed mood, and b) the physician should contact the PI directly if the 
physician has any questions. 
  
The research assistants will be instructed not to reveal whether a person is a participant in the study and 
will report to the PI any outside requests for information about a participant or any breaches in 
confidentiality. All requests by participant’s physicians and other medical providers will be referred directly 
to the PI.  
 
Trial Efficacy 
The Data and Safety Monitoring Board (see below) may request a blinded interim efficacy report (blinded 
to the PI and research team) for review while the trial is ongoing. Final (fully unblinded) efficacy analysis 
will occur after all participants have completed all follow-ups. 
 
Data and Safety Monitoring Plan Administration 
The PI will be responsible for monitoring the trial, with additional oversight provided by study co-
Investigators. The PI will examine monthly the outcomes database for missing data, unexpected 
distributions or responses, and outliers. The PI will check weekly the AE database prepared by the research 
assistant immediately prior to the lab meeting. A DSM report will be filed with the IRB and funding agency 
on a yearly basis, unless greater than expected problems occur. The report will include participant 

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characteristics, retention and disposition of study participants, quality assurance issues and reports of AEs, 
significant/unexpected AEs and serious AEs. We will report efficacy at the end of the trial. 
 
Data and Safety Monitoring Board Plan 
We will create a Data and Safety Monitoring Board (DSMB), comprised of 3 clinicians with expertise in 
depression treatment, primary care, and clinical trials, as well as a statistician. The DSMB will meet 
annually (more frequently as needed for emergent situations) to review any AEs related to the study, as well 
as review any data management related errors. The board may be called at any point if needed for 
unexpected, serious AEs, etc. Modification will be made in the procedures and/or the protocol if necessary 
based on the findings of the board.  
 
13.0 Risks to Subjects 
 
The potential risks in this study include those related to: a) depressive symptoms, b) clinical deterioration, 
c) confidentiality, d) potential data breach from the app database, and e) frustration. All risk mitigation 
strategies outlined below were approved by the MUSC IRB and implemented with success during our 
Phase I trial. 
 
a) Depressive symptoms: Depressive symptoms will be monitored via the BDI-II. All participants will 
complete the BDI-II weekly online via a REDCap survey that is accessible via mobile phone web 
browsers. All participants will own smartphones. Thus, all participants should have access to the online 
BDI-II assessments. 
 
b) Clinical deterioration: Dr. Dahne and her research team will monitor participant PHQ-9 and BDI-II 
scores for possible clinical deterioration (i.e., increasing depressive symptoms and/or the development of 
suicidal ideation) throughout the course of the study as participants complete the PHQ-9 and BDI-II 
weekly. Clinical deterioration will be defined as an increase of 10 or more points on the BDI-II from the 
baseline BDI-II assessment or a response of “I would like to kill myself” or “I would kill myself if I had 
the chance” on the suicidal thoughts or wishes item of the BDI-II. Our team has developed alerts within 
REDCap reports that the investigative team checks daily to determine whether a participant completed a 
BDI-II assessment and evidenced evidences clinical deterioration. This system has now been 
implemented with success across multiple studies. In the event that a participant evidences clinical 
deterioration, Dr. Dahne will contact the participant via phone and will provide referrals for local mental 
health resources for depression treatment. Dr. Dahne will suggest that the participant seek treatment and 
then will follow-up with the participant via phone one week later. In the event that a participant reports 
suicidal ideation either during study screening or during subsequent assessments, Dr. Dahne will 1) page 
the participant’s attending primary care provider and 2) complete a risk assessment with the participant 
via phone. Dr. Dahne will query the participant for details regarding the suicidal ideation, including a 
likelihood of harming oneself imminently and a plan for committing suicide. If the participant reports an 
imminent likelihood of harming him/herself or a plan for committing suicide, Dr. Dahne will call 
emergency services and will remain on the phone with the participant until emergency services arrive. 
Contact information including home address will be collected during study screening and will be provided 
if necessary to emergency services personnel. Dr. Dahne is a licensed clinical psychologist and has more 
than 10 years of experience conducting suicide risk assessments. In the event that a participant evidences 
clinical deterioration, the participant will be allowed to continue in the trial, but we will recruit an 
additional participant for data collection purposes.  
 
c) Confidentiality: Participants will be made aware of limits to confidentiality at the beginning of 
screening and during informed consent which includes a report of suicidal or homicidal intent or report of 

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abuse or neglect. If the participant reports suicidal or homicidal intent or abuse/neglect during screening 
or at any point during the trial, Dr. Dahne will take appropriate action as outlined by the MUSC IRB, 
NIH, and the State of South Carolina, which may include paging the participant’s physician, contacting 
the authorities and/or pursuing involuntary commitment at a mental health facility. If participants present 
no imminent danger but also need more extensive treatment of mental health concerns, they will be given 
appropriate referrals.  
 
d) Data breach: Although health information will be collected within Moodivate (e.g., daily mood 
ratings, activities, values), personally identifiable information will intentionally not be collected within 
the app (e.g., name, phone number, email address, etc.), and thus we will not collect nor will we retain 
protected health information (PHI). In the event of a data breach, it is important to note that health 
information will not be able to be tracked back to specific individual users. By refraining from collecting 
PHI within the mobile app, we ensure HIPPA compliance while also protecting the identities of our users. 
In the event of a data breach, all app users will be notified via email.   
 
e) Frustration: Participants may become frustrated while completing questionnaires or while using 
Moodivate. Participants will be informed that they may refuse to answer any question(s) that they do not 
wish to answer and that they may discontinue use of Moodivate at any time. 
 
Since patients will all currently be receiving medical care at MUSC, there are no additional risks 
associated with participation in this study. 
 
Adequacy of Protection Against Risks  
Recruitment and Informed Consent 
Study participants will be recruited from local primary care/family medicine clinics associated with 
MUSC’s Primary Care Integrated Center of Clinical Excellence (ICCE). Study recruitment will occur 
proactively prior to a patient’s scheduled primary care visit. We will utilize available EHR data to identify 
Primary Care ICCE patients with depressive symptoms. As part of routine clinical care, all primary care 
patients complete the Patient Health Questionnaire-2 (PHQ-2) to screen for depressive symptoms during 
each primary care visit. Information on depression is also available via the EHR within each patient’s 
problem list and billing codes. Adult (age 18+) patients will be identified who have an upcoming visit 
scheduled within the next four weeks and have either: 1) a score of > 3 on their last PHQ-2 assessment 
(i.e., the optimal cut-point for identifying those who are symptomatic56), 2) depression listed on their 
problem list, or 3) a depression-related billing code associated with their last visit. With the permission of 
their attending physician, these patients will be contacted by the study team via the patient EHR portal 
(MyChart) to invite them to participate in the research study. If participants do not respond to this initial 
invitation within 72-hours, we will contact them via phone, e-mail, and/or text-message (based on patient 
communication preferences within the EHR) to ensure they received the MyChart message. We currently 
implement this recruitment approach in an ongoing trial of an electronic visit for smoking cessation and 
our experience has been that it is feasible and efficient to generate a recruitment report via the EHR and 
that all physicians associated with the MUSC Primary Care ICCE have agreed to have their patients 
contacted for the purpose of our studies. If interested, participants will complete an eligibility screening 
via REDCap to determine preliminary study eligibility. After completing determination of preliminary 
eligibility, a member of the research team will schedule a phone call with the participant to determine 
final study eligibility (no suicidal ideation reported on the BDI-II). After determining final study 
eligibility, a member of the research team will complete informed consent with the participant. All 
participants will electronically/in writing sign informed consent forms that have been IRB-approved once 
the study is explained to them in full and they have stated that they understand what is being asked of 
them. Participants will be given the opportunity to ask questions about their participation throughout the 
course of the study. A copy of the informed consent will be kept centrally at our study office within 

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locked filing cabinets and a copy will also be given to each study participant. Participants will be given a 
study phone number and e-mail address to contact for questions. 
 
Protections Against Risk 
All screening information will be kept in a password protected REDCap database. Only key study 
personnel will have access to the database. If an individual is not eligible to participate, his/her screener 
will include his/her first name and last initial and the reason for disqualification. Eligible participants’ full 
name, telephone number and e-mail address will be recorded in the database. This is the only place where 
participants’ names and subject identification numbers appear together. Eligible participants will be 
assigned a subject number, will complete informed consent (see procedures above), will be randomized 
(Aim 2), will complete baseline assessments, and subsequently will receive their randomized intervention 
(or will complete usability testing ). 
 
Upon completing eligibility screening, if study eligible, individuals will be provided with a verbal 
overview of the study, asked to review a consent form, and asked to provide informed consent. 
Participants will be informed of limitations of confidentiality (i.e., abuse or neglect, intention to harm self 
or someone else) both verbally and in writing during the informed consent process. The consent form will 
include the participant’s name, but not his/her subject number. Consent forms will be provided in English. 
As utilization of Moodivate requires that participants are able to read, participants unable to read the 
consent form on their own will not be included. 
 
Regarding questionnaire data, data will be obtained for research purposes only. All data will be collected, 
stored, and managed via REDCap, which is a secure, web-based application designed exclusively to 
support data capture for research studies. REDCap includes real time validation rules with automated data 
type and range checks at the time of entry. The underlying database is hosted in a secure data center at 
MUSC and includes redundancy, failover capability, backups and extensive security checks. The system 
has several layers of protection including user/group account management, "Data Access Groups" which 
allow data to be entered by multiple groups in one database with segmented user rights for entered data, 
audit trails for all changes, queries and reports, and Secure Sockets Layer (SSL) encryption. Name and 
relevant contact information will be obtained to provide compensation and every effort will be made to 
maintain subject confidentiality, in accordance with HIPAA. All data will be identified only by code 
numbers (participant IDs). Participant IDs will be linked to participants’ names in a password-protected 
file that is accessible only to the PI and trained research staff.  
 
Regarding user privacy while using Moodivate, users will create a username and password in order to 
login to their account without storing their true identity. Users will be instructed not to use identifiable 
information in their username (e.g., name, birthdate, etc) to protect identity. In Moodivate, we will store a 
one-way hashed version of the patient’s email address to support password reset and unique identification, 
but that identification will not be traceable back to the user’s true identity. We will not store any 
personally identifiable information (e.g., first name, last name, email address, phone number) of users in 
our app database. We will use a HIPAA-compliant server protected by industry-standard safeguards to 
prevent unauthorized access. Since we are not associating patient health information with personally 
identifiable information there would not be a risk of unauthorized release of patient medical data in the 
event of a security breach. User personal information will be contained behind secured networks and will 
only be accessible by the investigators, who will have special access rights to such systems. In addition, 
all sensitive information users supply will be encrypted via Secure Socket Layer (SSL) technology. We 
will not sell, trade, or otherwise transfer personally identifiable information to outside parties. Our privacy 
policy will be available within the app for users to view at any time. This plan is consistent with that used 
by our partnering app development company, MountainPass Technology LLC, in several other previous 
products with similar protection requirements.     

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Protection against risk resulting from depressive symptoms includes the following: Regarding suicidal 
ideation and broader mental health concerns, Dr. Dahne, a licensed clinical psychologist, will take 
appropriate action as outlined by the MUSC IRB, NIH, and the State of South Carolina, which may 
include paging the participant’s referring physician, contacting the authorities and/or pursuing involuntary 
commitment at a mental health facility. If participants present no imminent danger but also need more 
extensive treatment of mental health concerns, they will be given appropriate referrals. As noted above, 
BDI-II data will be monitored over time in order to detect any possible clinical deterioration. BDI-II data 
will be monitored using only the participants’ subject numbers. Should a participant evidence clinical 
deterioration, Dr. Dahne will then use the participant database in order to obtain contact information for 
the participant based on their subject number. We will also form a Data Safety and Monitoring Board 
(DSMB). If the percent of serious or severe AEs related to clinical deterioration appears to be greater than 
5% the DSMB will be notified to make a decision on early termination of the study.  
 
14.0 Potential Benefits to Subjects or Others 
 
All participants in this trial will receive at minimum treatment as usual via MUSC’s Primary Care ICCE 
clinics. We will not augment treatment as usual as provided by these clinics. The majority of participants 
will also receive a mobile app developed to improve depressive symptoms. The major benefit to society 
will be whether Moodivate and/or Moodivate + EHR integration improves depression treatment outcomes 
relative to TAU. Potential issues of clinical deterioration, confidentiality, data security, and frustration are 
a high priority and will be closely monitored throughout the study. Consequently, the risk to benefit ratio 
in the proposed study appears to be acceptable. 
 
 
15.0 Sharing of Results with Subjects 
 
Study outcomes will not be shared with subjects. 
 
 
 

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